CN109508848B - Enterprise production security risk assessment and management system - Google Patents
Enterprise production security risk assessment and management system Download PDFInfo
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Abstract
The invention discloses an enterprise production security risk assessment and management system, which comprises a client, a security production management platform, a data server and a device access platform, wherein the client is connected with the security production management platform; the equipment access platform is used for collecting enterprise security feature data in real time and storing the enterprise security feature data into the data server; the data server comprises a basic database, a distributed file server and a security risk model library, wherein the basic database stores enterprise security characteristic parameters, including real-time acquisition data and environment dynamic parameters; the distributed file server stores a security management platform configuration file and various resource files; storing a preset enterprise security risk assessment model by a security risk model bank; and the safety production management platform reads the enterprise safety feature data and the enterprise safety risk assessment model through the data server, calculates enterprise safety production scores by combining with the input parameter configuration of the client, and sends the scoring results to the client in a chart form for visual display to a user.
Description
Technical Field
The invention relates to the field of risk assessment and management, in particular to an enterprise production safety risk assessment and management system.
Background
The related safety production management activities are developed, the occurrence of serious safety production accidents is fundamentally reduced or stopped, the safety production accidents are caught from the safety production sources, the social and economic science and sustainable development are realized, and the safety production risk management method has great significance in safety production risk management, safety and health of staff are ensured, and sociality modernization construction is promoted.
At present, the safety production management of enterprises mainly depends on a few supervisory personnel and is responsible for executing related emergency treatment after the safety accident occurs. In the supervision mode, the safety production condition of enterprises cannot be timely and comprehensively mastered. How to advance the safety production supervision work from the basic level, identify, analyze, evaluate and manage the safety production risk of enterprises, and effectively prevent the occurrence of risk accidents, thereby effectively providing the safety production supervision level of enterprises, which is a problem to be solved urgently.
Disclosure of Invention
Aiming at the defect that the safety production management of enterprises in the prior art mainly depends on few supervisory personnel and cannot timely and comprehensively grasp the safety production condition of the enterprises, the invention provides a standardized risk scoring system standard, and can simplify the enterprise production safety risk assessment and management system of the operation flow.
The technical scheme adopted for solving the technical problems is as follows:
the enterprise production security risk assessment and management system comprises a client, a security production management platform, a data server and a device access platform; wherein,,
the equipment access platform is used for collecting enterprise security feature data in real time and storing the enterprise security feature data into the data server;
the data server comprises a basic database, a distributed file server and a security risk model library, wherein the basic database stores enterprise security characteristic parameters, including real-time acquisition data and environment dynamic parameters; the distributed file server stores a security management platform configuration file and various resource files; storing a preset enterprise security risk assessment model by a security risk model bank;
and the safety production management platform reads the enterprise safety feature data and the enterprise safety risk assessment model through the data server, calculates enterprise safety production scores by combining with the input parameter configuration of the client, and sends the scoring results to the client in a chart form for visual display to a user.
The invention also provides an enterprise production security risk assessment and management method, which is characterized by comprising the following steps:
presetting a security risk assessment model to a data server;
the client transmits relevant configuration parameters to the safe production management platform;
the equipment access platform collects real-time environment data, stores the real-time environment data into a basic database and sends the real-time environment data to the safety production management platform;
substituting the environmental data and the configuration parameters into a security risk assessment model by the security production management platform, calculating to obtain a risk assessment result, and pushing the risk assessment result to the client;
the client displays the risk assessment results to the user in the form of a chart and the like.
The invention also provides an enterprise production security risk assessment and management method, which is characterized by comprising the following steps:
s1, presetting an enterprise safety production risk assessment tree diagram in a database server; the enterprise safety production risk assessment tree diagram is specifically constructed based on daily production activities of enterprises:
s1.1, summarizing and summarizing related contents of enterprise safety production management, and setting up overall scoring items;
s1.2, based on the overall score in the step S1.1, further refining the score according to the actual conditions of enterprises, and analyzing and setting all the sub-items;
s1.3, selectively repeating the operation of the step S2 according to actual demands and the number of the scoring items, namely, dividing the scoring sub-items listed in the step 2 again, setting scoring standards for all sub-scoring points, and directly reserving the sub-items as the sub-items if part of the scoring sub-items cannot finely divide the scoring points again;
s2, when the safety production management platform server is started, traversing leaf nodes of the tree diagram in the step S1 to obtain final scoring sub-items, traversing the tree diagram, and establishing corresponding scoring standards for the scoring items on each leaf node;
s3, constructing a mesh chart of enterprise safety production risk assessment by referring to enterprise safety management experience according to an enterprise safety production risk assessment tree chart preset in a database server; analyzing the coupling relation among all the scoring sub-items, and associating all the scoring sub-items by using a directed graph so as to form a mesh graph for enterprise safety production risk assessment, wherein the arrow head scoring sub-items influence the arrow tail scoring sub-items by a certain weight;
s4, the safety production management platform server reads the mesh map from the safety risk model library, constructs a judgment matrix A and calculates the weight value P of each node in the mesh map i And carrying in real-time data and environmental parameters read from the basic database to calculate the score S of each scoring sub-item i The method specifically comprises the following steps:
s4.1, the scoring value of the node i in the mesh map is influenced by n child nodes, an n-order judgment matrix is constructed, and the maximum eigenvalue lambda of the judgment matrix is calculated max And a corresponding feature vector W;
s4.2, for the maximum eigenvalue lambda in the above steps max Performing consistency test, if not, repeating the step S4;
s4.2, carrying out normalization processing on the feature vector W to obtain W 1 ＝[w 0 ,w 1 …w n ]Namely the child node weight w under the node i j Distribution;
s4.4. node i has a score value of S i ＝∑S j w j Where j= … n, n is the total number of children nodes of node i;
s5, the safety production management platform server calculates the proportion of unqualified items in all scoring sub-items, and obtains an enterprise safety risk level R according to the safety production scoring table 1 1 ；
Table 1 safety production risk scoring table 1
S6, the safety production management platform calculates node weights in the tree diagram through constructing a judgment matrix according to the tree diagram in the step S1, so that the score of each scoring sub-item is calculated, and enterprise safety production risk assessment level scores are obtained;
s7, the safety production management platform scores the levels of the step S6 according to the safety production scoring table 2 to obtain an enterprise safety risk level R 2 ；
Table 2 safety production risk scoring table 2
S8, the safety production management platform server synthesizes the enterprise safety risk level R in the step S5 and the step S7 1 ,R 2 And taking the item with higher risk level as the final result of the enterprise risk level.
Further, the scoring total scoring item content specifically comprises 11 items of education and training, management personnel, fire facilities, hidden danger correction, special equipment safety management, operation safety management, safety inspection, emergency management, accident management, safety early warning and hazard source management.
Further, the education and training comprises training data, a training plan, a training record and 4 sub-items of an online examination;
the management personnel comprise obligation fire departments and personnel qualification 2 subitems; the fire-fighting facility comprises 5 sub-items of state, inspection, detection, maintenance and recording;
the hidden danger correction comprises major hidden danger, and the hidden danger does not close 2 sub-items in time;
the special equipment safety management comprises equipment type management, detection, deactivation, recovery and statistics of 5 sub-items;
the operation safety management comprises 8 sub-items of fire operation, limited space operation, overhead operation, hoisting operation, temporary electricity operation, soil operation, equipment maintenance operation and operation statistics;
the security check comprises 3 sub-items of check standard, check record and check statistics;
the emergency management comprises an emergency organization, an emergency plan, an exercise evaluation and 5 sub-items of emergency materials;
the accident management comprises 7 sub-items of accident level setting, accident report, event registration, industrial injury information, accident statistics and action tracking;
the safety precaution comprises precaution parameter configuration, precaution index and precaution report 3 sub items;
the dangerous source management comprises 3 sub-items of important dangerous source management, key device key parts and dangerous chemical management.
The invention has the beneficial effects that: according to the invention, the enterprise safety production risk level is quantitatively evaluated from two aspects of hierarchical grading and overall duty ratio grading, so that the enterprise is helped to more accurately control the current situation of the self safety production risk.
Further, the weight distribution of each scoring sub-item is derived by adopting a judgment matrix method, which is different from conventional expert scoring, avoids subjective assessment deviation in experience judgment, and simultaneously establishes a clear scoring criterion for each sub-item, so that the method has higher operability, is simple and easy to implement, and has lower use cost.
Furthermore, the invention starts from the actual situation in the enterprise safety production, utilizes the mesh map to identify the coupling relation among all scoring sub-items, greatly compensates the problem of incomplete association relation in the common hierarchical assessment method, and provides a more robust safety production risk assessment system for enterprises.
Furthermore, the enterprise security risk assessment method provided by the invention is widely applicable to security risk self-assessment and standardized risk scoring system standards of general enterprises, greatly simplifies the operation flow and provides scientific basis and support for enterprise risk assessment.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic diagram of an enterprise security risk assessment system according to the present invention;
FIG. 2 is a flow chart of the enterprise security risk assessment of the present invention;
FIGS. 3a-3f are tree diagrams of enterprise security risk assessment of the present invention;
FIG. 4 is a mesh diagram of enterprise security risk assessment according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The enterprise production security risk assessment and management system of the embodiment of the invention, as shown in fig. 1, comprises a client, a security production management platform, a data server and a device access platform. The equipment access platform covers an enterprise production environment (comprising a key device, key parts, dangerous chemicals, warning sign equipment, access to an existing archive and job ticket management subsystem of an enterprise and the like) and is responsible for collecting enterprise safety feature data in real time, storing the enterprise safety feature data into a data server, wherein the safety production management platform service is positioned between the data server and a client, reads the enterprise safety feature data based on a safety risk assessment model, calculates enterprise safety production scores according to input parameter configuration of the client, sends the scoring results to the client in a chart form, and displays the scoring results to a user in a visual mode. Wherein:
the data server comprises a basic database, a distributed file server and a security risk model library, wherein the basic database stores enterprise security characteristic parameters, including real-time acquisition data and environment dynamic parameters; the distributed file server stores a security management platform configuration file and various resource files; the security risk model storage stores preset enterprise security risk assessment models.
The secure production management platform server establishes connection with the client by providing REST API and supports short message and mail service.
The client comprises a WEB browser and a mobile APP.
The enterprise production security risk management method of the enterprise production security risk assessment and management system of the invention specifically comprises the following steps:
1. presetting an enterprise security production risk assessment tree diagram in a database server. And constructing a tree diagram of enterprise safety production risk assessment based on the daily production activities of the enterprise.
And constructing a tree diagram of enterprise safety production risk assessment based on the daily enterprise production activities, and presetting the tree diagram of enterprise safety production risk assessment in a database server.
1.1 summarizing and summarizing the related content of enterprise safety production management, and setting up overall scoring items. In the embodiment of the invention, the scoring content relates to various aspects of safety management, such as a system, facilities, activities and the like, and specifically comprises 11 contents of education and training, management personnel, fire-fighting facilities, hidden danger correction, special equipment safety management, operation safety management, safety inspection, emergency management, accident management, safety precaution and hazard source management.
1.2. And (3) based on the overall score in the step (1.1), further refining the score according to the actual conditions of enterprises, and analyzing and setting all the sub-items contained. In the embodiment of the invention, education and training comprise training materials, training plans, training records and 4 on-line examination sub-items, management personnel comprise obligation fire departments and 2 personnel qualification sub-items, fire facilities comprise states, inspection, detection, maintenance and recording 5 sub-items, hidden danger correction comprises major hidden danger, hidden danger is not closed in time for 2 sub-items, special equipment safety management comprises equipment type management, detection, deactivation, recovery and statistics for 5 sub-items, operation safety management comprises fire operation, limited space operation, high-altitude operation, hoisting operation, temporary electricity operation, soil operation, equipment maintenance operation and 8 operation statistics sub-items, safety inspection comprises inspection standards, inspection records and 3 inspection statistics sub-items, emergency management comprises emergency organization, emergency pre-plan, exercise evaluation, 5 sub-items of emergency materials, accident management comprises accident level setting, accident quick report, accident report, event registration, industrial injury information, accident statistics and 7 sub-items of action tracking, safety pre-warning comprises pre-warning parameter configuration, pre-warning index and 3 sub-items, hazard source management comprises major hazard management devices and 3 critical hazard management key positions.
1.3. And (3) selectively repeating the operation of the step (2) according to the actual demand and the number of the scoring items, namely, refining the scoring sub-items listed in the step (2) again, and establishing scoring standards for each sub-scoring point, wherein part of scoring sub-items cannot score the scoring points again, and the scoring sub-items are directly reserved as leaf nodes.
2. When the safety production management platform server is started, traversing the leaf nodes of the tree diagram in the step 1 to obtain final scoring sub-items, traversing the tree diagram, and formulating corresponding scoring standards for the scoring items on each leaf node.
3. And constructing a mesh map of enterprise safety production risk assessment, and presetting the mesh map of enterprise safety production risk assessment in a database server. And (3) analyzing the coupling relation among the leaf nodes in the step (1) by referring to enterprise safety management experience, and associating scoring sub-items by using a directed graph so as to form a mesh graph for enterprise safety production risk assessment, wherein the arrow head scoring item influences the arrow end scoring item by a certain weight.
4. The safety production management platform server reads enterprise mesh graph from the risk model library, constructs a judgment matrix A, and calculates the weight value P of each node in the mesh graph i And carrying in real-time data and environmental parameters read from the basic database to calculate the score S of each scoring sub-item i 。
4.1 node i scoring values in the mesh map are affected by n child nodes, an n-order judgment matrix A is constructed, and the maximum eigenvalue lambda of the judgment matrix A is calculated max And a corresponding feature vector W.
4.2. For the maximum eigenvalue lambda in the above step max And (4) performing consistency test, and if the consistency test does not meet the consistency test, repeating the step (4).
4.3. Normalizing the feature vector W to obtain W 1 ＝[w 0 ,w 1 …w n ]Namely the child node weight w under the node i j Distribution.
4.4 node i score value S i ＝∑S j w j Where j= … n, n is the total number of children nodes of node i.
5. The safety production management platform server calculates the proportion of unqualified items in all scoring sub-items, and obtains enterprise safety wind according to the safety production scoring table 1Risk level R 1 。
Table 1 safety production risk scoring table 1
6. And (3) the safety production management platform server calculates node weights in the tree diagram by constructing a judgment matrix according to the tree diagram system in the step (1), so as to calculate the score of each scoring sub-item and obtain the enterprise safety production risk assessment level score.
7. The security production management platform server obtains the enterprise security risk level R according to the security production scoring table 2 and the hierarchy scoring of the step 6 2 。
Table 2 safety production risk scoring table 2
8. The safety production management platform server synthesizes the enterprise risk level R in the step 5 and the step 7 1 ,R 2 And taking the item with higher risk level as the final result of the enterprise risk level.
9. The client visually presents the process data and the final result in the steps in a chart form.
Fig. 2 is a flow chart of security production risk assessment for enterprises, mainly comprising the steps of:
s1, presetting a security risk assessment model to a data server;
s2, the client transmits relevant configuration parameters to the safety production management platform;
s3, the equipment access platform collects real-time environment data, stores the real-time environment data into a basic database and sends the real-time environment data to the safety production management platform;
s4, substituting the environment and the configuration parameters into a risk scoring model by the safety production management platform, calculating to obtain a risk assessment result, and pushing the risk assessment result to the client;
and S5, the client displays the risk assessment result to the user in the form of a chart and the like.
Wherein S4 comprises the steps of:
1. the scores of the enterprise security risk scoring sub-items listed in figure 3 are calculated. Wherein,,
JCJY001: the training data about the safety production-related content is 100 points, 10 percent is 60 points, and less than 10 percent is 0 point, which is 20 percent of the total training data of the company.
JCJY002: training is planned to be no less than twice in each quarter, and training is completed according to the plan, which is full score; at least one safety training per quarter is planned to be completed for 60 points, otherwise for 0 points.
JCJY003: the method has detailed training records including time, personnel, places, tasks, training contents and the like, and is full; otherwise, the score is not 0.
JCJY004: the score of the online examination item is the qualification rate of the reference person, and the score of the item is taken as the score of the item (the examination lack person belongs to unqualified); when a plurality of sets of examination papers are associated in one park, the first examination paper has 5 examination persons, the examination is qualified for 3 persons, the second examination paper has 5 examination persons, and the examination is qualified for 5 persons, the qualification rate is = (3+5)/(5+5).
GLRY001: the number of the obligation fire department exceeds the total number of the company by 30 percent, and is full; otherwise, the score is 0.
GLRY002: the number of qualified personnel accounts for a percentage of the total number of the obligation fire department.
XFSS001: the normal operating facility duty cycle takes the molecules of the percentage value as the value of the item.
XFSS002: and calculating the duty ratio of the patrol task completed in time within one month, taking the molecule of the percentage value as the score of the item, wherein the total number of patrol in the current month is 100 when the total number of patrol in the current month is 0.
XFSS003: the duty ratio of the detection task completed in time within one year takes the molecule of the percentage value as the score of the item, and when the total number of detection in one year is 0, the total number is 100.
XFSS004: and calculating the on-time maintenance task duty ratio within half a year, taking the molecule of the percentage value as the score of the item, and taking the total number of the on-time maintenance as 100 when the total number of the on-time maintenance is 0 within half a year.
XFSS005: the record (inspection, detection, maintenance and repair) of the fire-fighting equipment is complete, and the fire-fighting equipment record item is full, otherwise, is 0.
YHZG001: 1-the major hidden danger proportion in the current month, taking the molecule of the percentage value as the score of the item (excluding hidden danger that the audit fails).
YHZG002: 1-the hidden danger of untimely closed loop in the current month accounts for the percentage of the total hidden danger (excluding hidden danger of failed audit).
TZSB001: there is a management record of full score, otherwise 0 score.
TZSB002: the duty ratio of the detection task completed in time within one year is calculated, the molecule of the percentage value is taken as the score of the item, and the total detection number in the year is 100 when the total detection number in the year is 0.
TZSB003: whether the disabling or resuming use meets the safety requirements.
TZSB004: whether the person holds a valid certificate or training.
TZSB005: the device registers whether the record is complete and correctly full, otherwise it is 0.
DHZY001, DHZY002, DHZY003, SXKJ001, SXKJ002, SXKJ003, GCZY001, GCZY002, GCZY003, DZZY001, DZZY002, DZZY003, LSYD001, LSYD002, LSYD003, DTZY001, DTZY002, DTZY003, SBJX001, SBJX002, SBJX003, SXKJ001, GJZZ001: there is 100 points for this item of content, otherwise 0 points.
AQJC001: there is a clear check criterion of 100 points, otherwise 0 points.
AQJC002: the record of the security check is complete and is full, otherwise, the record is 0.
AQJC003: the statistical result of the security check is correct, which is a full score, otherwise, the statistical result is a score of 0.
YJGL001: the emergency management organization is full, otherwise, the emergency management organization is 0.
YJGL002: and the complete emergency plan is full, otherwise, the emergency plan is 0.
YJGL003: there is a detailed exercise schedule and the exercises are done periodically to full score, otherwise 0 score.
YJGL004: the evaluation results after exercise were good, medium and bad, which were 100, 60 and 0 points, respectively.
YJGL005: the number of maintenance tasks completed with the available emergency supplies is a fraction of the total number of tasks.
SGGL001: the level setting is reasonably full, otherwise, 0 is the level.
SGGL002: whether the timely accident is rapidly reported as full score or not is reported as 0 score or not.
SGGL003: the accident report shows whether all the occurrence factors are full score, otherwise, the accident report shows that the accident report shows 0 score.
SGGL004: event registration is exactly full score, otherwise 0 score.
SGGL005: the industrial injury information is registered as full score, otherwise, 0 score.
SGGL006: and if the accident statistics is accurate and public, the accident statistics is full score, otherwise, the accident statistics is 0 score.
SGGL007: whether there is a modification plan and implementation, the action tracking can be checked as full score, otherwise, 0 score.
AQYJ001: and if the pre-warning parameter configuration description document is full, otherwise, the pre-warning parameter configuration description document is 0.
AQYJ002: the early warning index accords with the fact that the situation is full score, otherwise, the early warning index is 0 score.
AQYJ003: and if the warning report is full, the warning report is 0.
ZDWX001, WHPGL001: the safety management regulation system is full, otherwise, the safety management regulation system is 0.
ZDWX002, GJZZ002, WHPGL002: and registering important dangerous sources for gear establishment, wherein the consistency is full score, otherwise, the score is 0.
ZDWX003, WHPGL003: the checking task duty cycle is completed monthly.
2. The judgment matrix a is constructed for the scoring items including the scoring sub-items on the basis of the correlation shown in fig. 4 using the judgment matrix scale shown in table 3.
Table 3 judges the matrix scale and its meaning
3. Calculating and judging maximum eigenvalue lambda of matrix A by Matlab software max And a corresponding feature vector W.
Such as input feature matrixPressing Enter; input [ W, L ]]=eig (a), eigenvalue decomposition is performed by Enter, where W represents one eigenvector of matrix a per column, L is a diagonal matrix, and the elements on the diagonal represent eigenvalues of matrix a; input [ m n ]]=find (l= max (L)), calculate the row and column where the largest feature root is located, m=1, λ, according to Enter max ＝4.1311。
4. Calculating consistency index of judgment matrix AWherein lambda is max And n is the A order, which is the largest characteristic root of the matrix A.
5. Calculating the random uniformity ratio of matrix AWherein RI is a random uniformity index, obtained by looking up Table 4 below.
TABLE 4 random uniformity index RI
When CR <0.1, the judgment matrix a is considered to have consistency. Otherwise, repeating the second step, and carrying out comparison again, and adjusting the elements in the judgment matrix until the judgment matrix has satisfactory consistency.
6. Input W 1 And (3) carrying out normalization processing on the feature vector which is obtained by carrying out normalization processing on the maximum feature column of the judgment matrix A passing the consistency test to obtain each scoring item weight value.
7. According toWeight calculation of score S for each scoring sub-item i ＝∑S j w j . Calculating the proportion of unqualified items in all scoring sub-items, and obtaining the enterprise security risk level R according to the security production scoring table 1 1 。
8. And referring to the third, fourth and fifth steps, deriving each weight value according to the correlation shown in fig. 3, and calculating the total enterprise security risk score.
9. Evaluating the hierarchical risk level R of an enterprise according to the security scoring table 2 2 。
10. Comprehensive R 1 ,R 2 R is the value of 1 And R is 2 The risk level of the enterprise safety production risk assessment method is the final result of the enterprise safety production risk assessment.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
Claims (4)
1. The enterprise production security risk assessment and management system is characterized by comprising a client, a security production management platform, a data server and a device access platform; wherein,,
the equipment access platform is used for collecting enterprise security feature data in real time and storing the enterprise security feature data into the data server;
the data server comprises a basic database, a distributed file server and a security risk model library, wherein the basic database stores enterprise security characteristic parameters, including real-time acquisition data and environment dynamic parameters; the distributed file server stores a security management platform configuration file and various resource files; storing a preset enterprise security risk assessment model by a security risk model bank;
the safety production management platform reads enterprise safety feature data and an enterprise safety risk assessment model through the data server, calculates enterprise safety production scores in combination with input parameter configuration of the client, and sends the scoring results to the client in a chart form for visual display to a user;
the enterprise production security risk assessment and management method of the system comprises the following steps:
s1, presetting an enterprise safety production risk assessment tree diagram in a database server; the enterprise safety production risk assessment tree diagram is specifically constructed based on daily production activities of enterprises:
s1.1. Summarizing and summarizing related contents of enterprise safety production management, and setting up overall scoring items;
s1.2, based on the total score in the step S1.1, further refining the score according to the actual condition of the enterprise, and analyzing and setting all the sub-items;
s1.3, selectively repeating the operation of the step S2 according to actual demands and the number of the scoring items, namely, dividing the scoring sub-items listed in the step 2 again, setting scoring standards for all sub-scoring points, and directly reserving the sub-items as the sub-items if part of the scoring sub-items cannot finely divide the scoring points again;
s2, when the safety production management platform server is started, traversing leaf nodes of the tree diagram in the step S1 to obtain final scoring sub-items, traversing the tree diagram, and establishing corresponding scoring standards for the scoring items on each leaf node;
s3, constructing a mesh chart of enterprise safety production risk assessment by referring to enterprise safety management experience according to an enterprise safety production risk assessment tree chart preset in a database server; analyzing the coupling relation among all the scoring sub-items, and associating all the scoring sub-items by using a directed graph so as to form a mesh graph for enterprise safety production risk assessment, wherein the arrow head scoring sub-items influence the arrow tail scoring sub-items by a certain weight;
s4, the safety production management platform server reads the mesh map from the safety risk model library, and constructs a judgment matrixCalculating weight value +.>And brought into reading from the underlying databaseThe obtained real-time data and environmental parameters calculate the score of each scoring sub-item>The method specifically comprises the following steps:
s4.1. nodes in mesh graphThe scoring value is->Influence of child node, construction->The order judgment matrix calculates the maximum eigenvalue +.>And corresponding feature vector->；
S4.2, for the maximum characteristic value in the stepsPerforming consistency test, if not, repeating the step S4;
s4.2. pair of eigenvectorsNormalizing to obtain->Namely node->Lower child node weight->Distribution;
s4.4. nodeThe score value of (2) is->Wherein->，/>For node->The total number of child nodes;
s5, the safety production management platform server calculates the proportion of unqualified items in all scoring sub-items, and obtains the enterprise safety risk level according to the safety production scoring table 1；
Table 1 safety production risk scoring table 1
S6, the safety production management platform calculates node weights in the tree diagram through constructing a judgment matrix according to the tree diagram in the step S1, so that the score of each scoring sub-item is calculated, and enterprise safety production risk assessment level scores are obtained;
s7, the safety production management platform scores the levels of the step S6 according to the safety production scoring table 2 to obtain the enterprise safety risk level；
Table 2 safety production risk scoring table 2
S8, the safety production management platform server synthesizes the enterprise safety risk levels in the step S5 and the step S7And taking the item with higher risk level as the final result of the enterprise risk level.
2. The system of claim 1, wherein the enterprise production security risk assessment and management method of the system further comprises the steps of:
presetting a security risk assessment model to a data server;
the client transmits relevant configuration parameters to the safe production management platform;
the equipment access platform collects real-time environment data, stores the real-time environment data into a basic database and sends the real-time environment data to the safety production management platform;
substituting the environmental data and the configuration parameters into a security risk assessment model by the security production management platform, calculating to obtain a risk assessment result, and pushing the risk assessment result to the client;
the client displays the risk assessment results to the user in the form of a chart and the like.
3. The system of claim 1, wherein the scoring population scoring item content specifically comprises 11 items of education training, management personnel, fire facilities, hidden trouble shooting, special equipment safety management, job safety management, safety inspection, emergency management, accident management, safety precaution, and hazard source management.
4. The system of claim 1, wherein the educational training comprises 4 sub-items of training material, training program, training record, on-line exam;
the management personnel comprise obligation fire departments and personnel qualification 2 subitems; the fire-fighting facility comprises 5 sub-items of state, inspection, detection, maintenance and recording;
the hidden danger correction comprises major hidden danger, and the hidden danger does not close 2 sub-items in time;
the special equipment safety management comprises equipment type management, detection, deactivation, recovery and statistics of 5 sub-items;
the operation safety management comprises 8 sub-items of fire operation, limited space operation, overhead operation, hoisting operation, temporary electricity operation, soil operation, equipment maintenance operation and operation statistics;
the security check comprises 3 sub-items of check standard, check record and check statistics;
the emergency management comprises an emergency organization, an emergency plan, an exercise evaluation and 5 sub-items of emergency materials;
the accident management comprises 7 sub-items of accident level setting, accident report, event registration, industrial injury information, accident statistics and action tracking;
the safety precaution comprises precaution parameter configuration, precaution index and precaution report 3 sub items;
the dangerous source management comprises 3 sub-items of important dangerous source management, key device key parts and dangerous chemical management.
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| CN116384819A (en) * | 2023-03-31 | 2023-07-04 | 山东省地质矿产勘查开发局第八地质大队(山东省第八地质矿产勘查院) | Enterprise safety code information data management system |
| CN117151478B (en) * | 2023-10-31 | 2024-01-26 | 南京南工应急科技有限公司 | A risk early warning method and system for chemical enterprises based on convolutional neural network |
| CN119046954A (en) * | 2024-11-04 | 2024-11-29 | 江西通慧科技集团股份有限公司 | Special equipment safety monitoring management cloud platform |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104142662A (en) * | 2013-05-09 | 2014-11-12 | 洛克威尔自动控制技术股份有限公司 | Industrial data analytics in a cloud platform |
| CN104283967A (en) * | 2014-10-23 | 2015-01-14 | 武汉华大优能信息有限公司 | Third-party data service system based on Internet-of-Things data collecting |
| CN105678446A (en) * | 2015-12-31 | 2016-06-15 | 浙江图讯科技股份有限公司 | Method used for enterprise safety production risk early warning |
| CN106355319A (en) * | 2016-08-26 | 2017-01-25 | 南京安全无忧网络科技有限公司 | Safety risk assessment system and method |
| CN107409126A (en) * | 2015-02-24 | 2017-11-28 | 思科技术公司 | Systems and methods for securing an enterprise computing environment |
-
2018
- 2018-08-08 CN CN201810898421.9A patent/CN109508848B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104142662A (en) * | 2013-05-09 | 2014-11-12 | 洛克威尔自动控制技术股份有限公司 | Industrial data analytics in a cloud platform |
| CN104283967A (en) * | 2014-10-23 | 2015-01-14 | 武汉华大优能信息有限公司 | Third-party data service system based on Internet-of-Things data collecting |
| CN107409126A (en) * | 2015-02-24 | 2017-11-28 | 思科技术公司 | Systems and methods for securing an enterprise computing environment |
| CN105678446A (en) * | 2015-12-31 | 2016-06-15 | 浙江图讯科技股份有限公司 | Method used for enterprise safety production risk early warning |
| CN106355319A (en) * | 2016-08-26 | 2017-01-25 | 南京安全无忧网络科技有限公司 | Safety risk assessment system and method |
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