Safety production risk research and judgment method and equipment based on industry and trade enterprise monitoring data

Abstract

The invention discloses a safety production risk research and judgment method based on industrial and trade enterprise monitoring data. The method comprises the steps of obtaining equipment operation state real-time data of an industrial and trade enterprise; establishing a fuzzy relation between the index set and the comprehensive safety evaluation set; establishing a fuzzy relationship between the index set and the comprehensive safety evaluation set, obtaining an alarm level of each index of the index set corresponding to each monitoring time period, and constructing a normalized fuzzy evaluation matrix; calculating a comprehensive evaluation set of the industry and trade enterprises; determining a score range corresponding to each industry and trade enterprise risk comprehensive evaluation grade; and obtaining a score of the industry and trade enterprise risk comprehensive evaluation grade. According to the invention, the monitoring data risk level fuzzy mathematical model is established for the equipment operation state real-time data of the industry and trade enterprise, and dynamic research and judgment of the comprehensive risk evaluation level of the industry and trade enterprise are realized. And the risk management and control capability of the enterprise is further improved, and the guarantee of targeted suggestions is provided for enterprise production.

Description

Technical Field

[0001] The present invention relates to the technical field of work safety in industrial and trading enterprises, and particularly to a method and device for judging work safety risks based on monitoring data of industrial and trading enterprises. Background Art

[0002] The technological processes of industrial and trading enterprises are complex, with numerous operation links and a large number of employees, facing many unstable potential major risks and hazards. The construction of a work safety risk monitoring and early warning system, through the introduction of advanced monitoring equipment in key high-risk operation areas, realizes real-time monitoring and early warning of equipment operation and personnel work. Through data analysis technology, it can quantitatively evaluate the on-site safety risks of industrial and trading enterprises. The fuzzy mathematics safety evaluation method uses the real-time online monitoring data obtained by the work safety risk monitoring and early warning system of industrial and trading enterprises to conduct a safety evaluation of the enterprise's operation status, providing a scientific and reliable decision-making basis for the enterprise and relevant departments.

[0003] The existing work safety risk monitoring and early warning system uses video monitoring analysis equipment and Internet of Things collection equipment to collect the data of monitoring points of hazardous chemical enterprises in real time, and uploads the abnormal data to the monitoring and early warning system for display and statistical analysis. The video intelligent analysis equipment can identify illegal behaviors or abnormal information, generate early warning information, and feedback it to relevant enterprises and regulatory departments at the same time; the Internet of Things equipment sets an alarm threshold. Once the data at the monitoring point exceeds the threshold, the monitoring and early warning system will immediately generate early warning information and feedback it to relevant enterprises and regulatory departments. However, due to the differences in the internal algorithms of the system and the empirical nature of setting the thresholds of monitoring points, it is difficult to automatically realize the dynamic assessment of the risks of enterprise units. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for judging work safety risks based on monitoring data of industrial and trading enterprises in view of the above problems existing in the prior art.

[0005] The above object of the present invention is achieved by the following technical means:

[0006] A method for judging work safety risks based on monitoring data of industrial and trading enterprises includes the following steps:

[0007] Step 1: Obtain the real-time data of the equipment operation status of industrial and trading enterprises;

[0008] Step 2: Establish an index set A and a comprehensive safety evaluation set V, and establish a fuzzy relationship between the index set A and the comprehensive safety evaluation set V.

[0009] Step 3: Based on the real-time data of the equipment operation status of industrial and trading enterprises in multiple consecutive monitoring time periods, establish a fuzzy relationship between the index set A and the comprehensive safety evaluation set V, obtain the alarm levels of each index of the index set A corresponding to each monitoring time period, and construct a normalized fuzzy evaluation matrix R.

[0010] Step 4: Calculate the comprehensive evaluation set B of industrial and trading enterprises, B = M × R. Normalize the comprehensive evaluation set B to obtain the normalized set B′ = [b′ 1 , b′ 2 , b′ 3 , b′ 4 , where b′ 1 , b′ 2 , b′ 3 , b′ 4 are the elements in the normalized set B′ respectively, and M is the index weight set;

[0011] Step 5: Determine the score range corresponding to the comprehensive risk evaluation level of each industrial and trading enterprise;

[0012] Step 6: Multiply the upper limit of the score range corresponding to the comprehensive risk evaluation level of each industrial and trading enterprise by the corresponding element of the normalized set B′ respectively and then sum them to obtain the score C of the comprehensive risk evaluation level of the industrial and trading enterprise. Judge the comprehensive risk evaluation level of the industrial and trading enterprise according to the calculated score C of the comprehensive risk evaluation level of the industrial and trading enterprise.

[0013] As described above, the index set A includes four indexes, namely equipment offline A 1 , data alarm A 2 , number of repeated alarms A 3 , alarm elimination duration A 4 , and maximum alarm duration A 5 ;

[0014] Comprehensive safety evaluation set V = {V 1 , V 2 , V 3 , V 4}, V 1 , V 2 , V 3 , V 4 are first-level alarm, second-level alarm, third-level alarm, and fourth-level alarm respectively;

[0015] The comprehensive risk evaluation levels of industrial and trading enterprises are divided into low risk, general risk, relatively large risk, and first-level major risk.

[0016] As described above, each row of the normalized fuzzy evaluation matrix R corresponds to each index of the index set A respectively, each column of the normalized fuzzy evaluation matrix R corresponds to the alarm level respectively, and each element of the normalized fuzzy evaluation matrix R is the normalized number of times that the index corresponding to the row is judged as the alarm level corresponding to the column.

[0017] As described above, establishing the fuzzy relationship between the index set A and the comprehensive safety evaluation set V is to establish the fuzzy relationship between each index of the index set A corresponding to the first-level alarm V1 、 Secondary alarm V 2 、 Tertiary alarm V 3 、 Quaternary alarm V 4 The alarm range.

[0018] As described above, the normalized fuzzy evaluation matrix R is constructed based on the following steps: Normalize the number of times of each alarm level of the same index in the index set A for each monitoring time period to obtain the normalized number of times of each alarm level of the same index.

[0019] As described above, equipment offline A 1 The full score is 10 points, and 1 point is added for each offline equipment.

[0020] Data alarm A 2 The full score is 10 points. 1 point is added for each low-threshold alarm that appears at the monitoring point, and 2 points are added for each high-threshold alarm that appears.

[0021] Number of repeated alarms A 3 The full score is 20 points. If the cumulative number of alarms at the monitoring point ≥ 5 times, 5 points are added to the corresponding monitoring point; if 2 ≤ cumulative number of alarms at the monitoring point < 5, 2 points are added to the corresponding monitoring point.

[0022] Alarm elimination duration A 4 The full score is 30 points. If the alarm elimination duration corresponding to the monitoring point ≥ 30 min, 2 points are added to the alarm elimination duration of the corresponding monitoring point for each 1 min increase in the alarm elimination duration relative to 30 min.

[0023] Maximum alarm duration A 5 The full score is 30 points. If the maximum alarm duration corresponding to the monitoring point ≥ 30 min, 2 points are added for each 10 min increase in the maximum alarm duration relative to 30 min.

[0024] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned judgment method are implemented.

[0025] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned judgment method are implemented.

[0026] A computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned judgment method are implemented.

[0027] Compared with the prior art, the beneficial effects and advantages of the present invention:

[0028] The present invention provides a method for judging safety production risks based on monitoring data of industrial and trading enterprises, as well as corresponding devices, readable storage media, and program products. A fuzzy mathematical model of the risk level of monitoring data is established for the real-time data of the operation status of the equipment in industrial and trading enterprises, realizing the dynamic judgment of the comprehensive evaluation level of risks in industrial and trading enterprises. It solves the problem that the risk data of production equipment in industrial and trading enterprises is complex and it is difficult to scientifically judge the risk level, further improves the enterprise's risk control ability, and provides a guarantee for targeted suggestions for enterprise production. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] For the convenience of those of ordinary skill in the art to understand and implement the present invention, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0031] Embodiment 1:

[0032] As Figure 1 shown, the method for judging safety production risks based on monitoring data of industrial and trading enterprises includes the following steps:

[0033] Step 1: Obtain the real-time data of the operation status of the equipment in industrial and trading enterprises. During the production process of industrial and trading enterprises, intelligent acquisition hardware for monitoring vibration, temperature, pressure, etc. is installed at corresponding monitoring points of production equipment to monitor the real-time data of the operation status of the equipment, and technologies such as intelligent sensing, Internet of Things, and mobile communication are used to upload the real-time data of the operation status of the equipment to the data acquisition server, and then the real-time data of the operation status of the equipment is processed and stored.

[0034] Step 2: Establish an index set A and a comprehensive safety evaluation set V, and establish a fuzzy relationship between the index set A and the comprehensive safety evaluation set V,

[0035] Establish a fuzzy mathematical model for the risk level of the monitoring data of industrial and trading enterprises. Among them, first establish the index set A: A = {A 1 , A 2 , A 3 , A 4 , A 5}, the index set A includes four indexes, namely: equipment offline A 1 , data alarm A 2 , number of repeated alarms A 3 , alarm elimination duration A 4 , and maximum alarm duration A 5 ,

[0036] Equipment offline A 1The full score is 10 points, and 1 point is added for each offline device.

[0037] Data Alarm A 2 The full score is 10 points. Among them, 1 point is added for each low-threshold alarm at the monitoring point, and 2 points are added for each high-threshold alarm.

[0038] Number of Repeated Alarms A 3 The full score is 20 points. Repeated alarms mean that the cumulative number of alarms at the monitoring point within 24 hours is greater than or equal to 2 times. Among them, when the cumulative number of alarms at the monitoring point is ≥ 5 times, 5 points are added to the corresponding monitoring point; when 2 ≤ cumulative number of alarms at the monitoring point < 5, 2 points are added to the corresponding monitoring point.

[0039] Alarm Elimination Duration A 4 The full score is 30 points. Alarm elimination duration means the cumulative alarm duration within 24 hours at the monitoring point / the cumulative number of alarms. Among them, when the alarm elimination duration corresponding to the monitoring point is ≥ 30 min, for each additional 1 min of the alarm elimination duration compared to 30 min, 2 points are added to the corresponding monitoring point's alarm elimination duration.

[0040] Maximum Alarm Duration A 5 The full score is 30 points, representing the maximum continuous duration without alarm elimination. Among them, when the maximum alarm duration corresponding to the monitoring point is ≥ 30 min, for each additional 10 min of the maximum alarm duration compared to 30 min, 2 points are added.

[0041] Construct the index weight set M = [0.1, 0.1, 0.2, 0.3, 0.3].

[0042] Establish the fuzzy relationship between each index in the index set A and the evaluation object, divide according to the alarm level, for the risk indicators of industrial and trade enterprises, construct the comprehensive safety evaluation set V, and establish the analog relationship between the index set A and the comprehensive safety evaluation set V.

[0043] Comprehensive safety evaluation set V: V = {V 1 , V 2 , V 3 , V 4}

[0044] In the comprehensive safety evaluation set V, V 1 , V 2 , V 3 , V 4 are the first-level alarm, second-level alarm, third-level alarm, and fourth-level alarm respectively.

[0045] Establish the fuzzy relationship between the index set A and the comprehensive safety evaluation set V, that is, establish the correspondence between each index in the index set A and the first-level alarm V 1 , second-level alarm V 2 , third-level alarm V 3 , fourth-level alarm V4 The alarm range. Table 1 shows the alarm ranges corresponding to each index in index set A for level 1 alarm V 1 , level 2 alarm V 2 , level 3 alarm V 3 , and level 4 alarm V 4 .

[0046] Table 1

[0047] Index type <![CDATA[Fourth-level alarm V 4 > <![CDATA[Level 3 Alarm V 3 > <![CDATA[Secondary alarm V 2 > <![CDATA[Level 1 Alarm V 1 > <![CDATA[A 1 > (0,2) (3,4) (5,7) (8，10) <![CDATA[A 2 > (0,2) (3,4) (5,7) (8，10) <![CDATA[A 3 > (0,5) (6，10) (11，15) (16，20) <![CDATA[A 4 > (0,7) (8.14) (15，22) (23，30) <![CDATA[A 5 > (0,7) (8.14) (15，22) (23，30)

[0048] Step 3: Based on the real-time data of the equipment operation status obtained from multiple consecutive monitoring time periods of industrial and trading enterprises, establish the fuzzy relationship between index set A and the comprehensive safety evaluation set V, obtain the alarm levels of each index in index set A corresponding to each monitoring time period, normalize the number of times of each alarm level of the same index in index set A for each monitoring time period, construct the normalized fuzzy evaluation matrix R. Each row of the normalized fuzzy evaluation matrix R corresponds to each index in index set A, and each column of the normalized fuzzy evaluation matrix R corresponds to the alarm levels (level 1 alarm V 1 , level 2 alarm V 2 , level 3 alarm V 3 , level 4 alarm V 4 ). Each element of the normalized fuzzy evaluation matrix R is the normalized number of times that the index corresponding to the row is judged as the alarm level corresponding to the column.

[0049]

[0050] Among them, r ij is the number of times that the index in the i-th row is judged as the alarm level in the j-th column, where i ∈ {1 - 5} and j ∈ {1 - 4}.

[0051] Step 4: Calculate the comprehensive evaluation set B of the industrial and trading enterprise, B = M × R = [b 1 , b 2 , b 3 , b 4 . b 1 , b 2 , b 3 , b 4 are the elements of the comprehensive evaluation set B. Calculate the weight set M and the normalized fuzzy rating matrix R using the product calculation method of simulation mathematics, calculate the comprehensive evaluation set B of the industrial and trading enterprise, and normalize the elements of the comprehensive evaluation set B of the industrial and trading enterprise to obtain the normalized set B' = [b' 1 , b' 2 , b' 3 , b' 4 . Further determine the comprehensive risk evaluation level of the industrial and trading enterprise according to the normalized set B'.

[0052] Step 5: Classify the comprehensive risk assessment levels of industrial and trading enterprises into low risk, general risk, major risk, and significant risk, corresponding to four warning levels: blue warning, yellow warning, orange warning, and red warning respectively. The total score for the assessment is 100 points, and the score ranges corresponding to the comprehensive risk assessment levels of industrial and trading enterprises are as follows: low risk (blue warning) is 0 - 60 points, general risk (yellow warning) is 60 - 80 points, major risk (orange warning) is 80 - 90 points, and significant risk (red warning) is 90 - 100 points.

[0053] Step 6: Multiply the upper limits of the score ranges corresponding to each comprehensive risk assessment level of industrial and trading enterprises by the corresponding elements in the normalized set B′ and then sum them up to obtain the score C of the comprehensive risk assessment level of industrial and trading enterprises. For example, multiply the upper limit of the score range for low risk, which is 60, by b′ 1 (corresponding to level 4 alarm), multiply the upper limit of the score range for general risk, which is 80, by b′ 2 (corresponding to level 3 alarm), multiply the upper limit of the score range for major risk, which is 90, by b′ 3 (corresponding to level 2 alarm), multiply the upper limit of the score range for significant risk, which is 100, by b′ 4 (corresponding to level 1 alarm), and calculate the score C of the comprehensive risk assessment level of industrial and trading enterprises as follows:

[0054] C = b′ 1 ×60 + b′ 2 ×80 + b′ 3 ×90 + b′ 4 ×100

[0055] Based on the calculated score C of the comprehensive risk assessment level of industrial and trading enterprises, judge the comprehensive risk assessment level of industrial and trading enterprises (low risk, general risk, major risk, significant risk).

[0056] According to the comprehensive risk assessment level of industrial and trading enterprises, formulate corresponding supervision measures and conduct classified early warnings for different comprehensive risk assessment levels of industrial and trading enterprises. For example, the following classified measures can be adopted:

[0057] Red warning (significant risk): Immediately activate the emergency plan, take emergency measures to ensure the safety of personnel, property, and the environment. At the same time, report to the superior government and relevant departments in a timely manner and cooperate to address the risks.

[0058] Orange warning (major risk): Strengthen risk monitoring and assessment, formulate and implement targeted preventive measures to prevent the risk from escalating. At the same time, regularly report the risk prevention and response situation to the superior government and relevant departments.

[0059] Yellow Warning (General Risk): For the evaluation units under yellow warning, risk inspections and investigations should be carried out, and daily supervision should be strengthened to ensure that risks are within a controllable range. At the same time, information communication and reporting work should be done well, and changes in risks and countermeasures should be reported in a timely manner.

[0060] Blue Warning (Low Risk): For the evaluation units under blue warning, risk education and training should be strengthened to enhance employees' awareness of risk prevention. At the same time, a sound risk prevention system should be established, and risk assessments and potential hazard investigations should be carried out regularly.

[0061] The present invention establishes a fuzzy mathematical model for monitoring data risk levels based on the real-time data of the equipment operation status of industrial and trading enterprises, and realizes the dynamic judgment of the comprehensive risk evaluation level of industrial and trading enterprises. It solves the problem that the risk data of production equipment in industrial and trading enterprises is complex and it is difficult to scientifically judge the risk level, further improves the enterprise's risk control ability, and provides a guarantee for targeted suggestions for enterprise production.

[0062] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments.

[0063] Embodiment 2:

[0064] In this embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0065] Embodiment 3:

[0066] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0067] Embodiment 4:

[0068] In this embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0069] It should be noted that the embodiments described in the present invention are only examples of the spirit of the present invention. Those skilled in the technical field to which the present invention belongs can make various modifications or supplements to the described embodiments or use similar ways to replace them, but will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A method for evaluating production safety risks based on monitoring data of industrial and trade enterprises, characterized in that: The following steps are involved: Step 1: Obtain real-time data on the equipment operation status of industrial and trade enterprises; Step 2: Establish the indicator set A and the comprehensive safety evaluation set V, and establish the fuzzy relationship between the indicator set A and the comprehensive safety evaluation set V. Step 3: Based on the real-time data of equipment operation status in multiple continuous monitoring time periods of industrial and trade enterprises, establish the fuzzy relationship between the indicator set A and the comprehensive safety evaluation set V, obtain the alarm level of each indicator of the indicator set A corresponding to each monitoring time period, and construct the normalized fuzzy evaluation matrix R; Step 4: Calculate the comprehensive evaluation set B = M × R of the industrial and trade enterprises, normalize the comprehensive evaluation set B, and obtain the normalized set B′ = [b′1, b′2, b′3, b′4], where b′1, b′2, b′3, b′4 are the elements in the normalized set B′, and M is the indicator weight set; Step 5: Determine the scoring range corresponding to the comprehensive risk assessment level of each industrial and trade enterprise; Step 6: Multiply the upper limits of the scoring range corresponding to each comprehensive risk evaluation level of industrial and trade enterprises by the elements corresponding to the normalized set B′ and then sum them up to obtain the score C of the comprehensive risk evaluation level of the industrial and trade enterprises. According to the calculated score C of the comprehensive risk evaluation level of the industrial and trade enterprises, judge the comprehensive risk evaluation level of the industrial and trade enterprises.

2. The method for evaluating production safety risks based on industrial and trade enterprise monitoring data according to claim 1 is characterized in that: The indicator set A includes four indicators, namely, device offline A1, data alarm A2, number of repeated alarms A3, alarm elimination time A4, and maximum alarm duration A5; Comprehensive safety evaluation set V = {V1, V2, V3, V4}, V1, V2, V3, V4 are level 1 alarm, level 2 alarm, level 3 alarm, and level 4 alarm respectively; The comprehensive risk assessment levels for industrial and trade enterprises are divided into low risk, general risk, high risk, and level one major risk.

3. The method for evaluating production safety risks based on industrial and trade enterprise monitoring data according to claim 2 is characterized in that: Each row of the normalized fuzzy evaluation matrix R corresponds to each indicator of the indicator set A, each column of the normalized fuzzy evaluation matrix R corresponds to the alarm level, and each element of the normalized fuzzy evaluation matrix R is the normalized number of times the indicator of the corresponding row is determined as the alarm level of the corresponding column.

4. The method for evaluating production safety risks based on industrial and trade enterprise monitoring data according to claim 2 is characterized in that: The fuzzy relationship between the indicator set A and the comprehensive safety evaluation set V is established to establish that each indicator of the indicator set A corresponds to the alarm range of the first level alarm V1, the second level alarm V2, the third level alarm V3, and the fourth level alarm V4.

5. The method for evaluating production safety risks based on industrial and trade enterprise monitoring data according to claim 3 is characterized in that: The normalized fuzzy evaluation matrix R is constructed based on the following steps: normalizing the number of each alarm level of the same indicator in the indicator set A of each monitoring time period to obtain the normalized number of each alarm level of the same indicator.

6. The method for evaluating production safety risks based on industrial and trade enterprise monitoring data according to claim 2 is characterized in that: The full score of A1 for device offline is 10 points, and 1 point is added for each device offline; The full score of data alarm A2 is 10 points. Each time a low threshold alarm occurs at a monitoring point, 1 point will be added, and each time a high threshold alarm occurs, 2 points will be added. The maximum score of repeated alarm times A3 is 20 points. If the monitoring point has 5 or more cumulative alarms, the corresponding monitoring point will be awarded 5 points. If the monitoring point has 2 or less cumulative alarms <5, the corresponding monitoring point will be awarded 2 points. The full score of the alarm extinction time A4 is 30 points. The alarm extinction time corresponding to the monitoring point is ≥ 30 minutes. For every 1 minute increase in the alarm extinction time relative to 30 minutes, the alarm extinction time of the corresponding monitoring point increases by 2 points. The maximum alarm duration A5 has a full score of 30 points. The maximum alarm duration corresponding to the monitoring point is ≥30 minutes. For every 10 minutes increase in the maximum alarm duration relative to 30 minutes, 2 points will be added.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the analysis method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the analysis method according to any one of claims 1 to 5 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the analysis method described in any one of claims 1 to 5 are implemented.

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