A Construction Method and System for a Process Safety Management Grading Evaluation Model

Abstract

The present invention relates to the technical field of information processing, and in particular to a method and system for constructing a process safety management grading evaluation model, including step S1 of collecting evaluation data; step S2 of processing the evaluation data; step S3 of constructing an operation model and a state model according to the evaluation data of the previous evaluation period; step S4 of obtaining the enterprise operation safety level and the enterprise state safety level according to the operation model and the state model; step S5 of setting a weight parameter matrix according to the historical grading evaluation data; step S6 of performing a grading evaluation on the enterprise process safety management according to the weight parameter matrix; step S7 of performing a specific correction on the screening process of the historical grading evaluation data of the enterprise; and step S8 of performing a general correction on the screening process of the historical grading evaluation data. The present invention improves the applicability of the process safety management grading evaluation and refines the information analysis, thereby improving the efficiency of the process safety management grading evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing, and particularly relates to a method and system for constructing a process safety management grading evaluation model. Background Art

[0002] Process safety management grading evaluation is an important means to prevent and reduce production accidents. Through process safety management grading evaluation, enterprises can more clearly understand the safety status of each link in the process, provide a basis for decision-making in the production process, and the state has clear regulatory requirements for process safety management. Enterprises need to regularly evaluate their own process safety status to ensure compliance with relevant regulatory requirements. Through process safety management grading evaluation, enterprises can demonstrate their process safety management level, and enterprises need to continuously improve their process safety management level according to the evaluation results. Enterprises should attach great importance to the process safety management grading evaluation work to ensure its effective implementation.

[0003] Chinese Patent Publication No.: CN106094747B discloses a method for building a multi-resolution factory safety model for petrochemical enterprises, including: (1) establishing a multi-level factory topology model; (2) establishing a mapping relationship between adjacent levels of the multi-level factory topology model; (3) constructing a node information set model, a hazard source information set model, and an accident risk information set model in each level of the topology model; (4) establishing a mapping relationship between the information set models in each level of the topology model and performing risk assessment to form a multi-level risk assessment information set model; (5) according to the node geographical location information in each level of the topology model, associating each information set model with the multi-level factory topology model to form a multi-resolution factory safety model. However, this solution does not classify data and adjust the weight parameters of various types of data, and this solution is not applicable to the process safety management grading evaluation of multiple objects. Summary of the Invention

[0004] Therefore, the present invention provides a method and system for constructing a process safety management grading evaluation model to overcome the problem of low grading evaluation efficiency caused by few applicable objects and insufficiently refined information analysis when chemical enterprises conduct process safety management grading evaluation.

[0005] To achieve the above object, on the one hand, the present invention provides a method for constructing a process safety management grading evaluation model, including:

[0006] Step S1, collecting the equipment working hours, equipment maintenance time interval, safety facility status, number of personnel wearing safety equipment, standing area of non-wearing personnel, and standing duration of non-wearing personnel during the evaluation period to obtain evaluation data;

[0007] Step S2, processing the evaluation data;

[0008] Step S3, construct an operation model and a status model based on the evaluation data of the previous evaluation period;

[0009] Step S4, obtain the enterprise operation safety level and the enterprise status safety level according to the operation model and the status model;

[0010] Step S5, set the weight parameter matrix according to the historical grading evaluation data;

[0011] Step S6, conduct a grading evaluation of the enterprise process safety management according to the weight parameter matrix;

[0012] Step S7, perform a specific correction on the screening process of the historical grading evaluation data of the enterprise according to the number of process equipment failures of the enterprise in the current evaluation period;

[0013] Step S8, perform a general correction on the screening process of the historical grading evaluation data according to the number of specific corrections of the grading evaluation.

[0014] Further, in the step S2, when processing the evaluation data, obtain the service life t2 of each device, calculate the device working parameter T according to the device working duration t1, set T = t1 / t2, calculate the personnel wearing ratio p according to the number of personnel wearing safety equipment n and the total number of people n0 in the current working area, set p = n / n0, compare the personnel wearing ratio p with the preset personnel wearing ratio p0, and judge the personnel wearing situation according to the comparison result, where:

[0015] When p≥p0, it is determined that the personnel wearing situation is normal;

[0016] When p < p0, it is determined that the personnel wearing situation is abnormal, and set an adjustment coefficient v, 0.87 < v < 0.98, adjust the safety facility coverage rate u according to the adjustment coefficient v, and the adjusted safety facility coverage rate is uv, set uv = v×u.

[0017] Further, in the step S2, correct the judgment process of the personnel wearing situation according to the standing area of the non-wearing personnel, where:

[0018] When the standing area of the non-wearing personnel is a first-level dangerous area, compare the standing duration i of the non-wearing personnel with the first preset standing duration i1, where:

[0019] If i≤i1, do not correct the judgment process of the personnel wearing situation;

[0020] If i > i1, the judgment process of personnel wearing situation is corrected. A correction coefficient J is set, where 1.02 < J < 1.2. The preset personnel wearing ratio p0 in the judgment process of personnel wearing situation is corrected. The corrected preset personnel wearing ratio is pj0. Set pj0 = J × p0, and when pj0 > 1, pj0 takes the value of 1;

[0021] When the standing area of the un-worn personnel is a secondary dangerous area, the standing duration i of the un-worn personnel is compared with the second preset standing duration i2, where:

[0022] If i ≤ i2, the judgment process of personnel wearing situation is not corrected;

[0023] If i > i2, the judgment process of personnel wearing situation is corrected. A correction coefficient J is set, where 1.01 < J < 1.15. The preset personnel wearing ratio p0 in the judgment process of personnel wearing situation is corrected. The corrected preset personnel wearing ratio is pj0. Set pj0 = J × p0, and when pj0 > 1, pj0 takes the value of 1;

[0024] When the standing area of the un-worn personnel is a safe area, the judgment process of personnel wearing situation is not corrected;

[0025] The equipment operating parameters and equipment maintenance time intervals are used as operating data, and the safety facility status and safety facility coverage rate are used as status data.

[0026] Furthermore, in step S3, when constructing the operating model and the status model, the operating model is constructed according to the safety facility status and the corresponding operating safety level in the previous evaluation period, and the status model is constructed according to the status data and the corresponding status safety level in the previous evaluation period.

[0027] Furthermore, in step S4, the evaluation data of the current evaluation period is input into the operating model and the status model, and the enterprise operating safety level and the enterprise status safety level are output respectively.

[0028] Furthermore, in step S5, when setting the weight parameter matrix, the weight parameter matrix is initialized. The weight parameter matrix is set as [w1, w2, w3], where w1 is the weight parameter of the enterprise operating safety level, w2 is the weight parameter of the enterprise status safety level, w3 is the weight parameter of the enterprise average process safety level, and w1 + w2 + w3 = 1;

[0029] The weight parameter matrix is trained according to the historical grading evaluation data, and when the correct rate reaches the preset correct rate, the weight parameter matrix is output.

[0030] Further, in the step S5, the number of enterprise process equipment failures y in each historical evaluation period is compared with the preset average standard number of enterprise process equipment failures y0, and the historical grading evaluation data is screened according to the comparison result, where:

[0031] When y ≤ y0, the grading evaluation data of this historical evaluation period is selected as the historical grading evaluation data;

[0032] When y > y0, the grading evaluation data of this historical evaluation period is not selected as the historical grading evaluation data.

[0033] Further, in the step S6, when conducting a grading evaluation of enterprise process safety management, the enterprise process safety level H is calculated according to the enterprise operation safety level a, the enterprise status safety level b, the enterprise average process safety level c, and the weight parameter matrix. It is set that H = a×w1 + b×w2 + c×w3, and the enterprise process safety level is used as the grading evaluation result.

[0034] Further, in the step S7, the number of enterprise process equipment failures y1 in the current evaluation period is compared with the preset level standard number of enterprise process equipment failures y10, and a judgment on specific correction is made according to the comparison result, where:

[0035] When y1 ≤ y10, it is determined that no specific correction is performed;

[0036] When y1 > y10, it is determined that specific correction is performed. The specific correction coefficient r1 is set, and r1 = 0.85 + e -0.5×(y1-y10)-2 , where e is the base of the natural logarithm. The preset average standard number of enterprise process equipment failures y0 in the screening process of the historical grading evaluation data of this enterprise is specifically corrected according to the specific correction coefficient r1. The specifically corrected preset average standard number of enterprise process equipment failures is yr10, and it is set that yr10 = r1×y0;

[0037] The number of specific corrections m for grading evaluation is compared with the preset number of specific corrections m0 for grading evaluation, and a judgment on universal correction is made according to the comparison result, where:

[0038] When m ≤ m0, it is determined that no universal correction is performed;

[0039] When m > m0, it is determined that universal correction is performed. The universal correction coefficient r2 is set, and r2 = 0.9 + e -0.7×(m-m0)-2.5 , where e is the base of the natural logarithm. The preset average standard number of enterprise process equipment failures y0 in the screening process of the historical grading evaluation data is universally corrected according to the universal correction coefficient r2. The universally corrected preset average standard number of enterprise process equipment failures is yr20, and it is set that yr20 = r2×y0.

[0040] On the other hand, the present invention also provides a construction system for a process safety management grading evaluation model, including:

[0041] A data acquisition module for collecting the equipment working hours, equipment maintenance time intervals, safety facility status, number of personnel wearing safety equipment, standing areas of non-wearing personnel, and standing durations of non-wearing personnel within an evaluation period to obtain evaluation data;

[0042] A data processing module for processing the evaluation data;

[0043] A model construction module for constructing an operation model and a status model based on the evaluation data of the previous evaluation period;

[0044] An operation status evaluation module for obtaining the enterprise operation safety level and the enterprise status safety level based on the operation model and the status model;

[0045] A weight parameter setting module for setting a weight parameter matrix according to historical grading evaluation data;

[0046] A grading evaluation module for conducting a grading evaluation of the enterprise process safety management according to the weight parameter matrix;

[0047] A specificity correction module for specifically correcting the screening process of the enterprise's historical grading evaluation data according to the number of enterprise process equipment failures in the current evaluation period;

[0048] A universality correction module for generally correcting the screening process of the historical grading evaluation data according to the number of specificity corrections in the grading evaluation.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows. The method collects evaluation data through step S1 to facilitate the process safety management grading evaluation based on the evaluation data. The method processes the evaluation data through step S2 to classify the data, which is convenient for subsequent model construction. The method constructs an operation model and a state model according to the evaluation data of the previous evaluation period through step S3 to classify and analyze the data based on the operation model and the state model. The method obtains the enterprise operation safety level and the enterprise state safety level according to the operation model and the state model through step S4 to facilitate the subsequent process safety management grading evaluation based on the enterprise operation safety level and the enterprise state safety level. The method sets the weight parameter matrix according to the historical grading evaluation data through step S5 and conducts the process safety management grading evaluation of the enterprise according to the weight parameter matrix through step S6 to facilitate the precise grading evaluation of the enterprise process safety management. The applicable enterprises for the process safety management grading evaluation are expanded through the weight parameter matrix, thereby improving the efficiency of the process safety management grading evaluation. The method performs specific correction on the screening process of the historical grading evaluation data of the enterprise through step S7 to improve the accuracy of the process safety management grading evaluation of the enterprise. The method performs universal correction on the screening process of the historical grading evaluation data through step S8 to improve the accuracy of the process safety management grading evaluation of each enterprise and further improve the efficiency of the process safety management grading evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic flow chart of the construction method of the process safety management grading evaluation model of this embodiment;

[0051] Figure 2 It is a schematic structural diagram of the construction system of the process safety management grading evaluation model of this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0054] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] Please refer to Figure 1 shown in the figure, which is a schematic flowchart of the method for constructing a process safety management grading evaluation model in this embodiment. The method includes:

[0056] Step S1, collect the equipment working hours, equipment maintenance time intervals, safety facility status, number of personnel wearing safety equipment, standing areas of non-wearing personnel, and standing durations of non-wearing personnel during the evaluation period to obtain evaluation data;

[0057] Step S2, process the evaluation data;

[0058] Step S3, construct an operation model and a status model based on the evaluation data of the previous evaluation period;

[0059] Step S4, obtain the enterprise operation safety level and the enterprise status safety level based on the operation model and the status model;

[0060] Step S5, set the weight parameter matrix according to the historical grading evaluation data;

[0061] Step S6, conduct a grading evaluation of the enterprise process safety management according to the weight parameter matrix;

[0062] Step S7, perform a specific correction on the screening process of the historical grading evaluation data of the enterprise according to the number of enterprise process equipment failures in the current evaluation period;

[0063] Step S8, perform a general correction on the screening process of the historical grading evaluation data according to the number of specific corrections in the grading evaluation.

[0064] Specifically, the method is set in a system for process safety management of enterprises. By constructing a process safety management grading evaluation model, the process safety management of enterprises is graded and evaluated. The data is classified, and the weight parameters of various types of data are adjusted so that the process safety management grading evaluation model is applicable to the process safety management grading evaluation of multiple objects, improving the efficiency of process safety management grading evaluation. The method collects evaluation data through step S1 to facilitate the process safety management grading evaluation based on the evaluation data. The method processes the evaluation data through step S2 to classify and process the data, facilitating subsequent model construction. The method constructs an operation model and a status model based on the evaluation data of the previous evaluation cycle through step S3 to classify and analyze the data according to the operation model and the status model. The method obtains the enterprise operation safety level and the enterprise status safety level based on the operation model and the status model through step S4 to facilitate the subsequent process safety management grading evaluation according to the enterprise operation safety level and the enterprise status safety level. The method sets the weight parameter matrix according to the historical grading evaluation data through step S5 and conducts the process safety management grading evaluation of the enterprise according to the weight parameter matrix through step S6 to facilitate the accurate grading evaluation of the enterprise's process safety management. By expanding the applicable enterprises of the process safety management grading evaluation through the weight parameter matrix, the efficiency of the process safety management grading evaluation is improved. The method performs a specific correction on the screening process of the enterprise's historical grading evaluation data through step S7 to improve the accuracy of the process safety management grading evaluation of the enterprise. The method performs a general correction on the screening process of the historical grading evaluation data through step S8 to improve the accuracy of the process safety management grading evaluation of each enterprise, further improving the efficiency of the process safety management grading evaluation.

[0065] Specifically, in step S1, the evaluation cycle refers to the preset time interval for the process safety management grading evaluation of enterprises. For example, if the evaluation cycle is set to one week, the process safety management grading evaluation of enterprises is conducted every week. The equipment working hours refer to the cumulative duration of the equipment in the operating state. In step S1, the equipment working hours are collected through the equipment work log. The equipment maintenance time interval refers to the interval between the two closest equipment maintenance time points in terms of time. In step S1, the equipment maintenance time interval is collected through the equipment work log. The safety facility status refers to the safety facilities set by the enterprise for dangerous situations caused by safety failures, such as fire hydrants, fire extinguishers, and gas masks. In step S1, the safety facility status is set according to the normal use situation of the safety facilities, where:

[0066] When all safety facilities can be used normally, the safety facility status is set to normal, and the safety facility status parameter is 1;

[0067] When there are facilities that cannot be used normally in the safety facilities, set the status of the safety facilities as abnormal, and the safety facility status parameter is 0;

[0068] This embodiment does not limit the method for obtaining the normal use situation of the safety facilities. Those skilled in the art can freely set it according to the actual situation, as long as it meets the requirement of reflecting the use situation of the safety facilities. For example, through the Internet of Things method, intelligent sensors can be set in each safety facility, and the normal use situation of the safety facilities can be obtained through sensor data. The number of personnel wearing safety equipment refers to the number of personnel wearing safety devices in the enterprise work area, such as the number of people wearing safety helmets. This embodiment does not limit the collection method of the number of personnel wearing safety equipment. Those skilled in the art can freely set it according to the actual situation, as long as it meets the requirement of accurately obtaining the number of personnel wearing safety equipment. For example, it can be set to collect the number of personnel wearing safety equipment through the temperature data of the temperature sensors set on the safety devices. The standing area of non-wearing personnel refers to the position area where the personnel not wearing safety devices stand in the enterprise work area. In step S1, the standing area of non-wearing personnel is collected by the cameras set in the enterprise work area. The standing duration of non-wearing personnel refers to the standing duration of the personnel not wearing safety devices in the enterprise work area. In step S1, the standing time point of the non-wearing personnel in the enterprise work area is obtained by processing the images collected by the cameras in the enterprise work area to obtain the standing duration of the non-wearing personnel.

[0069] Specifically, in step S2, when processing the evaluation data, the service life t2 of each device is obtained, the device working parameter T is calculated according to the device working duration t1, and T = t1 / t2 is set. The personnel wearing ratio p is calculated according to the number of personnel wearing safety equipment n and the total number of people n0 in the current work area, and p = n / n0 is set. The personnel wearing ratio p is compared with the preset personnel wearing ratio p0, and the personnel wearing situation is judged according to the comparison result, where:

[0070] When p ≥ p0, it is determined that the personnel wearing situation is normal;

[0071] When p < p0, it is determined that the personnel wearing situation is abnormal, and an adjustment coefficient v is set, 0.87 < v < 0.98. The safety facility coverage rate u is adjusted according to the adjustment coefficient v, and the adjusted safety facility coverage rate is uv, and uv = v × u is set;

[0072] The judgment process of the personnel wearing situation is corrected according to the standing area of non-wearing personnel, where:

[0073] When the standing area of non-wearing personnel is a first-level dangerous area, the standing duration i of non-wearing personnel is compared with the first preset standing duration i1, where:

[0074] If i ≤ i1, the judgment process of personnel wearing situation is not corrected;

[0075] If i > i1, the judgment process of personnel wearing situation is corrected. A correction coefficient J is set, where 1.02 < J < 1.2. The preset personnel wearing ratio p0 in the judgment process of personnel wearing situation is corrected. The corrected preset personnel wearing ratio is pj0. Set pj0 = J × p0, and when pj0 > 1, pj0 takes the value of 1;

[0076] When the standing area of the un-worn personnel is a secondary dangerous area, the standing duration i of the un-worn personnel is compared with the second preset standing duration i2, where:

[0077] If i ≤ i2, the judgment process of personnel wearing situation is not corrected;

[0078] If i > i2, the judgment process of personnel wearing situation is corrected. A correction coefficient J is set, where 1.01 < J < 1.15. The preset personnel wearing ratio p0 in the judgment process of personnel wearing situation is corrected. The corrected preset personnel wearing ratio is pj0. Set pj0 = J × p0, and when pj0 > 1, pj0 takes the value of 1;

[0079] When the standing area of the un-worn personnel is a safe area, the judgment process of personnel wearing situation is not corrected;

[0080] The equipment working parameters and the equipment maintenance time interval are used as operation data, and the safety facility status and the safety facility coverage rate are used as status data.

[0081] Specifically, the lifespan of each device refers to the duration for which the enterprise's production process equipment can operate normally. In step S2, the lifespan of each device is obtained by reading the attributes of the enterprise's production process equipment. The safety facility coverage rate refers to the radiation degree of the installation locations of safety facilities on the enterprise's working area. The total number of people in the current working area refers to the total number of people in the enterprise's working area at the current time. In this embodiment, the method for obtaining the total number of people in the current working area is not limited, and those skilled in the art can freely set it according to the actual situation, as long as the accurate acquisition requirement of the total number of people in the current working area is met. For example, the total number of people in the current enterprise's working area can be collected through the cameras in the enterprise's working area. The preset personnel wearing ratio refers to a preset value reflecting abnormal personnel wearing situations. For example, the preset personnel wearing ratio can be set to 95%. In this embodiment, the preferred value of the adjustment coefficient is v = 0.92. The enterprise's working area refers to the area where the enterprise conducts production activities. According to the danger level of production activities, the enterprise's working area is divided into a first-level dangerous area, a second-level dangerous area, and a safe area. In this embodiment, the division method of the enterprise's working area is not limited, and those skilled in the art can freely set it according to the actual situation, as long as the requirement for distinguishing dangerous areas is met. For example, the area within the safe preset distance from radioactive equipment can be set as the first-level dangerous area, the area within the safe preset distance from dangerous gas equipment can be set as the second-level dangerous area, and other enterprise working areas are safe areas. In this embodiment, the method for the safety facility coverage rate is not limited, and those skilled in the art can freely set it according to the actual situation. For example, the area within the preset use distance from safety facilities can be set as the safety facility coverage area, and the union of each safety facility coverage area is processed to obtain the total safety facility coverage area. The safety facility coverage rate is obtained by dividing the area of the total safety facility coverage area by the area of the enterprise's working area. The first preset standing duration refers to a preset duration value for correcting the judgment process of personnel wearing situations when the standing area of non-wearing personnel is in the first-level dangerous area. For example, it can be set to 10 mins. The second preset standing duration refers to a preset duration value for correcting the judgment process of personnel wearing situations when the standing area of non-wearing personnel is in the second-level dangerous area. For example, it can be set to 30 mins, 0 < i1 < i2.

[0082] Specifically, in step S3, when constructing the operation model and the state model, the operation model is constructed according to the safety facility state and the corresponding operation safety level in the previous evaluation period, and the state model is constructed according to the state data and the corresponding state safety level in the previous evaluation period.

[0083] It can be understood that this embodiment does not limit the construction methods of the operation model and the status model, as long as the accurate output requirements of the operation model and the status model for the enterprise operation safety level and the enterprise status safety level are met. For example, the safety facility status and the corresponding operation safety level at each moment in the previous evaluation period can be set to be obtained and used as the operation model construction data. 70% of the operation model construction data is set as the operation model training set, and 30% of the operation model construction data is set as the operation model test set. The operation model training set is input into the neural network model for training, and the trained neural network model is tested according to the operation model test set. When the accuracy rate of the test result reaches 95%, the neural network model is output as the operation model. The status data and the corresponding status safety level at each moment in the previous evaluation period are obtained and used as the status model construction data. 70% of the status model construction data is set as the status model training set, and 30% of the status model construction data is set as the status model test set. The status model training set is input into the neural network model for training, and the trained neural network model is tested according to the operation model test set. When the accuracy rate of the test result reaches 95%, the neural network model is output as the status model.

[0084] Specifically, in the step S4, the evaluation data of the current evaluation period is input into the operation model and the status model, and the enterprise operation safety level and the enterprise status safety level are respectively output.

[0085] Specifically, in the step S5, when setting the weight parameter matrix, the weight parameter matrix is initialized, and the weight parameter matrix is set as [w1, w2, w3]. w1 is the weight parameter of the enterprise operation safety level, w2 is the weight parameter of the enterprise status safety level, and w3 is the weight parameter of the enterprise average process safety level, and w1 + w2 + w3 = 1;

[0086] The weight parameter matrix is trained according to the historical grading evaluation data, and when the accuracy rate reaches the preset accuracy rate, the weight parameter matrix is output.

[0087] Specifically, in this embodiment, the initial values of the weight parameter matrix are set as w1 = 0.5, w2 = 0.5, w3 = 0. The historical grading evaluation data refers to the enterprise operation safety level, enterprise status safety level, and enterprise process safety level in each historical evaluation period. The average enterprise process safety level refers to the value obtained by dividing the sum of the enterprise process safety levels in each historical evaluation period by the total number of historical evaluation periods. In this embodiment, the training method of the weight parameter matrix is not limited, and those skilled in the art can freely set it according to the actual situation, as long as it meets the model calculation requirements for the enterprise process safety level. For example, it can be set to train the weight parameter matrix through the backpropagation algorithm combined with the gradient descent method. The correct rate refers to the ratio of the number of times the test result is consistent with the enterprise process safety level in each historical evaluation period to the total number of test times when testing the training result of the weight parameter matrix. The preset correct rate refers to the preset correct rate at which the training result of the weight parameter matrix reaches the output standard. For example, the preset correct rate can be set to 93%.

[0088] Specifically, in the step S5, the number of enterprise process equipment failures y in each historical evaluation period is compared with the preset average standard number of enterprise process equipment failures y0, and the historical grading evaluation data is screened according to the comparison result, where:

[0089] When y ≤ y0, the grading evaluation data of this historical evaluation period is selected as the historical grading evaluation data;

[0090] When y > y0, the grading evaluation data of this historical evaluation period is not selected as the historical grading evaluation data.

[0091] Specifically, the number of enterprise process equipment failures in each historical evaluation period refers to the number of times the enterprise process equipment fails in each such historical evaluation period. The preset average standard number of enterprise process equipment failures refers to the preset value of the number of failures that reflects that the grading evaluation data of the historical evaluation period is not suitable as the historical grading evaluation data. For example, the preset average standard number of enterprise process equipment failures can be set to 5 times.

[0092] Specifically, in the step S6, when conducting a grading evaluation of enterprise process safety management, the enterprise process safety level H is calculated according to the enterprise operation safety level a, enterprise status safety level b, average enterprise process safety level c, and the weight parameter matrix. It is set that H = a × w1 + b × w2 + c × w3, and the enterprise process safety level is used as the grading and rating result.

[0093] Specifically, in the step S7, the number of enterprise process equipment failures y1 in the current evaluation period is compared with the preset level standard number of enterprise process equipment failures y10, and the specific correction is judged according to the comparison result, where:

[0094] When y1 ≤ y10, it is determined that no specific correction is to be made;

[0095] When y1 > y10, it is determined that specific correction is to be made. Set the specific correction coefficient r1, and set r1 = 0.85 + e -0.5×(y1-y10)-2 , where e is the base of the natural logarithm. According to the specific correction coefficient r1, perform specific correction on the preset average standard enterprise process equipment failure times y0 during the screening process of the enterprise's historical grading evaluation data. The preset average standard enterprise process equipment failure times after specific correction is yr10, and set yr10 = r1 × y0.

[0096] Specifically, the enterprise process equipment failure times in the current evaluation period refer to the number of times the enterprise process equipment fails during the current evaluation period. In step S7, the enterprise process equipment failure times are obtained through the enterprise process equipment log. The preset level standard enterprise process equipment failure times refer to the preset values of the enterprise process equipment failure times corresponding to the enterprise process safety level of the enterprise. For example, when the enterprise process safety level of the enterprise is level 3, the preset level standard enterprise process equipment failure times is 5 times; when the enterprise process safety level of the enterprise is level 1, the preset level standard enterprise process equipment failure times is 2 times. The specific correction refers to the situation where when the enterprise process equipment failure times y in the current evaluation period of the enterprise is greater than the preset level standard enterprise process equipment failure times, only the preset average standard enterprise process equipment failure times y0 of the enterprise are corrected.

[0097] Specifically, in step S8, compare the specific correction times m of the grading evaluation with the preset specific correction times m0 of the grading evaluation, and judge the universal correction according to the comparison result, where:

[0098] When m ≤ m0, it is determined that no universal correction is to be made;

[0099] When m > m0, it is determined that universal correction is to be made. Set the universal correction coefficient r2, and set r2 = 0.9 + e -0.7×(m-m0)-2.5 , where e is the base of the natural logarithm. According to the universal correction coefficient r2, perform universal correction on the preset average standard enterprise process equipment failure times y0 during the screening process of the historical grading evaluation data. The preset average standard enterprise process equipment failure times after universal correction is yr20, and set yr20 = r2 × y0.

[0100] Specifically, the number of specific corrections for grading evaluation refers to the total number of specific corrections made by each enterprise. The preset number of specific corrections for grading evaluation refers to a preset value (such as 30 times) that reflects the number of corrections required for general corrections. General correction refers to the situation where when the total number of specific corrections made by each enterprise is greater than the preset number of specific corrections for grading evaluation, the preset average number of process equipment failures of standard enterprises for each enterprise is corrected.

[0101] Please refer to Figure 2 as shown, which is a schematic structural diagram of the system for constructing a process safety management grading evaluation model in this embodiment. The system includes:

[0102] A data collection module for collecting the equipment working hours, equipment maintenance time intervals, safety facility status, number of personnel wearing safety equipment, standing areas of non-wearing personnel, and standing durations of non-wearing personnel during the evaluation period to obtain evaluation data;

[0103] A data processing module for processing the evaluation data and connecting;

[0104] A model construction module for constructing an operation model and a status model based on the evaluation data of the previous evaluation period. The model construction module is connected to the data processing module;

[0105] An operation status evaluation module for obtaining the enterprise operation safety level and the enterprise status safety level based on the operation model and the status model. The operation status evaluation module is connected to the model construction module;

[0106] A weight parameter setting module for setting the weight parameter matrix according to the historical grading evaluation data. The weight parameter setting module is connected to the operation status evaluation module;

[0107] A grading evaluation module for conducting a grading evaluation of the enterprise's process safety management according to the weight parameter matrix. The grading evaluation module is connected to the weight parameter setting module;

[0108] A specific correction module for specifically correcting the screening process of the enterprise's historical grading evaluation data according to the number of process equipment failures of the enterprise in the current evaluation period. The specific correction module is connected to the grading evaluation module;

[0109] A general correction module for generally correcting the screening process of the historical grading evaluation data according to the number of specific corrections for grading evaluation. The general correction module is connected to the specific correction module.

[0110] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A construction method of a process safety management grading evaluation model, characterized in that, Including: Step S1: Collect the equipment working hours, equipment maintenance time intervals, safety facility status, number of personnel wearing safety equipment, standing areas of personnel not wearing safety equipment, and standing durations of personnel not wearing safety equipment during the evaluation period to obtain evaluation data; Step S2: Process the evaluation data; Step S3: Construct an operation model and a status model based on the evaluation data of the previous evaluation period; Step S4: Obtain the enterprise operation safety level and the enterprise status safety level based on the operation model and the status model; Step S5: Set the weight parameter matrix according to the historical grading evaluation data; Step S6: Conduct a grading evaluation of the enterprise process safety management according to the weight parameter matrix; Step S7: Specifically correct the screening process of the historical grading evaluation data of the enterprise according to the number of process equipment failures of the enterprise in the current evaluation period; Step S8: Universally correct the screening process of the historical grading evaluation data according to the number of specific corrections in the grading evaluation; In the said Step S2, correct the judgment process of personnel wearing safety equipment according to the standing areas of personnel not wearing safety equipment, where: When the standing area of personnel not wearing safety equipment is a first-level dangerous area, compare the standing duration i of personnel not wearing safety equipment with the first preset standing duration i1, where: If i ≤ i1, do not correct the judgment process of personnel wearing safety equipment; If i > i1, correct the judgment process of personnel wearing safety equipment. Set a correction coefficient J, where 1.02 < J < 1.2, correct the preset personnel wearing ratio p0 in the judgment process of personnel wearing safety equipment, and the corrected preset personnel wearing ratio is pj0. Set pj0 = J × p0, and when pj0 > 1, pj0 takes the value of 1; When the standing area of personnel not wearing safety equipment is a second-level dangerous area, compare the standing duration i of personnel not wearing safety equipment with the second preset standing duration i2, where: If i ≤ i2, do not correct the judgment process of personnel wearing safety equipment; If i > i2, correct the judgment process of personnel wearing safety equipment. Set a correction coefficient J, where 1.01 < J < 1.15, correct the preset personnel wearing ratio p0 in the judgment process of personnel wearing safety equipment, and the corrected preset personnel wearing ratio is pj0. Set pj0 = J × p0, and when pj0 > 1, pj0 takes the value of 1; When the standing area of personnel not wearing safety equipment is a safe area, do not correct the judgment process of personnel wearing safety equipment; Regard the equipment working parameters and equipment maintenance time intervals as operation data, and regard the safety facility status and safety facility coverage rate as status data; In the said Step S3, when constructing the operation model and the status model, construct the operation model according to the safety facility status and the corresponding operation safety level of the previous evaluation period, and construct the status model according to the status data and the corresponding status safety level of the previous evaluation period; In the said Step S4, input the evaluation data of the current evaluation period into the operation model and the status model, and output the enterprise operation safety level and the enterprise status safety level respectively; In step S5, when setting the weight parameter matrix, initialize the weight parameter matrix and set the weight parameter matrix as [w1, w2, w3]. Here, w1 is the weight parameter of the enterprise operation safety level, w2 is the weight parameter of the enterprise status safety level, and w3 is the weight parameter of the enterprise average process safety level, with w1 + w2 + w3 = 1; Train the weight parameter matrix based on historical grading evaluation data, and when the correct rate reaches the preset correct rate, output the weight parameter matrix; In step S5, compare the number of enterprise process equipment failures y in each historical evaluation period with the preset average standard number of enterprise process equipment failures y0, and screen the historical grading evaluation data according to the comparison result, where: When y ≤ y0, select the grading evaluation data of this historical evaluation period as the historical grading evaluation data; When y > y0, do not select the grading evaluation data of this historical evaluation period as the historical grading evaluation data; In step S6, when conducting a grading evaluation of enterprise process safety management, calculate the enterprise process safety level H according to the enterprise operation safety level a, the enterprise status safety level b, the enterprise average process safety level c, and the weight parameter matrix. Set H = a × w1 + b × w2 + c × w3, and use the enterprise process safety level as the grading evaluation result; In step S7, compare the number of enterprise process equipment failures y1 in the current evaluation period with the preset level standard number of enterprise process equipment failures y10, and judge the specific correction according to the comparison result, where: When y1 ≤ y10, it is determined that no specific correction is required; When y1 > y10, it is determined that specific correction is required. Set the specific correction coefficient r1, where r1 = 0.85 + e^(-0.5×(y1 - y10)) - 2, and e is the base of the natural logarithm. Specifically correct the preset average standard number of enterprise process equipment failures y0 in the screening process of the historical grading evaluation data of this enterprise according to the specific correction coefficient r1. The specifically corrected preset average standard number of enterprise process equipment failures is yr10, and set yr10 = r1 × y0; Compare the number of specific corrections m of the grading evaluation with the preset number of specific corrections m0 of the grading evaluation, and judge the general correction according to the comparison result, where: when m ≤ m0, it is determined that no general correction is required; When m > m0, it is determined that general correction is required. Set the general correction coefficient r2, where r2 = 0.9 + e^(-0.7×(m - m0)) - 2.5, and e is the base of the natural logarithm. Specifically correct the preset average standard number of enterprise process equipment failures y0 in the screening process of the historical grading evaluation data according to the general correction coefficient r2. The generally corrected preset average standard number of enterprise process equipment failures is yr20, and set yr20 = r2 × y0.

2. The method for constructing a process safety management grading evaluation model according to claim 1, wherein In the step S2, when processing the evaluation data, the service life t2 of each device is obtained, the device working parameter T is calculated according to the device working duration t1, T = t1 / t2 is set, the personnel wearing ratio p is calculated according to the number of personnel wearing safety equipment n and the total number of people n0 in the current working area, p = n / n0 is set, the personnel wearing ratio p is compared with the preset personnel wearing ratio p0, and the personnel wearing situation is judged according to the comparison result, where: when p≥p0, it is determined that the personnel wearing situation is normal; when p<p0, it is determined that the personnel wearing situation is abnormal, and an adjustment coefficient v is set, 0.87<v<0.98, the safety facility coverage rate u is adjusted according to the adjustment coefficient v, and the adjusted safety facility coverage rate is uv, uv = v×u is set.

3. A system for a method of constructing a process safety management grading and evaluation model applied to the process safety management grading and evaluation model according to any one of claims 1-2, characterized in that, Including: A data acquisition module for collecting the device working duration, device maintenance time interval, safety facility status, number of personnel wearing safety equipment, standing area of non-wearing personnel, and standing duration of non-wearing personnel during the evaluation period to obtain evaluation data; A data processing module for processing the evaluation data; A model construction module for constructing an operation model and a status model according to the evaluation data of the previous evaluation period; An operation status evaluation module for obtaining the enterprise operation safety level and the enterprise status safety level according to the operation model and the status model; A weight parameter setting module for setting the weight parameter matrix according to the historical grading evaluation data; A grading evaluation module for grading and evaluating the enterprise process safety management according to the weight parameter matrix; A specificity correction module for specifically correcting the screening process of the historical grading evaluation data of the enterprise according to the number of enterprise process equipment failures in the current evaluation period; A universality correction module for generally correcting the screening process of the historical grading evaluation data according to the number of specific corrections in the grading evaluation.

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