A chemical production information management platform

Abstract

The present invention relates to the technical field of chemical production control, and specifically to a chemical production information management platform, comprising: a process identification module, used for automatically identifying a chemical process and operating parameters corresponding to the chemical process; a production process monitoring module, used for generating a production flow chart according to the identified chemical process, and determining the corresponding execution state and operating parameters in the production flow chart under each process; a process verification module, used for verifying the corresponding execution time and parameter use position under the execution state of each chemical process, ensuring that the chemical process is executed according to the predetermined time and parameters, and recording the actual execution situation; a risk identification module, used for extracting risk parameters and process problems about the current chemical process from the actual execution situation, and determining the risk parameters existing under each process problem; partitioning the extracted risk parameters and process problems according to the position information on the production line to generate multiple risk areas; improving the safety of chemical production management and the efficiency of production.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical production control and management, and in particular to a chemical production information management platform. Background Art

[0002] Chemical production refers to the production process of chemically processing raw materials to ultimately obtain valuable products. Due to the diversity of raw materials and products and the complexity of the production process, tens of thousands of chemical production processes have been formed. Looking at the numerous and complex chemical production processes, they are all composed of an organic combination of chemical reactions and several physical operations. Among them, chemical reactions and reactors are the core of chemical production, and physical processes play the role of preparing suitable reaction conditions for chemical reactions and separating and purifying the reactants to obtain the final products.

[0003] For example, Chinese patent publication number CN116911619A discloses an intelligent control system for chemical production based on data analysis, including an intelligent control platform, a real-time detection module for operating parameters, an operation summary analysis module, an operation control detection module, an auxiliary comprehensive supervision module and a chemical production early warning module; the present invention performs real-time detection and analysis of the operating parameters of production equipment to accurately feedback the degree of risk of deviation of the operating parameters of each production equipment, and performs operation summary analysis on the production equipment and performs control detection analysis on it when generating control detection analysis signals to ensure the safe and stable operation of the production equipment and reduce the difficulty of management, and performs auxiliary comprehensive supervision analysis on the analysis area of ​​the production equipment to evaluate its regional safety.

[0004] When analyzing the relevant duration and warning conditions in chemical production, the existing technology tends to analyze the occurrence of warnings and the length of time the warnings are generated, ignoring the location where the warnings appear and the related processes. As a result, the control of chemical production cannot monitor and process chemical tasks according to the order of chemical production implementation, resulting in the inability to track and control risks in production tasks in a timely manner. Summary of the invention

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a chemical production information management platform, including: a process identification module, which is used to automatically identify chemical processes and operating parameters corresponding to the chemical processes.

[0006] The production process monitoring module is used to generate a production flow chart based on the identified chemical process and determine the corresponding execution status and operating parameters in the production flow chart for each process.

[0007] The process verification module is used to verify the corresponding execution time and parameter usage position under the execution status of each chemical process, ensure that the chemical process is executed according to the scheduled time and parameters, and record the actual execution status.

[0008] The risk identification module is used to extract risk parameters and process problems about the current chemical process from the actual execution situation, determine the risk parameters existing under each process problem; partition the extracted risk parameters and process problems according to the location information on the production line to generate multiple risk areas.

[0009] The risk assessment module is used to monitor the risk parameters in the risk area. When the risk parameters exceed the threshold, a trend curve related to the risk parameters exceeding the threshold is generated, and the trend curve is analyzed to determine the risk situation of each risk area.

[0010] The beneficial effects of the present invention are as follows: 1. The present invention reduces manual intervention and errors and improves production efficiency by automatically identifying chemical processes and monitoring production processes in real time.

[0011] 2. The present invention effectively reduces the safety risks in the production process and improves the safety of production by verifying the process execution time and parameter usage location, as well as real-time monitoring and evaluation of risk parameters.

[0012] 3. The present invention realizes risk zoning through a decision tree and obtains risk indicators based on comprehensive evaluation values ​​and failure probabilities, which provides enterprise managers with a more accurate basis for risk management and helps optimize risk management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0014] Figure 1 It is a system framework diagram of a chemical production information management platform.

[0015] Figure 2 It is a system implementation diagram of a chemical production information management platform.

[0016] Figure 3 It is a risk warning diagram of a chemical production information management platform. DETAILED DESCRIPTION

[0017] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. If no specific techniques or conditions are specified in the embodiments, the techniques or conditions described in the literature in the art or the product specifications are used.

[0018] See also Figure 1 , Figure 2A chemical production information management platform includes: a process identification module, a production process monitoring module, a process verification module, a risk identification module, and a risk assessment module; wherein the process identification module is connected to the production process monitoring module, and the process identification module sends the currently identified chemical process and operating parameters to the production process monitoring module; after receiving the corresponding data, the production process monitoring module verifies whether the current production task can be executed correctly; the process verification module determines the actual execution status of the current chemical process; the process verification module is connected to the risk identification module, and the risk identification module is used to identify the existing risk parameters. The output result of the risk identification module is input into the risk assessment module, which is used to monitor the identified risk parameters to obtain the risk status of different areas.

[0019] The process identification module is used to automatically identify the chemical process and the operating parameters corresponding to the chemical process; the identified chemical process can be an electrolysis process, nitration process, fluorination process, oxidation process, hydrogenation process, etc., and the identified operating parameters include the temperature, flow rate, pressure and corresponding time point when the chemical process is running.

[0020] The production process monitoring module is used to generate a production flow chart based on the identified chemical process, determine the corresponding execution status and operating parameters in the production flow chart for each process; use the production flow chart and real-time monitoring data to display the current production process and forward status to ensure that production proceeds as planned.

[0021] The process verification module is used to verify the corresponding execution time and parameter usage position under the execution status of each chemical process, ensure that the chemical process is executed according to the scheduled time and parameters, and record the actual execution status.

[0022] The risk identification module is used to extract risk parameters and process problems about the current chemical process from the actual execution situation, and determine the risk parameters existing under each process problem; the extracted risk parameters and process problems are partitioned according to the location information on the production line to generate multiple risk areas; at this time, based on historical data and real-time data, algorithms are used to identify parameters or process execution problems that may cause risks, and production areas are partitioned according to risk levels.

[0023] The risk assessment module is used to monitor the risk parameters in the risk area. When the risk parameters exceed the threshold, a trend curve related to the risk parameters exceeding the threshold is generated, and the trend curve is analyzed to determine the risk situation of each risk area. This module monitors the risk area, issues an alarm for exceeding the limit, and performs trend analysis. At this time, a risk threshold is set, and once the monitored data exceeds the threshold, the alarm system is immediately triggered.

[0024] The operating parameters to be verified at this time include the temperature during the chemical reaction, the pressure of the reaction vessel, the flow rate, concentration and liquid level of the product processing, which can directly affect the production quality of chemical products; for example, in the process of producing a certain chemical, it may be necessary to control the reaction temperature at 80°C, the pressure at 2 atmospheres, the flow rate of the raw materials at 10 liters per hour, and the concentration of the reactants within a certain range. The setting of these parameters needs to be determined according to the specific chemical reaction and production requirements.

[0025] The process identification module identifies the corresponding chemical process, and each chemical production step that needs to be identified, such as reaction, distillation, extraction, etc.; and extracts the operating parameters associated with each chemical process from each chemical process, such as temperature, pressure, flow, etc.; these identified chemical processes and operating parameters are used to determine whether there are problems with the currently implemented chemical processes under chemical production management at this time, and whether the relevant chemical processes are in normal production.

[0026] The process identification module collects historical data containing various chemical processes and their corresponding operating parameters; extracts features that can represent different processes from the collected data, such as specific temperature and pressure ranges, etc.; compares real-time data with the features of different processes to identify the current chemical process and its operating parameters.

[0027] At this time, the characteristics of different processes are classified in advance according to the corresponding chemical processes and the characteristics of the operating parameters under each chemical process. The input real-time data is compared with the classified data to identify the chemical process corresponding to the current real-time data and the operating parameters under the chemical process.

[0028] Since each chemical process requires the current operating parameters to meet the reaction conditions of the chemical reaction, and the reaction conditions cannot be modified in chemical processing, it is necessary to supervise the production process of each chemical process to determine the operating parameters and specific implementation content required for each production task at this time.

[0029] Therefore, the production process monitoring module needs to generate a production flow chart based on the identified chemical process, determine the corresponding execution status and operating parameters in the production flow chart for each process, and determine the task progress of the current production task. According to the changes in the task progress of the production task, the change range of the operating parameters under the corresponding execution state is determined, and the change range of the operating parameters is compared with the preset safety range to determine abnormal conditions in the production process; the execution status at this time includes whether the current process is in different stages such as startup, operation, pause, stop, completion, etc.; and whether the equipment in the current process is shut down, working normally, or shut down.

[0030] The execution time in the process verification module indicates the planned start and end time of each chemical process; the parameter usage position indicates the corresponding time point and position on the corresponding equipment when each operating parameter in the chemical process reaches the preset standard value or range.

[0031] The implementation method for verifying the execution time and parameter usage position corresponding to the execution status of each chemical process in the process verification module is as follows: obtain the execution time and parameter usage position under each chemical process, determine the time difference between the current execution time and the previous execution time, calculate the process execution interval, compare the process execution interval with the preset time interval, and if the process time interval exceeds the preset time interval, obtain the parameter usage position under the current process execution interval, and determine whether the current parameter usage position is located at the corresponding equipment. If it is located, generate alarm information related to the current process execution interval; if it is not located, record the current parameter usage position, and generate an alarm signal about the parameter usage position; if the process time interval is less than the preset time interval, verify the start time and end time of the current chemical process, set the corresponding start time and end time as the abnormal duration, and generate an alarm signal corresponding to the abnormal duration, and output the verified alarm signal as the actual execution status.

[0032] When the execution interval of a process exceeds the preset time interval, the system will generate an alarm message indicating that the execution progress of the process is lagging behind; for example, if a chemical process is preset to be executed every two hours, but the actual execution interval exceeds two hours, the system will issue such an alarm.

[0033] In the case of a process execution interval timeout, if the system detects that the current parameter usage location is still at the corresponding device, it indicates that there may be equipment failure or operational error, and therefore generates an alarm message related to the current process execution interval; for example, if the temperature or pressure parameters in the reactor have not changed for a long time and exceed the predetermined time, the system determines that the equipment may have failed and issues an alarm.

[0034] When the process execution interval times out and the system detects that the current parameter usage location is not at the corresponding equipment, it indicates that the parameter may be used incorrectly elsewhere. At this time, the system will record the current parameter usage location and generate an alarm signal about the parameter usage location; for example, if the use of a catalyst is detected in a process where the catalyst should not be used, the system will issue this alarm signal.

[0035] When the process time interval is less than the preset time interval, that is, the process is executed too quickly, it may indicate that there is an abnormality or error in the operation process. At this time, the system will verify the start time and end time of the chemical process, and mark this time as an abnormal duration, and then generate a corresponding alarm signal; for example, a process that takes one hour to complete is completed in half an hour. This may mean that some steps in the operation process are omitted or accelerated, resulting in product quality problems, and the system will therefore issue an alarm.

[0036] At this time, after multiple alarm signals are identified, the chemical processes and operating parameters corresponding to these alarm signals will be marked as data that may pose risks, and this existing data will be identified and processed to determine the corresponding risk situation when problems occur in the actual chemical process.

[0037] like Figure 3 As shown, the risk identification module extracts risk parameters and process problems about the current chemical process from the actual execution situation. The risk parameters represent the corresponding operating parameters when an alarm signal appears, and the process problems represent the process problems that can be caused by the current alarm signals and risk parameters when risk parameters and alarm signals appear; the risk parameters existing under each process problem are determined; the extracted risk parameters and process problems are partitioned according to the location information on the production line to generate multiple risk areas.

[0038] The method for extracting risk parameters and process problems about the current chemical process from the actual execution situation is to map the alarm signal to the process problem by setting up a knowledge base, obtain the corresponding process problem, and compare the corresponding parameters under the process problem with the operating parameters to obtain the risk parameters.

[0039] The acquired alarm signals are mapped to process problems, where each alarm signal corresponds to a process problem. When an alarm signal is associated with multiple process problems, the process problems are associated with the alarm signals according to the different scenarios corresponding to the process problems. The parameters of the associated process problems and the operating parameters corresponding to the alarm signals are obtained. The parameters of the process problems are compared with the operating parameters corresponding to the alarm signals to obtain the risk parameters existing in the operating parameters.

[0040] At this time, the comparison method is to calculate the correlation coefficient by comparing the current operating parameters with the parameters of the process problem, and extract the operating parameters with correlation coefficient values ​​greater than the preset correlation threshold to obtain the operating parameters that are currently at risk. The preset correlation threshold set at this time can be any one of 0.6, 0.5 and 0.7 to select the parameters that may be at risk; the correlation coefficient can be calculated using the Pearson correlation coefficient to quantify the correlation between the current operating parameters and the parameters under the corresponding process problem.

[0041] The processing method of partitioning the risk parameters and process problems according to the location information on the production line includes: identifying the fault causes corresponding to the current risk parameters and process problems, the fault characteristics corresponding to the risk parameters and process problems, and the equipment characteristics of the equipment corresponding to the location information.

[0042] The cause of the failure is extracted by comparing the identified risk parameters and process problems with the data in the database. At this time, the cause of the failure is obtained through the correlation coefficient. The correlation threshold is set to 0.7 to obtain the cause of the failure corresponding to the current risk parameters and process problems. The correlation coefficient uses the Pearson correlation coefficient to calculate the risk parameters and process problems in turn with the data set in the database, and extracts the part with the calculated value greater than the correlation threshold. At this time, the extracted data contains the corresponding cause of the failure.

[0043] The fault feature is obtained by extracting parameters related to the current risk parameters and process problems from the fault cause, and using the correlation coefficient to obtain the corresponding fault feature. The correlation threshold is set to 0.8 at this time, which is used to determine whether there are parameters related to the current problem under the corresponding fault cause. The fault feature at this time is to calculate the current risk parameters and the parameters in the database after calculating the fault cause, and extract the data with a calculated value greater than 0.8 as the current fault feature. The calculation method used at this time is to use the Pearson correlation coefficient to calculate.

[0044] For equipment characteristics, the position of the equipment where the current risk parameters appear is converted into coordinates to indicate the corresponding position when the current fault occurs. At the same time, the characteristic value of the equipment characteristic is set to the impact of the current coordinate position on the overall chemical process. At this time, the characteristic value of the equipment characteristic is set according to the ratio of the number of faults at the corresponding position to the total number of faults.

[0045] The identified fault causes, fault characteristics and equipment characteristics are input into the decision tree, and risk zoning is implemented according to the decision tree to obtain the corresponding risk areas.

[0046] When using a decision tree to implement risk partitioning, the fault causes, equipment features, and fault features are combined to obtain combined features. The child nodes of the decision tree represent the combined features, the root node of the decision tree represents all process problems to be evaluated, and the leaf nodes of the decision tree represent the output risk partitioning results.

[0047] The Gini impurity of the fault feature of each child node of the decision tree is calculated in turn, the decision tree is divided according to the Gini impurity of the fault feature, and sorted according to the eigenvalue size of the equipment feature. The leaf node of the decision tree after the final sorting and division is used as the current risk partition result. According to the obtained risk partition result, the corresponding risk area is obtained.

[0048] The risk zoning results obtained at this time can indicate the risk levels corresponding to the current risk parameters and related process problems. The risk area obtained means that after the corresponding equipment identifies the relevant risk parameters, the area represented by the current position is at low risk, medium risk or high risk.

[0049] The risk monitoring module monitors the risk parameters in the risk area. When the risk parameters exceed the threshold, it generates a trend curve related to the risk parameters exceeding the threshold, analyzes the trend curve, and determines the risk situation of each risk area.

[0050] The analysis of the trend curve at this time is to determine whether the relevant risk parameters in the current risk area have changed, and whether the change will affect the stability of the equipment in the corresponding area; the originally set risk parameters indicate that there are certain abnormal data. When the risk parameters exceed the threshold, it means that there is an obvious error in the risk parameters. At this time, the probability of causing risk increases, so it is necessary to generate a trend curve to determine the change in the current risk parameters.

[0051] The trend curve analysis method includes, according to the obtained trend curve, calculating the possibility coefficient, sensitivity coefficient and severity coefficient corresponding to the trend curve in sequence; according to the possibility coefficient, sensitivity coefficient and severity coefficient, calculating a comprehensive evaluation value; obtaining the failure frequency corresponding to the comprehensive evaluation value, and obtaining the risk index based on the comprehensive evaluation value and the failure probability, and outputting the risk index as the risk situation of each risk area.

[0052] The possibility coefficient represents the variance of the change in the probability of a fault occurring after the risk parameter changes. At this time, the possibility coefficient is calculated by dividing the trend curve according to the time points existing in the trend curve, calculating the change in the probability of a fault occurring at two adjacent time points in turn, and taking the variance of all the calculated change values ​​of the fault probability. The result calculated at this time is used as the output possibility coefficient. The possibility coefficient is used to quantify the corresponding change in the probability of a fault occurring when the value of the risk parameter changes in the trend curve, so as to quantify whether a corresponding fault will occur at present.

[0053] The sensitivity coefficient is used to quantify the impact of each risk parameter on the overall output, obtain the expected value variance corresponding to each risk parameter and the total variance corresponding to the risk parameter, and obtain the sensitivity coefficient corresponding to the risk parameter.

[0054] ;in, represents the sensitivity coefficient, represents the number of risk parameters, represents the expected value variance corresponding to the i-th risk parameter, represents the total variance corresponding to the risk parameter, .

[0055] Here the expected value variance corresponding to each risk parameter is calculated as shown below.

[0056] ;in, represents the expected value variance corresponding to the i-th risk parameter, Denoted as the i-th risk parameter, represents the probability of failure. It is expressed as given as the i-th risk parameter Under the condition of The conditional expected value of Expressed as The expected value of The conditional expected value The average value of .

[0057] When is the total variance of the risk parameter, the total variance of the risk parameter is calculated as shown below.

[0058] ;in, Represents the total variance corresponding to the risk parameter.

[0059] The severity coefficient indicates the loss that can be caused by the current fault. At this time, the severity coefficient selects the coefficient value corresponding to the maximum value of the current fault probability from the preset database to judge the severity of the current fault. As shown in Table 1, the currently set severity coefficient can be set to the corresponding value.

[0060] Table 1 Severity coefficient example table

[0061]

[0062] The corresponding comprehensive evaluation value is expressed as the weighted sum of the likelihood coefficient, sensitivity coefficient and severity coefficient.

[0063] After obtaining the relevant comprehensive evaluation value, the risk index is expressed as: the failure frequency of the corresponding failure in the current risk area is obtained, the comprehensive evaluation value and the failure frequency are normalized, and the risk index of the current risk area is obtained.

[0064] ;in, is a risk indicator, Represents the comprehensive evaluation value, Indicates the fault frequency, represents the weight of the comprehensive evaluation value, The weight representing the failure frequency.

[0065] The final risk indicators are output to obtain the risk assessment of the current chemical process.

[0066] After obtaining the relevant risk assessment information, it will be sent to the external terminal, and according to the content input at this time, a chart corresponding to the current risk indicator will be generated to display the development status and related risks corresponding to the current process problems, thereby reminding external personnel to issue timely warnings on the current handling methods.

[0067] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. A chemical production information management platform, It is characterized in that include: Process identification module, used to automatically identify chemical processes and corresponding operating parameters of chemical processes; The production process monitoring module is used to generate a production flow chart according to the identified chemical process and determine the corresponding execution status and operating parameters in the production flow chart under each process; The process verification module is used to verify the corresponding execution time and parameter usage position under the execution status of each chemical process, ensure that the chemical process is executed according to the scheduled time and parameters, and record the actual execution status; The risk identification module is used to extract the risk parameters and process problems of the current chemical process from the actual execution situation and determine the risk parameters existing under each process problem; The extracted risk parameters and process problems are partitioned according to the location information on the production line to generate multiple risk areas; The risk assessment module is used to monitor the risk parameters in the risk area. When the risk parameters exceed the threshold, a trend curve related to the risk parameters exceeding the threshold is generated, and the trend curve is analyzed to determine the risk situation of each risk area; The trend curve analysis method includes, according to the obtained trend curve, sequentially calculating the possibility coefficient, sensitivity coefficient and severity coefficient corresponding to the trend curve; According to the possibility coefficient, sensitivity coefficient and severity coefficient, the comprehensive evaluation value is calculated; Obtain the failure frequency corresponding to the comprehensive evaluation value, obtain the risk index based on the comprehensive evaluation value and the failure frequency, and output the risk index as the risk situation of each risk area; The likelihood coefficient is expressed as the variance of the change in the probability of failure after the risk parameter changes; The sensitivity coefficient is expressed as follows: the expected value variance corresponding to each risk parameter and the total variance corresponding to the risk parameter are obtained to obtain the sensitivity coefficient corresponding to the risk parameter; The severity coefficient is expressed as selecting the coefficient value corresponding to the maximum value of the current fault occurrence probability from the preset database; The comprehensive assessment value is expressed as the weighted sum of the likelihood coefficient, sensitivity coefficient, and severity coefficient; The sensitivity coefficient is used to quantify the impact of each risk parameter on the overall output, obtain the expected value variance corresponding to each risk parameter and the total variance corresponding to the risk parameter, and obtain the sensitivity coefficient corresponding to the risk parameter; ； in, represents the sensitivity coefficient, represents the number of risk parameters, represents the expected value variance corresponding to the i-th risk parameter, represents the total variance corresponding to the risk parameter, ; The expected value variance corresponding to each risk parameter is calculated as follows: ； in, represents the expected value variance corresponding to the i-th risk parameter, is represented as the i-th risk parameter, represents the probability of failure. It is expressed as given as the i-th risk parameter Under the condition of The conditional expected value of Expressed as The expected value of Under the value of The average value of The total variance of the risk parameter is calculated as follows: ； in, represents the total variance corresponding to the risk parameter; The risk index is expressed as follows: obtain the fault frequency of the corresponding fault in the current risk area, normalize the comprehensive evaluation value and the fault frequency, and obtain the risk index of the current risk area: ； in, is a risk indicator, Represents the comprehensive evaluation value, Indicates the fault frequency, represents the weight of the comprehensive evaluation value, The weight representing the failure frequency.

2. A chemical production information management platform according to claim 1, It is characterized in that The implementation method for verifying the execution time and parameter usage position corresponding to the execution status of each chemical process in the process verification module is as follows: obtain the execution time and parameter usage position under each chemical process, determine the time difference between the current execution time and the previous execution time, calculate the process execution interval, compare the process execution interval with the preset time interval, and if the process time interval exceeds the preset time interval, obtain the parameter usage position under the current process execution interval, and determine whether the current parameter usage position is located at the corresponding equipment. If it is located, generate alarm information related to the current process execution interval; if it is not located, record the current parameter usage position, and generate an alarm signal about the parameter usage position; if the process time interval is less than the preset time interval, verify the start time and end time of the current chemical process, set the corresponding start time and end time as the abnormal duration, and generate an alarm signal corresponding to the abnormal duration, and output the verified alarm signal as the actual execution status.

3. A chemical production information management platform according to claim 2, It is characterized in that The implementation method for extracting risk parameters and process problems about the current chemical process from the actual execution situation includes: mapping the acquired alarm signal to the process problem, wherein each alarm signal corresponds to a process problem; when an alarm signal is associated with multiple process problems, the process problem is associated with the alarm signal according to different scenarios corresponding to the process problem; the parameters of the associated process problem and the operating parameters corresponding to the alarm signal are obtained; the parameters of the process problem are compared with the operating parameters corresponding to the alarm signal to obtain the risk parameters existing in the operating parameters.

4. A chemical production information management platform according to claim 1, It is characterized in that The processing method of partitioning the risk parameters and process problems according to the location information on the production line includes: identifying the fault causes corresponding to the current risk parameters and process problems, the fault characteristics corresponding to the risk parameters and process problems, and the equipment characteristics of the equipment corresponding to the location information; The identified fault causes, fault characteristics and equipment characteristics are input into the decision tree, and risk zoning is implemented according to the decision tree to obtain the corresponding risk areas.

5. A chemical production information management platform according to claim 4, It is characterized in that When using a decision tree to implement risk partitioning, the fault cause, equipment characteristics, and fault characteristics are combined to obtain a combined characteristic. The child nodes of the decision tree represent the combined characteristics. The root node of the decision tree represents all process problems to be evaluated. The leaf nodes of the decision tree represent the output risk partition results. The Gini impurity of the fault feature of each child node of the decision tree is calculated in turn, the decision tree is divided according to the Gini impurity of the fault feature, and sorted according to the eigenvalue size of the equipment feature. The leaf node of the decision tree after the final sorting and division is used as the current risk partition result. According to the obtained risk partition result, the corresponding risk area is obtained.

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