Special equipment risk assessment method and system, terminal and storage medium

Abstract

The invention relates to the technical field of special equipment assessment, in particular to a special equipment risk assessment method and system, a terminal and a storage medium, and the assessment method comprises the steps: obtaining different types of real-time data and historical data of special equipment; based on the real-time data and a preset contrast standard, obtaining single risk levels corresponding to the real-time data of different classifications; based on the single risk level and a preset calculation standard, obtaining a total risk level of the special equipment; obtaining a grade calibration factor based on the historical data; based on the grade calibration factor, calibrating the total risk grade and generating a calibration grade; and the corresponding risk assessment report is generated based on the calibration level, so that the accuracy of the assessment result can be improved. According to the system, the terminal and the storage medium provided by the invention, the evaluation method can be executed under adaptive conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of special equipment assessment, and in particular to a special equipment risk assessment method, system, terminal and storage medium. Background Art

[0002] Special equipment refers to boilers, pressure vessels (including gas cylinders, the same below), pressure piping, elevators, lifting machinery, passenger ropeways, large amusement facilities, and special motor vehicles used within a site (factory) that involve life safety and pose a high risk. Boilers, pressure vessels (including gas cylinders), and pressure piping are pressure-bearing special equipment; elevators, lifting machinery, passenger ropeways, and large amusement facilities are electromechanical special equipment.

[0003] Special equipment is closely connected to our daily lives and production. Accidents involving special equipment directly impact the lives and property of the general public, making risk assessment of special equipment essential. Existing technologies rely on the equipment's current operational status to determine risk assessment results. However, simply basing risk assessments on current operational status is a relatively simplistic approach, prone to inaccurate results and ultimately leading to serious safety incidents. Summary of the Invention

[0004] In response to the technical problem of low accuracy in existing special equipment risk assessment, the present invention provides a special equipment risk assessment method, system, terminal and storage medium. The total risk level is calibrated by a calibration factor, which helps to improve the accuracy of the total risk level and thus further improve the accuracy of risk assessment.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a special equipment risk assessment method, comprising the following steps:

[0007] Obtain real-time and historical data of different categories of special equipment;

[0008] Based on the real-time data and preset comparison standards, obtaining individual risk levels corresponding to the real-time data of different categories;

[0009] Based on the individual risk levels and preset calculation standards, obtaining the overall risk level of the special equipment;

[0010] obtaining a level calibration factor based on the historical data;

[0011] Calibrate the total risk level based on the level calibration factor and generate a calibrated level;

[0012] Based on the calibration level, a corresponding risk assessment report is generated.

[0013] The special equipment risk assessment method provided by the present invention obtains real-time data and historical data of different categories of special equipment, obtains the individual risk levels corresponding to the real-time data of different categories based on the real-time data and preset comparison standards, and obtains the total risk level of the special equipment by integrating the preset calculation standards. At the same time, based on the historical data, a level calibration factor is obtained, and based on the level calibration factor, the total risk level is calibrated and a calibration level is generated. Finally, a corresponding risk assessment report is generated based on the calibration level. Therefore, calibrating the total risk level through the calibration factor helps to improve the accuracy of the total risk level, thereby further improving the accuracy of the risk assessment and ensuring the operational safety of the special equipment.

[0014] In an optional embodiment of the present application, the real-time data includes real-time feature data and real-time operation data; and the specific steps of obtaining individual risk levels corresponding to different categories of the real-time data based on the real-time data and preset comparison standards include:

[0015] If the real-time data is the real-time feature data, comparing the real-time feature data with the preset feature data and obtaining feature abnormality data;

[0016] Matching the characteristic abnormal data with the preset control standard to obtain a characteristic risk level corresponding to the characteristic abnormal data;

[0017] Using the characteristic risk level as the individual risk level corresponding to the real-time data;

[0018] If the real-time data is the real-time operation data, the real-time data is compared with the preset safety data, and operation abnormality data is obtained;

[0019] Matching the abnormal operation data with the preset control standard to obtain the operation risk level corresponding to the abnormal operation data;

[0020] The operational risk level is used as the single risk level corresponding to the real-time data.

[0021] In an optional embodiment of the present application, the specific steps of obtaining the total risk level of the special equipment based on the individual risk levels and the preset calculation standard include:

[0022] Classify all the individual risk levels and generate classification results;

[0023] Get the number of categories of individual risk levels corresponding to different categories;

[0024] Based on the classification results and the number of classifications of individual risk levels corresponding to different categories, the total risk level of the special equipment is obtained.

[0025] In an optional embodiment of the present application, the specific step of obtaining the level calibration factor based on the historical data includes:

[0026] Based on the historical data, obtaining illegal operation records;

[0027] Based on the illegal operation record, obtaining the number of illegal operations;

[0028] Determining whether the number of illegal operations is greater than or equal to a preset threshold;

[0029] If the number of illegal operations is greater than or equal to a preset threshold, obtaining a violation level based on the illegal operation records;

[0030] using the number of violations and the violation level as the level calibration factor;

[0031] If the number of illegal operations is less than the preset number threshold, determining whether a designated adjustment instruction is detected;

[0032] If the designated adjustment instruction is detected, obtaining the designated adjustment content and the designated adjustment range based on the designated adjustment instruction;

[0033] The designated adjustment content and the designated adjustment range are used as the level calibration factor.

[0034] In an optional embodiment of the present application, the method further includes:

[0035] Acquiring environmental data corresponding to the special equipment;

[0036] Acquire a first weight coefficient corresponding to the environmental data;

[0037] Based on the historical data, obtaining historical influencing factors;

[0038] Obtaining the current value corresponding to the historical influencing factor;

[0039] Obtaining a second weight coefficient corresponding to the historical influencing factor;

[0040] Obtaining a calibration score based on the environmental data, the first weight coefficient, the historical influencing factors, and the second weight coefficient;

[0041] Based on the calibration score, calibrating the overall risk level and generating a calibrated level;

[0042] The calculation model of the calibration score is:

[0043] Where Y is the calibration score, is the weight coefficient of the i-th environmental data, u i is the current value of the i-th environmental data, is the historical average value of the i-th environmental data, is the weight coefficient of the jth historical influencing factor, e j is the current value of the jth historical influencing factor, m is the number of environmental data items, and n is the number of historical influencing factors.

[0044] In an optional embodiment of the present application, the specific steps of calibrating the total risk level based on the level calibration factor and generating a calibration level include:

[0045] If the level calibration factor is the number of violations and the level of violation, then obtaining the number of violations corresponding to different levels of violation respectively;

[0046] generating the calibration level based on the number of violations corresponding to different violation levels and the total risk level;

[0047] If the level calibration factor is a specified adjustment content and a specified adjustment range, acquiring initial data based on the abnormal real-time data corresponding to the adjustment content and the adjustment range;

[0048] Determining whether the initial data meets preset data requirements;

[0049] If the initial data meets the preset data requirement, a calibration level is generated based on the different adjustment contents and the total risk level.

[0050] In an optional implementation manner of the present application, after determining whether the initial data meets the preset data requirements, the method further includes:

[0051] If the initial data does not meet the preset data requirement, obtaining an adjusted operation risk level corresponding to the abnormal operation data based on the initial data and the preset comparison standard;

[0052] A calibration level is generated based on the different adjustment contents, the adjustment operation risk level, and the total risk level.

[0053] In a second aspect, the present invention provides a special equipment risk assessment system, comprising:

[0054] The first acquisition module is used to obtain real-time data and historical data of different categories of special equipment;

[0055] A second acquisition module is configured to acquire, based on the real-time data and a preset comparison standard, individual risk levels corresponding to the real-time data of different categories;

[0056] A third acquisition module is used to obtain the total risk level of the special equipment based on the individual risk level and a preset calculation standard;

[0057] a fourth acquisition module, configured to acquire a level calibration factor based on the historical data;

[0058] a calibration module, configured to calibrate the total risk level based on the level calibration factor and generate a calibrated level;

[0059] The risk assessment module is used to generate a corresponding risk assessment report based on the calibration level.

[0060] The special equipment risk assessment system provided by the present invention includes a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module, a calibration module and a risk assessment module. The first acquisition module can acquire real-time data and historical data of different categories of special equipment, the second acquisition module can acquire the individual risk levels corresponding to the real-time data of different categories based on real-time data and preset control standards, the third acquisition module can acquire the total risk level of special equipment based on the individual risk level and preset calculation standards, the fourth acquisition module can acquire the level calibration factor based on historical data, the calibration module can calibrate the total risk level based on the level calibration factor and generate a calibration level, and the risk assessment module can generate a corresponding risk assessment report based on the calibration level, thereby calibrating the total risk level through the calibration factor, which helps to improve the accuracy of the total risk level and the accuracy of risk assessment, so as to ensure the safe operation of special equipment.

[0061] In a third aspect, the present invention provides an intelligent terminal comprising a memory and a processor, wherein the memory is used to store a computer program that can be run on the processor, and when the processor loads the computer program, the above method is executed.

[0062] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is loaded by a processor, the above method is executed.

[0063] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0064] 1. The special equipment risk assessment method provided by the present invention obtains real-time data and historical data of different categories of special equipment, then obtains the individual risk levels corresponding to the real-time data of different categories based on the real-time data and preset comparison standards, and obtains the total risk level of the special equipment by integrating preset calculation standards. Simultaneously, a level calibration factor is obtained based on the historical data, and the total risk level is calibrated based on the level calibration factor to generate a calibrated level. Finally, a corresponding risk assessment report is generated based on the calibrated level. Thus, calibrating the total risk level using the calibration factor helps improve the accuracy of the total risk level, thereby further improving the accuracy of the risk assessment and ensuring the safe operation of the special equipment.

[0065] 2. The special equipment risk assessment system provided by the present invention includes a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module, a calibration module and a risk assessment module. The first acquisition module can acquire real-time data and historical data of different categories of special equipment; the second acquisition module can acquire the individual risk levels corresponding to the real-time data of different categories based on real-time data and preset control standards; the third acquisition module can acquire the total risk level of special equipment based on the individual risk level and preset calculation standards; the fourth acquisition module can acquire the level calibration factor based on historical data; the calibration module can calibrate the total risk level based on the level calibration factor and generate a calibration level; the risk assessment module can generate a corresponding risk assessment report based on the calibration level, thereby calibrating the total risk level through the calibration factor, which helps to improve the accuracy of the total risk level and the accuracy of risk assessment, so as to ensure the safe operation of special equipment.

[0066] 3. The terminal provided by the present invention includes a memory and a processor. The memory is used to store a computer program that can be run on the processor. When the processor loads the computer program, it executes the above method to calibrate the total risk level through the calibration factor, which helps to improve the accuracy of the total risk level, thereby further improving the accuracy of risk assessment and ensuring the safe operation of special equipment.

[0067] 4. The computer-readable storage medium provided by the present invention stores a computer program. When the computer program is loaded by the processor, the above method is executed to calibrate the total risk level through the calibration factor, which helps to improve the accuracy of the total risk level, thereby further improving the accuracy of risk assessment and ensuring the safe operation of special equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope.

[0069] In this accompanying figure:

[0070] Figure 1 A schematic diagram of the main process of the special equipment risk assessment method provided in the embodiment of the present application;

[0071] Figure 2 This is a flowchart of steps S201 to S206 of an embodiment of the present application;

[0072] Figure 3 This is a flowchart of steps S301 to S303 of an embodiment of the present application;

[0073] Figure 4 This is a flowchart of steps S401 to S408 of an embodiment of the present application;

[0074] Figure 5 This is a flowchart of steps S501 to S507 of an embodiment of the present application;

[0075] Figure 6 This is a flowchart of steps S601 to S605 of an embodiment of the present application;

[0076] Figure 7 This is a flowchart of steps S701 to S702 of an embodiment of the present application;

[0077] Figure 8 This is a module diagram of a special equipment risk assessment system provided in an embodiment of the present application.

[0078] Description of reference numerals:

[0079] 1. First acquisition module; 2. Second acquisition module; 3. Third acquisition module; 4. Fourth acquisition module; 5. Calibration module; 6. Risk assessment module. DETAILED DESCRIPTION

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0081] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0082] It should be noted that the embodiments and features of the embodiments in this application may be combined with each other unless they conflict. Furthermore, in the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other embodiments, well-known methods are not specifically described to avoid obscuring the present invention.

[0083] Example 1

[0084] Combine Figure 1 This embodiment provides a special equipment risk assessment method, comprising the following steps:

[0085] Step S101: Acquire real-time data and historical data of different categories of special equipment.

[0086] Specifically, in this embodiment, real-time data is a collection of various real-time data of special equipment, including real-time feature data and real-time operation data. Real-time feature data refers to the external features or internal features of various components of special equipment, and real-time operation data is real-time data during the operation of special equipment. In this embodiment, real-time data can be acquired through cameras and various sensors, and the acquired real-time data will be stored in the corresponding database according to different categories. When the real-time data of the next moment is acquired, the real-time data of the previous moment becomes historical data; historical data is factual data of the historical period, and the historical period can be the past 5 or 10 years, or it can be from the time the special equipment was officially put into use to the present; it is worth noting that in this embodiment, the real-time data of special equipment is classified in advance, and can be classified according to the different components of special equipment and various important data during the operation of special equipment.

[0087] Step S102: Based on the real-time data and the preset comparison standard, the individual risk levels corresponding to the real-time data of different categories are obtained.

[0088] Specifically, the preset control standard is a pre-set control standard for obtaining a single risk level based on real-time data. The preset control standard includes the type and range of different real-time data, as well as the single risk level corresponding to the type and range. The single risk level is the risk level corresponding to different classified real-time data. In this embodiment, the single risk level can be divided according to relevant industry standards corresponding to different special equipment. For example, the single risk level is divided into Class A, Class B, Class C, Class D and Class E, among which Class A has the highest risk and Class E has the lowest risk.

[0089] Step S103: Based on the individual risk levels and preset calculation standards, the total risk level of the special equipment is obtained.

[0090] Specifically, in this embodiment, the preset calculation standard is a pre-set calculation standard for calculating the total risk level based on the individual risk level. The total risk level is the comprehensive risk level of the entire special equipment, including the primary level and the secondary level. The primary level is the level corresponding to different individual risk levels, and the secondary level is the number corresponding to the same individual risk level. For example, the total risk level is B5, the primary level is B, and the secondary level is 5, which means that the special equipment has a total of 5 individual risk levels of level B.

[0091] Step S104: Obtaining a level calibration factor based on historical data.

[0092] Specifically, in this embodiment, the level calibration factor is a relevant factor used to calibrate the total risk level.

[0093] Step S105: Based on the level calibration factor, calibrate the total risk level and generate a calibration level.

[0094] Specifically, in this embodiment, the calibration level is the total risk level after calibration by the level calibration factor.

[0095] Step S106: Generate a corresponding risk assessment report based on the calibration level.

[0096] Specifically, in this embodiment, the risk assessment report is a report generated after a comprehensive assessment of the risks existing in the special equipment.

[0097] That is to say, the special equipment risk assessment method provided in this embodiment obtains the individual risk levels corresponding to real-time data of different categories based on real-time data and preset control standards, and then calculates the total risk level of the special equipment based on the individual risk levels and preset calculation standards; obtains the level calibration factor from historical data, calibrates the total risk level through the level calibration factor and generates a calibration level, and finally generates a risk assessment report based on the calibration level; calibrating the total risk level through the calibration factor helps to improve the accuracy of the total risk level, thereby further improving the accuracy of the risk assessment.

[0098] Reference Figure 2 In this embodiment, the specific steps of step S102 for obtaining the individual risk levels corresponding to different categories of real-time data based on real-time data and preset comparison standards include steps S201 to S206:

[0099] Step S201: If the real-time data is real-time feature data, the real-time feature data is compared with the preset feature data and feature abnormality data is obtained.

[0100] Specifically, the preset characteristic data is the standard characteristic data of the special equipment that is set in advance. In this embodiment, the preset characteristic data is the characteristic data corresponding to the special equipment when it leaves the factory, including the initial condition and shape of each component of the special equipment when it leaves the factory; the real-time characteristic data is compared with the preset characteristic data to compare the distinguishing features between the two, and the distinguishing features are the characteristic abnormality data, such as the breakage or rust of the boom components.

[0101] Step S202: Match the characteristic abnormality data with a preset control standard to obtain a characteristic risk level corresponding to the characteristic abnormality data.

[0102] Specifically, in this embodiment, the characteristic abnormality data is matched one by one with all ranges of the type corresponding to the characteristic abnormality data in the preset control standard, and the characteristic risk level corresponding to the characteristic abnormality data finally obtained is the characteristic risk level corresponding to the range matching the characteristic abnormality data; the characteristic risk level is the risk level corresponding to the real-time characteristic data.

[0103] Step S203: taking the characteristic risk level as the individual risk level corresponding to the real-time data.

[0104] Step S204: If the real-time data is real-time operation data, the real-time data is compared with the preset safety data, and operation abnormality data is obtained.

[0105] Specifically, in this embodiment, the preset safety data is the standard operating data of the special equipment that is set in advance. In this embodiment, the preset safety data is the data range for the normal and safe operation of the special equipment. The real-time operating data is compared with the preset safety data to determine whether the real-time operating data is within the data range corresponding to the preset safety data. If the real-time operating data is not within the data range, the real-time operating data is determined to be abnormal operating data.

[0106] Step S205: Match the abnormal operation data with a preset comparison standard to obtain an operation risk level corresponding to the abnormal operation data.

[0107] Specifically, in this embodiment, the abnormal operation data is matched one by one with all ranges of the type corresponding to the abnormal operation data in the preset control standard, and the characteristic risk level corresponding to the abnormal operation data finally obtained is the characteristic risk level corresponding to the range matching the abnormal operation data.

[0108] Step S206: The operation risk level is used as the single risk level corresponding to the real-time data.

[0109] Specifically, in this embodiment, the individual risk levels can be set to A, B, C, D and E and a, b, c, d and e, among which A to E are classifications of characteristic risk levels, with A being the highest risk and E being the lowest risk, and a to e are classifications of operational risk levels, with a being the highest risk and e being the lowest risk.

[0110] That is, the special equipment risk assessment method provided in this embodiment determines whether the real-time data is real-time characteristic data or real-time operation data, and compares the judgment result with the corresponding preset characteristic data or preset operation data to obtain the corresponding characteristic abnormality data or operation abnormality data, and then matches the characteristic abnormality data or operation abnormality data with the preset control standard to obtain the corresponding characteristic risk level and operation risk level; according to the different types of real-time data, the corresponding method and standard are selected to obtain the corresponding single risk level, which helps to improve the accuracy of the single risk level.

[0111] Reference Figure 3 In this embodiment, step S103 obtains the total risk level of special equipment based on the individual risk level and the preset calculation standard, and the specific steps include steps S301 to S303:

[0112] Step S301: Classify all individual risk levels and generate classification results.

[0113] Specifically, the classification result is the result of classifying individual risk levels according to different levels.

[0114] Step S302: Obtain the number of categories of individual risk levels corresponding to different categories.

[0115] Specifically, the number of categories is the number of individual risk levels corresponding to different categories. For example, if there are 5 individual risk levels of level A, the number of categories corresponding to level A is 5.

[0116] Step S303: Based on the classification results and the number of categories of individual risk levels corresponding to different categories, the total risk level of the special equipment is obtained.

[0117] Specifically, in this embodiment, the total risk level is the sum of all individual risk levels. For example, the classification result is that the number of categories corresponding to level A is 5, the number of categories corresponding to level B is 3, the number of categories corresponding to level C is 2, the number of categories corresponding to level D is 0, and the number of categories corresponding to level E is 1, then the total risk level is A5B3C2E1.

[0118] The special equipment risk assessment method provided in this embodiment classifies all individual risk levels and obtains the number of classifications corresponding to different categories. Finally, all individual risk levels and the number of classifications are combined to calculate the total risk level.

[0119] Reference Figure 4 In this embodiment, the specific steps of obtaining the level calibration factor based on historical data in step S104 include steps S401 to S408:

[0120] Step S401: Obtain illegal operation records based on historical data.

[0121] Specifically, in this embodiment, the illegal operation record is a historical record of illegal operations, including illegal operation time, illegal operation type, illegal operation level, and the like.

[0122] Step S402: Based on the illegal operation records, the number of illegal operations is obtained.

[0123] Specifically, the number of illegal operations refers to the total number of illegal operations. In this embodiment, the number of illegal operations refers to the sum of all illegal operations between the last risk assessment and the current risk assessment.

[0124] Step S403: Determine whether the number of illegal operations is greater than or equal to a preset number threshold.

[0125] Specifically, the preset quantity threshold is a pre-set criterion for determining whether there is an illegal operation. In this embodiment, the preset quantity threshold is 1 time.

[0126] Step S404: If the number of illegal operations is greater than or equal to a preset threshold, a violation level is obtained based on the illegal operation records.

[0127] Specifically, the violation level is the severity level of the illegal operation. In this embodiment, the violation level can be set to level one to level five, where level one is the most serious and level five is the least serious. Levels one to five correspond to levels A to E and levels a to e, respectively.

[0128] Step S405: Using the number of violations and the violation level as level calibration factors.

[0129] Step S406: If the number of illegal operations is less than a preset threshold, determine whether a designated adjustment instruction is detected.

[0130] Specifically, in this embodiment, the designated adjustment instruction is an instruction designated by the user for adjusting the special equipment.

[0131] Step S407: If a designated adjustment instruction is detected, the designated adjustment content and the designated adjustment range are obtained based on the designated adjustment instruction.

[0132] Specifically, in this embodiment, specifying the adjustment content means specifying the specific adjustment items for adjusting the special equipment contained in the adjustment instruction; specifying the adjustment range means specifying the adjustment range corresponding to the specific adjustment items for adjusting the special equipment contained in the adjustment instruction.

[0133] Step S408: Using the designated adjustment content and the designated adjustment range as level calibration factors.

[0134] Therefore, the special equipment risk assessment method provided by this embodiment obtains records of illegal operations based on historical data, and then obtains the number of illegal operations based on the illegal operation records, and determines whether the illegal operation data is greater than or equal to a preset number threshold. If it exceeds, it indicates that there is an illegal operation, and then the violation level of the illegal operation is further obtained, and the number of violations and the violation level are used as level calibration factors; illegal operations will affect the quality of special equipment, thereby increasing the safety risk of special equipment, so the number of violations and the violation level corresponding to the violation records are used as level calibration factors to calibrate the total risk level, which helps to improve the accuracy of the total risk level.

[0135] Determine whether the specified adjustment instruction is detected. If detected, it indicates that the user has made relevant adjustments to the special equipment according to actual needs. Further obtain the specified adjustment content and the specified adjustment range, and use the specified adjustment content and the specified adjustment range as the level calibration factor; the staff adjusts the special equipment according to the specified adjustment instruction based on the actual situation and user needs. After the adjustment, the real-time operation data may become abnormal operation data, but there is actually no safety risk. Therefore, the specified adjustment content and the specified adjustment range contained in the specified adjustment instruction are used as the level calibration factor to calibrate the total safety level, which helps to improve the accuracy of the total safety level.

[0136] Reference Figure 5 , this embodiment further includes steps S501 to S507:

[0137] Step S501: Acquire environmental data corresponding to special equipment.

[0138] Specifically, in this embodiment, the environmental data refers to relevant data in the environment surrounding the special equipment, such as ambient temperature, ambient humidity, and air dust content.

[0139] Step S502: Acquire a first weight coefficient corresponding to the environmental data.

[0140] Specifically, in this embodiment, the first weight coefficient may be determined according to the impact of different environmental data on the risk assessment, or may be determined according to user needs and actual conditions.

[0141] Step S503: Obtain historical influencing factors based on historical data;

[0142] Specifically, historical influencing factors refer to relevant factors that have occurred in history and have an impact on risk assessment, such as fault maintenance and lack of normal maintenance;

[0143] Step S504: obtaining the current value corresponding to the historical influencing factor;

[0144] Specifically, the current value is the number of times the historical influencing factors appear, such as the number of fault repairs and the number of missed normal maintenances.

[0145] Step S505: Obtain a second weight coefficient corresponding to the historical influencing factor.

[0146] Specifically, the second weight coefficient may be determined based on the impact of different historical influencing factors on the risk assessment, or may be determined based on user needs and actual conditions.

[0147] Step S506: Obtain a calibration score based on the environmental data, the first weight coefficient, the historical influencing factors, and the second weight coefficient.

[0148] Specifically, the calculation model of the calibration score is:

[0149] Where Y is the calibration score, is the weight coefficient of the i-th environmental data, u i is the current value of the i-th environmental data, is the historical average value of the i-th environmental data, is the weight coefficient of the jth historical influencing factor, e j is the current value of the jth historical influencing factor, m is the number of environmental data items, and n is the number of historical influencing factors.

[0150] Step S507: Based on the calibration score, calibrate the total risk level and generate a calibration level.

[0151] Specifically, a relevant mechanism for calibrating the total risk level according to the calibration score can be pre-set. Once the calibration score is known, the corresponding calibration situation can be matched (for example, increasing the total risk level or reducing the total risk level), and then the total risk level can be calibrated according to the calibration situation.

[0152] Reference Figure 6 In this embodiment, the specific steps of step S105 for calibrating the total risk level and generating the calibration level based on the level calibration factor include steps S601 to S605:

[0153] Step S601: If the level calibration factor is the number of violations and the violation level, then the number of violations corresponding to different violation levels is obtained respectively.

[0154] Specifically, in this embodiment, the number of violations is the number corresponding to different violation levels. For example, if there are 5 level 3 violation operations in total, the number of violations corresponding to the level 3 violation operations is 3.

[0155] Step S602: Generate a calibration level based on the number of violations corresponding to different violation levels and the total risk level.

[0156] Specifically, in this embodiment, each time a violation operation is performed, the number of sub-levels of the corresponding main level in the total risk level is increased by one according to the violation level. For example, the total risk level is A5B3C2E1, and the number of violations and violation levels corresponding to the violation operations are 1 time for level one, 2 times for level two, 3 times for level three, 4 times for level four, and 5 times for level five, respectively. The calibration level is A(5+1)B(3+2)C(2+3)D(0+4)E(1+5), that is, A6B2C5D4E6.

[0157] Step S603: If the level calibration factor specifies adjustment content and a specified adjustment range, initial data is acquired based on the abnormal real-time data corresponding to the adjustment content and the adjustment range.

[0158] Specifically, the initial data is the initial data corresponding to the abnormal real-time data. In this embodiment, the initial data is the value obtained by subtracting or adding the adjustment range from the abnormal real-time data. Specifically, whether it is the abnormal real-time data minus the adjustment range or the abnormal real-time data plus the adjustment range depends on whether the adjustment range is positive or negative, or whether it is adjusted up or down.

[0159] Step S604: Determine whether the initial data meets the preset data requirements.

[0160] Specifically, the preset data requirement is a pre-set basis for determining whether the initial data is within the safe data range. In this embodiment, determining whether the initial data meets the preset data requirement is determining whether the initial data is within the data range included in the preset safe data.

[0161] Step S605: If the initial data meets the preset data requirements, a calibration level is generated based on different adjustment contents and the total risk level.

[0162] Specifically, each time a designated adjustment instruction is detected, the number of sub-levels of the main level corresponding to the level size of the designated adjustment content is reduced by one; for example, the total risk level is A6B2C5D4E6, and the levels of the individual levels corresponding to the designated adjustment instructions are level one, level three, level five, level four, level four, and level three, respectively. Then the calibration level is A(6-1)B(2-0)C(5-2)D(4-2)E(6-1), that is, A5B2C3D2E5.

[0163] Therefore, in the special equipment risk assessment method provided by this embodiment, if the level calibration factor is the number of violations and the violation level, the number of violations corresponding to different violation levels is obtained, and then the calibration level is generated based on the number of violations and the total risk level; if the level calibration factor is the specified adjustment content and the specified adjustment range, the initial data is obtained and it is determined whether the initial data meets the preset data requirements. If so, it indicates that the initial data is within the safe data range, and therefore the calibration level is generated based on different adjustment contents and the total risk level; the total risk level is calibrated by selecting the corresponding calibration method according to different calibration factors. Calibration helps to improve the accuracy of the total risk level.

[0164] Reference Figure 7 In this embodiment, after determining whether the initial data meets the preset data requirements in step S604, the process further includes steps S701 to S702:

[0165] Step S701: If the initial data does not meet the preset data requirements, then based on the initial data and the preset comparison standard, an adjusted operation risk level corresponding to the abnormal operation data is obtained.

[0166] Specifically, in this embodiment, the adjusted operation risk level is the risk level obtained by matching the initial data with the preset data standard.

[0167] Step S702: Generate a calibration level based on different adjustment contents, adjustment operation risk levels, and the total risk level.

[0168] Specifically, in this embodiment, the individual risk level corresponding to the abnormal operation data may be replaced by the adjusted operation risk level in the overall risk level.

[0169] Therefore, the special equipment risk assessment method provided in this embodiment, when the initial data does not meet the preset data requirements, obtains the adjusted operation risk level corresponding to the abnormal operation data based on the initial data and the preset control standards, and then generates a calibration level based on different adjustment contents, adjusted operation risk levels and total risk levels.

[0170] Example 2

[0171] Reference Figure 8 , this embodiment provides a special equipment risk assessment system, including:

[0172] The first acquisition module is used to obtain real-time data and historical data of different categories of special equipment;

[0173] A second acquisition module is configured to acquire, based on the real-time data and a preset comparison standard, individual risk levels corresponding to the real-time data of different categories;

[0174] A third acquisition module is used to obtain the total risk level of the special equipment based on the individual risk level and a preset calculation standard;

[0175] a fourth acquisition module, configured to acquire a level calibration factor based on the historical data;

[0176] a calibration module, configured to calibrate the total risk level based on the level calibration factor and generate a calibrated level;

[0177] The risk assessment module is used to generate a corresponding risk assessment report based on the calibration level.

[0178] That is to say, the special equipment risk assessment system provided in this embodiment includes a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module, a calibration module and a risk assessment module. The first acquisition module can acquire real-time data and historical data of different categories of special equipment, the second acquisition module can acquire the individual risk levels corresponding to the real-time data of different categories based on real-time data and preset control standards, the third acquisition module can acquire the total risk level of special equipment based on the individual risk level and preset calculation standards, the fourth acquisition module can acquire the level calibration factor based on historical data, the calibration module can calibrate the total risk level based on the level calibration factor and generate a calibration level, and the risk assessment module can generate a corresponding risk assessment report based on the calibration level, thereby calibrating the total risk level through the calibration factor, which helps to improve the accuracy of the total risk level and the accuracy of risk assessment to ensure the safe operation of special equipment.

[0179] Example 3

[0180] This embodiment provides an intelligent terminal, including a memory and a processor. The memory is used to store a computer program that can be run on the processor. When the processor loads the computer program, it executes the special equipment risk assessment method described in Example 1.

[0181] Example 4

[0182] This embodiment provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is loaded by a processor, the special equipment risk assessment method described in Example 1 is executed.

[0183] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A special equipment risk assessment method, characterized in that: include: Obtain real-time and historical data of different categories of special equipment; Based on the real-time data and preset comparison standards, obtaining individual risk levels corresponding to the real-time data of different categories; Obtaining the total risk level of the special equipment based on the individual risk levels and preset calculation standards; obtaining a level calibration factor based on the historical data; Calibrate the total risk level based on the level calibration factor and generate a calibrated level; Based on the calibration level, a corresponding risk assessment report is generated.

2. A special equipment risk assessment method according to claim 1, characterized in that: The real-time data includes real-time feature data and real-time operation data. The specific steps of obtaining the individual risk levels corresponding to the real-time data of different categories based on the real-time data and the preset comparison standard include: If the real-time data is the real-time feature data, comparing the real-time feature data with the preset feature data and obtaining feature abnormality data; Matching the characteristic abnormal data with the preset control standard to obtain a characteristic risk level corresponding to the characteristic abnormal data; Using the characteristic risk level as the individual risk level corresponding to the real-time data; If the real-time data is the real-time operation data, the real-time data is compared with the preset safety data, and operation abnormality data is obtained; Matching the abnormal operation data with the preset control standard to obtain the operation risk level corresponding to the abnormal operation data; The operational risk level is used as the single risk level corresponding to the real-time data.

3. A special equipment risk assessment method according to claim 1, characterized in that: The specific steps of obtaining the total risk level of the special equipment based on the individual risk levels and the preset calculation standard include: Classify all the individual risk levels and generate classification results; Get the number of categories of individual risk levels corresponding to different categories; Based on the classification results and the number of classifications of individual risk levels corresponding to different categories, the total risk level of the special equipment is obtained.

4. A special equipment risk assessment method according to claim 1, characterized in that: The specific steps of obtaining the level calibration factor based on the historical data include: Based on the historical data, obtaining illegal operation records; Based on the illegal operation record, obtaining the number of illegal operations; Determining whether the number of illegal operations is greater than or equal to a preset threshold; If the number of illegal operations is greater than or equal to a preset threshold, obtaining a violation level based on the illegal operation records; using the number of violations and the violation level as the level calibration factor; If the number of illegal operations is less than the preset number threshold, determining whether a designated adjustment instruction is detected; If the designated adjustment instruction is detected, obtaining the designated adjustment content and the designated adjustment range based on the designated adjustment instruction; The designated adjustment content and the designated adjustment range are used as the level calibration factor.

5. A special equipment risk assessment method according to claim 4, characterized in that: Also includes: Acquiring environmental data corresponding to the special equipment; Acquire a first weight coefficient corresponding to the environmental data; Based on the historical data, obtaining historical influencing factors; Obtaining the current value corresponding to the historical influencing factor; Obtaining a second weight coefficient corresponding to the historical influencing factor; Obtaining a calibration score based on the environmental data, the first weight coefficient, the historical influencing factors, and the second weight coefficient; Based on the calibration score, calibrating the overall risk level and generating a calibrated level; The calculation model of the calibration score is: Where Y is the calibration score, is the weight coefficient of the i-th environmental data, u i is the current value of the i-th environmental data, is the historical average value of the i-th environmental data, is the weight coefficient of the jth historical influencing factor, e j is the current value of the jth historical influencing factor, m is the number of environmental data items, and n is the number of historical influencing factors.

6. A special equipment risk assessment method according to claim 1, characterized in that: The specific steps of calibrating the total risk level based on the level calibration factor and generating a calibration level include: If the level calibration factor is the number of violations and the level of violation, then obtaining the number of violations corresponding to different levels of violation respectively; generating the calibration level based on the number of violations corresponding to different violation levels and the total risk level; If the level calibration factor is a specified adjustment content and a specified adjustment range, acquiring initial data based on the abnormal real-time data corresponding to the adjustment content and the adjustment range; Determining whether the initial data meets preset data requirements; If the initial data meets the preset data requirement, a calibration level is generated based on the different adjustment contents and the total risk level.

7. A special equipment risk assessment method according to claim 6, characterized in that: After determining whether the initial data meets the preset data requirements, the method further includes: If the initial data does not meet the preset data requirement, obtaining an adjusted operation risk level corresponding to the abnormal operation data based on the initial data and the preset comparison standard; A calibration level is generated based on the different adjustment contents, the adjustment operation risk level, and the total risk level.

8. A special equipment risk assessment system, characterized in that: include: The first acquisition module is used to obtain real-time data and historical data of different categories of special equipment; A second acquisition module is configured to acquire, based on the real-time data and a preset comparison standard, individual risk levels corresponding to the real-time data of different categories; A third acquisition module is used to obtain the total risk level of the special equipment based on the individual risk level and a preset calculation standard; a fourth acquisition module, configured to acquire a level calibration factor based on the historical data; a calibration module, configured to calibrate the total risk level based on the level calibration factor and generate a calibrated level; The risk assessment module is used to generate a corresponding risk assessment report based on the calibration level.

9. An intelligent terminal comprising a memory and a processor, characterized in that: The memory is used to store a computer program that can be run on the processor, and when the processor loads the computer program, it executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, wherein: When the computer program is loaded into a processor, the method according to any one of claims 1 to 7 is executed.

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