A method and system for monitoring and identifying abnormal fluctuations in total quality management

Abstract

The present application discloses a comprehensive quality management abnormal fluctuation monitoring and identification system and method, the system includes: a first monitoring system, used to monitor the operating conditions of the target object to be measured, and send the first abnormal operating condition to the server platform after monitoring the first abnormal operating condition; the server platform, used to obtain the first influencing factor type corresponding to the first abnormal operating condition; obtain the corresponding influencing factor characteristic data; determine the actual influencing factor type of the first abnormal operating condition based on the influencing factor characteristic data; increase the warning value corresponding to the management clause data to obtain the corresponding first warning value; when the first warning value exceeds the warning threshold, a warning prompt signal is issued. By using the solution of this application, the timeliness of the modification of the management clause content and the processing efficiency of the modification and improvement work can be improved, so as to improve the high matching degree between the management system and the actual situation and ensure the quality of enterprise / project operation.

Description

Technical Field

[0001] The present invention relates to data anomaly monitoring and analysis technology, and in particular to a system and method for monitoring, identifying and analyzing abnormal fluctuations in total quality management. Background Art

[0002] To ensure the stability and reliability of power supply, relevant companies develop and implement relevant management systems. Generally speaking, management system clauses are formulated to improve the overall quality of the project or company. Therefore, the closer the clauses align with actual operational conditions, the better. However, actual operation and implementation vary depending on the specific application scenario, and practical problems arising during the process are often delayed in reaching management. Consequently, there is a significant lag in the timing of the improvement or revision of management system clauses, resulting in the clauses being ineffective in practice and poor management quality. Furthermore, when revising or revising management clauses, there is a lack of statistical analysis to support the relevant data, making it difficult to accurately implement the revisions or modifications. Furthermore, system reviewers and approvers require extensive, repetitive communication with relevant personnel to ensure the accuracy and relevance of the revised or revised clauses. This significantly increases the workload for review and approval, leading to very low processing efficiency. Summary of the Invention

[0003] This application aims to solve at least one of the technical problems existing in the prior art. To this end, this application proposes a method and system for monitoring and identifying abnormal fluctuations in comprehensive quality management, which analyzes and compiles the monitored abnormal fluctuation data and uses it as a basis for early warning and prompting of clause modifications, thereby improving the efficiency and timeliness of clause modifications / enhancements.

[0004] In a first aspect, an embodiment of the present application provides a comprehensive quality management abnormal fluctuation monitoring and identification system, the system comprising:

[0005] A first monitoring system is configured to monitor the operating status of the target object to obtain first monitoring data, and after determining the presence of a first abnormal operating status based on the first monitoring data, transmit the first abnormal operating status to the server platform;

[0006] The server platform includes at least one processor configured to load a program to execute the following steps:

[0007] S1. After receiving a first abnormal operation condition, obtain a plurality of first influencing factor types corresponding to the first abnormal operation condition;

[0008] S2. Acquire corresponding influencing factor characteristic data according to a plurality of first influencing factor types;

[0009] S3. Determine the actual influencing factor type of the first abnormal operating condition based on the influencing factor characteristic data;

[0010] S4. When it is determined that there is management clause data matching the actual influencing factor type, the warning value corresponding to the management clause data is incremented to obtain a first warning value corresponding to the management clause data;

[0011] S5. When the first warning value corresponding to the management clause data exceeds the first warning threshold, a first warning prompt signal is issued.

[0012] In some embodiments, obtaining a plurality of first influencing factor types corresponding to the first abnormal operating condition specifically includes:

[0013] Using a clustering algorithm, querying from the first database the historical abnormal operation situation that has the highest matching degree with the first abnormal operation situation, wherein the historical abnormal operation situation that has the smallest Euclidean distance with the first abnormal operation situation is the historical abnormal operation situation with the highest matching degree;

[0014] From the mapping relationship between the historical abnormal operation conditions and the historical influencing factor types, the historical influencing factor types corresponding to the queried historical abnormal operation conditions are obtained.

[0015] In some embodiments, the step S4 of increasing the warning value corresponding to the management clause data specifically includes:

[0016] S401. Obtain the severity of the impact of the first abnormal operating condition;

[0017] S402: Determine a first weighted value corresponding to the severity of the impact, wherein the severity of the impact and the first weighted value are in direct proportion to each other;

[0018] S403: Use the reference value and the first weighted value to increase the warning value corresponding to the management clause data.

[0019] In some embodiments, step S403 specifically includes:

[0020] S4031. When a first satisfaction evaluation value corresponding to the management clause data is obtained from the evaluation system, a second weighted value corresponding to the first satisfaction evaluation value is determined, wherein the first satisfaction evaluation value and the second weighted value are in inverse proportion to each other;

[0021] S4032: Use the reference value, the first weighted value, and the second weighted value to increase the warning value corresponding to the management clause data.

[0022] In some embodiments, determining the second weighted value corresponding to the first satisfaction evaluation value specifically includes:

[0023] S40311. Randomly extract a number of second satisfaction evaluation values ​​corresponding to the management clause data from the evaluation system according to a preset sampling number;

[0024] S40312. Calculate the standard deviation of the plurality of second satisfaction evaluation values ​​to obtain a first standard deviation value;

[0025] S40313. Determine whether the first standard deviation value is less than or equal to the first stability threshold. If so, determine the first satisfaction evaluation value based on the average of several second satisfaction evaluation values; otherwise, return to execute step S40311 until the first standard deviation value is less than or equal to the first stability threshold.

[0026] In some embodiments, the system further comprises:

[0027] a second monitoring system for monitoring the maintenance status of the fault target to obtain second monitoring data, and after determining the presence of a first maintenance abnormality based on the second monitoring data, transmitting the first maintenance abnormality to the server platform; wherein the first maintenance abnormality at least includes abnormal maintenance duration data;

[0028] The server platform is further configured to load a program to perform the following steps:

[0029] S6. Determine corresponding management clause data based on the abnormal maintenance duration type corresponding to the abnormal maintenance duration data;

[0030] S7. Determine a second warning value of the corresponding management clause data according to the maintenance time difference included in the abnormal maintenance time data;

[0031] S8. When the second warning value corresponding to the management clause data exceeds the second warning threshold, a second warning prompt signal is issued.

[0032] In some embodiments, step S7 specifically includes:

[0033] S701: Determine a third weighted value corresponding to the maintenance time difference, wherein the maintenance time difference and the third weighted value are in direct proportion;

[0034] S702: Use the reference value and the third weighted value to increase the warning value corresponding to the corresponding management clause data, thereby obtaining a second warning value.

[0035] In some embodiments, the maintenance time difference includes a first maintenance time difference, where the first maintenance time difference refers to a maintenance time difference corresponding to executing a maintenance sub-step, and the first maintenance time difference is determined based on an actual maintenance time corresponding to the maintenance sub-step, wherein the actual maintenance time corresponding to the maintenance sub-step is obtained and determined by the following steps:

[0036] In response to a click signal of the start timing button, the current moment is used as the first maintenance actual start time node;

[0037] In response to a click signal of a maintenance information upload button, after obtaining the uploaded maintenance status information, determining whether the maintenance status information meets a first maintenance requirement, wherein the first maintenance requirement corresponds to a maintenance sub-step in a one-to-one manner;

[0038] When it is determined that the maintenance status information meets the first maintenance requirement, the current time is used as the actual end time node of the first maintenance;

[0039] The time difference between the first maintenance actual start time node and the first maintenance actual end time node is used as the actual maintenance duration of the maintenance sub-step;

[0040] The start timing button and the maintenance information upload button are arranged on a second-level display interface for displaying a plurality of maintenance sub-steps.

[0041] In some embodiments, the second-level display interface is further provided with a play button for triggering the playback of a maintenance demonstration video corresponding to a maintenance sub-step.

[0042] In a second aspect, an embodiment of the present application provides a method for monitoring and identifying abnormal fluctuations in comprehensive quality management, the method comprising the following steps:

[0043] S1. After receiving a first abnormal operation condition, obtain a plurality of first influencing factor types corresponding to the first abnormal operation condition;

[0044] S2. Acquire corresponding influencing factor characteristic data according to a plurality of first influencing factor types;

[0045] S3. Determine the actual influencing factor type of the first abnormal operating condition based on the influencing factor characteristic data;

[0046] S4. When it is determined that there is management clause data matching the actual influencing factor type, the warning value corresponding to the management clause data is incremented to obtain a first warning value corresponding to the management clause data;

[0047] S5. When the first warning value corresponding to the management clause data exceeds the first warning threshold, a first warning prompt signal is issued.

[0048] The present application can achieve at least one of the following technical effects: after the present application scheme performs abnormal monitoring and identification on the target object to be measured (such as power supply equipment, power supply layout lines, electrical equipment, etc.), when it is determined that there is data abnormality, it is sent to the server platform. The server platform will obtain the influencing factor type that matches the abnormal data from the first database based on the abnormal data; then, after obtaining the corresponding influencing factor characteristic data based on these influencing factor types, the actual influencing factor type is judged and identified based on the influencing factor characteristic data. Then, when it is determined that there is management clause data matching it based on the actual influencing factor type, the warning value corresponding to the management clause data is increased to obtain the first warning value corresponding to the management clause data, and when the first warning value corresponding to the management clause data exceeds the first warning threshold, an early warning prompt signal is issued. It can be seen that in this way, the statistical analysis results of abnormal situations can be used as an early warning basis for the modification and improvement of management clauses. Not only can the corresponding management clause data be improved / modified in a timely manner to make it more in line with actual operation, ensuring the quality of power operation management, thereby improving the reliability and stability of power operation, but also for the modification / improvement process of management clauses (including early revisions and improvements and later approval and review), it is conducive to rapid data tracing of the basis for the modification of clauses, greatly reducing the workload of staff, improving work processing efficiency, and improving the accuracy of clause modifications and improvements. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0050] Figure 1 A schematic diagram of the architecture of a comprehensive quality management abnormal fluctuation monitoring and identification system is provided for an embodiment of the present application;

[0051] Figure 2 A schematic flow chart of the steps of a method for monitoring and identifying abnormal fluctuations in comprehensive quality management is provided for an embodiment of the present application;

[0052] Figure 3 An interface diagram of the second-level display interface is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions, and advantages of this application more clear, the following will refer to the drawings in the embodiments of this application to clearly and completely describe the technical solutions of this application through implementation methods. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] The purpose of formulating management system clauses for an enterprise / project is to ensure the reliability and stability of its operation. Therefore, the content of the management clauses should be as close to the actual operation as possible. However, in the current process of formulating management clause data, due to the lack of appropriate data as support, the modification / improvement of the management clause data has been seriously delayed, resulting in a mismatch between the specific management regulations and the actual situation. This not only fails to guarantee the quality of enterprise / project operation, but also leads to excessive ineffective management process work, reducing the efficiency of the entire enterprise / project implementation. At the same time, for system review and approval personnel, a large amount of repetitive communication with relevant personnel is required during the review and approval process to ensure the accuracy of the improved / modified clauses and their relevance to the actual situation. This greatly increases the workload of the review and approval work, resulting in very low work processing efficiency.

[0055] In view of the above-mentioned problems, after studying and analyzing the overall situation of the current enterprise / project and the relevant management clause data, it was found that the factors affecting operational reliability and stability mainly come from the factors that cause abnormal operating conditions, that is, the factors that cause abnormal operating conditions. Among them, these factors can include environmental factors (such as operating environment temperature, humidity, electromagnetic interference, lightning strikes, earthquakes and other environmental conditions), connected loads, equipment failure factors (which include failures in the equipment's own circuits / electronic components, equipment aging / loss factors, etc.), human factors (such as manual cable cutting, equipment parameter setting errors, operational errors, etc.), technical factors (such as unreasonable layout of power system architecture / cables, lines, etc., equipment performance does not meet actual operating conditions, etc.). Therefore, in order to avoid the occurrence of these factors, the current management system's institutional content is basically formulated around these factors. For example: 1. The management clauses formulated for the environmental influencing factor may include equipment specifications, protective measures, monitoring measures, emergency warning plans, etc. selected for different specific environments (such as excessively high / low temperatures, excessive humidity, lightning strikes, and high levels of electromagnetic interference). 1. To avoid the problem of abnormal power operation caused by extreme / abnormal weather; 2. The management clauses formulated for the influencing factor of access load mainly include the content of load management strategy, such as demand-side electricity management regulations, peak-shifting electricity management regulations, etc., so as to avoid the interference of electricity metering data due to sudden changes in load (such as large power equipment being started and running), resulting in subsequent measurement errors; 3. The management clauses formulated for equipment failure factors may include: equipment specification and performance selection regulations, equipment detection (such as fault detection, equipment aging / loss detection) management regulations; 4. The management clauses formulated for human factors may include: various types of operation / design use / setting and other operation regulations; 5. The management clauses formulated for technical factors may include: system architecture / cable layout design and other design management regulations. It can be seen that there is an inevitable mapping relationship between the factors that affect abnormal operation and the management clauses. When the operation is often abnormal due to a certain factor, it means that the content of the management clause corresponding to the factor is basically not suitable for the current usage scenario. At this time, it is necessary to focus on analyzing, improving and modifying these management clause data in a timely manner. Based on this, the embodiment of the present application optimizes the existing monitoring and control system architecture so that it can communicate with multiple management terminals for data transmission, so as to realize statistical analysis and processing based on the number of occurrences of factors that cause abnormal situations, and use this as a basis for early warning prompts for the modification / improvement of the content of the management clauses, and feed back this data to the corresponding management terminal.

[0056] Reference Figure 1A comprehensive quality management abnormal fluctuation monitoring and identification system includes a first monitoring system and a server platform. The first monitoring system and the server platform are communicatively connected, and the server platform further includes a first data interface for communication with a management terminal. The first monitoring system and the server platform are described below.

[0057] The first monitoring system is mainly used to monitor the operating status of the target object to be measured to obtain first monitoring data. After determining the existence of a first abnormal operating status based on the first monitoring data, the first abnormal operating status is sent to the server platform.

[0058] Specifically, for the above-mentioned first monitoring data, it can be data obtained after monitoring the attribute data such as voltage, circuit, impedance, frequency, power, equipment status, etc. of the target object to be measured. And for the step of determining the existence of the first abnormal operation condition based on the first monitoring data, it can be specifically as follows: the value and / or value change trend of the monitored first monitoring data is compared and identified with the historical normal value and / or historical normal value change trend. When the difference between the value and / or value change trend of the monitored first monitoring data and the historical normal value and / or historical normal value change trend exceeds the specified difference threshold, it is judged that the current target object to be measured has an abnormal operation condition. Of course, in addition to determining whether there is an abnormal operation condition by identifying and comparing the value / value change trend, other technical means can also be used to achieve it. It can be set according to actual needs and is not specifically limited here.

[0059] In addition, for the first abnormal operation condition sent to the server platform, the information that may be included includes the type of monitoring data, the equipment specifications of the monitored object, the environmental conditions of the equipment (including indoor and outdoor environmental conditions), the comparison and identification results between the monitoring data and historical data, the project / enterprise information of the monitored object, the geographical environment information of the monitored object, etc.

[0060] The server platform comprises at least one processor for loading a program to execute the steps of a method for monitoring and identifying abnormal fluctuations in comprehensive quality management.

[0061] Reference Figure 2 , an embodiment of the present application provides a comprehensive quality management abnormal fluctuation monitoring and identification method, which includes the following specific implementation steps.

[0062] S1. After receiving a first abnormal operating condition, obtain a plurality of first influencing factor types corresponding to the first abnormal operating condition.

[0063] Specifically, the multiple first influencing factor types may be influencing factor types obtained based on factor analysis of historical abnormal operating conditions, wherein the total number of first influencing factor types may be m. A specific implementation of obtaining the multiple first influencing factor types corresponding to the first abnormal operating condition may include the following.

[0064] Method ①, based on the mapping relationship between historical abnormal operation conditions and historical influencing factor types: It is achieved by constructing a mapping relationship between historical abnormal operation conditions and historical influencing factor types, that is, by performing data identification, comparison and judgment on historical abnormal operation conditions, analyzing and processing the judgment results and combining them with other attribute data to obtain the corresponding historical influencing factor types, and then constructing a mapping relationship between historical abnormal operation conditions and corresponding historical influencing factor types; then in actual use, the first abnormal operation condition determined by monitoring can be matched and compared with the historical abnormal operation condition to obtain the first historical abnormal operation condition that is closest to the first abnormal operation condition. At this time, based on the mapping relationship between the historical abnormal operation condition and the historical influencing factor type, several first historical influencing factor types corresponding to the first historical abnormal operation condition can be obtained. The several first historical influencing factor types obtained at this time are the required first influencing factor types with a high degree of matching.

[0065] Method ② is implemented based on a deep learning model: the data identification, comparison and judgment of historical abnormal operation conditions and the combination of other attribute data are used as training input data, and the corresponding historical influencing factor types obtained by analysis and processing are used as training output data. Then, the preset deep learning model is trained using the above training input data and training output data to obtain a trained deep learning model; then, in actual use, the data identification, comparison and judgment of the first abnormal operation condition and the combination of other attribute data are obtained as the first data input matrix, and then input into the trained deep learning model for data processing to obtain the corresponding data output results, that is, several first influencing factor types.

[0066] For the above methods ① and ②, they can be selected and set accordingly according to actual conditions, and no special restrictions are made here.

[0067] Alternatively, if N influencing factor types are obtained after impact analysis of all historical anomalies, then obtaining several first influencing factor types corresponding to the first abnormal operating condition can be done directly by directly obtaining N influencing factor types, which is equivalent to obtaining all influencing factor types. Compared to the above two methods, this method eliminates the need for matching query processing and data processing using deep learning models, significantly reducing processing workload.

[0068] S2. Acquire corresponding influencing factor characteristic data according to a plurality of first influencing factor types.

[0069] Specifically, since the obtained several first influencing factor types may be different from the actual situation, after obtaining several first influencing factor types, corresponding influencing factor characteristic data can be obtained based on these types to determine whether these first influencing factor types are actual influencing factors. For example, the obtained first influencing factor types include equipment failure factors and environmental factors. Then, the evaluation data corresponding to the two aspects of equipment failure and environment, i.e., influencing factor characteristic data, can be used to determine whether the circuit of the equipment is short-circuited / open-circuited, whether the loss rate of the equipment is lower than the specified requirements, whether the environment in which the equipment is located has extreme weather conditions, etc., so as to determine whether the first influencing factor type currently obtained is the actual influencing factor of the current abnormal operation based on these data.

[0070] S3. Determine the actual influencing factor type of the first abnormal operating condition based on the influencing factor characteristic data.

[0071] Specifically, the step S3 may include the following steps.

[0072] S301: Screen and determine an actual influencing factor type from a plurality of first influencing factor types based on influencing factor characteristic data.

[0073] S302. When the number of actual influencing factor types screened and determined from several first influencing factor types is 0 and the number of first influencing factor types is less than N, then a second influencing factor type is obtained, and then the actual influencing factor type of the first abnormal operating condition is determined based on the influencing factor characteristic data corresponding to the second influencing factor type; wherein N is the total number of all influencing factor types corresponding to all abnormal operating conditions; the second influencing factor type refers to the influencing factor type other than the first influencing factor type in the N influencing factor types, for example, a total of the 1st to 8th influencing factor types are included. At this time, the first influencing factor types obtained are the 3rd, 5th, 7th, and 8th influencing factor types, respectively, and the 1st, 2nd, 4th, and 6th influencing factor types belong to the second influencing factor type.

[0074] Specifically, after analyzing, identifying, and judging all historical abnormal operating conditions, a total of N influencing factor types will be obtained. If, after analyzing, identifying, and judging based on the influencing factor characteristic data corresponding to the currently obtained first influencing factor type, it is determined that the specified conditions that produce these influencing factor types do not exist in the actual situation, for example, there is no circuit fault in the equipment, the loss rate / aging degree of the equipment meets the specified requirements, and there is no extreme weather, etc., then at this time, in order to improve the accuracy and comprehensiveness of the identification and judgment, the remaining influencing factor types can be obtained for judgment, thereby determining the actual influencing factor type. Of course, if the number of actual influencing factor types screened and judged from several first influencing factor types is not 0, the actual influencing factor types screened and judged will be used as the final actual influencing factor type required.

[0075] S303: When the number of actual influencing factor types screened and determined from the plurality of first influencing factor types or second influencing factor types is not 0, the screened and determined actual influencing factor types are used as the final required actual influencing factor types.

[0076] S304: When the number of actual influencing factor types determined from the plurality of first and second influencing factor types is zero, this indicates the presence of a new influencing factor type, which has caused an abnormal operating condition. The current first abnormal operating condition is then stored in the corresponding first designated area, and a fourth early warning signal is transmitted to prompt relevant personnel to conduct further analysis of the specific influencing factors of this abnormal operating condition. This data processing step allows for the rapid and timely discovery of new influencing factors, thereby increasing the update rate of influencing factor types and avoiding the omission of new influencing factors in detection and analysis.

[0077] S4. If matching management clause data is determined based on the actual influencing factor type, the warning value corresponding to the management clause data is incremented to obtain a first warning value corresponding to the management clause data. The first warning value is primarily used to provide a warning prompt for timely modification and improvement of the management clause data.

[0078] Specifically, for an influencing factor type, if it is judged and identified once, it means that the number of occurrences of the influencing factor type is increased by 1, and it also means that the management clause data content that matches the influencing factor type is less suitable for the current usage scenario. For the actual influencing factor type, it is the influencing factor type that is finally identified. Therefore, based on these identified influencing factor types, the warning value of the corresponding management clause data is increased. That is to say, for this warning value, it is used to indicate the degree of mismatch between the corresponding management clause data content and the current actual scenario. Among them, when the influencing factor type is identified once, the warning value is increased by 1, or it can also be increased according to other preset values. This can be selected and set according to the actual situation, and the increase processing is not particularly limited here.

[0079] Furthermore, in view of the variability of actual usage scenarios, the update speed of the influencing factor types for abnormal operation conditions will also be relatively fast, that is, there will be situations where there is no management clause data that matches the actual influencing factor type. For example, there are a total of 8 original influencing factor types, and in the existing management clause content, there are management clause data that match these 8 original influencing factor types. Based on factors such as changes in scenarios and monitoring technology, new influencing factor types may appear on this basis in the future, such as the 9th, 10th, and 11th influencing factor types. Therefore, when the actual influencing factor type identified is a new influencing factor type, there will be a situation where there is no management clause data that matches the actual influencing factor type. At this time, this abnormal operation condition and its corresponding actual influencing factor type can be stored in a designated area, and when the number of times a new actual influencing factor type is identified exceeds the preset warning threshold, a corresponding warning prompt signal is output to prompt relevant staff to formulate corresponding management clause data for this influencing factor type as soon as possible. It can be seen that the method steps of the embodiment of the present application can also include the following steps.

[0080] S9. When it is determined based on the actual influencing factor type that there is no matching management clause data, the actual influencing factor type and its corresponding first abnormal operating condition are stored in the second designated area, and the number of occurrences of the actual influencing factor type is increased (generally, the number of occurrences is increased by 1).

[0081] S10. When the number of occurrences of the actual influencing factor type exceeds the first preset number threshold, a third early warning signal is issued to promptly remind relevant staff to formulate corresponding management clauses for this actual influencing factor type as soon as possible.

[0082] It can be seen that the above-mentioned data processing method can greatly improve the timeliness of the formulation of new management systems, thereby improving the reliability and stability of actual operation conditions and improving management quality; moreover, when formulating the specific content of management clauses, all corresponding abnormal operation information can be directly screened out from the designated area according to the actual influencing factor type, which provides great convenience for the formulation of new management systems, reduces the workload of staff in collecting sample data, and thus improves work efficiency.

[0083] S5. When the first warning value corresponding to the management clause data exceeds the first warning threshold, a first warning prompt signal is issued to prompt relevant staff to promptly and quickly analyze, modify and improve the management clause data.

[0084] Based on the solution content of the above embodiment, this solution can timely warn and remind relevant staff to make corresponding improvements / modifications to the corresponding management clause data to make it more in line with actual operation, thereby ensuring the quality of power operation management, thereby improving the reliability and stability of power operation, and the modification / improvement process of management clauses is conducive to rapid data tracing of the basis for modifying clauses and avoiding excessive repetitive communication and statistical work, greatly reducing the workload of staff, improving work processing efficiency, and improving the accuracy of clause modifications and improvements.

[0085] In some embodiments, when using the mapping relationship between historical abnormal operating conditions and historical influencing factor types to obtain the first influencing factor type required for matching, clustering methods in big data analysis technology can be used to search for the historical abnormal operating conditions that have the highest degree of matching with the first abnormal operating condition, thereby ensuring the accuracy of the matching data search. Therefore, for the acquisition of several first influencing factor types corresponding to the first abnormal operating condition described in step S1, it specifically includes:

[0086] S101. Query a first database using a clustering algorithm to obtain a historical abnormal operation condition that has the highest degree of matching with a first abnormal operation condition, wherein the historical abnormal operation condition that has the smallest Euclidean distance to the first abnormal operation condition is the historical abnormal operation condition that has the highest degree of matching.

[0087] S102 : From the mapping relationship between the historical abnormal operation conditions and the historical influencing factor types, obtain the historical influencing factor type corresponding to the queried historical abnormal operation conditions, which is the first influencing factor type obtained by the queried.

[0088] In some embodiments, different abnormal operating conditions can be configured with different impact severity levels based on different specific abnormal details such as the location of the abnormal point, the degree of data fluctuation, and the degree of subsequent negative impact (for example, one abnormal operating condition only causes a single device to fail to operate normally, while another abnormal operating condition causes the paralysis of a local power architecture). The impact severity can be represented by a numerical value, wherein the greater the impact severity, the larger the numerical value of the target, and the larger the numerical value of the increase in the warning value should also be. This means that when the severity of the abnormal operational condition is greater, the type of influencing factor that causes it to appear is more important. Therefore, the numerical value of the increase in the warning value should be greater, so that the management clause data corresponding to the influencing factor type should be improved and modified more promptly. For example, if a certain influencing factor type is identified 10 times, and the abnormal operating conditions corresponding to 5 of them will cause relatively serious impacts, then the management clause data corresponding to this influencing factor type should be analyzed, improved and modified more quickly.

[0089] Therefore, the process of increasing the warning value corresponding to the management clause data in step S4 specifically includes the following steps.

[0090] S401: Obtain the severity of the impact of a first abnormal operating condition.

[0091] S402: Determine a first weighted value corresponding to the severity of the impact, wherein the severity of the impact and the first weighted value are in direct proportion. The first weighted value should be greater than or equal to 1. Further preferably, the first weighted value may be in the range [a, b], where a is 1 and b is greater than or equal to 2. Alternatively, the specific value of b may be determined based on the number of levels of severity of the impact.

[0092] S403: The warning value corresponding to the management clause data is increased using the baseline value and the first weighted value. The baseline value is a unit baseline value, such as 1, which means that, ignoring the severity of the impact, when the type of impact factor corresponding to the abnormal operating condition is identified once, the current warning value corresponding to the management clause data is increased by 1 to obtain a new warning value.

[0093] Specifically, step S403 may include: using the product of the base value and the first weighted value as the added value, and then summing the added value with the current warning value of the management clause data to obtain a new warning value. As can be seen, the calculation formula of the new warning value is: i+1 =M*k1 i+1 +I i , where Ii The warning value obtained after the i-th execution of the added processing is equivalent to the current warning value of the management clause data; k1 i+1 It is represented as the first weighted value of i+1; M is represented as the reference value; I i+1 The warning value obtained after the i+1th execution of the added processing. That is, when a certain actual influencing factor type is identified for the i+1th time, the first weighted value k1 is calculated based on the reference value M and the i+1th first weighted value k1. i+1 After the product is performed, the product result is used as the increase value required in the i+1th increase process, and then it is combined with the warning value I obtained after the i-th increase process is completed. i Add them together to get the warning value I obtained after the current i+1th execution of the increase process i+1 , and when the warning value corresponding to the management clause data exceeds the threshold, an early warning signal is issued.

[0094] It can be seen that the severity of the impact of abnormal operating conditions is introduced as a consideration to increase the warning value of the clause. In this way, the greater the severity of the abnormal operating conditions, the greater the increase in value, so that the warning value is more likely to exceed the warning threshold and send a warning prompt signal, thereby further improving the timeliness of the modification and improvement of the corresponding management clause data.

[0095] In some embodiments, in addition to considering the severity of the impact of abnormal operating conditions, the satisfaction evaluation corresponding to the existing management terms can also be considered. Generally, for this satisfaction evaluation, it is possible to collect the satisfaction scores of each user on the content of these management terms through existing questionnaires. Generally, the more satisfied, the higher the score. Therefore, if the satisfaction score corresponding to the management term is lower, it means that from the perspective of employee execution, the rationality of the use and execution of the term is lower. At this time, the priority of reviewing, revising and improving the management terms should be greater. Of course, the determination and acquisition method of the satisfaction evaluation score corresponding to the management terms can also be achieved by other existing technical methods, which are not specifically limited here.

[0096] Therefore, the step S403 may further specifically include the following steps.

[0097] S4031. When a first satisfaction evaluation value corresponding to the management clause data is obtained from the evaluation system, a second weighted value corresponding to the first satisfaction evaluation value is determined, wherein the first satisfaction evaluation value and the second weighted value are inversely proportional.

[0098] Specifically, in this embodiment, the server platform further includes a second data interface for communicating with the evaluation system to facilitate data transmission and processing between the evaluation system and the server platform. The second weighted value can be greater than or equal to 1, or can be in the range of [c, d], where c is 1 and d is determined based on the first satisfaction rating (i.e., the satisfaction rating score).

[0099] S4032: Use the reference value M, the first weighted value k1, and the second weighted value k2 to increase the warning value corresponding to the management clause data.

[0100] Specifically, first, according to the reference value M and the i+1th first weighted value k1 i+1 After the product is performed, the product result is used as the increase value required in the i+1th increase process, and then it is combined with the warning value I obtained after the i-th increase process is completed. i Add them together to get the warning value I obtained after the current i+1th execution of the increase process i+1 Then, calculate k2 and warning value I i+1 The summation result between (i.e. k2+I i+1 ) or the product result (i.e. k2*I i+1 ), and then the summation result or the product result is subjected to a warning threshold judgment, and when it exceeds the first warning threshold, a first warning prompt signal is issued.

[0101] It can be seen that by introducing the second weighted value corresponding to the satisfaction evaluation score, where the satisfaction evaluation score and the second weighted value are inversely proportional, the lower the satisfaction evaluation score, the higher the second weighted value, and the faster the warning value approaches the warning threshold. This is equivalent to shortening the waiting time for modification / improvement of management terms and improving the priority and timeliness of improvement and modification of management terms data.

[0102] In some embodiments, in order to improve the accuracy of determining the second weighted value, the process of determining the second weighted value corresponding to the first satisfaction evaluation value may specifically include the following steps.

[0103] S40311. According to the preset sampling quantity, a number of second satisfaction evaluation values ​​corresponding to the management clause data are randomly extracted from the evaluation system.

[0104] S40312. After performing standard deviation calculation on a plurality of second satisfaction evaluation values, a first standard deviation value is obtained.

[0105] S40313. Determine whether the first standard deviation value is less than or equal to the first stability threshold. If so, determine the first satisfaction evaluation value based on the average of several second satisfaction evaluation values; otherwise, return to execute step S40311 until the first standard deviation value is less than or equal to the first stability threshold.

[0106] S40314. Determine a corresponding second weighted value based on the first satisfaction evaluation value obtained in step S40311.

[0107] It can be seen that for any management clause data, by randomly extracting several corresponding satisfaction scores, and then after their standard deviation value is less than the first stable threshold, the average value of several satisfaction scores is used as the final required satisfaction score as the basis for determining the second weighted value. This can avoid extreme values ​​of the evaluation, so that the obtained satisfaction score can be more in line with the actual use evaluation situation, greatly improving the accuracy of identifying and determining the second weighted value.

[0108] In some embodiments, the system of this embodiment further includes:

[0109] The second monitoring system is used to monitor the maintenance status of the fault target to obtain second monitoring data. After determining the existence of a first maintenance anomaly based on the second monitoring data, the first maintenance anomaly is sent to the server platform; wherein the first maintenance anomaly at least includes maintenance duration anomaly data. That is, in the embodiment of the present application, the maintenance anomaly is mainly identified and judged based on the maintenance duration. When the time difference between the actual maintenance duration and the preset maintenance duration (i.e., the maintenance duration difference) is greater than or equal to the first time threshold, it is determined that a maintenance anomaly exists.

[0110] Correspondingly, the method of this embodiment may further include the following steps.

[0111] S6. Determine corresponding management clause data according to the abnormal maintenance time type corresponding to the abnormal maintenance time data.

[0112] Specifically, for the duration anomaly type, if the second monitoring data is the actual maintenance time required to perform all maintenance steps, then the duration anomaly type belongs to the first maintenance anomaly type, and the first management clause matching it mainly involves maintenance training management clauses (such as training examination regulations, training frequency regulations, etc.). This is because, if there is an abnormality in the actual maintenance time required to perform all maintenance steps, then it means that the current training examination regulations, training frequency, etc. can no longer effectively guarantee the efficiency of the maintenance work; if the second monitoring data is the actual maintenance time required to perform any maintenance sub-step, then the duration anomaly type belongs to the second maintenance anomaly type, and the second management clause matching it mainly involves maintenance operation regulations. This is because, if there is an abnormality in the actual maintenance time required to perform any maintenance sub-step, then it means that the maintenance operation step content designed in the maintenance operation regulations (this operation step content can be presented in the form of text description or video animation) has design defects or presentation defects in the operation process steps (that is, the text content and / or video animation presented in the maintenance operation step cannot be easily understood by maintenance personnel, or it itself has expression defects), and then it needs to be improved and modified.

[0113] S7. Determine a second warning value of the corresponding management clause data according to the maintenance time difference included in the abnormal maintenance time data.

[0114] Specifically, step S7 may include the following steps.

[0115] S701. Determine a third weighted value corresponding to the maintenance duration difference. The maintenance duration difference and the third weighted value are in direct proportion, i.e., the larger the maintenance duration difference, the larger the corresponding third weighted value. In other words, the larger the maintenance duration difference, the less suitable the current management regulations are for actual maintenance conditions. Therefore, adjusting the warning value using a weighted value determined based on the maintenance duration difference can improve the timeliness of management clause data modifications and avoid the continued use of inappropriate management regulations for maintenance training, operations, and other tasks.

[0116] S702: Use the reference value and the third weighted value to increase the warning value corresponding to the corresponding management clause data, thereby obtaining a second warning value. The reference value refers to a unit reference value, such as 1.

[0117] Specifically, the product of the baseline value and the third weighted value is first used as the incremental value. The incremental value is then summed with the current warning value for the management clause data to obtain a new warning value. This method of updating and determining the warning value corresponding to the management clause data is highly accurate and more responsive to actual conditions, improving the timeliness of revisions to the corresponding training / maintenance management regulations.

[0118] In some embodiments, when the maintenance time difference includes a maintenance time difference corresponding to executing a maintenance sub-step (i.e., a first maintenance time difference), the first maintenance time difference is determined based on the actual maintenance time corresponding to the maintenance sub-step. Specifically, the first maintenance time difference is determined by the time difference between the actual maintenance time corresponding to executing a maintenance sub-step and the preset maintenance time.

[0119] On this basis, the increase in the warning value corresponding to the management clause data can also take into account the importance of the maintenance sub-step. If the maintenance sub-step is of low importance, then the priority and timeliness of revising and improving the corresponding maintenance operation regulations do not need to be too high. Conversely, the timeliness of revising and improving the maintenance sub-steps of high importance needs to be increased. Therefore, step S701 can specifically include the following: S7011, determining a fourth weighted value corresponding to the first maintenance time difference, wherein the first maintenance time difference and the fourth weighted value are in direct proportion. Correspondingly, step S702 can specifically include the following steps.

[0120] S7021. Determine a first importance of the maintenance sub-step corresponding to the first maintenance time difference;

[0121] S7022: Determine a fifth weighted value corresponding to the first importance, wherein the greater the first importance, the greater the fifth weighted value, that is, there is a positive proportional relationship between the first importance and the fifth weighted value;

[0122] S7023: Increase the warning value corresponding to the corresponding management clause data using the reference value, the fourth weighted value, and the fifth weighted value, thereby obtaining a second warning value.

[0123] Specifically, the product of the baseline value, the fourth weighted value and the fifth weighted value is calculated and used as the added value, and then the added value and the current warning value of the management clause data are summed to obtain a new warning value.

[0124] S8. When the second warning value corresponding to the management clause data exceeds the second warning threshold, a second warning prompt signal is issued to remind relevant staff to review, modify and improve the content of relevant management clauses such as training and maintenance.

[0125] In some embodiments, the first maintenance time difference is obtained by calculating the time difference between the actual maintenance duration of a maintenance sub-step and the preset maintenance duration. The actual maintenance duration of a maintenance sub-step can generally be determined by the time difference between the maintenance start and end time nodes. This actual maintenance duration can generally be directly obtained by maintenance personnel entering maintenance information, thereby directly obtaining the maintenance duration and maintenance start / end time nodes. However, this process involves manual operation records, and therefore the accuracy of the actual maintenance duration is relatively low. Therefore, in the assistant management software currently used by staff, a data collection and analysis function is designed to collect the actual maintenance duration corresponding to the maintenance sub-steps.

[0126] For the above-mentioned functional data collection and analysis function, the first-level display interface is provided with options for maintenance operation steps corresponding to various target objects such as different situations, different equipment, and different cables. When you click on the option for the maintenance operation step corresponding to the required target object, you will enter the next-level display interface; Figure 3 As shown, the second-level display interface displays several corresponding maintenance sub-steps, such as maintenance sub-steps 1, 2, 3, etc. For each maintenance sub-step, the second-level display interface is configured with a start timer button and a maintenance information upload button. Regarding this function, the specific steps for obtaining and determining the actual maintenance time for a maintenance sub-step may include the following steps.

[0127] A1. In response to a click signal from the start timing button, the timing function is triggered to start, and the current moment is used as the first actual start time of maintenance. That is, when the maintenance personnel starts maintenance, they can click this button to collect and record the maintenance start time point.

[0128] A2. In response to a click signal from the maintenance information upload button, after obtaining the uploaded maintenance status information, determine whether the maintenance status information meets the first maintenance requirement, wherein the first maintenance requirement corresponds one-to-one to the maintenance sub-step, that is, different maintenance sub-steps correspond to different maintenance requirements.

[0129] Specifically, after a maintenance sub-step is completed, its maintenance status must be consistent with the specified requirements to indicate that the maintenance sub-step has been successfully executed. Upon determining that the maintenance sub-step has been successfully executed, execution of the sub-step can be terminated. For example, if after executing a maintenance sub-step, the current circuit wiring status is consistent with the specified requirements, then the maintenance sub-step can be terminated. Therefore, after executing a maintenance sub-step, when the maintenance status information is determined to be consistent with the first maintenance requirement, this moment can be used as the actual maintenance completion time node. The acquisition and collection of maintenance status information can be achieved using existing technical means and is not specifically limited here.

[0130] The start timing button and the maintenance information upload button are arranged on a second-level display interface for displaying a plurality of maintenance sub-steps, and one maintenance sub-step corresponds to a start timing button and a maintenance information upload button.

[0131] A3. When it is determined that the maintenance status information meets the first maintenance requirement, the current time is used as the actual end time node of the first maintenance.

[0132] A4. The time difference between the first actual maintenance start time node and the first actual maintenance end time node is used as the actual maintenance time of the maintenance sub-step.

[0133] Furthermore, in order to improve the accuracy of obtaining the actual start time node of the first maintenance and avoid erroneous triggering operations that result in obtaining the start time node of the next maintenance sub-step before the previous maintenance sub-step is completed, the above-mentioned step A1 may specifically include:

[0134] In response to the click signal of the start timing button corresponding to the i+1th maintenance sub-step, it is detected and determined whether there is a first maintenance actual end time node corresponding to the i-th maintenance sub-step. If so, the timing function is triggered to start, and the current moment is used as the first maintenance actual start time node of the i+1th maintenance sub-step. Otherwise, an early warning prompt signal is output to remind the maintenance personnel of the operation error and to confirm whether the end time node of the i-th maintenance sub-step has been collected.

[0135] In some embodiments, a play button is provided on the second-level display interface for triggering the playback of a maintenance demonstration video corresponding to a maintenance sub-step, with each maintenance sub-step corresponding to a play button. This helps maintenance personnel quickly and correctly perform the corresponding maintenance operations through the playback of the maintenance demonstration video, further ensuring the accuracy of maintenance work and improving processing efficiency.

[0136] In addition, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above method embodiment.

[0137] For the processors mentioned in the above storage medium embodiment and system embodiment, the number can be at least one, and at least any step in the above method embodiment can be executed. When the number is at least two, at least two processors can be connected to each other for communication, not limited to wired or wireless communication connection, and the at least one processor can be connected to various intelligent terminal devices for communication. In addition, the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0138] Finally, it should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0139] Note that the above are only preferred embodiments of the present application and the technical principles employed. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present application. The scope of the present application is determined by the scope of the appended claims.

Claims

1. A comprehensive quality management abnormal fluctuation monitoring and identification system, characterized by: The system includes: A first monitoring system is configured to monitor the operating status of the target object to obtain first monitoring data, and after determining the presence of a first abnormal operating status based on the first monitoring data, transmit the first abnormal operating status to the server platform; The server platform includes at least one processor configured to load a program to execute the following steps: S1. After receiving a first abnormal operation condition, obtain a plurality of first influencing factor types corresponding to the first abnormal operation condition; S2. Acquire corresponding influencing factor characteristic data according to a plurality of first influencing factor types; S3. Determine the actual influencing factor type of the first abnormal operation condition based on the influencing factor characteristic data; step S3 specifically includes the following steps: S301. Screen and determine an actual influencing factor type from a plurality of first influencing factor types based on influencing factor characteristic data; S302: When the number of actual influencing factor types screened and determined from the plurality of first influencing factor types is 0 and the number of first influencing factor types is less than N, a second influencing factor type is obtained, and then the actual influencing factor type of the first abnormal operating condition is determined based on the influencing factor characteristic data corresponding to the second influencing factor type; wherein N is the total number of all influencing factor types corresponding to all abnormal operating conditions; and the second influencing factor type refers to an influencing factor type other than the first influencing factor type among the N influencing factor types; S303: When the number of actual influencing factor types screened and determined from the plurality of first influencing factor types or second influencing factor types is not 0, the screened and determined actual influencing factor types are used as the final required actual influencing factor types; S304: When the number of actual influencing factor types screened and determined from the plurality of first influencing factor types and second influencing factor types is 0, storing the current first abnormal operation condition in the corresponding first designated area, and sending a fourth early warning prompt signal; S4. When it is determined that there is management clause data matching the actual influencing factor type, the warning value corresponding to the management clause data is incremented to obtain a first warning value corresponding to the management clause data. The incrementing of the warning value corresponding to the management clause data in step S4 specifically includes: S401. Obtain the severity of the impact of the first abnormal operating condition; S402: Determine a first weighted value corresponding to the severity of the impact, wherein the severity of the impact and the first weighted value are in direct proportion to each other; S403: using the benchmark value and the first weighted value to increase the warning value corresponding to the management clause data; wherein, step S403 specifically includes: S4031. When a first satisfaction evaluation value corresponding to the management clause data is obtained from the evaluation system, a second weighted value corresponding to the first satisfaction evaluation value is determined, wherein the first satisfaction evaluation value and the second weighted value are in inverse proportion to each other; S4032, using the reference value, the first weighted value and the second weighted value to increase the warning value corresponding to the management clause data, wherein the step S4032 is specifically: calculating the second weighted value k2 and the warning value I i+1 The sum or product result between i+1 =M*k1 i+1 +I i , Ii is the warning value obtained after the i-th execution of the increase process, I i+1 is the warning value obtained after the i+1th execution of the added processing, M represents the baseline value, k1 i+1 Represented as the i+1th first weighted value; S5. When the first warning value corresponding to the management clause data exceeds the first warning threshold, a first warning prompt signal is issued.

2. The system according to claim 1, wherein The obtaining of a plurality of first influencing factor types corresponding to the first abnormal operating condition specifically includes: Using a clustering algorithm, querying from the first database the historical abnormal operation situation that has the highest matching degree with the first abnormal operation situation, wherein the historical abnormal operation situation that has the smallest Euclidean distance with the first abnormal operation situation is the historical abnormal operation situation with the highest matching degree; From the mapping relationship between the historical abnormal operation conditions and the historical influencing factor types, the historical influencing factor types corresponding to the queried historical abnormal operation conditions are obtained.

3. The system according to claim 1, wherein: The determining of the second weighted value corresponding to the first satisfaction evaluation value specifically includes: S40311. Randomly extract a number of second satisfaction evaluation values ​​corresponding to the management clause data from the evaluation system according to a preset sampling number; S40312. Calculate the standard deviation of the plurality of second satisfaction evaluation values ​​to obtain a first standard deviation value; S40313. Determine whether the first standard deviation value is less than or equal to the first stability threshold. If so, determine the first satisfaction evaluation value based on the average of several second satisfaction evaluation values; otherwise, return to execute step S40311 until the first standard deviation value is less than or equal to the first stability threshold.

4. The system according to claim 1, wherein: The system also includes: a second monitoring system for monitoring the maintenance status of the fault target to obtain second monitoring data, and after determining the presence of a first maintenance abnormality based on the second monitoring data, transmitting the first maintenance abnormality to the server platform; wherein the first maintenance abnormality at least includes abnormal maintenance duration data; The server platform is further configured to load a program to perform the following steps: S6. Determine corresponding management clause data based on the abnormal maintenance duration type corresponding to the abnormal maintenance duration data; S7. Determine a second warning value of the corresponding management clause data according to the maintenance time difference included in the abnormal maintenance time data; S8. When the second warning value corresponding to the management clause data exceeds the second warning threshold, a second warning prompt signal is issued.

5. The system according to claim 4, wherein: The step S7 specifically includes: S701: Determine a third weighted value corresponding to the maintenance time difference, wherein the maintenance time difference and the third weighted value are in direct proportion; S702: Use the reference value and the third weighted value to increase the warning value corresponding to the corresponding management clause data, thereby obtaining a second warning value.

6. The system according to claim 4, wherein: The maintenance time difference includes a first maintenance time difference, which refers to a maintenance time difference corresponding to the maintenance sub-step. The first maintenance time difference is determined based on the actual maintenance time corresponding to the maintenance sub-step, wherein the actual maintenance time corresponding to the maintenance sub-step is obtained and determined by the following steps: In response to a click signal of the start timing button, the current moment is used as the first maintenance actual start time node; In response to a click signal of a maintenance information upload button, after obtaining the uploaded maintenance status information, determining whether the maintenance status information meets a first maintenance requirement, wherein the first maintenance requirement corresponds to a maintenance sub-step in a one-to-one manner; When it is determined that the maintenance status information meets the first maintenance requirement, the current time is used as the actual end time node of the first maintenance; The time difference between the first maintenance actual start time node and the first maintenance actual end time node is used as the actual maintenance duration of the maintenance sub-step; The start timing button and the maintenance information upload button are arranged on a second-level display interface for displaying a plurality of maintenance sub-steps.

7. The system according to claim 6, wherein: The second-level display interface is also provided with a play button for triggering the playback of the maintenance demonstration video corresponding to the maintenance sub-step.

8. A method for monitoring and identifying abnormal fluctuations in total quality management, characterized in that: The method comprises the following steps: S1. After receiving a first abnormal operation condition, obtain a plurality of first influencing factor types corresponding to the first abnormal operation condition; S2. Acquire corresponding influencing factor characteristic data according to a plurality of first influencing factor types; S3. Determine the actual influencing factor type of the first abnormal operation condition based on the influencing factor characteristic data; step S3 specifically includes the following steps: S301. Screen and determine an actual influencing factor type from a plurality of first influencing factor types based on influencing factor characteristic data; S302: When the number of actual influencing factor types screened and determined from the plurality of first influencing factor types is 0 and the number of first influencing factor types is less than N, a second influencing factor type is obtained, and then the actual influencing factor type of the first abnormal operating condition is determined based on the influencing factor characteristic data corresponding to the second influencing factor type; wherein N is the total number of all influencing factor types corresponding to all abnormal operating conditions; and the second influencing factor type refers to an influencing factor type other than the first influencing factor type among the N influencing factor types; S303: When the number of actual influencing factor types screened and determined from the plurality of first influencing factor types or second influencing factor types is not 0, the screened and determined actual influencing factor types are used as the final required actual influencing factor types; S304: When the number of actual influencing factor types screened and determined from the plurality of first influencing factor types and second influencing factor types is 0, storing the current first abnormal operation condition in the corresponding first designated area, and sending a fourth early warning prompt signal; S4. When it is determined that there is management clause data matching the actual influencing factor type, the warning value corresponding to the management clause data is incremented to obtain a first warning value corresponding to the management clause data. The incrementing of the warning value corresponding to the management clause data in step S4 specifically includes: S401. Obtain the severity of the impact of the first abnormal operating condition; S402: Determine a first weighted value corresponding to the severity of the impact, wherein the severity of the impact and the first weighted value are in direct proportion to each other; S403: using the benchmark value and the first weighted value to increase the warning value corresponding to the management clause data; wherein, step S403 specifically includes: S4031. When a first satisfaction evaluation value corresponding to the management clause data is obtained from the evaluation system, a second weighted value corresponding to the first satisfaction evaluation value is determined, wherein the first satisfaction evaluation value and the second weighted value are in inverse proportion to each other; S4032, using the reference value, the first weighted value and the second weighted value to increase the warning value corresponding to the management clause data, wherein the step S4032 is specifically: calculating the second weighted value k2 and the warning value I i+1 The sum or product result between i+1 =M*k1 i+1 +I i , I i The warning value obtained after the i-th execution is added, I i+1 is the warning value obtained after the i+1th execution of the added processing, M represents the baseline value, k1 i+1 Represented as the i+1th first weighted value; S5. When the first warning value corresponding to the management clause data exceeds the first warning threshold, a first warning prompt signal is issued.

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