Operation and maintenance management and control business acquisition and monitoring system for power supply station

Abstract

The invention discloses a power supply station operation and maintenance management and control business acquisition and monitoring system, and the system comprises a data acquisition module which is used for collecting equipment operation data and equipment environment data corresponding to all management and control equipment in a power supply station; the data processing module is used for determining the equipment environment data in the plurality of sub-periods and processing the equipment environment data into equipment environment period data corresponding to each sub-period; the data analysis module determines a first operation risk level of each management and control device according to the device operation data and the operation demand information of each management and control device; determining an environmental impact factor of each management and control device based on the first operation risk level of each management and control device, and determining a second operation risk level of each management and control device through an environment analysis model according to the device environment time period data of each sub time period corresponding to each management and control device and the environmental impact factor; and the operation and maintenance management and control module determines whether the management and control equipment needs to be maintained or not according to the first operation risk level and the second operation risk level of each management and control equipment.

Description

Technical Field

[0001] The present application relates to the technical field of power operation and maintenance, and in particular to a power supply station operation and maintenance management and control service collection and monitoring system. Background Art

[0002] In the operation and management of the power system, the power supply station is a key node, and the efficiency and accuracy of its operation and maintenance management business are directly related to the stability and reliability of the power supply. However, the traditional operation and maintenance management model of the power supply station mainly relies on manual inspections and post-maintenance. This model has several significant problems: the traditional method mainly relies on manual recording of equipment operation data and environmental parameters, which is not only labor-intensive, but also difficult to ensure the timeliness and accuracy of data collection, resulting in a lag in the assessment of the health status of the equipment; the lack of efficient data processing and analysis means that the assessment of equipment operation risks is often based on experience judgment, and it is difficult to accurately quantify the potential risks of each device, thus affecting the scientificity and effectiveness of maintenance decisions. After discovering equipment abnormalities or failures, it is often necessary to go through multiple layers of reporting and coordination to arrange maintenance, which leads to a longer operation and maintenance response time and increases the risk of power outages. Summary of the invention

[0003] In view of this, an embodiment of the present application provides a power supply station operation and maintenance control business collection and monitoring system, which can quickly identify the operating status and potential risks of each control equipment in the power supply station.

[0004] According to one aspect of the present application, a power supply station operation and maintenance management and control service collection and monitoring system is provided, including:

[0005] Data collection module, data processing module, data analysis module, operation and maintenance control module, and operation and maintenance evaluation module;

[0006] The data acquisition module is used to collect equipment operation data and equipment environment data corresponding to each control equipment in the power supply station;

[0007] The data processing module is used to take a preset first time period before the current time as a monitoring reference period, evenly divide the monitoring reference period into a plurality of sub-periods, and process the device environment data in each sub-period into device environment period data corresponding to each sub-period;

[0008] The data analysis module includes a first data analysis unit and a second data analysis unit; the first data analysis unit is used to determine the first operation risk level of each control device according to the device operation data and the operation requirement information of each control device; the second data analysis unit is used to determine the environmental impact factor of each control device based on the first operation risk level of each control device, and determine the second operation risk level of each control device according to the device environment time period data and the environmental impact factor of each sub-time period corresponding to each control device through the environmental analysis model; wherein the first operation risk level and the second operation risk level of the control device both include a major risk level, a relatively large risk level, a general risk level and a low risk level;

[0009] The operation and maintenance control module includes an operation and maintenance judgment unit, an operation and maintenance information generation unit and an operation and maintenance information push unit; the operation and maintenance judgment unit is used to determine whether maintenance is required for the control equipment according to the first operation risk level and the second operation risk level of each control equipment; the operation and maintenance information generation unit is used to determine the operation and maintenance work information according to the preset maintenance personnel information and the equipment type of the target control equipment to be maintained when it is determined that the control equipment needs to be maintained, wherein the operation and maintenance work information includes the target maintenance personnel information, the location information of the target control equipment, the equipment operation data and the equipment environment data; the operation and maintenance information push unit is used to reach the operation and maintenance work information to the target maintenance personnel corresponding to the target maintenance personnel information;

[0010] The operation and maintenance evaluation module is used to evaluate the operation and maintenance management and control services of the power supply station according to the first operation risk level and the second operation risk level of each management and control equipment.

[0011] By means of the above technical solution, the embodiment of the present application provides a power supply station operation and maintenance management and control business collection and monitoring system. Through real-time monitoring and data analysis, the system can quickly identify the operating status and potential risks of each control device in the power supply station, thereby quickly generating operation and maintenance work information and pushing it to the target maintenance personnel. This immediate response mechanism significantly shortens the time from fault discovery to processing, improves operation and maintenance efficiency, and reduces power outage time caused by equipment failure. The system makes operation and maintenance decisions based on dual risk assessment (equipment operation risk and environmental impact risk), avoids unnecessary maintenance activities, and reduces resource waste. At the same time, by accurately locating problem equipment and providing detailed operation and maintenance guidance information, it reduces the difficulty and labor intensity of maintenance personnel's work, and further reduces operation and maintenance costs. Taking into account the impact of the equipment's own status and environmental factors on equipment operation, it can warn of potential risks in advance and take corresponding measures, thereby effectively preventing the occurrence of equipment failures, improving the reliability and stability of the power supply system, and reducing power supply interruption events caused by equipment failures. By regularly evaluating the operation and maintenance control business of the power supply station, we can discover problems and deficiencies in operation and maintenance management, provide data support for management decisions, and help continuously optimize the operation and maintenance management process and improve the scientific nature and standardization of operation and maintenance management.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0014] Figure 1 A schematic diagram of the structure of a power supply station operation and maintenance management and control service collection and monitoring system provided in an embodiment of the present application is shown;

[0015] Figure 2 A structural schematic diagram of another power supply station operation and maintenance management and control service collection and monitoring system provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0016] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0017] In this embodiment, a power supply station operation and maintenance management and control service collection and monitoring system is provided. Figure 1As shown, it includes: data acquisition module, data processing module, data analysis module, operation and maintenance control module, and operation and maintenance evaluation module;

[0018] The data acquisition module is used to collect equipment operation data and equipment environment data corresponding to each control equipment in the power supply station;

[0019] The data processing module is used to take a preset first time period before the current time as a monitoring reference period, evenly divide the monitoring reference period into a plurality of sub-periods, and process the device environment data in each sub-period into device environment period data corresponding to each sub-period;

[0020] The data analysis module includes a first data analysis unit and a second data analysis unit; the first data analysis unit is used to determine the first operation risk level of each control device according to the device operation data and the operation requirement information of each control device; the second data analysis unit is used to determine the environmental impact factor of each control device based on the first operation risk level of each control device, and determine the second operation risk level of each control device according to the device environment time period data and the environmental impact factor of each sub-time period corresponding to each control device through the environmental analysis model; wherein the first operation risk level and the second operation risk level of the control device both include a major risk level, a relatively large risk level, a general risk level and a low risk level;

[0021] The operation and maintenance control module includes an operation and maintenance judgment unit, an operation and maintenance information generation unit and an operation and maintenance information push unit; the operation and maintenance judgment unit is used to determine whether maintenance is required for the control equipment according to the first operation risk level and the second operation risk level of each control equipment; the operation and maintenance information generation unit is used to determine the operation and maintenance work information according to the preset maintenance personnel information and the equipment type of the target control equipment to be maintained when it is determined that the control equipment needs to be maintained, wherein the operation and maintenance work information includes the target maintenance personnel information, the location information of the target control equipment, the equipment operation data and the equipment environment data; the operation and maintenance information push unit is used to reach the operation and maintenance work information to the target maintenance personnel corresponding to the target maintenance personnel information;

[0022] The operation and maintenance evaluation module is used to evaluate the operation and maintenance management and control services of the power supply station according to the first operation risk level and the second operation risk level of each management and control equipment.

[0023] In this embodiment, the power supply station operation and maintenance management and control business collection and monitoring system includes a data collection module, a data processing module, a data analysis module, an operation and maintenance management and control module, and an operation and maintenance evaluation module. The data collection module is responsible for collecting the operation data and equipment environment data of each control device in the power supply station as the basis for subsequent analysis and operation and maintenance decisions. Ensure the real-time and accuracy of the data, provide a reliable basis for subsequent data processing and analysis, and help to timely discover equipment abnormalities and potential risks. The data processing module divides the monitoring reference period (the preset first time before the current time) into multiple sub-periods, and processes the equipment environment data in each sub-period to generate equipment environment period data. The data analysis module includes a first data analysis unit and a second data analysis unit. The first data analysis unit determines the first operation risk level of each control device based on the equipment operation data and operation demand information, so as to directly perform risk assessment based on the equipment operation status and demand, quickly identify high-risk equipment, and provide a direct basis for operation and maintenance decisions. The second data analysis unit determines the environmental impact factor based on the first operation risk level, and determines the second operation risk level using the environmental impact factor and the equipment environment period data through the environmental analysis model. Thus, the equipment's own status and environmental factors are comprehensively considered, making the risk assessment more comprehensive and accurate, which helps to prevent equipment failures caused by environmental changes in advance. The operation and maintenance control module includes an operation and maintenance judgment unit, an operation and maintenance information generation unit, and an operation and maintenance information push unit. The operation and maintenance judgment unit determines whether each controlled equipment needs maintenance based on the first and second operation risk levels. Through double risk assessment, it ensures the scientificity and rationality of operation and maintenance decisions, avoids unnecessary maintenance costs, and ensures that high-risk equipment is maintained in a timely manner. Operation and maintenance information generation unit: Generates detailed operation and maintenance work information, including target maintenance personnel, equipment location, operation data, and environmental data, etc., in order to provide comprehensive operation and maintenance guidance information, help maintenance personnel quickly locate problems, and improve operation and maintenance efficiency. The operation and maintenance information push unit pushes the operation and maintenance work information to the target maintenance personnel in a timely manner to ensure the timely transmission of information, shorten the response time, and improve the operation and maintenance response speed. The operation and maintenance evaluation module evaluates the operation and maintenance control business of the power supply station according to the risk level of each controlled equipment. Through regular evaluation, the overall status of the operation and maintenance control of the power supply station can be understood, and existing problems and deficiencies can be found, providing data support for the continuous optimization of operation and maintenance management.

[0024] By applying the technical solution of this embodiment, the system can quickly identify the operating status and potential risks of each control equipment in the power supply station through real-time monitoring and data analysis, so as to quickly generate operation and maintenance work information and push it to the target maintenance personnel. This immediate response mechanism significantly shortens the time from fault discovery to processing, improves operation and maintenance efficiency, and reduces the power outage time caused by equipment failure. The system makes operation and maintenance decisions based on dual risk assessment (equipment operation risk and environmental impact risk), avoids unnecessary maintenance activities, reduces resource waste, and reduces the work difficulty and labor intensity of maintenance personnel by accurately locating problem equipment and providing detailed operation and maintenance guidance information, further reducing the operation and maintenance cost. Taking into account the impact of the equipment's own status and environmental factors on the operation of the equipment, it can warn of potential risks in advance and take corresponding measures, thereby effectively preventing the occurrence of equipment failures, improving the reliability and stability of the power supply system, and reducing power outages caused by equipment failures. By regularly evaluating the operation and maintenance control business of the power supply station, problems and deficiencies in operation and maintenance management can be discovered, providing data support for management decisions, which is conducive to continuously optimizing the operation and maintenance management process and improving the scientificity and standardization of operation and maintenance management.

[0025] In the embodiments of the present application, Figure 2 As shown, optionally, the equipment operation data includes current value, voltage value, power factor, and equipment temperature; the first data analysis unit includes an interval acquisition subunit and a first analysis subunit; the interval acquisition subunit is used to determine multiple current intervals, multiple voltage intervals, multiple power factor intervals, and multiple equipment temperature intervals corresponding to each controlled equipment according to the operation requirement information of each controlled equipment; the first analysis subunit is used to sum, for each controlled equipment, the current risk score corresponding to the current interval in which the current value falls, the voltage risk score corresponding to the voltage interval in which the voltage value falls, the power factor risk score corresponding to the power factor interval in which the power factor falls, and the equipment temperature risk score corresponding to the equipment temperature interval in which the equipment temperature falls, determine the target risk level score interval in which the sum falls among multiple risk level score intervals, and determine the operation risk level corresponding to the target risk level score interval as the first operation risk level corresponding to the controlled equipment.

[0026] In the above embodiment, the equipment operation data includes current value, voltage value, power factor, and equipment temperature. The current value reflects the current intensity of the equipment when it is working, and is an important indicator for evaluating the load and electrical performance of the equipment. The voltage value represents the potential difference between the two ends of the equipment, which has an important impact on the normal operation and stability of the equipment. The power factor measures the ability of the equipment to effectively utilize electrical energy in an AC circuit. A low power factor means that the reactive power loss of electrical energy is large. The equipment temperature reflects the thermal state of the equipment. Excessive temperature may cause the performance of the equipment to deteriorate or even damage it. The first data analysis unit interval acquisition subunit and the first analysis subunit. The interval acquisition subunit sets multiple current, voltage, power factor and equipment temperature intervals for each device according to the operation requirement information of each controlled device (such as rated current, rated voltage, recommended power factor range, allowable temperature range, etc.), and these intervals are the basis for subsequent risk assessment. For each controlled device, the first analysis subunit first determines the intervals into which its current, voltage, power factor and equipment temperature fall respectively, and searches for the corresponding risk score. Then, these risk scores are summed to obtain a sum value. Finally, the interval in which the sum falls is searched in the preset multiple risk level scoring intervals, and the operation risk level corresponding to the interval is determined as the first operation risk level of the control device. The embodiment of the present application sets different intervals and risk scores for different devices, so that the risk assessment is more in line with the actual operation and needs of the equipment. Through the clear risk level division, it provides a clear decision-making basis for operation and maintenance personnel, which helps to quickly formulate effective operation and maintenance measures.

[0027] In an embodiment of the present application, optionally, multiple current intervals, multiple voltage intervals, multiple power factor intervals, and multiple device temperature intervals all include major risk intervals, larger risk intervals, general risk intervals, and low risk intervals; the first analysis subunit is also used for, for each managed device, if at least one of the current interval into which the current value falls, the voltage interval into which the voltage value falls, the power factor interval into which the power factor falls, and the device temperature interval into which the device temperature falls belongs to a major risk interval, or the current interval into which the current value falls, the voltage interval into which the voltage value falls, the power factor interval into which the power factor falls, and the device temperature interval into which the device temperature falls If at least two of the degree intervals belong to the larger risk interval, the first operation risk level corresponding to the controlled device is determined to be the major risk level; otherwise, the current risk score corresponding to the current interval that the current value falls into, the voltage risk score corresponding to the voltage interval that the voltage value falls into, the power factor risk score corresponding to the power factor interval that the power factor falls into, and the equipment temperature risk score corresponding to the equipment temperature interval that the equipment temperature falls into are summed, and the target risk level score interval in which the sum falls is determined among multiple risk level score intervals, and the operation risk level corresponding to the target risk level score interval is determined as the first operation risk level corresponding to the controlled device.

[0028] In this embodiment, multiple current intervals, voltage intervals, power factor intervals and equipment temperature intervals are clearly divided into major risk intervals, larger risk intervals, general risk intervals and low risk intervals. For each managed device, if any value of its current, voltage, power factor or equipment temperature falls into the major risk interval, or at least two values ​​fall into the larger risk interval, then the first operating risk level of the device will be directly determined as a major risk level. This fast judgment logic can quickly identify high-risk devices and ensure that they are given priority. If the above-mentioned fast judgment conditions are not met, the system will use a comprehensive scoring logic to determine the risk level. Specifically, the risk scores of the current, voltage, power factor and equipment temperature are summed, and then the interval in which the sum falls is found in the preset multiple risk level scoring intervals, and the first operating risk level of the device is finally determined. Through the fast judgment logic of the embodiment of the present application, the system can quickly identify high-risk devices and ensure that they are processed and maintained in a timely manner, thereby avoiding potential safety accidents and losses. The comprehensive scoring logic takes into account the combined impact of multiple factors, making the risk assessment results more comprehensive and accurate. At the same time, through the preset risk level scoring interval, the system can give a specific risk level division, providing a clear decision-making basis for operation and maintenance personnel. The automated risk assessment process reduces manual intervention and subjective judgment, improves operation and maintenance efficiency, and at the same time, through clear risk level division and priority processing mechanism, the system can guide operation and maintenance personnel to perform equipment maintenance and management in a more orderly and efficient manner. Through accurate risk assessment and priority processing mechanism, the system can avoid unnecessary maintenance activities and waste of resources, thereby reducing operation and maintenance costs. At the same time, by timely discovering and handling potential problems, the system can also reduce power outage time and economic losses caused by equipment failures.

[0029] In an embodiment of the present application, optionally, the device environment data includes ambient temperature, ambient humidity, ambient noise, and electromagnetic interference level; the device period environment data includes ambient temperature period data, ambient humidity period data, ambient noise period data, and electromagnetic interference level period data; the second data analysis unit includes a feature extraction subunit and a second analysis subunit; the feature extraction subunit is used to determine the ambient temperature and humidity feature vector according to the ambient temperature period data and ambient humidity period data corresponding to each sub-period, generate the ambient noise feature vector according to the ambient noise period data corresponding to each sub-period, and generate the electromagnetic interference level feature vector according to the electromagnetic interference level period data corresponding to each sub-period; the second analysis subunit is used to initialize the model parameters of the environmental analysis model based on the environmental influencing factors, and convert the environmental influencing factors into the environmental noise feature vector. The ambient temperature and humidity feature vector, the ambient noise feature vector, and the electromagnetic interference level feature vector are input into the environmental analysis model to determine the second operation risk level, wherein the ambient temperature and humidity feature vector W=[W(k), W(k-1), ... W(k-m+1)], W(i) represents the ambient temperature and humidity feature value corresponding to the i-th time period, i∈[k, k-m+1], the ambient noise feature vector N=[N(k), N(k-1), ... N(k-m+1)], N(i) represents the ambient noise feature value corresponding to the i-th time period, the electromagnetic interference level feature vector E=[E(k), E(k-1), ... E(k-m+1)], E(i) represents the ambient electromagnetic interference level feature value corresponding to the i-th time period, and m is the number of neurons in the first layer of the neural network in the environmental analysis model.

[0030] In an embodiment of the present application, optionally, the feature extraction subunit is also used to perform denoising on the ambient temperature, ambient humidity, ambient noise, and electromagnetic interference level in each sub-time period, and to respectively calculate the average of the ambient temperature, ambient humidity, ambient noise, and electromagnetic interference level in each sub-time period after the denoising process, and to respectively use the obtained averages as the ambient temperature time period data, the ambient humidity time period data, the ambient noise time period data, and the electromagnetic interference level time period data.

[0031] For the data in any sub-period, for example, multiple ambient temperature data in a sub-period, denoising is first performed, and then the denoised ambient temperature data are averaged to obtain the ambient temperature period data of the sub-period.

[0032] In the embodiment of the present application, optionally, the feature extraction subunit is further used to calculate the temperature and humidity characteristic values ​​of each control device according to the following formula: t(i) represents the ambient temperature period data corresponding to the i-th time period, ts represents the preset temperature threshold determined according to the current weather information, h(i) represents the ambient humidity period data corresponding to the i-th time period, hs represents the preset humidity threshold determined according to the current weather information, a represents the temperature influence factor of the corresponding control equipment, and b represents the humidity influence factor of the corresponding control equipment; the ambient noise characteristic value of each control equipment is calculated according to the following formula: Where n(i) represents the environmental noise period data corresponding to the i-th time period, and ns represents the preset noise threshold corresponding to the corresponding control equipment; the electromagnetic interference level characteristic value of each control equipment is calculated according to the following formula: Wherein, e(i) represents the electromagnetic interference level period data corresponding to the i-th time period, and es represents the preset electromagnetic interference level threshold corresponding to the corresponding control equipment.

[0033] In the above embodiment, the equipment environment data includes ambient temperature, ambient humidity, ambient noise and electromagnetic interference level, all of which may have an impact on the normal operation of the equipment. The equipment period environment data is a subdivision of the equipment environment data in the time dimension, that is, the data of ambient temperature, humidity, noise and electromagnetic interference level corresponding to each sub-period. This subdivision helps to capture the impact of dynamic changes in environmental factors on the operation of the equipment. The second data analysis unit extracts feature vectors that affect the operation of the control equipment from the period environment data. Specifically, it determines the ambient temperature and humidity feature vectors based on the ambient temperature period data and ambient humidity period data of each sub-period, generates the ambient noise feature vector based on the ambient noise period data, and generates the electromagnetic interference level feature vector based on the electromagnetic interference level period data. These feature vectors will be used as inputs to the environmental analysis model. The second analysis subunit processes the input feature vectors using the environmental analysis model to determine the second operation risk level of the equipment. The environmental analysis model is a complex machine learning model, such as a neural network, which can predict the impact of environmental factors on the equipment operation risk by learning historical data. Here, the model parameters are initialized according to the environmental impact factor to reflect the contribution of different environmental factors to the equipment operation risk. Then, the environmental temperature and humidity feature vector, the environmental noise feature vector and the electromagnetic interference level feature vector are input into the model, and the second operation risk level of the equipment is obtained by the calculation of the neural network. Among them, the environmental temperature and humidity feature vector W, the environmental noise feature vector N and the electromagnetic interference level feature vector E are all expressed in the form of time series, that is, they contain the feature values ​​of the past m sub-periods. This representation method helps the model capture the impact of the dynamic changes of environmental factors on the equipment operation risk. In the embodiment of the present application, by considering multiple environmental factors such as ambient temperature, humidity, noise and electromagnetic interference, and their changes in the time dimension, the system can more comprehensively evaluate the equipment operation risk. By processing the feature vector using a machine learning model (such as a neural network), the complex relationship between environmental factors and equipment operation risks can be captured, thereby improving the accuracy of risk assessment. Taking into account the dynamic changes of environmental factors, it is possible to support real-time or regular evaluation of equipment operation risks, which helps to timely discover and deal with potential risk issues.

[0034] In the embodiment of the present application, optionally, the data analysis module also includes a model training unit; the model training unit is used to: obtain the historical operation and maintenance data of at least one sample power supply station, the historical operation and maintenance data includes the equipment operation history data and equipment environment history data of each sample control equipment in the corresponding sample power supply station, the equipment operation history data includes historical current value, historical voltage value, historical power factor, historical equipment temperature, and the equipment environment history data includes historical environment temperature, historical environment humidity, historical environment noise, and historical electromagnetic interference level; for each sample power supply station, the equipment operation history data and the equipment environment history data corresponding to the sample power supply station are divided into time periods to obtain equipment operation history time period data and equipment environment history time period data of multiple sub-periods, and the operation risk level samples of each sub-period are determined according to the equipment operation history time period data of each sub-period, and the environmental impact factor samples corresponding to each sub-period are determined according to the operation risk level samples of each sub-period; according to the time window of m sub-periods and the step size of 1 sub-period, the equipment environment history time period data is divided into multiple sample data groups, each sample data group includes equipment environment history time period data of m sub-periods; the last time period corresponding to each sample data group is divided into The environmental impact factor samples corresponding to a sub-period are added to the sample data group, and the operation risk level samples corresponding to the next sub-period of the last sub-period are added to the sample data group; according to the difference of the environmental impact factor samples in each sample data group, each sample data group is divided into multiple classes; the equipment environment historical period data in each sample data group is processed into corresponding feature vector samples, and the feature vector samples include environmental temperature and humidity feature vector samples, environmental noise feature vector samples and electromagnetic interference level feature vector samples; based on multiple environmental analysis models with the same structure, the sample data groups under each class are used for training respectively to obtain the trained environmental analysis models corresponding to the corresponding classes, wherein in the model training process, the risk level is predicted based on the environmental temperature and humidity feature vector samples, environmental noise feature vector samples and electromagnetic interference level feature vector samples in the sample data group, and the model is optimized based on the prediction results and the operation risk level samples in the sample data group; the model parameters of the environmental analysis model corresponding to each class are extracted, and the model parameters corresponding to different environmental impact factors are recorded, so that the model parameters are initialized based on the model parameters corresponding to different environmental impact factors when the environmental analysis model is used for risk prediction.

[0035] In this embodiment, the model training unit in the data analysis module learns from the historical operation and maintenance data and constructs an environmental analysis model that can accurately predict the risk of equipment operation. Historical operation and maintenance data are collected from at least one sample power supply, and these data include equipment operation history data and equipment environment history data. The equipment operation history data includes historical current value, historical voltage value, historical power factor, historical equipment temperature, etc., and the equipment environment history data includes historical environment temperature, historical environment humidity, historical environment noise, historical electromagnetic interference level, etc., which describe the external environment conditions of equipment operation. The equipment operation history data and the equipment environment history data are divided according to the time dimension to obtain data of multiple sub-periods. Based on the equipment operation history data, an operation risk level sample is determined for each sub-period, and the specific method can refer to the determination method of the first operation risk level above. According to the operation risk level of each sub-period, the environmental impact factor sample corresponding to each sub-period is determined. According to the time window of m sub-periods and the step size of 1 sub-period, the equipment environment history period data is divided into multiple sample data groups. The environmental impact factor samples and operation risk level samples (of the next sub-period) of the last sub-period corresponding to each sample data group are added to the data group to enrich the information content of the data group. According to the different environmental impact factor samples in the sample data group, the sample data group is divided into multiple classes so that model training can be performed for different types of environmental conditions in the future. The equipment environment historical period data in each sample data group is processed into corresponding feature vector samples, including environmental temperature and humidity feature vector samples, environmental noise feature vector samples, and electromagnetic interference level feature vector samples. When training the model, multiple environmental analysis models with the same structure are used for training, and the sample data groups under each class are used for training respectively, so that the model can learn the changing laws of equipment operation risks under different environmental conditions. During the training process, the model will predict the risk level according to the input feature vector samples, and compare the prediction results with the actual operation risk level samples in the sample data group, and optimize the model parameters through algorithms such as back propagation. After the training is completed, the model parameters of the environmental analysis model corresponding to each class are extracted, and the model parameters corresponding to different environmental impact factors are recorded, so that in the subsequent risk prediction process, the model parameters can be quickly initialized according to different environmental impact factors to improve the prediction efficiency and accuracy. Through the above process, the model training unit can build a high-precision environmental analysis model for different environmental conditions, providing strong support for the operation risk assessment of the equipment.

[0036] In an embodiment of the present application, optionally, the operation and maintenance information generating unit is also used to execute the following process for each controlled device: if the first operation risk level of the controlled device is a major risk level, the controlled device is determined to be a first target controlled device that requires equipment maintenance, and according to the maintenance personnel capability information and the maintenance personnel scheduling information in the preset maintenance personnel information, the maintenance personnel with equipment maintenance capabilities for the equipment type of the first target controlled device are screened as the first target maintenance personnel, and first operation and maintenance work information of the equipment maintenance type is generated; if the first operation risk level of the controlled device is not a major risk level, and the second operation risk level is a major risk level or a large risk level, the controlled device is determined to be a second target controlled device that requires environmental maintenance, and according to the maintenance personnel capability information and the maintenance personnel scheduling information in the preset maintenance personnel information, the maintenance personnel with environmental maintenance capabilities for the equipment type of the target controlled device are screened as the second target maintenance personnel, and second operation and maintenance work information of the environmental maintenance type is generated.

[0037] In this embodiment, the operation and maintenance information generation unit is responsible for generating specific operation and maintenance work information based on data processing and risk assessment, so as to guide maintenance personnel to perform corresponding maintenance work. For each controlled device, first obtain its first operation risk level (usually obtained based on the evaluation of the device operation data) and the second operation risk level (possibly obtained based on the evaluation of the device environment data). If the first operation risk level is a major risk level, the controlled device is determined to be the first target controlled device that needs equipment maintenance. According to the preset maintenance personnel information (including maintenance personnel capability information and maintenance personnel scheduling information), the maintenance personnel with equipment maintenance capabilities for the equipment type are screened out as the first target maintenance personnel, and the first operation and maintenance work information of the equipment maintenance type is generated. The information should include key information such as equipment type, maintenance type (equipment maintenance), maintenance personnel, and estimated maintenance time. If the first operation risk level is not a major risk level, but the second operation risk level is a major risk level or a large risk level, the controlled device is determined to be the second target controlled device that needs environmental maintenance. It should be noted here that even if the first risk level is not high, if the environmental risk level is high, it may also pose a threat to the long-term stable operation of the equipment, so environmental maintenance is required. Similarly, according to the preset maintenance personnel information, the maintenance personnel who have the environmental maintenance ability for the equipment type (or the environment in which it is located) are screened as the second target maintenance personnel. Generate the second operation and maintenance work information of the environmental maintenance type, which should include key information such as equipment location, environmental risk type (such as excessive humidity, excessive noise, etc.), maintenance type (environmental maintenance), maintenance personnel, and estimated maintenance time. It should be noted that the professional skills, experience, and equipment type or environment type that each maintenance personnel is good at maintaining should be recorded in detail in order to accurately match tasks and maintenance personnel. Considering the timeliness of maintenance work and the reasonable allocation of human resources, the most suitable maintenance personnel should be determined to perform tasks based on the scheduling information. In addition, the generated operation and maintenance work information should be as detailed as possible to ensure that the maintenance personnel can clearly understand the specific requirements of the maintenance tasks, thereby improving maintenance efficiency and quality. Through the above process, the operation and maintenance information generation unit can effectively generate targeted operation and maintenance work information according to the operation risk level and environmental risk level of the equipment, providing strong guarantee for the stable operation of the equipment.

[0038] In an embodiment of the present application, optionally, the operation and maintenance assessment module is also used to obtain the latest first operation risk level of the first target control equipment after receiving the preset second time period of the maintenance end information fed back by the first target maintenance personnel, and perform equipment maintenance work assessment on the first target maintenance personnel based on the latest first operation risk level; after receiving the preset third time period of the maintenance end information fed back by the second target maintenance personnel, obtain the latest second operation risk level of the second target control equipment, and perform environmental maintenance work assessment on the second target maintenance personnel based on the latest second operation risk level.

[0039] In this embodiment, the operation and maintenance evaluation module evaluates the maintenance effect after the maintenance work is completed to ensure the effectiveness and quality of the maintenance work. Specifically, when the first target maintenance personnel completes the equipment maintenance work, the maintenance end information will be fed back to the system. In order to avoid the evaluation error caused by system delay or untimely data update, the system will wait for a preset second time (such as 10 minutes) after receiving the maintenance end information to ensure the availability of the latest data. After the preset second time, the system obtains the latest first operation risk level of the first target control equipment, which should reflect the equipment operation status after the maintenance work. Based on the latest first operation risk level, the system evaluates the equipment maintenance work of the first target maintenance personnel. If the latest risk level is significantly reduced, it means that the maintenance work is effective; if the risk level does not change or increases, it may be necessary to further analyze the reasons and review or reschedule the maintenance work. In addition, for the evaluation of environmental maintenance work, similar to equipment maintenance, when the second target maintenance personnel completes the environmental maintenance work, the maintenance end information will be fed back to the system, and the system will also wait for a preset third time (which may be different from the second time, determined according to the characteristics and needs of environmental maintenance) to ensure the accuracy and stability of environmental data. After the preset third period of time, the system obtains the latest second operation risk level of the second target control equipment, which should reflect the equipment environment status after the maintenance work. Based on the latest second operation risk level, the system evaluates the environmental maintenance work of the second target maintenance personnel. If the latest risk level is reduced, it means that the environmental maintenance work is effective; if the risk level remains unchanged or increases, it is necessary to further analyze the environmental influencing factors and make necessary adjustments or redeploy the maintenance work. Through the above process, the operation and maintenance evaluation module can effectively evaluate the effects of equipment maintenance and environmental maintenance, and provide a scientific decision-making basis for operation and maintenance management.

[0040] In an embodiment of the present application, optionally, it also includes: an operation and maintenance archive module, which is used to receive maintenance process information fed back by target maintenance personnel, construct archive prompt words according to the maintenance process information and the maintenance archive template corresponding to the target control equipment, and call the large language model to generate maintenance archive information of the target control equipment according to the archive prompt words.

[0041] In this embodiment, the operation and maintenance archive module is responsible for recording and collating detailed information during the equipment maintenance process for subsequent query, analysis and audit. The operation and maintenance archive module first receives the maintenance process information fed back by the target maintenance personnel, which may include the maintenance start time, end time, maintenance steps, replaced parts, problems found, solutions, etc. According to the received maintenance process information and the maintenance archive template corresponding to the target control equipment, the module will intelligently construct a series of archive prompts, which are intended to guide the subsequent information generation process and ensure the comprehensiveness and standardization of the archive content. The maintenance archive template is pre-defined, and it is customized according to factors such as equipment type and maintenance type, and contains necessary archive fields and format requirements. The operation and maintenance archive module will call a large language model (such as GPT, BERT, etc.) to process these archive prompts. The large language model has a powerful natural language processing capability and can generate text content that conforms to grammatical specifications and clear logic according to the prompt words. In this process, the module will take the maintenance process information as input, and automatically fill it into the corresponding fields of the archive template through the generation algorithm of the large language model to form a complete maintenance archive information. The generated maintenance archive information will be saved in the database for subsequent query and analysis. The system provides a convenient query interface, allowing users to quickly retrieve relevant archive information based on conditions such as equipment number and maintenance time. At the same time, archive information can also serve as an important basis for operation and maintenance management, used to evaluate the work quality of maintenance personnel, analyze the causes of equipment failures, optimize operation and maintenance processes, etc. Through the application of the operation and maintenance archive module, the standardization and efficiency of operation and maintenance management can be greatly improved, providing strong support for the digital transformation and intelligent upgrade of enterprises.

[0042] By applying the technical solution of this embodiment, the following beneficial effects can be achieved: Improve operation and maintenance efficiency: Through automated risk assessment and operation and maintenance information generation, the time for manual judgment and decision-making is reduced, so that operation and maintenance work can respond to and handle potential problems more quickly. The use of large language models to generate maintenance archive information simplifies the cumbersome manual recording process and improves the efficiency and accuracy of archiving. Enhance the scientific nature of operation and maintenance decisions: Based on the evaluation of equipment operation risk level and environmental risk level, scientific data support is provided for operation and maintenance decisions, making the decisions more reasonable and effective. By comparing and evaluating the risk levels before and after maintenance, the effect of maintenance work can be objectively evaluated, providing a basis for future operation and maintenance strategy adjustments. Optimize human resource allocation: Intelligently match tasks according to the capabilities of maintenance personnel and scheduling information to ensure that the most suitable personnel perform maintenance work at the most suitable time, optimize the allocation of human resources, and reduce operation and maintenance delays and errors caused by personnel skill mismatch or time conflicts. Improve the standardization and traceability of operation and maintenance work: Introduce maintenance archive templates and large language models to generate archive information to ensure the standardization of the maintenance process and the consistency of archive content. The detailed recording and preservation of archived information provides a reliable basis for subsequent query, analysis and audit, and enhances the traceability of operation and maintenance work.

[0043] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0044] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A power supply station operation and maintenance management and control business collection and monitoring system, characterized in that: include: Data collection module, data processing module, data analysis module, operation and maintenance control module, and operation and maintenance evaluation module; The data acquisition module is used to collect equipment operation data and equipment environment data corresponding to each control equipment in the power supply station; The data processing module is used to take a preset first time period before the current time as a monitoring reference period, evenly divide the monitoring reference period into a plurality of sub-periods, and process the device environment data in each sub-period into device environment period data corresponding to each sub-period; The data analysis module includes a first data analysis unit and a second data analysis unit; the first data analysis unit is used to determine a first operation risk level of each controlled device according to the device operation data and the operation requirement information of each controlled device; The second data analysis unit is used to determine the environmental impact factor of each controlled device based on the first operation risk level of each controlled device, and determine the second operation risk level of each controlled device according to the device environment time period data and the environmental impact factor of each sub-period corresponding to each controlled device through the environmental analysis model; wherein the first operation risk level and the second operation risk level of the controlled device both include a major risk level, a relatively large risk level, a general risk level and a low risk level; The operation and maintenance control module includes an operation and maintenance judgment unit, an operation and maintenance information generation unit and an operation and maintenance information push unit; the operation and maintenance judgment unit is used to determine whether maintenance is required for the control equipment according to the first operation risk level and the second operation risk level of each control equipment; the operation and maintenance information generation unit is used to determine the operation and maintenance work information according to the preset maintenance personnel information and the equipment type of the target control equipment to be maintained when it is determined that the control equipment needs to be maintained, wherein the operation and maintenance work information includes the target maintenance personnel information, the location information of the target control equipment, the equipment operation data and the equipment environment data; the operation and maintenance information push unit is used to reach the operation and maintenance work information to the target maintenance personnel corresponding to the target maintenance personnel information; The operation and maintenance evaluation module is used to evaluate the operation and maintenance management and control services of the power supply station according to the first operation risk level and the second operation risk level of each management and control equipment.

2. The power supply station operation and maintenance management and control service collection and monitoring system according to claim 1 is characterized in that: The equipment operation data includes current value, voltage value, power factor, and equipment temperature; The first data analysis unit includes an interval acquisition subunit and a first analysis subunit; The interval acquisition subunit is used to determine multiple current intervals, multiple voltage intervals, multiple power factor intervals, and multiple device temperature intervals corresponding to each controlled device according to the operation requirement information of each controlled device; The first analysis subunit is used to sum, for each managed and controlled device, the current risk score corresponding to the current interval in which the current value falls, the voltage risk score corresponding to the voltage interval in which the voltage value falls, the power factor risk score corresponding to the power factor interval in which the power factor falls, and the equipment temperature risk score corresponding to the equipment temperature interval in which the equipment temperature falls, determine the target risk level score interval in which the sum falls among multiple risk level score intervals, and determine the operating risk level corresponding to the target risk level score interval as the first operating risk level corresponding to the managed and controlled device.

3. The power supply station operation and maintenance management and control service collection and monitoring system according to claim 2 is characterized in that: Multiple current intervals, multiple voltage intervals, multiple power factor intervals, and multiple equipment temperature intervals all include major risk intervals, relatively large risk intervals, general risk intervals, and low risk intervals; The first analysis subunit is further configured to, for each managed and controlled device, determine that the first operation risk level corresponding to the managed and controlled device is a major risk level if at least one of the current interval into which the current value falls, the voltage interval into which the voltage value falls, the power factor interval into which the power factor falls, and the device temperature interval into which the device temperature falls belongs to a major risk interval, or at least two of the current interval into which the current value falls, the voltage interval into which the voltage value falls, the power factor interval into which the power factor falls, and the device temperature interval into which the device temperature falls belong to a relatively large risk interval; Otherwise, the current risk score corresponding to the current interval that the current value falls into, the voltage risk score corresponding to the voltage interval that the voltage value falls into, the power factor risk score corresponding to the power factor interval that the power factor falls into, and the equipment temperature risk score corresponding to the equipment temperature interval that the equipment temperature falls into are summed, and the target risk level score interval in which the sum falls is determined among multiple risk level score intervals, and the operating risk level corresponding to the target risk level score interval is determined as the first operating risk level corresponding to the managed and controlled equipment.

4. The power supply station operation and maintenance management and control service collection and monitoring system according to claim 1 is characterized in that: The equipment environment data includes environment temperature, environment humidity, environment noise, and electromagnetic interference level; the equipment period environment data includes environment temperature period data, environment humidity period data, environment noise period data, and electromagnetic interference level period data; The second data analysis unit includes a feature extraction subunit and a second analysis subunit; The feature extraction subunit is used to determine the ambient temperature and humidity feature vector according to the ambient temperature time period data and the ambient humidity time period data corresponding to each sub-time period, generate the ambient noise feature vector according to the ambient noise time period data corresponding to each sub-time period, and generate the electromagnetic interference level feature vector according to the electromagnetic interference level time period data corresponding to each sub-time period, wherein the ambient temperature and humidity feature vector W=[W(k), W(k-1), ... W(k-m+1)], W(i) represents the ambient temperature and humidity feature value corresponding to the i-th time period, i∈[k, k-m+1], the ambient noise feature vector N=[N(k), N(k-1), ... N(k-m+1)], N(i) represents the ambient noise feature value corresponding to the i-th time period, the electromagnetic interference level feature vector E=[E(k), E(k-1), ... E(k-m+1)], E(i) represents the ambient electromagnetic interference level feature value corresponding to the i-th time period, and m is the number of neurons in the first layer of the neural network in the environmental analysis model; The second analysis subunit is used to initialize the model parameters of the environmental analysis model based on the environmental influencing factors, and input the environmental temperature and humidity feature vector, the environmental noise feature vector, and the electromagnetic interference level feature vector into the environmental analysis model to determine a second operation risk level.

5. The power supply station operation and maintenance management and control service collection and monitoring system according to claim 4 is characterized in that: The feature extraction subunit is also used to perform denoising on the ambient temperature, ambient humidity, ambient noise, and electromagnetic interference level in each sub-time period, and to average the ambient temperature, ambient humidity, ambient noise, and electromagnetic interference level in each sub-time period after denoising, and to use the obtained averages as the ambient temperature time period data, the ambient humidity time period data, the ambient noise time period data, and the electromagnetic interference level time period data, respectively.

6. The power supply station operation and maintenance management and control service collection and monitoring system according to claim 5 is characterized in that: The feature extraction subunit is also used to calculate the temperature and humidity characteristic values ​​of each control device according to the following formula: t(i) represents the ambient temperature period data corresponding to the i-th time period, ts represents the preset temperature threshold value determined according to the current weather information, h(i) represents the ambient humidity period data corresponding to the i-th time period, hs represents the preset humidity threshold value determined according to the current weather information, a represents the temperature influence factor of the corresponding control equipment, and b represents the humidity influence factor of the corresponding control equipment; The environmental noise characteristic value of each control device is calculated according to the following formula: Where n(i) represents the environmental noise period data corresponding to the i-th time period, and ns represents the preset noise impact threshold corresponding to the corresponding control device; The electromagnetic interference level characteristic value of each control equipment is calculated according to the following formula: Wherein, e(i) represents the electromagnetic interference level period data corresponding to the i-th time period, and es represents the preset electromagnetic interference level threshold corresponding to the corresponding control equipment.

7. The power supply station operation and maintenance management and control service collection and monitoring system according to claim 6 is characterized in that: The data analysis module further includes a model training unit; the model training unit is used to: Obtaining historical operation and maintenance data of at least one sample power supply station, wherein the historical operation and maintenance data includes equipment operation history data and equipment environment history data of each sample control equipment in the corresponding sample power supply station, wherein the equipment operation history data includes historical current value, historical voltage value, historical power factor, and historical equipment temperature, and the equipment environment history data includes historical environment temperature, historical environment humidity, historical environment noise, and historical electromagnetic interference level; For each sample power supply station, the equipment operation history data and the equipment environment history data corresponding to the sample power supply station are divided into time periods to obtain equipment operation history time period data and equipment environment history time period data of multiple sub-periods, and the operation risk level samples of each sub-period are determined according to the equipment operation history time period data of each sub-period, and the environmental impact factor samples corresponding to each sub-period are determined according to the operation risk level samples of each sub-period; According to the time window of m sub-periods and the step length of 1 sub-period, the equipment environment historical period data is divided into multiple sample data groups, each sample data group includes the equipment environment historical period data of m sub-periods; the environmental impact factor sample corresponding to the last sub-period corresponding to each sample data group is added to the sample data group, and the operation risk level sample corresponding to the next sub-period of the last sub-period is added to the sample data group; According to the difference of the environmental impact factor samples in each sample data group, each sample data group is divided into multiple classes; the equipment environment historical period data in each sample data group is processed into corresponding feature vector samples, wherein the feature vector samples include environmental temperature and humidity feature vector samples, environmental noise feature vector samples and electromagnetic interference level feature vector samples; Based on multiple environmental analysis models of the same structure, the sample data groups under each class are used for training respectively to obtain the trained environmental analysis models corresponding to the corresponding classes, wherein, during the model training process, the risk level is predicted based on the environmental temperature and humidity feature vector samples, the environmental noise feature vector samples, and the electromagnetic interference level feature vector samples in the sample data group, and the model is optimized based on the prediction results and the operation risk level samples in the sample data group; The model parameters of the environmental analysis model corresponding to each class are extracted, and the model parameters corresponding to different environmental impact factors are recorded, so that the model parameters can be initialized based on the model parameters corresponding to different environmental impact factors when the environmental analysis model is used for risk prediction.

8. The power supply station operation and maintenance management and control service collection and monitoring system according to any one of claims 1 to 7, characterized in that: The operation and maintenance information generating unit is further used to execute the following process for each managed device: If the first operation risk level of the controlled device is a major risk level, the controlled device is determined to be a first target controlled device that requires equipment maintenance, and according to the maintenance personnel capability information and maintenance personnel scheduling information in the preset maintenance personnel information, a maintenance personnel with equipment maintenance capability for the equipment type of the first target controlled device is selected as the first target maintenance personnel, and first operation and maintenance work information of the equipment maintenance type is generated; If the first operating risk level of the controlled device is not a major risk level, and the second operating risk level is a major risk level or a large risk level, the controlled device is determined to be a second target controlled device that requires environmental maintenance, and based on the maintenance personnel capability information and maintenance personnel scheduling information in the preset maintenance personnel information, maintenance personnel with environmental maintenance capabilities for the equipment type of the target controlled device are screened as second target maintenance personnel, and second operation and maintenance work information of the environmental maintenance type is generated.

9. The power supply station operation and maintenance management and control service collection and monitoring system according to claim 8 is characterized in that: The operation and maintenance assessment module is further used to obtain the latest first operation risk level of the first target controlled equipment after receiving the preset second time period of the maintenance completion information fed back by the first target maintenance personnel, and to evaluate the equipment maintenance work of the first target maintenance personnel based on the latest first operation risk level; and to obtain the latest second operation risk level of the second target controlled equipment after receiving the preset third time period of the maintenance completion information fed back by the second target maintenance personnel, and to evaluate the environmental maintenance work of the second target maintenance personnel based on the latest second operation risk level.

10. The power supply station operation and maintenance management and control service collection and monitoring system according to any one of claims 1 to 7, characterized in that: Also includes: The operation and maintenance archive module is used to receive the maintenance process information fed back by the target maintenance personnel, construct archive prompt words according to the maintenance process information and the maintenance archive template corresponding to the target control equipment, and call the large language model to generate the maintenance archive information of the target control equipment according to the archive prompt words.

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