Data Quality Monitoring and Storage System Based on Equipment Electrical State Assessment

Abstract

The present invention discloses a data quality monitoring and storage system based on the evaluation of the electrical state of equipment, which relates to the technical field of data quality monitoring and storage. The data quality monitoring and storage system based on the evaluation of the electrical state of equipment includes: an environmental state evaluation module, a cabinet state interference evaluation module, a cabinet defect evaluation module, and a data storage module. By evaluating the environmental state and determining whether to perform environmental interference adjustment, then evaluating the cabinet state interference and determining whether to perform cabinet state interference adjustment, then evaluating the cabinet defects to obtain the cabinet heating defect evaluation coefficient and determining whether to perform defect optimization, and finally storing the qualified cabinet data obtained, the present invention achieves the effect of improving the accuracy of the electrical state data acquisition and storage quality monitoring of high-voltage switchgears, and solves the problem of low accuracy of the electrical state data acquisition and storage quality monitoring of high-voltage switchgears in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of data quality monitoring and storage, and particularly to a data quality monitoring and storage system based on the evaluation of the electrical state of equipment. Background Art

[0002] With the advancement of the construction of smart grids, the on-line monitoring of power equipment has been greatly developed and has become a trend. The monitoring data has become increasingly large, gradually forming big data for the monitoring of power equipment. This poses a very big technical challenge to the on-line monitoring system of power equipment in terms of data storage and processing. Therefore, it is necessary to adopt efficient data storage technologies to meet the storage requirements of massive data. Data quality monitoring is an important link to ensure the accuracy, integrity, timeliness, and consistency of data. The evaluation of the electrical state of equipment refers to the work of dynamically monitoring the various parameters, working states, and service lives of electrical equipment and evaluating its reliability and safety. After long-term operation in harsh environments such as high temperature, high humidity, and haze, the internal insulation of high-voltage switchgear is extremely likely to deteriorate. This deterioration will cause the insulation performance of the switchgear electrical equipment to deteriorate and the voltage tolerance to decrease, thus increasing the risk of faults. With the rapid development of modern power systems, the state evaluation and prediction technologies of power equipment have become an important research direction in the power industry. In the evaluation of the electrical state of equipment, data quality directly affects the accuracy and reliability of the evaluation results.

[0003] Existing systems mainly install sensors on key electrical equipment to collect electrical state data of the equipment, such as current, voltage, power factor, etc. Through a data collector or a monitoring system, the data output by the sensors is collected in real time and transmitted to the data storage system.

[0004] For example, a novel comprehensive evaluation method for the operating state of high-voltage switchgear disclosed in the patent application of invention with the publication number of CN112561226A includes: Step 1, setting the operating state level of the high-voltage switchgear and constructing an evaluation index system for the operating state of the high-voltage switchgear; Step 2, using the fuzzy set-valued statistics method to determine the subjective weight of the evaluation index to obtain the initial weight, and dynamically correcting the initial weight by using the entropy method; Step 3, establishing an evaluation standard matrix for the operating state of the switchgear, mapping the evaluation samples in the input space to a high-dimensional feature space by using the Gaussian kernel function, establishing a vector space model in the high-dimensional feature space, obtaining the closeness between the input sample and the standard sample by using the vector space model, and obtaining the comprehensive evaluation result of the state of the high-voltage switchgear according to the closeness.

[0005] For example, the method for evaluating the health status of sensors applied to intelligent high-voltage switchgear disclosed in the invention patent application with the publication number of CN116050888A includes: acquiring sensor data and performing data cleaning and processing on the data; according to the data collected by the sensors, using an unsupervised clustering algorithm to distinguish data in different health states and constructing a health status evaluation data set; according to the processed sensor data, using a tensor fusion network to perform multi-modal information learning to obtain the correlation relationship between different modal data; and evaluating the historical working status of the high-voltage switchgear through a health status evaluation inference sub-network to determine whether it can continue to operate healthily.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it is found that the above technology has at least the following technical problems:

[0007] In the prior art, due to the existence of an electromagnetic interference environment, the quality of feature extraction of the high-voltage switchgear based on the detection data is affected, resulting in the problem of low accuracy in monitoring the acquisition and storage quality of the electrical state data of the high-voltage switchgear. Summary of the Invention

[0008] The embodiments of the present application provide a data quality monitoring and storage system based on the evaluation of the electrical state of the device, which solves the problem of low accuracy in monitoring the acquisition and storage quality of the electrical state data of the high-voltage switchgear in the prior art, and realizes the improvement of the accuracy in monitoring the acquisition and storage quality of the electrical state data of the high-voltage switchgear.

[0009] The embodiments of the present application provide a data quality monitoring and storage system based on the evaluation of the electrical state of the device, including an environmental state evaluation module, a cabinet state interference evaluation module, a cabinet defect evaluation module, and a data storage module: among them, the environmental state evaluation module is used to monitor the high-voltage switchgear to obtain live detection parameters and environmental detection parameters, and perform environmental state evaluation according to the obtained data and judge whether to perform environmental interference adjustment; the cabinet state interference evaluation module is used to obtain cabinet state interference data according to the obtained cabinet state parameters and qualified environmental evaluation coefficients, perform cabinet state interference evaluation based on the cabinet state interference data and judge whether to perform cabinet state interference adjustment; the cabinet defect evaluation module is used to obtain cabinet heating defect evaluation data according to the obtained cabinet defect parameters and qualified cabinet state evaluation coefficients, perform cabinet defect evaluation based on the cabinet heating defect evaluation data to obtain a cabinet heating defect evaluation coefficient and judge whether to perform defect optimization, the qualified cabinet state evaluation coefficient represents a cabinet state evaluation coefficient not lower than a preset cabinet state evaluation threshold, and the cabinet heating defect evaluation coefficient is used to evaluate the abnormal heating condition of the high-voltage switchgear during the monitoring process of the high-voltage switchgear; the data storage module is used to store the obtained qualified cabinet data, and the qualified cabinet data is used for electrical state evaluation.

[0010] Further, the live detection parameters include the short-circuit current of the switchgear and the magnetic field intensity of the switchgear; the environmental detection parameters include the external environmental temperature, the external environmental humidity, and the cabinet load rate; the cabinet state parameters include the cabinet leakage current, the cabinet shutdown duration, and the insulation resistance; the cabinet defect parameters include the internal temperature of the cabinet, the heat generation power of the switchgear, and the circuit resistance of the cabinet.

[0011] Further, the specific process of evaluating the environmental state based on the acquired data is as follows: an interference influence factor is obtained by processing the live detection parameters and a preset interference threshold; environmental detection influence data is obtained by processing the environmental detection parameters, a preset environmental detection threshold, and the interference influence factor; an environmental evaluation coefficient is obtained by processing the environmental detection influence data and a preset environmental detection weight factor obtained from the database; the interference influence factor is used to reflect the degree of electromagnetic interference within a preset time period; the preset interference threshold includes the maximum preset short-circuit current and the maximum preset magnetic field intensity; the environmental detection influence data includes a temperature influence evaluation value, a humidity influence evaluation value, and a load rate influence evaluation value; the environmental detection influence data is used to reflect the influence of the environmental detection parameters by the interference influence factor; the preset environmental detection threshold includes the maximum preset external temperature, the maximum preset external humidity, and the maximum preset load rate; the preset environmental detection weight factor includes a temperature weight factor, a humidity weight factor, and a load rate weight factor; the environmental evaluation coefficient is used to evaluate the qualification degree of environmental monitoring during the monitoring of the high-voltage switchgear.

[0012] Further, the short-circuit current of the switchgear represents the maximum value of the short-circuit current formed by the high-voltage switchgear circuit within a preset time period; the cabinet leakage current represents the maximum value of the current leaked by the insulation material of the high-voltage switchgear within a preset time period; the cabinet shutdown duration represents the maximum value of the duration of abnormal shutdown of the high-voltage switchgear within a preset time period.

[0013] Further, the specific process of determining whether to perform environmental interference adjustment is as follows: determine whether the obtained environmental evaluation coefficient is not lower than the preset environmental interference threshold in the database; when the environmental evaluation coefficient is not lower than the preset environmental interference threshold, mark the corresponding environmental evaluation coefficient as a qualified environmental evaluation coefficient, otherwise perform environmental interference adjustment; when the environmental evaluation coefficient after environmental interference adjustment is still lower than the preset environmental interference threshold, send an alarm prompt to the preset personnel; the environmental interference adjustment includes anti-interference of the live detection value and structural shielding.

[0014] Further, the specific process of evaluating the cabinet state interference based on the cabinet state interference data is as follows: The cabinet state interference data is obtained by processing the environmental interference factors, cabinet state parameters, and preset cabinet state thresholds, and the environmental interference factors are obtained by processing the qualified environmental evaluation coefficients; The cabinet state evaluation coefficient is obtained by processing the cabinet state interference data and the preset cabinet state weight factors; The cabinet state interference data includes the leakage current evaluation value, cabinet interruption evaluation value, and cabinet insulation evaluation value; The cabinet state interference data is used to reflect the influence of environmental interference factors on cabinet state parameters; The preset cabinet state thresholds include the preset maximum leakage current, preset maximum stop operation duration, and preset maximum insulation resistance; The preset cabinet state weight factors include the leakage current weight factor, cabinet interruption weight factor, and cabinet insulation weight factor; The cabinet state evaluation coefficient is used to evaluate the qualification degree of the cabinet state of the high-voltage switchgear during the monitoring process.

[0015] Further, the specific process of determining whether to adjust the cabinet state interference is as follows: Determine whether the obtained cabinet state evaluation coefficient is not lower than the preset cabinet state evaluation threshold in the database; If the cabinet state evaluation coefficient is not lower than the preset cabinet state evaluation threshold, mark the corresponding cabinet state evaluation coefficient as the qualified cabinet state evaluation coefficient, otherwise, perform cabinet state interference adjustment; If the cabinet state evaluation coefficient is still lower than the preset cabinet state evaluation threshold after the cabinet state interference adjustment, send an alarm prompt to the preset personnel; The cabinet state interference adjustment includes insulation defect marking and air gap adjustment.

[0016] Further, the specific process of evaluating the cabinet defect based on the cabinet heating defect evaluation data is as follows: Obtain the cabinet state influence factor, and the cabinet state influence factor is obtained by processing the qualified cabinet state evaluation coefficient; The cabinet heating defect evaluation data is obtained by processing the cabinet state influence factor, cabinet defect parameters, and preset cabinet defect thresholds, and the cabinet heating defect evaluation data includes the cabinet internal temperature evaluation value, heating power evaluation value, and cabinet loop resistance evaluation value; The cabinet heating defect evaluation data is used to reflect the influence degree of cabinet defect parameters by the cabinet state influence factor; The cabinet heating defect evaluation coefficient is obtained by processing the cabinet heating defect evaluation data and the preset heating defect weight factors; The preset heating defect weight factors include the cabinet internal temperature weight factor, heating power weight factor, and cabinet loop resistance weight factor; The preset cabinet defect thresholds include the preset maximum cabinet internal temperature, preset maximum heating power, and preset maximum cabinet loop resistance; The cabinet heating defect evaluation coefficient is used to evaluate the abnormal heating condition of the high-voltage switchgear.

[0017] Further, the specific process of defect optimization is as follows: Determine whether the obtained cabinet heating defect evaluation coefficient is not higher than the preset heating threshold in the database; when the cabinet heating defect evaluation coefficient is not higher than the preset heating threshold, no defect optimization is performed, otherwise defect optimization is performed; when the cabinet heating defect evaluation coefficient is still higher than the preset heating threshold after defect optimization, an alarm prompt is sent to the preset personnel; the defect optimization includes node decoupling and overload protection.

[0018] Further, the qualified cabinet data includes qualified environmental detection influence data, qualified cabinet state interference data, and qualified cabinet heating defect evaluation data; the qualified environmental detection influence data represents the environmental detection influence data corresponding to the cabinet state evaluation coefficient not lower than the preset cabinet state evaluation threshold; the qualified cabinet state interference data represents the cabinet state interference data corresponding to the cabinet state evaluation coefficient not lower than the preset cabinet state evaluation threshold; the qualified cabinet heating defect evaluation data represents the cabinet heating defect evaluation data corresponding to the cabinet heating defect evaluation coefficient not higher than the preset heating threshold.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. By performing environmental state evaluation and determining whether to perform environmental interference adjustment, then performing cabinet state interference evaluation and determining whether to perform cabinet state interference adjustment, then performing cabinet defect evaluation to obtain the cabinet heating defect evaluation coefficient and determining whether to perform defect optimization, and finally storing the obtained qualified cabinet data, the accuracy of qualified cabinet data storage is improved, and further the accuracy of electrical state data collection and storage quality monitoring of high-voltage switchgear is improved, effectively solving the problem of low accuracy of electrical state data collection and storage quality monitoring of high-voltage switchgear in the prior art.

[0021] 2. By processing the environmental detection parameters, preset environmental detection threshold, and interference influence factor to obtain the environmental detection influence data, and then processing the environmental detection influence data and preset environmental detection weight factor to obtain the environmental evaluation coefficient, the reliability of evaluating the qualification degree of environmental monitoring is improved, and further the accurate quantification of the qualification degree of environmental monitoring during the monitoring of high-voltage switchgear is realized.

[0022] 3. By marking the cabinet state evaluation coefficient not lower than the preset cabinet state evaluation threshold as the qualified cabinet state evaluation coefficient and performing cabinet state interference adjustment on the cabinet state evaluation coefficient lower than the preset cabinet state evaluation threshold, the qualification degree of the cabinet state of high-voltage switchgear is improved, and further the accuracy of evaluating the qualification degree of the cabinet state of high-voltage switchgear is improved. Description of the Drawings

[0023] Figure 1 This is a schematic structural diagram of a data quality monitoring and storage system based on equipment electrical state assessment provided by an embodiment of the present application;

[0024] Figure 2 This is a statistical chart of the change in the external environmental temperature - temperature impact assessment value provided by an embodiment of the present application;

[0025] Figure 3 This is a schematic diagram of a multi - data fusion state assessment model provided by an embodiment of the present application. Detailed implementation manners

[0026] In the embodiment of the present application, by providing a data quality monitoring and storage system based on equipment electrical state assessment, the problem of low accuracy in the acquisition and storage quality monitoring of electrical state data of high - voltage switch cabinets in the prior art is solved. By monitoring the high - voltage switch cabinet to obtain live detection parameters and environmental detection parameters, then performing environmental state assessment based on the obtained data and judging whether to perform environmental interference adjustment, then obtaining cabinet state interference data according to the obtained cabinet body state parameters and qualified environmental assessment coefficients, performing cabinet state interference assessment based on the cabinet state interference data and judging whether to perform cabinet state interference adjustment, then obtaining cabinet heating defect assessment data according to the obtained cabinet defect parameters and qualified cabinet state assessment coefficients, performing cabinet defect assessment based on the cabinet heating defect assessment data to obtain a cabinet heating defect assessment coefficient and judging whether to perform defect optimization, and finally storing the obtained qualified cabinet data, the accuracy of the acquisition and storage quality monitoring of electrical state data of high - voltage switch cabinets is improved.

[0027] The technical solution in the embodiment of the present application for solving the problem of low accuracy in the acquisition and storage quality monitoring of electrical state data of the above - mentioned high - voltage switch cabinet has the following general idea:

[0028] By performing environmental state assessment and judging whether to perform environmental interference adjustment, then performing cabinet state interference assessment and judging whether to perform cabinet state interference adjustment, then performing cabinet defect assessment to obtain a cabinet heating defect assessment coefficient and judging whether to perform defect optimization, and finally storing the obtained qualified cabinet data, the effect of improving the accuracy of the acquisition and storage quality monitoring of electrical state data of high - voltage switch cabinets is achieved.

[0029] To better understand the above - mentioned technical solution, the above - mentioned technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0030] Such as Figure 1As shown in the figure, it is a schematic structural diagram of a data quality monitoring and storage system based on equipment electrical state assessment provided by an embodiment of the present application, including an environmental state assessment module, a cabinet state interference assessment module, a cabinet defect assessment module, and a data storage module: Among them, the environmental state assessment module is used to monitor the high-voltage switchgear to obtain live detection parameters and environmental detection parameters, and conduct environmental state assessment based on the obtained data and determine whether to perform environmental interference adjustment; the cabinet state interference assessment module is used to obtain cabinet state interference data according to the obtained cabinet state parameters and qualified environmental assessment coefficients, conduct cabinet state interference assessment based on the cabinet state interference data and determine whether to perform cabinet state interference adjustment, and the qualified environmental assessment coefficient represents an environmental assessment coefficient not lower than the preset environmental interference threshold; the cabinet defect assessment module is used to obtain cabinet heating defect assessment data according to the obtained cabinet defect parameters and qualified cabinet state assessment coefficients, conduct cabinet defect assessment based on the cabinet heating defect assessment data to obtain a cabinet heating defect assessment coefficient and determine whether to perform defect optimization, and the qualified cabinet state assessment coefficient represents a cabinet state assessment coefficient not lower than the preset cabinet state assessment threshold, and the cabinet heating defect assessment coefficient is used to evaluate the abnormal heating condition of the high-voltage switchgear during the monitoring process of the high-voltage switchgear; the data storage module is used to store the obtained qualified cabinet data, and the qualified cabinet data is used for electrical state assessment.

[0031] It should be added that the live detection parameters include the short-circuit current of the switchgear and the magnetic field intensity of the switchgear; the environmental detection parameters include the external environmental temperature, the external environmental humidity, and the cabinet load rate; the cabinet state parameters include the cabinet leakage current, the cabinet stop operation duration, and the insulation resistance; the cabinet defect parameters include the internal temperature of the cabinet, the heating power of the switchgear, and the cabinet loop resistance; the short-circuit current of the switchgear represents the maximum value of the short-circuit current formed by the high-voltage switchgear circuit within a preset time period; the cabinet leakage current represents the maximum value of the current leaked from the insulation material of the high-voltage switchgear within a preset time period; the cabinet stop operation duration represents the maximum value of the duration of abnormal stop operation of the high-voltage switchgear within a preset time period; the live detection parameters are the maximum values of the data obtained within a preset time period, and none of them is 0; the environmental detection parameters are the maximum values of the data obtained within a preset time period, and none of them is 0; the cabinet state parameters are the maximum values of the data obtained within a preset time period, and none of them is 0; the cabinet defect parameters are all the maximum values of the data obtained within a preset time period, and none of them is 0.

[0032] Among them, the maximum short-circuit current of the high-voltage switchgear within a preset time period is analyzed and counted through an ammeter and an oscilloscope to obtain the short-circuit current of the switchgear; the magnetic induction intensity at a preset position outside the high-voltage switchgear is measured by a gaussmeter, and its maximum value is counted to obtain the magnetic field intensity of the switchgear; the temperature at a preset position outside the high-voltage switchgear within a preset time period is measured by a temperature sensor, and its maximum value is counted to obtain the external ambient temperature; the humidity at a preset position outside the high-voltage switchgear within a preset time period is measured by a humidity sensor, and its maximum value is counted to obtain the external ambient humidity; the current and voltage at a preset position at the outgoing end of the high-voltage switchgear within a preset time period are monitored by a power quality analyzer, and the maximum value of the load rate of the high-voltage switchgear is obtained to get the cabinet load rate.

[0033] The current at a preset position of the insulation layer of the high-voltage switchgear within a preset time period is measured by a leakage current tester, and its maximum value is counted to obtain the cabinet leakage current; the maximum value of the stop operation time of the high-voltage switchgear within a preset time period is recorded by a timer to obtain the cabinet stop operation duration; the resistance at a preset position between the conductive circuit and the ground of the high-voltage switchgear within a preset time period is measured by an insulation resistance tester, and its maximum value is counted to obtain the insulation resistance.

[0034] The temperature at a preset position inside the high-voltage switchgear within a preset time period is measured by a temperature sensor, and its maximum value is counted to obtain the internal temperature of the cabinet; the heating power of the high-voltage switchgear within a preset time period is analyzed by a power monitor, and its maximum value is counted to obtain the heating power of the switchgear; the loop resistance of the high-voltage switchgear within a preset time period is measured by a loop resistance tester, and its maximum value is counted to obtain the cabinet loop resistance.

[0035] In this embodiment, when the environmental evaluation coefficient obtained by the environmental state evaluation module is lower than the preset environmental interference threshold, environmental interference adjustment is required; the cabinet state interference evaluation module further obtains the cabinet state evaluation coefficient through the environmental evaluation coefficient after the environmental interference adjustment is performed. When the cabinet state evaluation coefficient is lower than the preset cabinet state evaluation threshold in the database, cabinet state interference adjustment is required; the cabinet defect evaluation module further obtains the cabinet heating defect evaluation coefficient through the cabinet state evaluation coefficient after the cabinet state interference adjustment is performed. When the cabinet heating defect evaluation coefficient is higher than the preset heating threshold, defect optimization is required. Finally, the qualified cabinet data obtained is stored by the data storage module. The environmental state evaluation module, the cabinet state interference evaluation module, the cabinet defect evaluation module, and the data storage module complete the comprehensive monitoring, evaluation, and adjustment of the high-voltage switchgear through data transfer and interaction, thereby improving the accuracy of the electrical state data acquisition and storage quality monitoring of the high-voltage switchgear.

[0036] It should be noted that the high-voltage switchgear in this embodiment can be an inflatable ring main unit switchgear. By storing the qualified cabinet data corresponding to the inflatable ring main unit switchgear, an equipment database corresponding to the inflatable ring main unit switchgear can be established, and data statistics and data analysis can be carried out on the detection data and overall situation of the switchgear in the entire power grid. The stored qualified cabinet data is used as historical data for the next status assessment of the inflatable ring main unit switchgear, which is beneficial for the power-related departments to master the overall operation status of the equipment.

[0037] Furthermore, the specific process of environmental status assessment based on the acquired data is as follows: The interference influence factor is obtained by processing the live detection parameters and the preset interference threshold; the environmental detection influence data is obtained by processing the environmental detection parameters, the preset environmental detection threshold, and the interference influence factor; the environmental assessment coefficient is obtained by processing the environmental detection influence data and the preset environmental detection weight factor obtained from the database; the interference influence factor is used to reflect the degree of electromagnetic interference within a preset time period; the preset interference threshold includes the maximum preset short-circuit current and the maximum preset magnetic field intensity obtained from the database; the environmental detection influence data includes the temperature influence assessment value, the humidity influence assessment value, and the load rate influence assessment value; the environmental detection influence data is used to reflect the influence of the environmental detection parameters by the interference influence factor; the preset environmental detection threshold includes the maximum preset external temperature, the maximum preset external humidity, and the maximum preset load rate obtained from the database; the preset environmental detection weight factor includes the temperature weight factor, the humidity weight factor, and the load rate weight factor; the temperature weight factor is used to describe the influence degree of the temperature influence assessment value on the environmental assessment coefficient; the humidity weight factor is used to describe the influence degree of the humidity influence assessment value on the environmental assessment coefficient; the load rate weight factor is used to describe the influence degree of the load rate influence assessment value on the environmental assessment coefficient; the environmental assessment coefficient is used to evaluate the qualified degree of environmental monitoring during the monitoring of the high-voltage switchgear.

[0038] Among them, the limiting expression of the environmental assessment coefficient is as follows:

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] In the formula, represents the environmental assessment coefficient in the th preset time period, , represents the number of the preset time period, Represents the total number of preset time periods, Represents the temperature impact evaluation value within the Represents the humidity impact evaluation value within the Represents the load rate impact evaluation value within the Represents the temperature weight factor, Represents the humidity weight factor, Represents the load rate weight factor, and e represents the natural constant, Represents the interference impact factor within the Represents the short - circuit current of the switchgear within the Represents the magnetic field intensity of the switchgear within the Represents the external environmental temperature within the Represents the external environmental humidity within the Represents the cabinet load rate within the Represents the maximum value of the preset short - circuit current, Represents the maximum value of the preset magnetic field intensity, Represents the maximum value of the preset external temperature, Represents the maximum value of the preset external humidity, Represents the maximum value of the preset load rate.

[0044] In this embodiment, the aforementioned database is the database for storing various setting data in the data quality monitoring storage system for equipment electrical state evaluation provided by the embodiment of the present application. The database includes, but is not limited to, the maximum value of the preset external temperature, the maximum value of the preset external humidity, and the maximum value of the preset load rate, etc. The various numerical values therein are directly set by technicians; for example, the maximum value of the preset short - circuit current is represented by the maximum value of the short - circuit current of the high - voltage switchgear loop in the historical time period, the maximum value of the preset magnetic field intensity is represented by the maximum value of the magnetic field intensity corresponding to the high - voltage switchgear in the historical time period, the maximum value of the preset external temperature is represented by the maximum value of the external temperature corresponding to the high - voltage switchgear in the historical time period, the maximum value of the preset external humidity is represented by the maximum value of the external humidity corresponding to the high - voltage switchgear in the historical time period, and the maximum value of the preset load rate is represented by the maximum value of the load rate corresponding to the high - voltage switchgear in the historical time period.

[0045] Specifically, assume that the interference impact factor is fixed at 0.15, and the external environmental temperature ranges from 40 to 45 (degrees Celsius), and the preset maximum external temperature is fixed at 45 (degrees Celsius), as Figure 2 shown, which is a statistical chart of the change of the external environmental temperature - temperature influence evaluation value provided by the embodiment of the present application. It can be seen from Figure 2 this that as the external environmental temperature gradually increases, the temperature influence evaluation value gradually increases, indicating that the degree of influence of the high - voltage switchgear by the external temperature and electromagnetic interference gradually intensifies.

[0046] It should be explained that this embodiment provides a mapping set, which is obtained from a database. The mapping relationship in this mapping set can be a one - to - one or many - to - one relationship. The mapping relationship in this mapping set is that the temperature influence evaluation value, humidity influence evaluation value, and load rate influence evaluation value respectively correspond to the temperature weight factor, humidity weight factor, and load rate weight factor. By inputting the real - time temperature influence evaluation value, humidity influence evaluation value, and load rate influence evaluation value into the mapping set, the corresponding temperature weight factor, humidity weight factor, and load rate weight factor can be obtained. For example, in this embodiment, the value range of the weights involved is 0 - 1.

[0047] The algorithm of this embodiment combines the data analysis of environmental detection influence to obtain an environmental evaluation coefficient. The larger the short - circuit current and magnetic field intensity of the switchgear, the greater the influence of electromagnetic interference on the external environment of the high - voltage switchgear, resulting in a decrease in the environmental evaluation coefficient; the higher the external environmental temperature and external environmental humidity, the greater the possibility of the high - voltage switchgear malfunctioning, resulting in a decrease in the environmental evaluation coefficient; the larger the cabinet load rate, the greater the possibility of the high - voltage switchgear being overloaded, resulting in a decrease in the environmental evaluation coefficient. In summary, there is an inverse relationship between the environmental detection influence data and the environmental evaluation coefficient.

[0048] In the algorithm of this embodiment, the environmental detection influence data does not exist independently, and there is a mutual correlation between the independent variables, which requires comprehensive analysis. When the magnetic field intensity of the high - voltage switchgear is stronger, it may cause an increase in the short - circuit current of the high - voltage switchgear, which may further intensify the electromagnetic interference; when current passes through the conductors of the high - voltage switchgear, heat will be generated, and the increase in the magnetic field intensity may also lead to an increase in electromagnetic loss, further generating heat, resulting in an increase in the internal and external environmental temperature of the high - voltage switchgear; and when the high - temperature duration is longer, it may cause the external environmental humidity to be lower; when the cabinet load rate increases, the short - circuit current in the high - voltage switchgear may also increase accordingly, and then a stronger magnetic field is generated. By analyzing the comprehensive influence between parameters, the accurate evaluation of the detection qualification degree during the monitoring of the high - voltage switchgear is realized, and thus the accuracy of the electrical state data acquisition and storage quality monitoring of the high - voltage switchgear is improved.

[0049] Further, the specific process of determining whether to perform environmental interference adjustment is as follows: Determine whether the obtained environmental evaluation coefficient is not lower than the preset environmental interference threshold in the database; when the environmental evaluation coefficient is not lower than the preset environmental interference threshold, mark the corresponding environmental evaluation coefficient as a qualified environmental evaluation coefficient, otherwise perform environmental interference adjustment; when the environmental evaluation coefficient after environmental interference adjustment is still lower than the preset environmental interference threshold, send an alarm prompt to the preset personnel; environmental interference adjustment includes anti-interference of live detection values and structural shielding; anti-interference of live detection values means reducing electromagnetic interference through the anti-interference algorithm of live detection values; structural shielding means sending a prompt to the preset personnel to isolate the electromagnetic interference source (such as contact movement, insulation part discharge) from sensitive equipment (such as control circuit).

[0050] In this embodiment, the preset environmental interference threshold is represented by the average value of the environmental evaluation coefficients in the historical time period.

[0051] By isolating the electromagnetic interference source from the sensitive equipment, the influence of electromagnetic interference is reduced. In a high-voltage switchgear cabinet, the electromagnetic interference source may include contact movement, insulation part discharge, etc., and the sensitive equipment may be a control circuit, etc. To reduce electromagnetic interference, a metal shielding layer is set at the preset position point by the preset personnel to physically isolate the electromagnetic interference source from the sensitive equipment.

[0052] Through the anti-interference algorithm of live detection values, such as the anti-interference algorithm based on digital filtering technology, filtering the digital signals of the high-voltage switchgear cabinet through this algorithm can significantly reduce the influence of electromagnetic interference, improve the accuracy and reliability of detection, and thus improve the accuracy of the electrical state data acquisition and storage quality monitoring of the high-voltage switchgear cabinet.

[0053] Further, the specific process of evaluating the cabinet body status interference based on the cabinet body status interference data is as follows: The cabinet body status interference data is obtained by processing the environmental interference factors, the cabinet body status parameters, and the preset cabinet body status thresholds. The environmental interference factors are obtained by processing the qualified environmental evaluation coefficients. The cabinet body status evaluation coefficient is obtained by processing the cabinet body status interference data and the preset cabinet body status weight factors obtained from the database. The cabinet body status interference data includes the leakage current evaluation value, the cabinet body interruption evaluation value, and the cabinet body insulation evaluation value, and none of the cabinet body status interference data is 0. The cabinet body status interference data is used to reflect the influence of the environmental interference factors on the cabinet body status parameters. The preset cabinet body status thresholds include the preset maximum leakage current, the preset maximum stop operation duration, and the preset maximum insulation resistance obtained from the database. The preset cabinet body status weight factors include the leakage current weight factor, the cabinet body interruption weight factor, and the cabinet body insulation weight factor. The cabinet body status evaluation coefficient is used to evaluate the qualification degree of the cabinet body status of the high-voltage switchgear during the monitoring process. The leakage current weight factor is used to reflect the influence degree of the leakage current evaluation value on the cabinet body status evaluation coefficient. The cabinet body interruption weight factor is used to reflect the influence degree of the cabinet body interruption evaluation value on the cabinet body status evaluation coefficient. The cabinet body insulation weight factor is used to reflect the influence degree of the cabinet body insulation evaluation value on the cabinet body status evaluation coefficient.

[0054] Among them, the cabinet body status evaluation coefficient is obtained by the following method:

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] In the formula, represents the cabinet body status evaluation coefficient in the th first preset qualified time period, , represents the number of the first preset qualified time period, represents the total number of the first preset qualified time periods. The first preset qualified time period represents the preset time period corresponding to the qualified environmental evaluation coefficient. represents the leakage current evaluation value in the th first preset qualified time period, represents the cabinet body interruption evaluation value in the th first preset qualified time period, represents the cabinet body insulation evaluation value in the th first preset qualified time period, Represents the leakage current weight factor, Represents the cabinet interruption weight factor, Represents the cabinet insulation weight factor, where e represents the natural constant.

[0060] Represents at the th first preset qualified time period, the qualified environmental evaluation coefficient, Represents at the th first preset qualified time period, the cabinet leakage current, Represents at the th first preset qualified time period, the cabinet stop operation duration, Represents at the th first preset qualified time period, the insulation resistance, Represents the preset maximum leakage current value, Represents the preset maximum stop operation duration value, Represents the preset maximum insulation resistance value.

[0061] In this embodiment, the preset maximum leakage current value is represented by the maximum value of the leakage current corresponding to the high-voltage switchgear circuit in the historical time period, the preset maximum stop operation duration value is represented by the maximum value of the stop operation duration corresponding to the high-voltage switchgear circuit in the historical time period, and the preset maximum insulation resistance value is represented by the maximum value of the insulation resistance corresponding to the high-voltage switchgear circuit in the historical time period.

[0062] Specifically, assume that the leakage current evaluation value ranges from 0.5 to 0.8, the cabinet interruption evaluation value ranges from 0.5 to 0.8, the cabinet insulation evaluation value ranges from 0.5 to 0.8, the leakage current weight factor is fixed at 0.4, the cabinet interruption weight factor is fixed at 0.2, the cabinet insulation weight factor is fixed at 0.4. As shown in Table 1, it is the change statistical table of the cabinet state evaluation coefficient provided by the embodiment of the present application:

[0063] Table 1 Change Statistical Table of Cabinet State Evaluation Coefficient

[0064]

[0065] As can be seen from the above table, as the leakage current evaluation value, the cabinet interruption evaluation value, and the cabinet insulation evaluation value gradually increase, the cabinet state evaluation coefficient gradually decreases, indicating that the high-voltage switchgear is gradually affected by the leakage current, cabinet interruption, and insulation performance, and the qualified degree of the high-voltage switchgear state gradually decreases.

[0066] It should be noted that this embodiment provides a mapping set, which is obtained from a database. The mapping relationships in this mapping set can be one-to-one or many-to-one relationships. The mapping relationships in this mapping set are as follows: the leakage current evaluation value, the cabinet interruption evaluation value, and the cabinet insulation evaluation value correspond to the leakage current weight factor, the cabinet interruption weight factor, and the cabinet insulation weight factor respectively. By inputting the real-time leakage current evaluation value, the cabinet interruption evaluation value, and the cabinet insulation evaluation value into the mapping set, the corresponding leakage current weight factor, the cabinet interruption weight factor, and the cabinet insulation weight factor can be obtained. For example, in this embodiment, the value range of the weights involved is 0-1.

[0067] The algorithm of this embodiment combines the analysis of the cabinet state interference data to obtain the cabinet state evaluation coefficient. The larger the environmental evaluation coefficient is, the higher the qualified degree of the environmental monitoring during the monitoring of the high-voltage switchgear is, which is beneficial to improving the monitoring accuracy of the cabinet leakage current, the cabinet interruption duration, and the insulation resistance, resulting in an increase in the cabinet state evaluation coefficient; the larger the cabinet leakage current and the cabinet interruption duration are, the greater the possibility of abnormality of the high-voltage switchgear is, resulting in a decrease in the cabinet state evaluation coefficient; the larger the insulation resistance is, the better the insulation performance of the high-voltage switchgear is, resulting in an increase in the cabinet state evaluation coefficient. In summary, the cabinet state interference data and the cabinet state evaluation coefficient are in an inverse relationship.

[0068] In the algorithm of this embodiment, the cabinet state interference data does not exist independently, and there is a mutual correlation between the independent variables, which requires comprehensive analysis. When the environmental evaluation coefficient is larger, it means that the qualified degree of the environmental monitoring is higher, and the insulation performance may be better, which is usually beneficial to the normal operation of the high-voltage switchgear; when the insulation resistance decreases, the cabinet leakage current may increase because the decrease in insulation performance will cause the current to pass through the insulation layer more easily, thus forming a leakage current; the decrease in the insulation resistance may lead to an increase in the failure rate of the high-voltage switchgear, thereby increasing the cabinet stop operation duration. By analyzing the comprehensive influence between the parameters, the accurate evaluation of the qualified degree of the cabinet state of the high-voltage switchgear during the monitoring process of the high-voltage switchgear is realized, and further the accuracy of the electrical state data acquisition and storage quality monitoring of the high-voltage switchgear is improved.

[0069] Further, the specific process of determining whether to perform cabinet state interference adjustment is as follows: Determine whether the obtained cabinet state evaluation coefficient is not lower than the preset cabinet state evaluation threshold in the database; if the cabinet state evaluation coefficient is not lower than the preset cabinet state evaluation threshold, mark the corresponding cabinet state evaluation coefficient as a qualified cabinet state evaluation coefficient, otherwise perform cabinet state interference adjustment; if the cabinet state evaluation coefficient is still lower than the preset cabinet state evaluation threshold after the cabinet state interference adjustment, send an alarm prompt to the preset personnel; the cabinet state interference adjustment includes insulation defect marking and air gap adjustment; the insulation defect marking means monitoring the partial discharge amount and partial discharge times in the high-voltage switchgear through the partial discharge detection method and prompting the preset personnel to make abnormal markings; the air gap adjustment means sending a prompt to the preset personnel to adjust the air gap between the live part and the grounded part in the high-voltage switchgear.

[0070] In this embodiment, the preset cabinet state evaluation threshold is represented by the average value of the cabinet state evaluation coefficients in the historical time period; through partial discharge detection equipment, such as ultrasonic sensors and UHF sensors, the preset positions of the high-voltage switchgear are monitored and the partial discharge amount and partial discharge times in the high-voltage switchgear are obtained. When the monitored partial discharge amount and partial discharge times in the high-voltage switchgear exceed the safety range set by the preset personnel, abnormal markings are made; the preset personnel set partitions at the preset position points between the live part and the grounded part in the high-voltage switchgear to reduce the risk of electrical breakdown, thereby improving the accuracy of the electrical state data collection and storage quality monitoring of the high-voltage switchgear.

[0071] Further, the specific process of evaluating the cabinet body defects based on the cabinet body heating defect evaluation data is as follows: Obtain the cabinet body state influence factors, which are obtained by processing the qualified cabinet body state evaluation coefficients; Obtain the cabinet body heating defect evaluation data by processing the cabinet body state influence factors, cabinet body defect parameters, and preset cabinet body defect thresholds. The cabinet body heating defect evaluation data includes the cabinet body internal temperature evaluation value, heating power evaluation value, and cabinet body loop resistance evaluation value; The cabinet body heating defect evaluation data is used to reflect the influence degree of the cabinet body defect parameters by the cabinet body state influence factors; Obtain the cabinet body heating defect evaluation coefficient by processing the cabinet body heating defect evaluation data and the preset heating defect weight factors; The preset heating defect weight factors include the cabinet body internal temperature weight factor, heating power weight factor, and cabinet body loop resistance weight factor; The preset cabinet body defect thresholds include the preset maximum cabinet body internal temperature, preset maximum heating power, and preset maximum cabinet body loop resistance obtained from the database; The cabinet body internal temperature weight factor is used to describe the influence degree of the cabinet body internal temperature evaluation value on the cabinet body heating defect evaluation coefficient; The heating power weight factor is used to reflect the influence degree of the heating power evaluation value on the cabinet body heating defect evaluation coefficient; The cabinet body loop resistance weight factor is used to reflect the influence degree of the cabinet body loop resistance evaluation value on the cabinet body heating defect evaluation coefficient; The cabinet body heating defect evaluation coefficient is used to evaluate the abnormal heating condition of the high-voltage switch cabinet.

[0072] Among them, the cabinet body heating defect evaluation coefficient is obtained by the following method:

[0073] ;

[0074]

[0075] ;

[0076] ;

[0077] In the formula, represents the cabinet body heating defect evaluation coefficient in the th second preset qualified time period, , represents the number of the second preset qualified time period, represents the total number of the second preset qualified time periods. The second preset qualified time period represents the preset time period corresponding to the qualified cabinet body state evaluation coefficient, represents the cabinet body internal temperature evaluation value in the th second preset qualified time period, represents the heating power evaluation value in the th second preset qualified time period, represents the The evaluated value of the cabinet loop resistance within a second preset qualified time period Represents the weight factor of the internal temperature of the cabinet Represents the weight factor of the heating power Represents the weight factor of the cabinet loop resistance, and e represents the natural constant Represents the Influence factor of the cabinet state within a second preset qualified time period Represents the Evaluation coefficient of the qualified cabinet state within a second preset qualified time period Represents the Internal temperature of the cabinet within a second preset qualified time period Represents the Heating power of the switch cabinet within a second preset qualified time period Represents the Cabinet loop resistance within a second preset qualified time period Represents the maximum value of the preset internal temperature of the cabinet Represents the maximum value of the preset heating power Represents the maximum value of the preset cabinet loop resistance

[0078] In this embodiment, the maximum value of the preset internal temperature of the cabinet is represented by the maximum value of the internal temperature of the cabinet corresponding to the high-voltage switch cabinet loop in the historical time period, the maximum value of the preset heating power is represented by the maximum value of the heating power corresponding to the high-voltage switch cabinet loop in the historical time period, and the maximum value of the preset cabinet loop resistance is represented by the maximum value of the cabinet loop resistance corresponding to the high-voltage switch cabinet loop in the historical time period

[0079] It should be noted that this embodiment provides a mapping set, which is obtained from the database. The mapping relationship in this mapping set can be a one-to-one or many-to-one relationship. The mapping relationship in this mapping set is as follows: the evaluated value of the internal temperature of the cabinet, the evaluated value of the heating power, and the evaluated value of the cabinet loop resistance respectively correspond to the weight factor of the internal temperature of the cabinet, the weight factor of the heating power, and the weight factor of the cabinet loop resistance. By inputting the real-time evaluated value of the internal temperature of the cabinet, the evaluated value of the heating power, and the evaluated value of the cabinet loop resistance into the mapping set, the corresponding weight factor of the internal temperature of the cabinet, the weight factor of the heating power, and the weight factor of the cabinet loop resistance can be obtained. For example, in this embodiment, the value range of the involved weights is 0-1

[0080] The algorithm of this embodiment combines the analysis of the data on the heat generation defects of the cabinet body to obtain the evaluation coefficient of the heat generation defects of the cabinet body. The higher the internal temperature of the cabinet body, the heat generation power of the switch cabinet, and the loop resistance of the cabinet body, the greater the possibility of abnormal heat generation of the high-voltage switch cabinet, resulting in an increase in the evaluation coefficient of the heat generation defects of the cabinet body; the higher the evaluation coefficient of the cabinet body state, the smaller the possibility of defects in the high-voltage switch cabinet, resulting in a decrease in the evaluation coefficient of the heat generation defects of the cabinet body. In summary, the data on the heat generation defects of the cabinet body is directly proportional to the evaluation coefficient of the heat generation defects of the cabinet body.

[0081] In the algorithm of this embodiment, the data on the heat generation defects of the cabinet body does not exist independently, and there is a mutual correlation between the independent variables, which requires comprehensive analysis. When the heat generation power of the switch cabinet increases, the internal temperature of the cabinet body will rise accordingly. This is because an increase in the heat generation power of the switch cabinet means that more heat is generated per unit time. If the heat dissipation conditions remain unchanged, the internal temperature of the cabinet body will increase; when the loop resistance of the cabinet body increases, the heat generated when the current passes through the loop resistance will also increase, resulting in an increase in the internal temperature of the cabinet body; the heat generation power of the switch cabinet may lead to a decrease in the evaluation coefficient of the cabinet body state because the heat generation power of the switch cabinet may indicate that there are defects or abnormal operations in the equipment, such as poor contact, decreased insulation performance, etc., thus reducing the evaluation coefficient of the cabinet body state; when the evaluation coefficient of the cabinet body state is higher, it means that the qualified degree of the cabinet body state is higher, which helps to reduce the influence on the monitoring of the internal temperature of the cabinet body, the heat generation power of the switch cabinet, and the loop resistance of the cabinet body. By analyzing the comprehensive influence between the parameters, the accurate evaluation of the abnormal heat generation situation of the high-voltage switch cabinet is realized, and further, the accuracy of the electrical state data collection and storage quality monitoring of the high-voltage switch cabinet is improved.

[0082] Further, the specific process of defect optimization is as follows: Determine whether the obtained evaluation coefficient of the heat generation defects of the cabinet body is not higher than the preset heat generation threshold in the database; when the evaluation coefficient of the heat generation defects of the cabinet body is not higher than the preset heat generation threshold obtained from the database, no defect optimization is performed, otherwise defect optimization is performed; when the evaluation coefficient of the heat generation defects of the cabinet body is still higher than the preset heat generation threshold after defect optimization, an alarm prompt is sent to the preset personnel; the defect optimization includes node decoupling and overload protection; node decoupling means sending a prompt to the preset personnel to set a decoupling capacitor at the preset node; overload protection means sending a prompt to the preset personnel to set the set value of the overload protection of the high-voltage switch cabinet.

[0083] It should be added that the qualified cabinet data includes qualified environmental detection impact data, qualified cabinet status interference data, and qualified cabinet heating defect evaluation data; obtain and store the environmental detection impact data corresponding to the cabinet status evaluation coefficient not lower than the preset cabinet status evaluation threshold; obtain and store the cabinet status interference data corresponding to the cabinet status evaluation coefficient not lower than the preset cabinet status evaluation threshold; obtain and store the cabinet heating defect evaluation data corresponding to the cabinet heating defect evaluation coefficient not higher than the preset heating threshold; the qualified environmental detection impact data represents the environmental detection impact data corresponding to the cabinet status evaluation coefficient not lower than the preset cabinet status evaluation threshold; the qualified cabinet status interference data represents the cabinet status interference data corresponding to the cabinet status evaluation coefficient not lower than the preset cabinet status evaluation threshold; the qualified cabinet heating defect evaluation data represents the cabinet heating defect evaluation data corresponding to the cabinet heating defect evaluation coefficient not higher than the preset heating threshold.

[0084] In this embodiment, the preset heating threshold is represented by the average value of the cabinet heating defect evaluation coefficients in the historical time period.

[0085] When it is monitored that the cabinet heating defect evaluation coefficient is higher than the preset heating threshold, the preset personnel set decoupling capacitors at the preset nodes of the high-voltage switchgear according to the received prompt to reduce the capacitance coupling effect and reduce heating and electromagnetic interference; when the current at the preset position point of the outgoing line end of the monitored high-voltage switchgear is higher than the set value (such as the rated current) for setting the overload protection of the high-voltage switchgear, prompt the preset personnel to cut off the power supply to prevent the high-voltage switchgear from heating and malfunctioning due to overload, thereby improving the accuracy of the electrical state data acquisition and storage quality monitoring of the high-voltage switchgear.

[0086] As Figure 3 shown, it is a schematic diagram of the multi-data fusion state evaluation model provided by the embodiment of the present application. As Figure 3 can be seen, by storing the obtained qualified cabinet data, further performing data analysis based on the stored qualified cabinet data, or performing state parameter preprocessing on the stored qualified cabinet data, the update of the database data can be realized and the stored qualified cabinet data in the database can be used as state evaluation parameters to improve the accuracy of the operation state evaluation of the high-voltage switchgear.

[0087] In summary, by performing environmental status assessment and determining whether to perform environmental interference adjustment, then performing cabinet status interference assessment and determining whether to perform cabinet status interference adjustment, then performing cabinet defect assessment to obtain the cabinet heating defect assessment coefficient and determining whether to perform defect optimization, and finally storing the obtained qualified cabinet data, the accuracy of storing qualified cabinet data is improved, thereby improving the accuracy of electrical status data acquisition and storage quality monitoring of high-voltage switchgear, and effectively solving the problem of low accuracy of electrical status data acquisition and storage quality monitoring of high-voltage switchgear in the prior art.

[0088] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0090] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and the instructions executed on the computer or other programmable device provide for realizing the functions specified in Figure 1 one or more of the flows Figure 1Steps of the functions specified in one or more boxes.

[0092] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0093] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A data quality monitoring storage system based on equipment electrical status evaluation, characterized in that: It includes environmental status assessment module, cabinet status interference assessment module, cabinet defect assessment module and data storage module: The environmental status assessment module is used to monitor the high-voltage switchgear to obtain live detection parameters and environmental detection parameters, and to perform environmental status assessment based on the acquired data and determine whether to perform environmental interference adjustment; The cabinet state interference assessment module is used to obtain cabinet state interference data according to the acquired cabinet state parameters and qualified environment assessment coefficients, perform cabinet state interference assessment based on the cabinet state interference data, and determine whether to perform cabinet state interference adjustment; The cabinet defect assessment module is used to obtain cabinet heating defect assessment data according to the obtained cabinet defect parameters and the qualified cabinet state assessment coefficient, perform cabinet defect assessment based on the cabinet heating defect assessment data to obtain the cabinet heating defect assessment coefficient and determine whether to perform defect optimization, the qualified cabinet state assessment coefficient represents a cabinet state assessment coefficient that is not lower than a preset cabinet state assessment threshold, and the cabinet heating defect assessment coefficient is used to assess the abnormal heating condition of the high-voltage switch cabinet during the monitoring process of the high-voltage switch cabinet; The data storage module is used to store the acquired qualified cabinet data, and the qualified cabinet data is used to perform electrical status assessment; The interference influencing factor is obtained by processing the live detection parameters and the preset interference threshold; Environmental detection impact data is obtained by processing environmental detection parameters, preset environmental detection thresholds and interference impact factors; The environmental assessment coefficient is obtained by processing the environmental monitoring impact data and the preset environmental monitoring weight factors; Determine whether the obtained environmental assessment coefficient is not lower than the preset environmental interference threshold in the database; When the environmental assessment coefficient is not lower than the preset environmental interference threshold, the corresponding environmental assessment coefficient is marked as a qualified environmental assessment coefficient, otherwise the environmental interference adjustment is performed; The specific process of performing cabinet state interference evaluation based on cabinet state interference data is as follows: The cabinet state interference data is obtained by processing the environmental interference factor, the cabinet state parameter and the preset cabinet state threshold, wherein the environmental interference factor is obtained by processing the qualified environment assessment coefficient; The cabinet state evaluation coefficient is obtained by processing the cabinet state interference data and the preset cabinet state weight factor.

2. The data quality monitoring storage system based on equipment electrical status evaluation according to claim 1, characterized in that: The live detection parameters include the switch cabinet short-circuit current and the switch cabinet magnetic field strength; The environmental detection parameters include external environmental temperature, external environmental humidity and cabinet load rate; The cabinet status parameters include cabinet leakage current, cabinet shutdown time and insulation resistance; The cabinet defect parameters include the cabinet internal temperature, switch cabinet heating power and cabinet loop resistance.

3. The data quality monitoring storage system based on equipment electrical status evaluation as claimed in claim 2, characterized in that: The specific process of performing environmental status assessment based on the acquired data is as follows: The interference impact factor is used to reflect the degree of electromagnetic interference within a preset time period; The preset interference threshold includes a preset short-circuit current maximum value and a preset magnetic field intensity maximum value; The environmental detection impact data includes a temperature impact assessment value, a humidity impact assessment value and a load rate impact assessment value; The environmental detection impact data is used to reflect the impact of environmental detection parameters on interference factors; The preset environmental detection thresholds include a preset maximum external temperature, a preset maximum external humidity, and a preset maximum load rate; The preset environmental detection weight factors include a temperature weight factor, a humidity weight factor and a load rate weight factor; The environmental assessment coefficient is used to assess the environmental monitoring qualification level during the monitoring process of the high-voltage switchgear.

4. The data quality monitoring storage system based on equipment electrical status evaluation as claimed in claim 2, characterized in that: The switch cabinet short-circuit current represents the maximum value of the short-circuit current formed by the high-voltage switch cabinet circuit within a preset time period; The cabinet leakage current represents the maximum value of the current leaked by the insulating material of the high-voltage switch cabinet within a preset time period; The cabinet stop operation duration represents the maximum duration of the high-voltage switch cabinet stopping operation due to an abnormality within a preset time period.

5. The data quality monitoring storage system based on equipment electrical status evaluation as claimed in claim 3, characterized in that: The specific process of determining whether to perform environmental interference adjustment is as follows: When the environmental assessment coefficient after environmental interference adjustment is still lower than the preset environmental interference threshold, an alarm is sent to the preset personnel; The environmental interference adjustment includes anti-interference of charged detection values ​​and structural shielding.

6. The data quality monitoring storage system based on equipment electrical status evaluation as claimed in claim 2, characterized in that: The cabinet state interference data includes a leakage current evaluation value, a cabinet interruption evaluation value and a cabinet insulation evaluation value; The cabinet state interference data is used to reflect the influence of the cabinet state parameters on the environmental interference factors; The preset cabinet state thresholds include a preset maximum leakage current value, a preset maximum shutdown time value, and a preset maximum insulation resistance value; The preset cabinet state weight factors include a leakage current weight factor, a cabinet interruption weight factor and a cabinet insulation weight factor; The cabinet state evaluation coefficient is used to evaluate the cabinet state qualification degree of the high-voltage switch cabinet during the monitoring process of the high-voltage switch cabinet.

7. The data quality monitoring storage system based on equipment electrical status evaluation as claimed in claim 6, characterized in that: The specific process of determining whether to perform cabinet state interference adjustment is as follows: Determine whether the obtained cabinet state assessment coefficient is not lower than a preset cabinet state assessment threshold in the database; If the cabinet state evaluation coefficient is not lower than the preset cabinet state evaluation threshold, the corresponding cabinet state evaluation coefficient is marked as a qualified cabinet state evaluation coefficient, otherwise the cabinet state interference adjustment is performed; If the cabinet state evaluation coefficient is still lower than the preset cabinet state evaluation threshold after the cabinet state interference adjustment, an alarm is sent to the preset personnel; The cabinet state interference adjustment includes insulation defect marking and air gap adjustment.

8. The data quality monitoring storage system based on equipment electrical status evaluation as claimed in claim 2, characterized in that: The specific process of performing cabinet defect assessment based on cabinet heating defect assessment data is as follows: Obtaining a cabinet state influencing factor, wherein the cabinet state influencing factor is obtained by processing a qualified cabinet state evaluation coefficient; The cabinet heating defect assessment data is obtained by processing the cabinet state influencing factor, the cabinet defect parameter and the preset cabinet defect threshold, wherein the cabinet heating defect assessment data includes the cabinet internal temperature assessment value, the heating power assessment value and the cabinet loop resistance assessment value; The cabinet heating defect assessment data is used to reflect the degree to which the cabinet defect parameters are affected by the cabinet state influencing factors; The cabinet heating defect assessment coefficient is obtained by processing the cabinet heating defect assessment data and the preset heating defect weight factor; The preset heating defect weight factors include a cabinet internal temperature weight factor, a heating power weight factor and a cabinet loop resistance weight factor; The preset cabinet defect thresholds include a preset cabinet internal temperature maximum value, a preset heating power maximum value, and a preset cabinet loop resistance maximum value; The cabinet heating defect assessment coefficient is used to assess the abnormal heating condition of the high-voltage switch cabinet.

9. The data quality monitoring storage system based on equipment electrical status evaluation as claimed in claim 8, characterized in that: The specific process of defect optimization is as follows: Determine whether the obtained cabinet heating defect assessment coefficient is not higher than a preset heating threshold in the database; When the cabinet heating defect assessment coefficient is not higher than the preset heating threshold obtained from the database, defect optimization is not performed, otherwise defect optimization is performed; When the cabinet heating defect assessment coefficient is still higher than the preset heating threshold after defect optimization, an alarm is sent to the preset personnel; The defect optimization includes node decoupling and overload protection.

10. The data quality monitoring storage system based on equipment electrical status evaluation according to claim 1, characterized in that: The qualified cabinet data includes qualified environmental detection impact data, qualified cabinet state interference data and qualified cabinet heating defect assessment data; The qualified environmental detection impact data represents environmental detection impact data corresponding to a cabinet state assessment coefficient that is not lower than a preset cabinet state assessment threshold; The qualified cabinet state interference data represents cabinet state interference data corresponding to a cabinet state evaluation coefficient that is not lower than a preset cabinet state evaluation threshold; The qualified cabinet heating defect assessment data represents cabinet heating defect assessment data corresponding to a cabinet heating defect assessment coefficient that is not higher than a preset heating threshold.

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