Electrical test digital cooperation system

Abstract

An electrical test digital cooperation system specifically relates to the field of electrical test digital cooperation, and adopts the scheme that a data acquisition assembly is configured to acquire electrical test data through a mobile terminal and an Internet of Things sensor, and retrieve historical test data stored in a cloud database; the credibility quantification component is configured to generate a data confidence coefficient weight, and the cross-project cooperative verification component is configured to output and generate a cooperative verification index by comparing the influence of the logic consistency related to the association test of the same equipment on the data confidence coefficient weight; the intelligent early warning and closed-loop optimization assembly is configured to fuse the cooperative verification index and the trend change degree, and perform early warning and correction of environmental interference according to an early warning rule of the early warning index. The transformation of the electrical test from'paper dispersed recording 'to'digital cooperative closed loop' is realized, so that the working efficiency is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of digital collaborative technology for electrical testing, specifically a digital collaborative system for electrical testing. Background Technology

[0002] High-voltage electrical testing and relay protection calibration are core components of power equipment operation, maintenance, and acceptance. Test reports, as the carriers of results, directly influence equipment safety decisions.

[0003] Currently, the industry relies on the traditional model of paper records and manual integration, which has the following drawbacks: First, existing technologies do not consider the impact of environmental interference and differences in collection methods on data quality. If insulation resistance data recorded manually in outdoor high-humidity environments is treated the same as sensor data in laboratory constant-temperature environments, it will lead to "large differences in data credibility and no benchmark for collaborative analysis." Second, data from different test items are stored in isolation without a correlation verification mechanism. For example, if the high-voltage test group measures "normal insulation resistance" while the relay protection group measures "excessive dielectric loss," the hidden risk of "normal single parameter but abnormal combination" will be ignored because the data is not analyzed in conjunction. In addition, existing technologies rely on manual comparison of historical data, which leads to a lag in anomaly identification, and the influencing factors such as environmental interference and collection methods are difficult to dynamically adjust, resulting in "small deviations accumulating into large anomalies over a long period of time."

[0004] Therefore, those skilled in the art provide a digital collaborative system for electrical testing to address the problems mentioned in the background section. Summary of the Invention

[0005] The technical problem solved by this invention is to provide a digital collaborative system for electrical testing, so as to realize the quantification and standardization of data reliability, cross-project data logic collaborative verification, and intelligent early warning and dynamic optimization of data quality in a closed loop.

[0006] To address the above problems, the present invention provides the following technical solution: A digital collaborative system for electrical testing, comprising: The data acquisition component is configured to collect electrical test data, including acquisition method, environmental interference, and related tests of the same equipment, through mobile terminals and IoT sensors, as well as retrieve historical test data stored in the cloud database. The trustworthy measurement component is configured to combine the collection method and environmental interference to distinguish the trustworthiness of data in different scenarios and generate data confidence weights. The cross-project collaborative verification component is configured to use a deviation analysis algorithm to compare the impact of logical consistency between electrical test data and historical test data related to the same equipment in the test on the data confidence weight, and output and generate a collaborative verification index. The intelligent early warning and closed-loop optimization component is configured to integrate the trend change degree of the collaborative verification index with the key data in the historical test data to generate an early warning index, and to issue early warnings and correct environmental interferences according to the early warning rules of the early warning index.

[0007] Further: The trusted metric component includes: The environmental interference factor calculation module is configured to collect test environment parameters in real time through temperature and humidity sensors and calculate the environmental interference factor using a linear regression algorithm. The data collection method weight allocation module is configured to assign different basic weight rules to the data collection methods and generate basic weights for data confidence based on the basic weight rules. The credibility calculation module is configured to generate the data confidence weight based on the degree of influence of the environmental interference factor on the basic weight of the data confidence.

[0008] Furthermore: the test environment parameters include actual humidity, standard humidity, actual temperature, and standard temperature, and the specific process of calculating the environmental interference factor based on the test environment parameters and using a linear regression algorithm includes: Humidity deviation values ​​are generated based on the degree of deviation between actual humidity and standard humidity. A temperature deviation value is generated based on the degree of deviation between the actual temperature and the standard temperature; The humidity deviation value and temperature deviation value are weighted separately and then summed to generate the environmental interference factor.

[0009] Furthermore: The cross-project collaborative verification component includes: The parameter ratio calculation module is configured to extract physically related parameters in the current test, calculate the actual parameter ratio, where physically related parameters include insulation resistance and dielectric loss, and generate the correlation matching coefficient of the actual parameter ratio based on the ratio relationship between insulation resistance and dielectric loss. The ratio of historical parameters with physical correlation in the last three tests of the same device is retrieved from the cloud database, and the average coefficient of correlation matching relationship is calculated using the moving average algorithm; The collaborative consistency verification module is configured to combine the data confidence weights output by the trusted quantification component, use the deviation rate algorithm to calculate the degree of deviation of the correlation matching relationship coefficient from the average coefficient of the correlation matching relationship, generate the deviation rate, and generate the collaborative verification index based on the influence of the product of the deviation rate and the data confidence weights.

[0010] Furthermore: the intelligent early warning and closed-loop optimization component includes: The trend deviation analysis module is configured to perform trend fitting on the key data of the same device over the past 12 months and generate a trend deviation rate. The trend deviation rate is combined with the collaborative verification index output by the cross-project collaborative verification component to generate the early warning index. The graded early warning and optimization module is configured to determine whether to correct the environmental interference factor and whether to issue an early warning based on the early warning rules. Specifically, the key data is insulation resistance.

[0011] Furthermore: the data collection methods include three types: manual input, automatic sensor data collection and recognition, and photographic evidence. The specific basic weighting rules are as follows: Manual input is set to 0.6; The sensor's automatic data acquisition setting is 0.8; The sensor's automatic data acquisition, combined with automatic identification and photographic evidence, is set to 0.9.

[0012] Furthermore: the warning rules include: When the warning index is greater than or equal to the preset warning threshold, it is determined to be in warning mode, and a high-risk warning signal is pushed to the test team through the mobile terminal APP, and a rectification work order is automatically generated. When the warning index is less than the preset warning threshold, it is determined to enter the correction mode. Based on the warning index, an adjustment factor is generated and directly input into the environmental interference factor calculation module. The adjustment factor is multiplied with the environmental interference factor to generate the corrected environmental interference factor.

[0013] Furthermore: the cloud database is configured as follows: It receives real-time electrical test data from mobile terminals and IoT sensors via 5G and WiFi, and stores it in a MySQL database. It also supports multi-team permission management, allowing test teams, including high-voltage test teams and relay protection calibration teams, to access data confidence weights, collaborative verification indices, and early warning indices across teams.

[0014] The effects of the above solution are as follows: 1. The reliability quantification component of this invention transforms the temperature and humidity deviations of environmental interference into calculable environmental interference factors to achieve data reliability assessment through "environmental normalization". By assigning differentiated weights to different collection methods, it solves the problem of "treating manual and automatic data equally" and makes the generated data confidence base weight a "data quality label" for cross-team collaboration. This provides a unified reliability benchmark for distributed data and reduces data entry errors.

[0015] 2. The cross-project collaborative verification component of this invention transforms isolated key data into physical correlation indicators and combines them with historical test data in the cloud database to output and generate a collaborative verification index, thereby realizing the "logical linkage between the data of the high-voltage test group and the relay protection group". In this way, by comparing the ratio of physical parameters with historical data, a cross-project collaborative verification algorithm is constructed, which upgrades the data of multiple teams from "isolated storage" to "logical correlation", thereby improving the efficiency of anomaly identification.

[0016] 3. The intelligent early warning and closed-loop optimization component of this invention integrates horizontal collaborative deviation and vertical trend deterioration to achieve comprehensive early warning of "static data + dynamic trend". When the preset early warning threshold is not reached, the component adjusts the environmental interference factor to force the optimization of the data collection environment on site, forming a closed loop of "early warning - adjustment - data quality improvement". This links the early warning index with the environmental interference factor to achieve dynamic collaboration of the entire process of "risk early warning - environmental optimization - data reliability", thereby improving the acceptance quality assurance capability of the equipment. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall framework of the digital collaborative system for electrical testing; Figure 2 This is a schematic diagram of the framework of the trusted measurement component in this invention; Figure 3 This is a schematic diagram of the framework of the cross-project collaborative verification component in this invention; Figure 4 This is a schematic diagram of the framework of the intelligent early warning and closed-loop optimization component in this invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0019] Example 1, please refer to Figures 1-4 A digital collaborative system for electrical testing, comprising: The data acquisition component is configured to collect electrical test data, including acquisition method, environmental interference, and related tests of the same equipment, through mobile terminals and IoT sensors, as well as retrieve historical test data stored in the cloud database. The trustworthy measurement component is configured to combine the collection method and environmental interference to distinguish the trustworthiness of data in different scenarios and generate data confidence weights. The cross-project collaborative verification component is configured to use a deviation analysis algorithm to compare the impact of logical consistency between electrical test data and historical test data related to the same equipment in the test on the data confidence weight, and output and generate a collaborative verification index. The intelligent early warning and closed-loop optimization component is configured to integrate the trend change degree of the collaborative verification index and key data in historical test data to generate an early warning index, and to issue early warnings and correct environmental interferences based on the early warning rules of the early warning index.

[0020] The cloud database is configured as follows: It receives real-time electrical test data from mobile terminals and IoT sensors via 5G and WiFi, and stores it in a MySQL database. It also supports multi-team permission management, allowing test teams, including high-voltage test teams and relay protection calibration teams, to access data confidence weights, collaborative verification indices, and early warning indices across teams.

[0021] In the above embodiments, this system achieves closed-loop management of the entire process through distributed acquisition, cloud collaboration, and intelligent algorithms of data acquisition components, reliable quantification components, cross-project collaborative verification components, and intelligent early warning and closed-loop optimization components. Specifically, the data acquisition component collects electrical test data via mobile terminals and IoT sensors, supporting multiple input methods and retrieval of historical test data. When the high-voltage test group conducts outdoor field tests, test personnel use a tablet APP equipped with the Vue3 front-end framework to input data. The APP automatically associates with the device ID (#2 main transformer), supports taking photos (such as photos of dielectric loss value tester readings), and voice annotations. Meanwhile, the deployed temperature and humidity sensors... Sensors (collecting environmental parameters) and intelligent high-voltage probes (collecting insulation resistance and dielectric loss values ​​in real time) transmit data to the cloud via 5G and WiFi. The relay protection team can retrieve historical protection settings of the equipment (such as overcurrent protection setting of 5A) through the system without having to go to the archives. The high-voltage test team can only modify test data, and the relay protection team can only view related parameters. The administrator has data review permissions (based on JWT permission control on the Spring Boot 3 backend). After the test is completed, the data is written to the database in real time. The relay protection team can access the system through a browser in the office and directly retrieve the historical insulation resistance data of the #2 main transformer (average of 4800MΩ for the last 3 tests) without waiting for the high-voltage team to send an email.

[0022] Example 2, as Figure 1 and Figure 2 As shown, based on Embodiment 1, the trusted quantification component provided in this embodiment of the invention includes: The environmental interference factor calculation module is configured to collect test environment parameters in real time through temperature and humidity sensors and calculate the environmental interference factor using a linear regression algorithm. The test environment parameters include actual humidity, standard humidity, actual temperature, and standard temperature. The specific process for calculating the environmental interference factor based on these parameters using a linear regression algorithm includes: Humidity deviation values ​​are generated based on the degree of deviation between actual humidity and standard humidity. A temperature deviation value is generated based on the degree of deviation between the actual temperature and the standard temperature; The humidity deviation value and the temperature deviation value are weighted separately and then summed to generate the environmental interference factor; The data collection method weight allocation module is configured to assign different basic weight rules to the data collection methods and generate basic weights for data confidence based on the basic weight rules. The confidence calculation module is configured to generate data confidence weights based on the degree of influence of environmental interference factors on the basic weights of data confidence.

[0023] In the above embodiments, the confidence quantification component supports the "IoT sensor acquisition" function. It dynamically adjusts the basic weight of data confidence through experimental environment parameters, thereby achieving "environmental normalization" data preprocessing. The specific formula for calculating the data confidence weight is as follows: ; ; In the formula: ZQ represents the data confidence weight. base Here, HX represents the basic weight for data confidence, S represents the environmental interference factor, and S represents the actual humidity. std Where W is the standard humidity and W is the actual temperature. std This is the standard temperature.

[0024] The calculation can dynamically correct the reliability of data under different test scenarios (such as outdoor high humidity and indoor constant temperature), avoiding misjudgment of the trust level of data from the same equipment by multiple teams (such as high voltage test group and relay protection calibration group) due to "environmental differences", and providing reliable data input with "environmental normalization" for the centralized platform. The calculations then provide "data quality labels" for multi-team collaboration: High-confidence data (data confidence weight ZQ greater than 0.8) can be directly used for cross-project analysis; Low-confidence data (data confidence weight ZQ less than 0.6) requires manual review to address the issues of inaccurate paper records and untraceable data.

[0025] The data collection methods include three types: manual input, automatic sensor data collection and recognition, and photographic evidence. The specific basic weighting rules are as follows: Manual input is set to 0.6; The sensor's automatic data acquisition setting is 0.8; The sensor's automatic data acquisition, combined with automatic identification and photographic evidence, is set to 0.9.

[0026] It is worth noting that electrical testing sites contain a large number of "non-fixed installation instruments" (such as portable megohmmeters and on-site handwritten records), making it difficult to rely entirely on automatic data acquisition from sensors. The flexibility of manual data entry must be retained, but if the data confidence level is based on a weight ZQ... base Because the system's data entry accuracy remained at 0.6 due to manual input, on-site personnel might resist using the manual input function due to concerns about "low data reliability and the need for repeated verification," resulting in insufficient data coverage. By increasing the accuracy to 0.8, "automatic recognition + photo verification" endowed manual input with the attributes of "quasi-automatic collection." This retained the convenience of manual operation (adapting to complex on-site environments) while using technical means to compensate for reliability shortcomings. Furthermore, through a weighted gradient design, a gradual digital transformation from "purely manual → enhanced manual → fully automatic collection" was achieved, avoiding system compatibility issues caused by technological iterations and ensuring the continuity of long-term collaborative capabilities.

[0027] Example 3, as Figure 1 and Figure 3 As shown, based on Embodiment 2, the cross-project collaborative verification component provided in this embodiment of the invention includes: The parameter ratio calculation module is configured to extract physically related parameters in the current test, calculate the actual parameter ratio, where physically related parameters include insulation resistance and dielectric loss, and generate the correlation matching coefficient of the actual parameter ratio based on the ratio relationship between insulation resistance and dielectric loss. The ratio of historical parameters with physical correlation in the last three tests of the same device is retrieved from the cloud database, and the average coefficient of correlation matching relationship is calculated using the moving average algorithm; The collaborative consistency verification module is configured to combine the data confidence weights output by the trusted quantification component, use the deviation rate algorithm to calculate the degree of deviation of the correlation matching coefficient from the average coefficient of the correlation matching, generate the deviation rate, and generate the collaborative verification index based on the effect of the product of the deviation rate and the data confidence weights.

[0028] In the above embodiments, independent data such as high-voltage tests and relay protection calibrations are transformed into physical correlation indicators by calculating the correlation matching coefficient. Combined with the average correlation matching coefficient in the cloud database, the logical consistency of the data is quantified, thereby supporting the "data analysis engine" function of the "centralized test report management platform." For example, when the insulation resistance measured by the high-voltage test group is normal but the dielectric loss value of the relay protection group exceeds the standard, the correlation matching coefficient deviates significantly from the average correlation matching coefficient, leading to a decrease in the collaborative verification index and triggering cross-team review. Deviation analysis using the data confidence weight ZQ can improve the accuracy of collaborative decision-making, thereby achieving the effect of "multi-location collaborative work, reducing personnel travel." The collaborative verification index automatically filters high-value collaborative data, reducing ineffective communication. The specific calculation formula for the collaborative verification index is as follows: ; P = X / Y; In the formula: KJ is the co-validation index, and P is the correlation matching coefficient. ref X represents the average coefficient of the correlation matching relationship, where X is the current test item - insulation resistance, and Y is the associated item - dielectric loss value.

[0029] It's worth noting that the calculation of P=X / Y transforms isolated single-item data (such as Team A measuring insulation resistance and Team B measuring dielectric loss) into a "correlation ratio"—a correlation matching coefficient P. This provides a unified benchmark for cross-team data comparison, avoiding the hidden risk of "normal single parameter but abnormal combination" (such as normal insulation resistance but excessive dielectric loss, indicating actual insulation aging). The calculated deviation rate can identify "logical anomalies" in cross-project data—when the deviation rate is >10%, it prompts multiple teams to review the data (e.g., "Poor coordination between insulation resistance and dielectric loss values; it is recommended that Team A review the insulation resistance measurement and Team B review the dielectric loss instrument"). This solves the pain point of "separate reports for different test items, making it impossible to conduct joint analysis." The calculation provides a "collaborative quality basis" for intelligent early warning, including: A cooperation verification index (KJ) greater than 0.8 indicates excellent cooperation. A cooperation verification index (KJ) of less than 0.6 indicates poor cooperation and requires close attention. In summary, the cross-project collaborative verification component achieves a digital collaborative closed loop of "data collection → cross-team verification".

[0030] Example 4, as Figure 1 and Figure 4 As shown, based on Embodiment 3, the intelligent early warning and closed-loop optimization component provided in this embodiment of the invention includes: The trend deviation analysis module is configured to perform trend fitting on key data of the same device over the past 12 months and generate a trend deviation rate. The trend deviation rate is combined with the collaborative verification index output by the cross-project collaborative verification component to generate an early warning index. The graded early warning and optimization module is configured to determine whether to correct environmental interference factors and whether to issue an early warning based on early warning rules. In the above embodiments, the intelligent early warning and closed-loop optimization components can reflect the horizontal collaborative deviation (cross-project consistency) of the current data based on the collaborative verification index, and can reflect the vertical trend deterioration by combining the trend deviation rate. This transforms implicit risks into quantifiable early warning indices to support the "data analysis engine in automatically identifying outliers and providing intelligent early warnings." The specific calculation formula for the early warning index is as follows: ; ; In the formula: WW is the early warning index, D trend X represents the trend deviation rate. avg This represents the historical average insulation resistance.

[0031] It is worth noting that The calculation supplements cross-project collaborative verification from the "vertical time dimension," avoiding missed judgments due to "current data being normal but the trend deteriorating" (e.g., insulation resistance decreasing by 10% annually, currently still within the acceptable range, but will exceed the standard in 3 years), thereby achieving a comprehensive risk assessment of "static data + dynamic trends." The calculation quantifies the degree of logical deviation of cross-project data, transforming "coordination consistency" into "risk contribution"—the higher the degree of coordination deviation, the greater the early warning risk, providing "horizontal coordination risk weights" for subsequent early warning index calculations. The overall calculation provides "quantitative risk signals" for multiple teams - automatically pushes "risk point location" when an early warning is issued, solving the problems of "cumbersome manual comparison of historical data and delayed anomaly identification".

[0032] In summary, the intelligent early warning and closed-loop optimization component combines real-time data collected by IoT sensors with collaborative verification results. When the early warning index exceeds the early warning threshold, the component pushes an early warning to the mobile terminal through a centralized platform, enabling early detection of equipment defects.

[0033] Example 5, as Figures 1-4 As shown, based on Embodiment 4, the early warning rules provided in this embodiment of the invention include: When the warning index is greater than or equal to the preset warning threshold, it is determined to be in warning mode, and a high-risk warning signal is pushed to the test team through the mobile terminal APP, and a rectification work order is automatically generated. When the warning index is less than the preset warning threshold, it is determined to enter the correction mode. Based on the warning index, an adjustment factor is generated and directly input into the environmental interference factor calculation module. The adjustment factor is multiplied with the environmental interference factor to generate the corrected environmental interference factor.

[0034] In the above embodiments, by using t=WW / 1000, the potential risk of not triggering a warning is converted into a fine-tuning amount of the environmental interference factor HX, where t is an adjustment factor, making the environmental interference factor HX more closely match the actual risk, for example: When the warning index WW remains around 40 due to prolonged slight humidity exceeding the standard, the environmental interference factor HX is gradually increased after the adjustment factor t is added, while the data confidence weight ZQ decreases accordingly. This suggests optimizing the on-site data collection environment (such as turning on dehumidifiers). Thus, the technical goal of "dynamically improving data acquisition quality" was achieved through "fine-tuning → gradual optimization," avoiding the accumulation of small deviations into large anomalies due to environmental interference.

[0035] In addition, when the warning index is greater than or equal to the preset warning threshold, the system automatically identifies the associated teams (such as the high-voltage testing team, relay protection calibration team, and equipment maintenance team) and automatically generates a "rectification work order" through the mobile terminal APP. This order includes an anomaly description (such as poor coordination between insulation resistance and dielectric loss, P=0.3), suggested measures, responsible team, and completion deadline. After rectification, newly collected data needs to be verified twice by P=X / Y (P≥0.8 is required for closed-loop operation). The anomaly and rectification results also need to be entered into the equipment digital twin model, and the average coefficient P of the correlation matching relationship needs to be updated. ref and historical average insulation resistance X avg This will prevent future misjudgments of similar anomalies and enable full-process digital traceability of "anomaly detection → rectification → verification," thus solving the problem of "difficulty in tracking rectification of paper records."

[0036] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A digital collaborative system for electrical testing, characterized in that, Include: The data acquisition component is configured to collect electrical test data, including acquisition method, environmental interference, and related tests of the same equipment, through mobile terminals and IoT sensors, as well as retrieve historical test data stored in the cloud database. The trustworthy measurement component is configured to combine the collection method and environmental interference to distinguish the trustworthiness of data in different scenarios and generate data confidence weights. The cross-project collaborative verification component is configured to use a deviation analysis algorithm to compare the impact of logical consistency between electrical test data and historical test data related to the same equipment in the test on the data confidence weight, and output and generate a collaborative verification index. The intelligent early warning and closed-loop optimization component is configured to integrate the trend change degree of the collaborative verification index with the key data in the historical test data to generate an early warning index, and to issue early warnings and correct environmental interferences according to the early warning rules of the early warning index.

2. The digital collaborative system for electrical testing according to claim 1, characterized in that, The trusted metric component includes: The environmental interference factor calculation module is configured to collect test environment parameters in real time through temperature and humidity sensors and calculate the environmental interference factor using a linear regression algorithm. The data collection method weight allocation module is configured to assign different basic weight rules to the data collection methods and generate basic weights for data confidence based on the basic weight rules. The credibility calculation module is configured to generate the data confidence weight based on the degree of influence of the environmental interference factor on the basic weight of the data confidence.

3. The digital collaborative system for electrical testing according to claim 2, characterized in that, The test environment parameters include actual humidity, standard humidity, actual temperature, and standard temperature. The specific process of calculating the environmental interference factor based on these test environment parameters using a linear regression algorithm includes: Humidity deviation values ​​are generated based on the degree of deviation between actual humidity and standard humidity; A temperature deviation value is generated based on the degree of deviation between the actual temperature and the standard temperature; The humidity deviation value and temperature deviation value are weighted separately and then summed to generate the environmental interference factor.

4. The digital collaborative system for electrical testing according to claim 1, characterized in that, The cross-project collaborative verification component includes: The parameter ratio calculation module is configured to extract physically related parameters in the current test, calculate the actual parameter ratio, where physically related parameters include insulation resistance and dielectric loss, and generate the correlation matching coefficient of the actual parameter ratio based on the ratio relationship between insulation resistance and dielectric loss. The historical parameter ratios with physical correlation in the last three tests of the same device are retrieved from the cloud database, and the average coefficient of the correlation matching relationship is calculated using the moving average algorithm. The collaborative consistency verification module is configured to combine the data confidence weights output by the trusted quantification component, use the deviation rate algorithm to calculate the degree of deviation of the correlation matching relationship coefficient from the average coefficient of the correlation matching relationship, generate the deviation rate, and generate the collaborative verification index based on the influence of the product of the deviation rate and the data confidence weights.

5. The digital collaborative system for electrical testing according to claim 2, characterized in that, The intelligent early warning and closed-loop optimization component includes: The trend deviation analysis module is configured to perform trend fitting on the key data of the same device over the past 12 months and generate a trend deviation rate. The trend deviation rate is combined with the collaborative verification index output by the cross-project collaborative verification component to generate the early warning index. The graded early warning and optimization module is configured to determine whether to correct the environmental interference factor and whether to issue an early warning based on the early warning rules. Specifically, the key data is insulation resistance.

6. The digital collaborative system for electrical testing according to claim 3, characterized in that, The data collection methods include three types: manual input, automatic sensor data collection and recognition, and photographic evidence. The specific basic weighting rules are as follows: Manual input is set to 0.6; The sensor's automatic data acquisition setting is 0.8; The sensor's automatic data acquisition, combined with automatic identification and photographic evidence, is set to 0.

9.

7. The digital collaborative system for electrical testing according to claim 5, characterized in that, The warning rules include: When the warning index is greater than or equal to the preset warning threshold, it is determined to be in warning mode, and a high-risk warning signal is pushed to the test team through the mobile terminal APP, and a rectification work order is automatically generated. When the warning index is less than the preset warning threshold, it is determined to enter the correction mode. Based on the warning index, an adjustment factor is generated and directly input into the environmental interference factor calculation module. The adjustment factor is multiplied with the environmental interference factor to generate the corrected environmental interference factor.

8. The digital collaborative system for electrical testing according to claim 7, characterized in that, The cloud database is configured as follows: It receives real-time electrical test data from mobile terminals and IoT sensors via 5G and WiFi, and stores it in a MySQL database. It also supports multi-team permission management, allowing test teams, including high-voltage test teams and relay protection calibration teams, to access data confidence weights, collaborative verification indices, and early warning indices across teams.

Images