CN-121998393-A - Digital cooperative system for electrical test

CN121998393ACN 121998393 ACN121998393 ACN 121998393ACN-121998393-A

Abstract

The invention relates to an electric test digital collaboration system, in particular to the electric test digital collaboration field, and the scheme comprises a data acquisition component, a reliability quantification component, a cross-item collaboration verification component, an intelligent early warning and closed-loop optimization component and a data analysis engine, wherein the data acquisition component is configured to acquire electric test data through a mobile terminal and an Internet of things sensor, and call historical test data stored in a cloud database, the reliability quantification component is configured to generate data confidence weight, the cross-item collaboration verification component is configured to output and generate a collaboration verification index by comparing the influence of logic consistency related to the same equipment association test on the data confidence weight, the intelligent early warning and closed-loop optimization component is configured to fuse the collaboration verification index and trend change degree, and early warning and environmental interference correction are carried out according to early warning rules of the early warning index.

Inventors

JIANG ZHIYONG
ZENG LIN
LIU QIANLI

Assignees

九江检安石化工程有限公司

Dates

Publication Date: 20260508
Application Date: 20251226

Claims (8)

1. An electrical test digital collaboration system, comprising: The data acquisition component is configured to acquire electric test data related to the same equipment association test in an acquisition mode, environmental interference and the like through the mobile terminal and the Internet of things sensor, and call historical test data stored in the cloud database; the credibility quantifying component is configured to distinguish the credibility of the data under different scenes and generate a data credibility weight by combining the acquisition mode with the environmental interference; the cross-project collaborative verification component is configured to compare the influence of logic consistency related to the same equipment association test in the electrical test data and the historical test data on the data confidence coefficient weight through a deviation analysis algorithm, and output and generate a collaborative verification index; The intelligent early warning and closed loop optimization component is configured to fuse the cooperative check index and the trend change degree of the key data in the historical test data, generate an early warning index, and perform early warning and environmental interference correction according to an early warning rule of the early warning index.
2. The digital synergy system of electrical testing of claim 1, wherein said reliability quantifying component comprises: the environment interference factor calculation module is configured to collect test environment parameters in real time through the temperature and humidity sensor and calculate environment interference factors by adopting a linear regression algorithm; the acquisition mode weight distribution module is configured to assign different basic weight rules to the acquisition modes and generate data confidence coefficient basic weights according to the basic weight rules; and the credibility calculation module is configured to generate the data confidence coefficient weight according to the influence degree of the environmental interference factor on the data confidence coefficient basic weight.
3. The digital collaboration system of claim 2, wherein the test environmental 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 environmental parameters and using a linear regression algorithm comprises: Generating a humidity deviation value based on the deviation degree of the actual humidity and the standard humidity; generating a temperature deviation value based on the deviation degree of the actual temperature and the standard temperature; And respectively weighting the humidity deviation value and the temperature deviation value, and then adding to generate the environment interference factor.
4. The digital collaboration system of claim 1, wherein the cross-project collaboration verification assembly comprises: The parameter ratio calculation module is configured to extract parameters with physical relevance in the current test, calculate an actual parameter ratio, wherein the parameters with physical relevance comprise insulation resistance and dielectric loss value, and generate an association matching relation coefficient which is the actual parameter ratio according to the ratio relation of the insulation resistance and the dielectric loss value; The historical parameter ratio with physical relevance in the near 3 experiments of the same equipment is called from the cloud database, and the average coefficient of the relevance matching relation is calculated by adopting a moving average algorithm; The collaborative consistency verification module is configured to calculate the deviation degree of the incidence matching relation coefficient on the incidence matching relation average coefficient by adopting a deviation rate algorithm in combination with the data confidence coefficient output by the reliability quantization component, generate a deviation rate, and generate the collaborative verification index according to the product influence of the deviation rate on the data confidence coefficient weight.
5. The digital synergy system of electrical testing of claim 2, wherein the intelligent early warning and closed loop optimization component comprises: The trend deviation analysis module is configured to perform trend fitting on the key data of the same equipment for about 12 months and generate a trend deviation rate, and 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 grading early warning and optimizing module is configured to judge whether to correct the environmental interference factor and whether to perform early warning according to the early warning rule; Wherein the key data is specifically insulation resistance.
6. The digital collaboration system of electrical tests according to claim 3, wherein the collection modes include three modes of manual input, automatic sensor collection, automatic identification and photographing evidence, and the basic weight rules are specifically as follows: setting the manual input to 0.6; The automatic acquisition of the sensor is set to 0.8; the sensor automatic acquisition combines automatic identification and photographing evidence setting to 0.9.
7. The digital collaboration system of electrical tests of claim 5, wherein the pre-warning rules comprise: When the early warning index is larger than or equal to a preset early warning threshold value, judging that an early warning mode is carried out, pushing a high-risk early warning signal to a test team through a mobile terminal APP, and automatically generating a rectification work order; When the early warning index is smaller than a preset early warning threshold, judging that a correction mode is performed, generating an adjustment factor according to the early warning index, directly inputting the adjustment factor into the environmental interference factor calculation module, and multiplying the adjustment factor by the environmental interference factor to generate a corrected environmental interference factor.
8. The electrical test digital collaboration system of claim 7, wherein the cloud database is configured to: The method comprises the steps of receiving real-time electric test data of a mobile terminal and an Internet of things sensor through 5G and WiFi, storing the electric test data into a MySQL database, supporting multi-team authority management, and allowing test teams including a high-voltage test team and a relay protection adjustment team to access data confidence weight, a collaborative check index and an early warning index of the cross teams.

Description

Digital cooperative system for electrical test Technical Field The invention relates to the technical field of digital coordination of electrical tests, in particular to a digital coordination system of electrical tests. Background The electric high-voltage test and relay protection adjustment are core links of operation, maintenance and acceptance of the power equipment, and the test report is a result carrier and directly influences the safety decision of the equipment. At present, the current industry relies on a traditional mode of paper recording and manual integration, and further has the defects that firstly, the influence of environmental interference and acquisition mode difference on data quality is not considered in the prior art, if insulation resistance data manually recorded in an outdoor high-humidity environment are treated equally with sensor data in a laboratory constant-temperature environment, the data reliability difference is large, collaborative analysis is free of references, and secondly, data of different test projects are stored in isolation and have no associated verification mechanism, wherein if a high-voltage test group detects that the insulation resistance is normal, a relay group detects that the dielectric loss value exceeds standard, but the data is not analyzed in a linkage mode, the hidden risk of single parameter is normal but combined abnormality is ignored, in addition, the prior art relies on manual comparison historical data, abnormality identification is lagged, influence factors such as environmental interference and acquisition mode are difficult to dynamically adjust mechanisms, and small deviation is accumulated as large abnormality for a long term. Accordingly, one skilled in the art would provide an electrical test digital synergistic system to solve the problems set forth in the background above. Disclosure of Invention The invention solves the technical problem of providing an electric test digital cooperative system to realize quantification and standardization of data reliability, cooperative verification of cross-project data logic, intelligent early warning and dynamic optimization of data quality. In order to solve the problems, the invention provides the following technical scheme: an electrical test digital collaboration system, comprising: The data acquisition component is configured to acquire electric test data related to the same equipment association test in an acquisition mode, environmental interference and the like through the mobile terminal and the Internet of things sensor, and call historical test data stored in the cloud database; the credibility quantifying component is configured to distinguish the credibility of the data under different scenes and generate a data credibility weight by combining the acquisition mode with the environmental interference; the cross-project collaborative verification component is configured to compare the influence of logic consistency related to the same equipment association test in the electrical test data and the historical test data on the data confidence coefficient weight through a deviation analysis algorithm, and output and generate a collaborative verification index; The intelligent early warning and closed loop optimization component is configured to fuse the cooperative check index and the trend change degree of the key data in the historical test data, generate an early warning index, and perform early warning and environmental interference correction according to an early warning rule of the early warning index. Further, the reliability quantization component comprises: the environment interference factor calculation module is configured to collect test environment parameters in real time through the temperature and humidity sensor and calculate environment interference factors by adopting a linear regression algorithm; the acquisition mode weight distribution module is configured to assign different basic weight rules to the acquisition modes and generate data confidence coefficient basic weights according to the basic weight rules; and the credibility calculation module is configured to generate the data confidence coefficient weight according to the influence degree of the environmental interference factor on the data confidence coefficient basic weight. Further, the test environment parameters comprise actual humidity, standard humidity, actual temperature and standard temperature, and the specific process for calculating the environment interference factor based on the test environment parameters and by adopting a linear regression algorithm comprises the following steps: Generating a humidity deviation value based on the deviation degree of the actual humidity and the standard humidity; generating a temperature deviation value based on the deviation degree of the actual temperature and the standard temperature; And respectively weighting the humidity deviation value and the temperature