CN117723818A - Voltage detection system based on server test - Google Patents
Voltage detection system based on server test Download PDFInfo
- Publication number
- CN117723818A CN117723818A CN202311667143.3A CN202311667143A CN117723818A CN 117723818 A CN117723818 A CN 117723818A CN 202311667143 A CN202311667143 A CN 202311667143A CN 117723818 A CN117723818 A CN 117723818A
- Authority
- CN
- China
- Prior art keywords
- module
- data
- server
- voltage
- test
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 46
- 238000001514 detection method Methods 0.000 title claims abstract description 33
- 238000004364 calculation method Methods 0.000 claims abstract description 29
- 230000005540 biological transmission Effects 0.000 claims abstract description 20
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000000034 method Methods 0.000 claims description 24
- 238000004140 cleaning Methods 0.000 claims description 23
- 238000007405 data analysis Methods 0.000 claims description 18
- 238000007781 pre-processing Methods 0.000 claims description 18
- 238000012423 maintenance Methods 0.000 claims description 14
- 238000012549 training Methods 0.000 claims description 13
- 230000005856 abnormality Effects 0.000 claims description 10
- 238000013500 data storage Methods 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 10
- 238000005457 optimization Methods 0.000 claims description 10
- 238000012795 verification Methods 0.000 claims description 10
- 238000001914 filtration Methods 0.000 claims description 9
- 230000006698 induction Effects 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000013480 data collection Methods 0.000 claims description 6
- 238000013461 design Methods 0.000 claims description 6
- 238000007619 statistical method Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000003066 decision tree Methods 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000003111 delayed effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000017525 heat dissipation Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Testing Electric Properties And Detecting Electric Faults (AREA)
Abstract
The invention relates to the technical field of server testing and discloses a voltage detection system based on server testing, which comprises a data acquisition and transmission module and a data test processing module, wherein the data acquisition and transmission module acquires and converts voltage, so that a voltage signal is converted into a digital signal which can be processed by a server and is transmitted to the server module, the data acquisition module comprises the voltage acquisition module and the data acquisition module, the data test processing module comprises the server module and a power calculation module, the server module receives the transmitted digital signal and converts the digital signal into corresponding voltage data, and the power calculation module is arranged.
Description
Technical Field
The invention relates to the technical field of server testing, in particular to a voltage detection system based on server testing.
Background
With the continuous progress of technology, the new generation of information technology of 5G and cloud computing is rapidly developed, but these technologies are not separated from a large amount of data processing, and when a server processes a large amount of data, in order to ensure that the power supply of the server operates normally, voltage detection is usually required to be performed on a power supply mechanism of the server.
In the process of detecting voltage, voltage signals are usually required to be converted into digital signals, so that a computer and a server can read and process the signals, and the power supply is monitored in real time, so that when the power supply fails, the voltage is unstable and the power overload problem occurs, a timely warning can be provided, and corresponding processing is performed.
The existing voltage detection system based on the server test usually detects immediately by data transmission, and when the server is used in a peak time period, the excessive load of the server is easily caused, the overall operation efficiency of the server is affected, the working efficiency of voltage detection is delayed, and when the existing voltage detection system based on the server test is considered, the abnormal voltage data is usually detected and then is alarmed, so that workers are reminded to maintain and detect, but a scheme and a direction for solving the problem cannot be provided for the workers, so that the workers with less working experience cannot quickly solve the hardware problem of the server in the first time.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a voltage detection system based on server test, which solves the problems that the excessive load of a server is easily caused when the voltage detection is detected during the use of the server in the peak time period, the overall operation efficiency of the server is influenced and the working efficiency of the voltage detection is delayed.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the voltage detection system based on the server test comprises a data acquisition and transmission module and a data test processing module, wherein the data acquisition and transmission module acquires and converts voltage, converts a voltage signal into a digital signal which can be processed by a server, and transmits the converted digital signal to the server module, and the data acquisition module comprises a voltage acquisition module, a data acquisition module, a maintenance and cleaning module, a preprocessing module, a transmission module and an encryption module;
the voltage acquisition module is connected with the data acquisition module, the data acquisition module processes the acquired and stored data through the maintenance and cleaning module, the preprocessing module processes the acquired and stored voltage data and comprises filtering, noise elimination and calibration, the transmission module transmits the acquired and stored data processed through the preprocessing module, the preprocessed acquired and stored data are transmitted to the server module, and the encryption module encrypts the digital signal in the transmission process;
the data test processing module comprises a server module, a power calculation module, a data storage module, a data analysis module, a statistical method module, an alarm module A, AI prediction module, a database module and a report generation module;
the server module receives the transmitted digital signals and converts the digital signals into corresponding voltage data, the power calculation module calculates and detects the power of the server, the data storage module stores the voltage data, the data analysis module learns and predicts the derived voltage data through the statistical method module in the process of detecting the derived voltage data, the alarm module A can send out an alarm to remind staff, the AI prediction module can identify a voltage abnormality mode, and the database module can store a large number of voltage data abnormality cases.
Preferably, the voltage acquisition module acquires voltage data from power equipment powered by the server, and the data acquisition module converts the voltage data acquired by the voltage acquisition module to enable the voltage data to be converted into digital signals.
Preferably, the maintenance cleaning module comprises a timing cleaning module and an identification and processing repeated data module, wherein the timing cleaning module determines the cleaning frequency according to the quality, type and collection frequency of data, and can clean the data once per week, and the identification and processing repeated data module is used for merging or deleting the same or very similar data items in the data.
Preferably, the power calculation module comprises an ADC analog-to-digital conversion module, a digital filtering module, a system calibration module and a heat induction module, wherein the ADC analog-to-digital conversion module can analyze and process sound, light intensity and temperature, the digital filtering module enables the power calculation module to filter noise when detecting server power, the system calibration module calibrates the system to reduce errors of the system in the power detection process, and the heat induction module can sense the temperature of the power calculation module and start heat dissipation through the heat induction module to reduce the temperature of the power calculation module.
Preferably, the AI prediction module comprises a data collection and preprocessing module, a feature selection module, a model design and training module, a model verification and test module, a model deployment module and a system evaluation and optimization module, wherein historical data is stored in the data collection and preprocessing module, the historical data comprises data of a normal running state and data when faults occur, and can be collected through various sensors, log files and maintenance reports, and then necessary preprocessing work is carried out.
Preferably, the data analysis module is divided into two groups after detecting the voltage data, when the voltage data is abnormal, the data analysis module starts the alarm module A, so that the alarm module A sends out a harsh alarm to remind a worker of finding out the problem of the power supply equipment at the first time, and when the voltage data is normal, the report generation module is started to generate a report for the voltage data and print.
Preferably, the feature selection module is used for selecting a large amount of data so as to determine the feature valuable for fault prediction, and the model design and training module comprises a neural network, a decision tree and a support vector machine and uses the collected historical data for training.
Preferably, the model verification and test module needs to verify and test the performance of the model through a verification set and a test set after model training is completed, which can help us evaluate the accuracy, recall rate and ROC curve index of the model, and further confirm whether the model has acceptable performance.
Preferably, the model deployment module can deploy the model into a real-time system after the model feedback is normal, in the real-time system, the model collects and processes the latest system operation data in real time, predicts the probability of possible faults, and sends out early warning when the probability exceeds a threshold value.
Preferably, the system evaluation and optimization module performs evaluation and optimization periodically by the system evaluation and optimization module after the model is operated for a period of time.
(III) beneficial effects
The invention provides a voltage detection system based on server testing. The beneficial effects are as follows:
(1) When the voltage detection system based on the server test is used, the power calculation module is arranged, so that when the server is in a peak period, voltage data are transmitted to the data storage module for temporary storage, and after the power of the server is gradually reduced to a proper standard along with the peak time, the voltage data stored in the data storage module are exported and detected, thereby achieving the purpose of peak staggering processing, avoiding damage caused by power overload of the server, and not delaying detection of the voltage data.
(2) When the voltage detection system based on the server test is used, through the AI prediction module and the database module, the voltage abnormality mode can be identified, the possible problem can be predicted, and early warning can be sent out timely, so that the problem can be solved before the problem is serious, and after the voltage abnormality occurs, the system can be conveniently connected to the solution provided by the system while the staff is connected to the alarm.
(3) When the voltage detection system based on the server test is used, the maintenance cleaning module is arranged, so that a large amount of data in the data acquisition module is cleaned at regular time, the influence of repeated data on the system performance is prevented, and the daily use memory and performance of the data acquisition module are maintained.
Drawings
FIG. 1 is a schematic diagram of a front view structure of the present invention;
FIG. 2 is a schematic diagram of a data acquisition and transmission module according to the present invention;
FIG. 3 is a schematic diagram of a data testing process according to the present invention;
FIG. 4 is a schematic diagram of a power calculation module according to the present invention;
FIG. 5 is a schematic diagram of the structure of the AI prediction module of the invention;
FIG. 6 is a schematic diagram of a maintenance cleaning module according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-6, the invention provides a voltage detection system based on server testing, which comprises a data acquisition and transmission module and a data testing and processing module, wherein the data acquisition and transmission module acquires and converts voltage, so that a voltage signal is converted into a digital signal which can be processed by a server, and the converted digital signal is transmitted to the server module, and the data acquisition module comprises a voltage acquisition module, a data acquisition module, a maintenance and cleaning module, a preprocessing module, a transmission module and an encryption module.
The voltage acquisition module acquires voltage data from power equipment powered by the server, and the data acquisition module converts the voltage data acquired by the voltage acquisition module so as to convert the voltage data into digital signals.
The data acquisition module processes the data stored after acquisition through the maintenance cleaning module, the maintenance cleaning module comprises a timing cleaning module and an identification and processing repeated data module, the timing cleaning module determines the cleaning frequency according to the quality, the type and the acquisition frequency of the data, if the data can be cleaned once per week, the data in the data acquisition module can be cleaned conveniently, the same or very similar data items in the data are identified and processed by the repeated data module, and then the data items are combined or deleted, so that the repeated data is prevented from affecting the system performance, and the daily use memory and performance of the data acquisition module are maintained.
The preprocessing module processes the collected voltage data, comprises filtering, noise elimination and calibration, achieves the effect of cleaning the collected data, the transmission module transmits the collected data processed by the preprocessing module, the preprocessed collected data is transmitted to the server module, the encryption module encrypts the digital signal in the transmission process, the preprocessed voltage data is safely transmitted to the main server or the cloud, and the safety of data transmission is ensured.
The data test processing module comprises a server module, a power calculation module, a data storage module, a data analysis module, a statistical method module, an alarm module A, AI prediction module, a database module and a report generation module, wherein the server module receives the transmitted digital signals and converts the digital signals into corresponding voltage data.
The power calculation module calculates and detects the power of the server, so that when the server is in a peak period and the service power of the server is high (full load), the power calculation module controls the server, and transmits the voltage data received by the server module to the data storage module for temporary storage, the data storage module stores the voltage data, and when the power of the server gradually decreases to normal power along with the peak period, the power calculation module derives the voltage data stored in the data storage module, and detects the derived voltage data through the data analysis module, and the data analysis module detects the voltage data and judges whether the voltage data is abnormal or not.
The power calculation module comprises an ADC analog-to-digital conversion module, a digital filtering module, a system calibration module and a heat induction module, wherein the ADC analog-to-digital conversion module can analyze and process sound, light intensity and temperature, so that the accuracy of the power calculation module in the process of detecting and calibrating the power of the server is higher, the digital filtering module enables the power calculation module to avoid being influenced by noise when detecting the power of the server, and the system calibration module calibrates the system to reduce errors of the system in the process of detecting the power, and further improves the accuracy of power detection.
The heat induction module can induce the temperature of the power calculation module, so that when the temperature of the power calculation module is high, the heat dissipation is started by the heat induction module to reduce the temperature of the power calculation module, the power calculation module is kept in a normal-temperature environment, and the power calculation module is kept free from the influence of the environmental temperature on the detection and calculation of the power of the server.
In the process of detecting the derived voltage data, the data analysis module enables the data analysis module to learn and predict from the data through the statistical method module, the accuracy and depth of the data analysis module on the voltage data analysis are improved, and the detection analysis effect is improved.
The alarm module A can send out an alarm to remind workers, the data analysis module is divided into two groups after detecting voltage data, when the voltage data is abnormal, the data analysis module starts the alarm module A, so that the alarm module A sends out an harsh alarm to remind the workers of finding out problems existing in power supply equipment at the first time, the power supply equipment is convenient to maintain and overhaul in time, the loss is further reduced, and when the voltage data is normal, the report generation module is started to generate a report for the voltage data and print, and the later manager or other stakeholders can conveniently carry out relevant examination and verification.
The AI prediction module can identify a voltage abnormality mode, predicts a possible problem and timely sends out early warning, so that the problem can be solved before the problem is serious, the database module can store a large number of voltage data abnormality cases, comparison can be carried out when voltage abnormality occurs conveniently, the voltage abnormality scheme stored in the database module is matched with the voltage data size detected to occur, the approximate direction of solving is given, the alarm module A receives abnormal data and sends out an alarm, and meanwhile, the AI prediction module and the database module are used for facilitating staff to receive the alarm and simultaneously receive a solution provided by a system, so that the staff can reasonably and quickly solve according to the scheme, and the efficiency of maintaining server hardware is improved.
The AI prediction module comprises a data collection and preprocessing module, a feature selection module, a model design and training module, a model verification and test module, a model deployment module and a system evaluation and optimization module, wherein a large amount of historical data including data of normal operation state and data in fault are stored in the data collection and preprocessing module, and can be collected by various sensors, log files and maintenance reporting modes, and then necessary preprocessing work such as cleaning, standardization and missing value processing is carried out.
The feature selection module is used for selecting a large amount of data so as to determine the feature valuable for fault prediction.
The model design and training module comprises a neural network, a decision tree and a support vector machine, and uses the collected historical data for training, and parameter tuning may be needed in the training process to optimize the performance of the model.
After model training is completed, the model verification and test module needs to verify and test the performance of the model through a verification set and a test set, which can help us evaluate the accuracy, recall rate and ROC curve index of the model, and further confirm whether the model has acceptable performance.
The model deployment module can deploy the model into a real-time system after the model feedback is normal, and in the real-time system, the model collects and processes the latest system operation data in real time, predicts the probability of possible faults and gives out early warning when the probability exceeds a threshold value.
The system evaluation and optimization module performs evaluation and optimization periodically after the model is operated for a period of time, which may include collecting new fault data, adjusting feature selection, optimizing model parameters, and adapting to new operating environments.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
Claims (10)
1. The utility model provides a voltage detection system based on server test, includes data acquisition transmission module and data test processing module, its characterized in that: the data acquisition and transmission module acquires and converts the voltage, converts the voltage signal into a digital signal which can be processed by the server, and transmits the converted digital signal to the server module, and the data acquisition module comprises a voltage acquisition module, a data acquisition module, a maintenance and cleaning module, a preprocessing module, a transmission module and an encryption module;
the voltage acquisition module is connected with the data acquisition module, the data acquisition module processes the acquired and stored data through the maintenance and cleaning module, the preprocessing module processes the acquired and stored voltage data and comprises filtering, noise elimination and calibration, the transmission module transmits the acquired and stored data processed through the preprocessing module, the preprocessed acquired and stored data are transmitted to the server module, and the encryption module encrypts the digital signal in the transmission process;
the data test processing module comprises a server module, a power calculation module, a data storage module, a data analysis module, a statistical method module, an alarm module A, AI prediction module, a database module and a report generation module;
the server module receives the transmitted digital signals and converts the digital signals into corresponding voltage data, the power calculation module calculates and detects the power of the server, the data storage module stores the voltage data, the data analysis module learns and predicts the derived voltage data through the statistical method module in the process of detecting the derived voltage data, the alarm module A can send out an alarm to remind staff, the AI prediction module can identify a voltage abnormality mode, and the database module can store a large number of voltage data abnormality cases.
2. The server test-based voltage detection system of claim 1, wherein: the voltage acquisition module acquires voltage data from power equipment powered by the server, and the data acquisition module converts the voltage data acquired by the voltage acquisition module to enable the voltage data to be converted into digital signals.
3. The server test-based voltage detection system of claim 1, wherein: the maintenance cleaning module comprises a timing cleaning module and an identification and processing repeated data module, wherein the timing cleaning module determines the cleaning frequency according to the quality, the type and the acquisition frequency of data, the data cleaning can be carried out once a week, and the identification and processing repeated data module is used for merging or deleting the same or very similar data items in the data.
4. The server test-based voltage detection system of claim 1, wherein: the power calculation module comprises an ADC analog-to-digital conversion module, a digital filtering module, a system calibration module and a heat induction module, wherein the ADC analog-to-digital conversion module can analyze and process sound, light intensity and temperature, the digital filtering module enables the power calculation module to filter noise when detecting server power, the system calibration module calibrates the system so as to reduce errors of the system in the power detection process, the heat induction module can sense the temperature of the power calculation module, and the heat induction module is started to radiate so as to reduce the temperature of the power calculation module.
5. The server test-based voltage detection system of claim 1, wherein: the AI prediction module comprises a data collection and preprocessing module, a feature selection module, a model design and training module, a model verification and test module, a model deployment module and a system evaluation and optimization module, wherein historical data is stored in the data collection and preprocessing module, the historical data comprises data in a normal running state and data in the case of failure, and can be collected through various sensors, log files and maintenance reports, and then necessary preprocessing work is carried out.
6. The server test-based voltage detection system of claim 1, wherein: the data analysis module is divided into two groups after detecting the voltage data, when the voltage data is abnormal, the data analysis module starts the alarm module A, so that the alarm module A sends out a pungent alarm to remind a worker of finding out the problem of the power supply equipment in the first time, and when the voltage data is normal, the data analysis module starts the report generation module to generate a report form for the voltage data and print the report form.
7. The server-test-based voltage detection system of claim 5, wherein: the feature selection module is used for selecting a large amount of data so as to determine the feature valuable for fault prediction, and the model design and training module comprises: neural networks, decision trees, support vector machines, and training using collected historical data.
8. The server-test-based voltage detection system of claim 5, wherein: the model verification and test module needs to verify and test the performance of the model through a verification set and a test set after model training is completed.
9. The server-test-based voltage detection system of claim 5, wherein: the model deployment module can deploy the model into a real-time system after the model feedback is normal, and in the real-time system, the model collects and processes the latest system operation data in real time, predicts the probability of possible faults and gives out early warning when the probability exceeds a threshold value.
10. The server-test-based voltage detection system of claim 5, wherein: the system evaluation and optimization module is used for performing evaluation and optimization periodically after the model is operated for a period of time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311667143.3A CN117723818A (en) | 2023-12-05 | 2023-12-05 | Voltage detection system based on server test |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311667143.3A CN117723818A (en) | 2023-12-05 | 2023-12-05 | Voltage detection system based on server test |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117723818A true CN117723818A (en) | 2024-03-19 |
Family
ID=90208041
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311667143.3A Pending CN117723818A (en) | 2023-12-05 | 2023-12-05 | Voltage detection system based on server test |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117723818A (en) |
-
2023
- 2023-12-05 CN CN202311667143.3A patent/CN117723818A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110596486A (en) | Intelligent early warning operation and maintenance method and system for charging pile | |
| CN106655522A (en) | Master station system suitable for operation and maintenance management of secondary equipment of power grid | |
| CN109724646A (en) | A kind of power distribution network switchgear cable connector monitoring method, server and system | |
| CN117474357B (en) | Power distribution room operation and maintenance management method and system based on deep learning | |
| CN112801313A (en) | Fully mechanized mining face fault judgment method based on big data technology | |
| CN104821789A (en) | Method for detecting reliability of photovoltaic power generation system | |
| CN114215705A (en) | Wind turbine generator fault early warning method and system | |
| CN117318614A (en) | Photovoltaic inverter fault prediction method | |
| CN116488351A (en) | Low-voltage distribution box remote monitoring method and system based on Internet of things | |
| CN116308304A (en) | New energy intelligent operation and maintenance method and system based on meta learning concept drift detection | |
| CN117113135A (en) | Carbon emission anomaly monitoring and analyzing system capable of sorting and classifying anomaly data | |
| CN117150418B (en) | Transformer operation detection period formulation method and system based on state characteristic fault tree | |
| CN114487705A (en) | Power grid equipment fault positioning detection method | |
| CN113193616B (en) | Health state evaluation method for power transmission channel monitoring equipment | |
| CN117614140A (en) | Power distribution equipment operation monitoring system and method thereof | |
| CN105548873A (en) | Method and system for achieving health diagnosis of switchgear in online monitoring device | |
| CN117723818A (en) | Voltage detection system based on server test | |
| CN116381380A (en) | Dynamic fault supervision system for intelligent substation fault recorder | |
| CN113098132A (en) | Improved machine learning fault diagnosis system based on group intelligent optimization | |
| CN113591909A (en) | Abnormality detection method, abnormality detection device, and storage medium for power system | |
| CN112698129A (en) | Power distribution network equipment reliability analysis method and system based on multi-system information fusion | |
| CN114428775A (en) | Oil field real-time data quality monitoring processing method and system | |
| CN118397771B (en) | Cable anti-theft system | |
| CN117993895B (en) | Full life cycle management system of power grid equipment | |
| CN118011115A (en) | Online lightning arrester monitoring system and method based on hardware capture mechanism |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |