Intelligent monitoring method and system for switching on and switching off of power grid equipment

Abstract

The invention relates to the technical field of equipment monitoring, and discloses an intelligent monitoring method and system for the switching of power grid equipment, and the method comprises the steps: monitoring and managing the operation state and health condition of the equipment, and collecting the performance data, working state and possible fault information data of the equipment in real time; processing and judging the data collected by the equipment commissioning and quitting system to obtain a conclusion and prediction; according to the conclusion and prediction, making the adjustment scheme for adjusting the operation strategy or path planning of the equipment and optimizing the operation efficiency and safety of the equipment; according to the adjustment scheme, adjusting the equipment, and collecting data after the equipment is adjusted in real time; and verifying the accuracy of the conclusion and prediction according to the adjusted data of the equipment. According to the invention, the convenience and smoothness of system operation are improved; and manpower resources are saved, and a lot of repeated and periodic work can be automatically completed by the system through a timed task, so that the burden of manual operation is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring, and in particular to a method and system for intelligent monitoring of power grid equipment. Background Art

[0002] As the scale and complexity of power systems continue to expand, traditional equipment monitoring and operation and maintenance methods can no longer meet the needs of real-time monitoring of equipment status and fault prevention. Therefore, the introduction of intelligent technology, combined with the dispatching and operation platform, to achieve intelligent analysis and monitoring of equipment investment and withdrawal and autonomous cruising has become the current development trend of the power industry. Through technical means such as data collection, data analysis and autonomous cruising, the efficiency of equipment operation can be improved, the operation and maintenance costs can be reduced, and the intelligent management of the power system can be realized.

[0003] There are already some similar equipment monitoring and autonomous cruising systems on the market, but they can only realize equipment monitoring and cannot perform intelligent analysis and autonomous cruising. Although other systems have autonomous cruising functions, they lack intelligent optimization and learning capabilities and cannot make intelligent adjustments based on real-time data. Summary of the invention

[0004] The present invention aims to at least solve the technical problems in the prior art that the system cannot simultaneously have the functions of intelligent analysis and autonomous cruising, lacks intelligent optimization and learning capabilities, and cannot make intelligent adjustments based on real-time data.

[0005] To this end, an object of the present invention is to provide a method for intelligent monitoring of power grid equipment switching on and off, comprising:

[0006] Monitor and manage the operating status and health of power grid equipment, and collect operation-related data of the power grid equipment, including equipment performance data, working status, and possible fault information data;

[0007] Processing and judging the collected operation-related data to obtain analysis results;

[0008] According to the analysis results, a corresponding adjustment plan is formulated to adjust the operation strategy or path planning of the power grid equipment to optimize the operation efficiency and safety of the equipment;

[0009] According to the adjustment plan, the power grid equipment is adjusted, and data of the adjusted power grid equipment is collected;

[0010] The accuracy of the analysis result is verified based on the adjusted data of the power grid equipment.

[0011] Furthermore, the collected operation-related data is processed and judged to obtain analysis results, including:

[0012] Cleaning, converting and preliminarily analyzing the collected operation-related data, and then conducting in-depth analysis to obtain the analysis results;

[0013] The in-depth analysis includes anomaly detection, predictive maintenance or performance optimization analysis.

[0014] The present invention provides an intelligent monitoring system for power grid equipment switching on and off, comprising:

[0015] The equipment commissioning and decommissioning system is responsible for monitoring and managing the operating status and health of the power grid equipment, collecting the operation-related data of the power grid equipment, the operation-related data including equipment performance data, working status and possible fault information data; adjusting the power grid equipment according to the adjustment plan, and collecting the adjusted data of the power grid equipment;

[0016] A data analysis module is used to process and judge the collected operation-related data to obtain analysis results; and receive data adjusted by the device;

[0017] An autonomous cruise module is used to formulate corresponding adjustment plans according to the analysis results to adjust the operation strategy or path planning of the power grid equipment; it is used to perform human-computer interaction with on-site personnel and abandon the method of manual intervention through timed tasks;

[0018] A data analysis module and an autonomous cruise module, used to verify the accuracy of the analysis results according to the data adjusted by the power grid equipment;

[0019] The dispatching and operation center integrates the equipment commissioning and decommissioning system, data analysis module and autonomous cruise module to achieve unified management and monitoring.

[0020] Furthermore, the method for constructing the model built into the data analysis module is as follows:

[0021] Obtain raw data and perform data cleaning;

[0022] Select the most relevant features from the cleaned data, reduce noise and redundancy, and extract new features from the data after reducing noise and redundancy;

[0023] Select a machine learning algorithm and use the machine learning algorithm to build a model on the training set;

[0024] evaluating the performance of the model;

[0025] Apply qualified models to new data for prediction or inference, and monitor the performance and stability of the models in real time or regularly to detect model drift and degradation to ensure the continued effectiveness of the models.

[0026] Further, the original data is obtained and data cleaning is performed, wherein the data cleaning includes:

[0027] Handle missing values, outliers, perform data conversion and standardization to ensure data quality and consistency.

[0028] Furthermore, the most relevant features are selected from the cleaned data to reduce noise and redundancy, and new features are extracted, including:

[0029] Use data transformation methods to extract new features from data after reducing noise and redundancy;

[0030] Principal component analysis and text processing techniques are used to extract new features.

[0031] Further, selecting a machine learning algorithm and using the machine learning algorithm to build a model on the training set includes:

[0032] Choose a machine learning algorithm based on the problem type, features to be extracted, and data size.

[0033] Furthermore, the performance of the model is evaluated including:

[0034] Use cross validation and indicator evaluation to evaluate the performance of the model;

[0035] Improve the model's predictive and generalization capabilities through hyperparameter adjustment and feature engineering optimization methods.

[0036] Furthermore, the scheduling operation center includes:

[0037] The task scheduler is responsible for managing and scheduling the execution of various tasks; it determines the time to start the task and the way to allocate resources according to the set conditions and rules to achieve maximum efficiency;

[0038] Task orchestration engine, which manages workflows, including multiple related tasks and dependencies; ensures that tasks are run in a predetermined order and according to the conditions, and handles task failures or exceptions;

[0039] Resource manager, which manages and allocates various resources of the system, including computing resources, storage resources, and network bandwidth. The resource manager ensures that tasks are executed smoothly when sufficient resources are available.

[0040] The monitoring and scheduling console provides a real-time monitoring and management interface for viewing task execution status, resource utilization, task logs, and alarm information; operators can manually trigger tasks, adjust scheduling strategies, or handle abnormal situations through the console;

[0041] Distributed storage system that stores intermediate data, results, and logs generated during task execution.

[0042] Furthermore, the autonomous cruise module also includes the following modules:

[0043] The clock synchronization algorithm module, specifically the network clock synchronization protocol, ensures the accuracy and synchronization of the device clock when the device is connected to an external clock source;

[0044] The inertial measurement calibration and error compensation module performs regular calibration and error compensation for the IMU used in the inertial navigation system and maintains navigation accuracy.

[0045] When predicting time series data through the data analysis module, it is possible to mine and identify repetitive patterns, seasonality, trends and abnormal events in the data.

[0046] The present invention also provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned intelligent monitoring method for switching on and off of power grid equipment when executing the computer program on the memory.

[0047] The present invention also provides a computer-readable storage medium, wherein the computer-readable medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned intelligent monitoring method for switching on and off of power grid equipment is implemented.

[0048] The computer-readable storage medium provided by the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. In the embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device, for example, the above-mentioned memory.

[0049] The present invention provides a method and system for intelligent monitoring of power grid equipment switching on and off, which has the following beneficial effects:

[0050] The present invention discloses an intelligent monitoring system for power grid equipment startup and shutdown, comprising: a data analysis module, which utilizes data analysis technology to monitor and analyze the equipment startup and shutdown process in real time, and intelligently identifies and analyzes the status of the equipment; an autonomous cruise module, which is used to perform human-computer interaction with on-site personnel and eliminate the need for manual intervention through scheduled tasks; a dispatching and operation center, which integrates the equipment startup and shutdown system, the data analysis module, and the autonomous cruise module to achieve unified management and monitoring; automated execution, in which scheduled tasks can automatically execute specific operations or programs according to a preset schedule without manual intervention, and this automated execution method can greatly improve the convenience and fluency of system operation; saves human resources, and through scheduled tasks, many repetitive and periodic tasks can be automatically completed by the system, thereby greatly reducing the burden of manual operation.

[0051] The present invention provides an intelligent monitoring method for power grid equipment startup and shutdown, which achieves higher real-time performance, lower latency and better resource utilization by introducing optimization means such as intelligent routing algorithm, dynamic load balancing and message compression, so as to solve the problems raised in the above background technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 This is a control flow chart of a method for intelligent monitoring of power grid equipment in an embodiment of the present invention;

[0054] Figure 2 It is a structural schematic diagram of an intelligent monitoring system for switching on and off of power grid equipment in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] Various aspects and features of the present invention are described herein with reference to the accompanying drawings.

[0056] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of the embodiments. Other modifications within the scope and spirit of the present invention will occur to those skilled in the art.

[0057] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the general description of the invention given above and the detailed description of the embodiments given below, serve to explain the principles of the invention.

[0058] These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the accompanying drawings.

[0059] It should also be understood that, although the invention has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the invention that have the characteristics recited in the claims and are therefore within the scope of protection defined thereby.

[0060] The above and other aspects, features and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0061] Specific embodiments of the present invention are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present invention, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present invention with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present invention in a variety of ways with substantially any suitable detailed structure.

[0062] Example

[0063] like Figure 1 As shown, this embodiment provides a method for intelligent monitoring of power grid equipment switching on and off, including:

[0064] Step S1: monitor and manage the operating status and health of power grid equipment, and collect operation-related data of the power grid equipment, wherein the operation-related data includes equipment performance data, working status, and possible fault information data;

[0065] Step S2: Processing and judging the collected operation-related data to obtain analysis results;

[0066] Step S3: formulating a corresponding adjustment plan according to the analysis result to adjust the operation strategy or path planning of the power grid equipment to optimize the operation efficiency and safety of the equipment;

[0067] Step S4: adjusting the power grid equipment according to the adjustment plan, and collecting data of the adjusted power grid equipment;

[0068] Step S5: verifying the accuracy of the analysis result based on the adjusted data of the power grid equipment.

[0069] According to another specific embodiment of the present invention, on the basis of the above embodiment, according to step S2, the collected operation-related data is processed and judged to obtain the analysis result, including: cleaning, converting and preliminarily analyzing the collected operation-related data, and then conducting in-depth analysis to obtain the analysis result;

[0070] The in-depth analysis includes anomaly detection, predictive maintenance or performance optimization analysis.

[0071] like Figure 2 As shown, this embodiment provides an intelligent monitoring system for power grid equipment switching on and off, including:

[0072] The equipment commissioning and decommissioning system is responsible for monitoring and managing the operating status and health of the power grid equipment, collecting the operation-related data of the power grid equipment, the operation-related data including equipment performance data, working status and possible fault information data; adjusting the power grid equipment according to the adjustment plan, and collecting the adjusted data of the power grid equipment;

[0073] A data analysis module is used to process and judge the collected operation-related data to obtain analysis results; and receive data adjusted by the device;

[0074] An autonomous cruise module is used to formulate corresponding adjustment plans according to the analysis results to adjust the operation strategy or path planning of the power grid equipment; it is used to perform human-computer interaction with on-site personnel and abandon the method of manual intervention through timed tasks;

[0075] A data analysis module and an autonomous cruise module, used to verify the accuracy of the analysis results according to the data adjusted by the power grid equipment;

[0076] The dispatching and operation center integrates the equipment commissioning and decommissioning system, data analysis module and autonomous cruise module to achieve unified management and monitoring.

[0077] According to another specific embodiment of the present invention, based on the above embodiment, the method for constructing the model built in the data analysis module is as follows:

[0078] Step H1: Obtain the original data and perform data cleaning;

[0079] Step H2: Select the most relevant features from the cleaned data, reduce noise and redundancy, and extract new features from the data after reducing noise and redundancy;

[0080] Step H3: Select a machine learning algorithm, and use the machine learning algorithm to build a model on the training set;

[0081] Step H4: Evaluate the performance of the model;

[0082] Step H5: Apply the qualified model to new data for prediction or inference, and monitor the performance and stability of the model in real time or regularly to detect model drift and degradation to ensure the continued effectiveness of the model.

[0083] According to another specific embodiment of the present invention, on the basis of the above embodiment, the step H1 is to obtain the original data and perform data cleaning, and the data cleaning includes:

[0084] Handle missing values, outliers, perform data conversion and standardization to ensure data quality and consistency.

[0085] According to another specific embodiment of the present invention, based on the above embodiment, in step H2, the most relevant features are selected from the cleaned data to reduce noise and redundancy, and extract new features, including:

[0086] Data transformation methods are used to extract new features from data after reducing noise and redundancy.

[0087] According to another specific embodiment of the present invention, on the basis of the above embodiment, in step H2, the most relevant features are selected from the cleaned data, noise and redundancy are reduced, and new features are extracted, including:

[0088] Principal component analysis and text processing techniques are used to extract new features.

[0089] According to another specific embodiment of the present invention, on the basis of the above embodiment, in step H3, selecting a machine learning algorithm, and using the machine learning algorithm to establish a model on a training set includes:

[0090] Choose a machine learning algorithm based on the problem type, features to be extracted, and data size.

[0091] According to another specific embodiment of the present invention, based on the above embodiment, in step H4, evaluating the performance of the model includes:

[0092] The performance of the model was evaluated using cross validation and indicator evaluation.

[0093] According to another specific embodiment of the present invention, based on the above embodiment, in step H4, evaluating the performance of the model includes:

[0094] Improve the model's predictive and generalization capabilities through hyperparameter adjustment and feature engineering optimization methods.

[0095] According to another specific embodiment of the present invention, based on the above embodiment, in step H5, the qualified model is applied to new data for prediction or inference, and the performance and stability of the model are monitored in real time or regularly to ensure the continuous effectiveness of the model, including:

[0096] Real-time or periodic monitoring of the model performance and stability is performed by detecting the model drift and degradation.

[0097] According to another specific embodiment of the present invention, on the basis of the above embodiment, the scheduling operation center includes:

[0098] The task scheduler is responsible for managing and scheduling the execution of various tasks; it decides when to start tasks and how to allocate resources to maximize efficiency based on the set conditions and rules;

[0099] Task orchestration engine, which manages workflows, including multiple related tasks and dependencies; ensures that tasks are run in a predetermined order and according to the conditions, and handles task failures or exceptions;

[0100] Resource manager, which manages and allocates various resources of the system, including computing resources, storage resources, and network bandwidth. The resource manager ensures that tasks are executed smoothly when sufficient resources are available.

[0101] The monitoring and scheduling console provides a real-time monitoring and management interface for viewing task execution status, resource utilization, task logs, and alarm information; operators can manually trigger tasks, adjust scheduling strategies, or handle abnormal situations through the console;

[0102] Distributed storage systems store intermediate data, results, and logs generated during task execution; distributed storage systems are usually able to provide high availability, fault tolerance, and scalability to support large-scale data processing and storage needs.

[0103] According to another specific embodiment of the present invention, on the basis of the above-mentioned embodiment, a comprehensive integration module is integrated in the dispatching operation center to organically combine the equipment deployment and withdrawal system, the data analysis module and the autonomous cruise module.

[0104] According to another specific embodiment of the present invention, on the basis of the above embodiment, the autonomous cruise module further includes the following modules:

[0105] The clock synchronization algorithm module, specifically the network clock synchronization protocol, ensures the accuracy and synchronization of the device clock when the device is connected to an external clock source;

[0106] The inertial measurement calibration and error compensation module performs regular calibration and error compensation on the IMU used in the inertial navigation system to reduce cumulative errors and maintain navigation accuracy.

[0107] When predicting time series data through the data analysis module, repeated patterns, seasonality, trends and abnormal events in the data can be mined and identified.

[0108] The present invention discloses an intelligent monitoring system for power grid equipment startup and shutdown, comprising: a data analysis module, which utilizes data analysis technology to monitor and analyze the equipment startup and shutdown process in real time, and intelligently identifies and analyzes the status of the equipment; an autonomous cruise module, which is used to perform human-computer interaction with on-site personnel and eliminate the need for manual intervention through scheduled tasks; a dispatching and operation center, which integrates the equipment startup and shutdown system, the data analysis module, and the autonomous cruise module to achieve unified management and monitoring; automated execution, in which scheduled tasks can automatically execute specific operations or programs according to a preset schedule without manual intervention, and this automated execution method can greatly improve the convenience and fluency of system operation; saves human resources, and through scheduled tasks, many repetitive and periodic tasks can be automatically completed by the system, thereby greatly reducing the burden of manual operation.

[0109] By introducing optimization methods such as intelligent routing algorithms, dynamic load balancing and message compression, higher real-time performance, lower latency and better resource utilization are achieved to solve the problems raised in the above background technology.

[0110] Another specific embodiment of the present invention is as follows:

[0111] Please refer to the following cases for details:

[0112] 1) After receiving the startup plan, the person in charge of each unit must complete the network signing, equipment status confirmation and operation steps writing before it can be transferred to execution.

[0113] 2) After receiving the startup plan, the system automatically notifies the person in charge of each unit to sign and confirm the setting status. Every five minutes, the system automatically notifies the person in charge of the unit who has not signed and confirmed the equipment status.

[0114] 1)) SMS content (receipt notification):

[0115] Please sign for the startup plan, order number: xxxxx, title: yyyyy,

[0116] Dispatcher: Zhang San Sending time: 2024-1-15 12:00:00

[0117] 2)) SMS content (device status confirmation):

[0118] Please confirm the equipment status of the startup plan, order number: xxxxx, title: yyyyy, the equipment is as follows: 1.xxxx, 2.yyyy.

[0119] Dispatcher: Zhang San Sending time: 2024-1-15 12:00:00;

[0120] The present disclosure also provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method for intelligent monitoring of power grid equipment when executing the computer program on the memory, including:

[0121] Step S1: monitor and manage the operating status and health of power grid equipment, and collect operation-related data of the power grid equipment, wherein the operation-related data includes equipment performance data, working status, and possible fault information data;

[0122] Step S2: Processing and judging the collected operation-related data to obtain analysis results;

[0123] Step S3: formulating a corresponding adjustment plan according to the analysis result to adjust the operation strategy or path planning of the power grid equipment to optimize the operation efficiency and safety of the equipment;

[0124] Step S4: adjusting the power grid equipment according to the adjustment plan, and collecting data of the adjusted power grid equipment;

[0125] Step S5: verifying the accuracy of the analysis result based on the adjusted data of the power grid equipment.

[0126] The electronic device is provided with an intelligent monitoring system for power grid equipment switching on and off, including:

[0127] The equipment commissioning and decommissioning system is responsible for monitoring and managing the operating status and health of the power grid equipment, collecting the operation-related data of the power grid equipment, the operation-related data including equipment performance data, working status and possible fault information data; adjusting the power grid equipment according to the adjustment plan, and collecting the adjusted data of the power grid equipment;

[0128] A data analysis module is used to process and judge the collected operation-related data to obtain analysis results; and receive data adjusted by the device;

[0129] An autonomous cruise module is used to formulate corresponding adjustment plans according to the analysis results to adjust the operation strategy or path planning of the power grid equipment; it is used to perform human-computer interaction with on-site personnel and abandon the method of manual intervention through timed tasks;

[0130] A data analysis module and an autonomous cruise module, used to verify the accuracy of the analysis results according to the data adjusted by the power grid equipment;

[0131] The dispatching and operation center integrates the equipment commissioning and decommissioning system, data analysis module and autonomous cruise module to achieve unified management and monitoring.

[0132] The method for constructing the model built into the data analysis module is as follows:

[0133] Step H1: Obtain the original data and perform data cleaning;

[0134] Step H2: Select the most relevant features from the cleaned data, reduce noise and redundancy, and extract new features from the data after reducing noise and redundancy;

[0135] Step H3: Select a machine learning algorithm, and use the machine learning algorithm to build a model on the training set;

[0136] Step H4: Evaluate the performance of the model;

[0137] Step H5: Apply the qualified model to new data for prediction or inference, and monitor the performance and stability of the model in real time or regularly to detect model drift and degradation to ensure the continued effectiveness of the model.

[0138] According to another specific embodiment of the present invention, on the basis of the above embodiment, the step H1 is to obtain the original data and perform data cleaning, and the data cleaning includes:

[0139] Handle missing values, outliers, perform data conversion and standardization to ensure data quality and consistency.

[0140] According to another specific embodiment of the present invention, based on the above embodiment, in step H2, the most relevant features are selected from the cleaned data to reduce noise and redundancy, and extract new features, including:

[0141] Data transformation methods are used to extract new features from data after reducing noise and redundancy.

[0142] According to another specific embodiment of the present invention, on the basis of the above embodiment, in step H2, the most relevant features are selected from the cleaned data, noise and redundancy are reduced, and new features are extracted, including:

[0143] Principal component analysis and text processing techniques are used to extract new features.

[0144] According to another specific embodiment of the present invention, on the basis of the above embodiment, in step H3, selecting a machine learning algorithm, and using the machine learning algorithm to establish a model on a training set includes:

[0145] Choose a machine learning algorithm based on the problem type, features to be extracted, and data size.

[0146] According to another specific embodiment of the present invention, based on the above embodiment, in step H4, evaluating the performance of the model includes:

[0147] The performance of the model was evaluated using cross validation and indicator evaluation.

[0148] According to another specific embodiment of the present invention, based on the above embodiment, in step H4, evaluating the performance of the model includes:

[0149] Improve the model's predictive and generalization capabilities through hyperparameter adjustment and feature engineering optimization methods.

[0150] According to another specific embodiment of the present invention, based on the above embodiment, in step H5, the qualified model is applied to new data for prediction or inference, and the performance and stability of the model are monitored in real time or regularly to ensure the continuous effectiveness of the model, including:

[0151] Real-time or periodic monitoring of the model performance and stability is performed by detecting the model drift and degradation.

[0152] According to another specific embodiment of the present invention, on the basis of the above embodiment, the scheduling operation center includes:

[0153] The task scheduler is responsible for managing and scheduling the execution of various tasks; it decides when to start tasks and how to allocate resources to maximize efficiency based on the set conditions and rules;

[0154] Task orchestration engine, which manages workflows, including multiple related tasks and dependencies; ensures that tasks are run in a predetermined order and according to the conditions, and handles task failures or exceptions;

[0155] Resource manager, which manages and allocates various resources of the system, including computing resources, storage resources, and network bandwidth. The resource manager ensures that tasks are executed smoothly when sufficient resources are available.

[0156] The monitoring and scheduling console provides a real-time monitoring and management interface for viewing task execution status, resource utilization, task logs, and alarm information; operators can manually trigger tasks, adjust scheduling strategies, or handle abnormal situations through the console;

[0157] Distributed storage systems store intermediate data, results, and logs generated during task execution; distributed storage systems are usually able to provide high availability, fault tolerance, and scalability to support large-scale data processing and storage needs.

[0158] According to another specific embodiment of the present invention, on the basis of the above-mentioned embodiment, a comprehensive integration module is integrated in the dispatching operation center to organically combine the equipment deployment and withdrawal system, the data analysis module and the autonomous cruise module.

[0159] According to another specific embodiment of the present invention, on the basis of the above embodiment, the autonomous cruise module further includes the following modules:

[0160] The clock synchronization algorithm module, specifically the network clock synchronization protocol, ensures the accuracy and synchronization of the device clock when the device is connected to an external clock source;

[0161] The inertial measurement calibration and error compensation module performs regular calibration and error compensation on the IMU used in the inertial navigation system to reduce cumulative errors and maintain navigation accuracy.

[0162] When predicting time series data through the data analysis module, repeated patterns, seasonality, trends and abnormal events in the data can be mined and identified.

[0163] In some embodiments, the processor executing the computer program may be a processing device including one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor running other instruction sets, or a processor running a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a system on a chip (SoC), etc.

[0164] The memory may be a read-only memory (ROM), a random access memory (RAM), a phase-change random access memory (PRAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), an electrically erasable programmable read-only memory (EEPROM), other types of random access memory (RAM), a flash disk or other form of flash memory, a cache, a register, a static memory, a compact disk read-only memory (CD-ROM), a digital versatile disk (DVD) or other optical storage, a cassette or other magnetic storage device, or any other possible non-temporary medium used to store information or instructions that can be accessed by a computer device.

[0165] The electronic devices of the embodiments of the present disclosure may include but are not limited to fixed terminal devices such as MCU controllers, servers, desktop computers, digital TVs, etc., and mobile terminal devices such as vehicle-mounted devices (such as head-up display devices), handheld devices (such as mobile phones, tablet computers, etc.), wearable devices (such as smart watches, smart bracelets, etc.), etc.

[0166] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein the computer-readable medium stores a computer program, and when the computer program is executed by a processor, the method for intelligent monitoring of power grid equipment switching on and off is implemented, including:

[0167] Step S1: monitor and manage the operating status and health of power grid equipment, and collect operation-related data of the power grid equipment, wherein the operation-related data includes equipment performance data, working status, and possible fault information data;

[0168] Step S2: Processing and judging the collected operation-related data to obtain analysis results;

[0169] Step S3: formulating a corresponding adjustment plan according to the analysis result to adjust the operation strategy or path planning of the power grid equipment to optimize the operation efficiency and safety of the equipment;

[0170] Step S4: adjusting the power grid equipment according to the adjustment plan, and collecting data of the adjusted power grid equipment;

[0171] Step S5: verifying the accuracy of the analysis result based on the adjusted data of the power grid equipment.

[0172] The electronic device is provided with an intelligent monitoring system for power grid equipment switching on and off, including:

[0173] The equipment commissioning and decommissioning system is responsible for monitoring and managing the operating status and health of the power grid equipment, collecting the operation-related data of the power grid equipment, the operation-related data including equipment performance data, working status and possible fault information data; adjusting the power grid equipment according to the adjustment plan, and collecting the adjusted data of the power grid equipment;

[0174] A data analysis module is used to process and judge the collected operation-related data to obtain analysis results; and receive data adjusted by the device;

[0175] An autonomous cruise module is used to formulate corresponding adjustment plans according to the analysis results to adjust the operation strategy or path planning of the power grid equipment; it is used to perform human-computer interaction with on-site personnel and abandon the method of manual intervention through timed tasks;

[0176] A data analysis module and an autonomous cruise module, used to verify the accuracy of the analysis results according to the data adjusted by the power grid equipment;

[0177] The dispatching and operation center integrates the equipment commissioning and decommissioning system, data analysis module and autonomous cruise module to achieve unified management and monitoring.

[0178] The method for constructing the model built into the data analysis module is as follows:

[0179] Step H1: Obtain the original data and perform data cleaning;

[0180] Step H2: Select the most relevant features from the cleaned data, reduce noise and redundancy, and extract new features from the data after reducing noise and redundancy;

[0181] Step H3: Select a machine learning algorithm, and use the machine learning algorithm to build a model on the training set;

[0182] Step H4: Evaluate the performance of the model;

[0183] Step H5: Apply the qualified model to new data for prediction or inference, and monitor the performance and stability of the model in real time or regularly to detect model drift and degradation to ensure the continued effectiveness of the model.

[0184] According to another specific embodiment of the present invention, on the basis of the above embodiment, the step H1 is to obtain the original data and perform data cleaning, and the data cleaning includes:

[0185] Handle missing values, outliers, perform data conversion and standardization to ensure data quality and consistency.

[0186] According to another specific embodiment of the present invention, based on the above embodiment, in step H2, the most relevant features are selected from the cleaned data to reduce noise and redundancy, and extract new features, including:

[0187] Data transformation methods are used to extract new features from data after reducing noise and redundancy.

[0188] According to another specific embodiment of the present invention, on the basis of the above embodiment, in step H2, the most relevant features are selected from the cleaned data, noise and redundancy are reduced, and new features are extracted, including:

[0189] Principal component analysis and text processing techniques are used to extract new features.

[0190] According to another specific embodiment of the present invention, on the basis of the above embodiment, in step H3, selecting a machine learning algorithm, and using the machine learning algorithm to establish a model on a training set includes:

[0191] Choose a machine learning algorithm based on the problem type, features to be extracted, and data size.

[0192] According to another specific embodiment of the present invention, based on the above embodiment, in step H4, evaluating the performance of the model includes:

[0193] The performance of the model was evaluated using cross validation and indicator evaluation.

[0194] According to another specific embodiment of the present invention, based on the above embodiment, in step H4, evaluating the performance of the model includes:

[0195] Improve the model's predictive and generalization capabilities through hyperparameter adjustment and feature engineering optimization methods.

[0196] According to another specific embodiment of the present invention, based on the above embodiment, in step H5, the qualified model is applied to new data for prediction or inference, and the performance and stability of the model are monitored in real time or regularly to ensure the continuous effectiveness of the model, including:

[0197] Real-time or periodic monitoring of the model performance and stability is performed by detecting the model drift and degradation.

[0198] According to another specific embodiment of the present invention, on the basis of the above embodiment, the scheduling operation center includes:

[0199] The task scheduler is responsible for managing and scheduling the execution of various tasks; it decides when to start tasks and how to allocate resources to maximize efficiency based on the set conditions and rules;

[0200] Task orchestration engine, which manages workflows, including multiple related tasks and dependencies; ensures that tasks are run in a predetermined order and according to the conditions, and handles task failures or exceptions;

[0201] Resource manager, which manages and allocates various resources of the system, including computing resources, storage resources, and network bandwidth. The resource manager ensures that tasks are executed smoothly when sufficient resources are available.

[0202] The monitoring and scheduling console provides a real-time monitoring and management interface for viewing task execution status, resource utilization, task logs, and alarm information; operators can manually trigger tasks, adjust scheduling strategies, or handle abnormal situations through the console;

[0203] Distributed storage systems store intermediate data, results, and logs generated during task execution; distributed storage systems are usually able to provide high availability, fault tolerance, and scalability to support large-scale data processing and storage needs.

[0204] According to another specific embodiment of the present invention, on the basis of the above-mentioned embodiment, a comprehensive integration module is integrated in the dispatching operation center to organically combine the equipment deployment and withdrawal system, the data analysis module and the autonomous cruise module.

[0205] According to another specific embodiment of the present invention, on the basis of the above embodiment, the autonomous cruise module further includes the following modules:

[0206] The clock synchronization algorithm module, specifically the network clock synchronization protocol, ensures the accuracy and synchronization of the device clock when the device is connected to an external clock source;

[0207] The inertial measurement calibration and error compensation module performs regular calibration and error compensation on the IMU used in the inertial navigation system to reduce cumulative errors and maintain navigation accuracy.

[0208] When predicting time series data through the data analysis module, repeated patterns, seasonality, trends and abnormal events in the data can be mined and identified.

[0209] The computer-readable storage medium of the embodiments of the present disclosure may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. In the embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device, for example, the memory described above.

[0210] The computer programs of the embodiments of the present disclosure may be organized into one or more computer executable components or modules. Any number and combination of such components or modules may be used to implement the various aspects of the present disclosure. For example, the various aspects of the present disclosure are not limited to the specific computer executable instructions or specific components or modules shown in the drawings and described herein. Other embodiments may include different computer executable instructions or components with more or less functions than those shown and described herein.

[0211] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

Claims

1. A method for intelligent monitoring of power grid equipment switching on and off, characterized in that: include: Monitor and manage the operating status and health of power grid equipment, and collect operation-related data of the power grid equipment, including equipment performance data, working status, and possible fault information data; Processing and judging the collected operation-related data to obtain analysis results; According to the analysis results, a corresponding adjustment plan is formulated to adjust the operation strategy or path planning of the power grid equipment; According to the adjustment plan, the power grid equipment is adjusted, and data of the adjusted power grid equipment is collected; The accuracy of the analysis result is verified based on the adjusted data of the power grid equipment.

2. The method for intelligent monitoring of power grid equipment startup and shutdown according to claim 1 is characterized in that: Process and judge the collected operation-related data to obtain analysis results, including: Cleaning, converting and preliminarily analyzing the collected operation-related data, and then conducting in-depth analysis to obtain the analysis results; The in-depth analysis includes anomaly detection, predictive maintenance or performance optimization analysis.

3. An intelligent monitoring system for power grid equipment switching on and off, characterized in that: include: The equipment commissioning and decommissioning system is responsible for monitoring and managing the operating status and health of the power grid equipment, collecting the operation-related data of the power grid equipment, including equipment performance data, working status, and possible fault information data; adjusting the power grid equipment according to the adjustment plan, and collecting the adjusted data of the power grid equipment; A data analysis module is used to process and judge the collected operation-related data to obtain analysis results; and receive data adjusted by the device; An autonomous cruise module, used to formulate a corresponding adjustment plan according to the analysis results to adjust the operation strategy or path planning of the power grid equipment; Used for human-computer interaction for on-site personnel, and to eliminate manual intervention through scheduled tasks; A data analysis module and an autonomous cruise module, used to verify the accuracy of the analysis results according to the data adjusted by the power grid equipment; The dispatching and operation center integrates the equipment commissioning and decommissioning system, data analysis module and autonomous cruise module to achieve unified management and monitoring.

4. The intelligent monitoring system for power grid equipment startup and shutdown according to claim 3 is characterized in that: The method for constructing the model built into the data analysis module is as follows: Obtain raw data and perform data cleaning; Select the most relevant features from the cleaned data, reduce noise and redundancy, and extract new features from the data after reducing noise and redundancy; Select a machine learning algorithm and use the machine learning algorithm to build a model on the training set; evaluating the performance of the model; Apply qualified models to new data for prediction or inference, and monitor the performance and stability of the models in real time or regularly to detect model drift and degradation to ensure the continued effectiveness of the models.

5. The intelligent monitoring system for power grid equipment startup and shutdown according to claim 4 is characterized in that: Obtaining raw data and performing data cleaning, wherein the data cleaning includes: Handle missing values, outliers, perform data conversion and standardization to ensure data quality and consistency.

6. The intelligent monitoring system for power grid equipment startup and shutdown according to claim 4 is characterized in that: Select the most relevant features from the cleaned data, reduce noise and redundancy, and extract new features, including: Use data transformation methods to extract new features from data after reducing noise and redundancy; Principal component analysis and text processing techniques are used to extract new features.

7. The intelligent monitoring system for power grid equipment startup and shutdown according to claim 4 is characterized in that: Select a machine learning algorithm and use the machine learning algorithm to build a model on the training set, including: Choose a machine learning algorithm based on the problem type, features to be extracted, and data size.

8. The intelligent monitoring system for power grid equipment startup and shutdown according to claim 4 is characterized in that: The performance of the model was evaluated by: Use cross validation and indicator evaluation to evaluate the performance of the model; Improve the model's predictive and generalization capabilities through hyperparameter adjustment and feature engineering optimization methods.

9. The intelligent monitoring system for power grid equipment startup and shutdown according to claim 3 is characterized in that: The dispatching operation platform includes: The task scheduler is responsible for managing and scheduling the execution of various tasks; it determines the time to start the task and the way to allocate resources according to the set conditions and rules to achieve maximum efficiency; Task orchestration engine, which manages workflows, including multiple related tasks and dependencies; ensures that tasks are run in a predetermined order and according to the conditions, and handles task failures or exceptions; Resource manager, which manages and allocates various resources of the system, including computing resources, storage resources, and network bandwidth. The resource manager ensures that tasks are executed smoothly when sufficient resources are available. The monitoring and scheduling console provides a real-time monitoring and management interface for viewing task execution status, resource utilization, task logs, and alarm information. Operators can manually trigger tasks, adjust scheduling strategies, or handle abnormal situations through the console. Distributed storage system that stores intermediate data, results, and logs generated during task execution.

10. The intelligent monitoring system for power grid equipment startup and shutdown according to claim 3 is characterized in that: The autonomous cruise module also includes the following modules: The clock synchronization algorithm module, specifically the network clock synchronization protocol, ensures the accuracy and synchronization of the device clock when the device is connected to an external clock source; The inertial measurement calibration and error compensation module performs regular calibration and error compensation for the IMU used in the inertial navigation system and maintains navigation accuracy. When predicting time series data through the data analysis module, it is possible to mine and identify repetitive patterns, seasonality, trends and abnormal events in the data.

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