An all-boundary power network data analysis method based on artificial intelligence technology

Abstract

The application provides a full-boundary power network data analysis method based on artificial intelligence technology, a power monitoring module collects and monitors power data in a power network; power operation data of a power data operation terminal is acquired by using an active collection mode; terminal information of the power data operation terminal is acquired by the power monitoring module, and the terminal information is analyzed; power data information is collected; keyword recognition and image recognition are used to realize monitoring of the power data and monitoring of the power data operation terminal, effectively solve the problem of power data monitoring difficulty, enhance the stability of power operation, provide the reliability of the power system, improve the communication efficiency and stability by multi-thread communication. It is beneficial to the rapid processing and judgment of power failure or power anomaly; key features can be extracted from the power data operation log and the monitoring log of the power monitoring module, and the past power data and power operation process are traced back.

Description

Technical Field

[0001] The present invention relates to the field of power network operation technology, and in particular to a full-boundary power network data analysis method based on artificial intelligence technology. Background Art

[0002] The power grid is the component of the power system beyond power generation and consumption. It encompasses multiple links, including substations, transmission, and distribution. It connects power plants and consumers across a vast geographic area, delivering centrally generated electricity to thousands of dispersed households. Power grid equipment primarily includes power lines, substations, and converter stations, which convert alternating current (AC) to direct current (DC) and vice versa. Network interconnection allows for the rational allocation of power between regions, improving power supply reliability and the utilization rate of power generation equipment.

[0003] Currently, the stable operation of the power grid is crucial to ensuring that thousands of households and businesses have access to electricity. However, during operation, power grids can experience faults and abnormalities. If these faults and abnormalities are not discovered or reported promptly, they can escalate, leading to unstable operation of the power grid and the power system, impacting electricity consumption. Summary of the Invention

[0004] The present invention provides a full-boundary power network data analysis method based on artificial intelligence technology. The method can perform large-scale monitoring and investigation in the power grid based on characteristic points in power failures and power abnormal states, thereby reducing power grid failures.

[0005] The method includes: a power monitoring module;

[0006] The power monitoring module collects and monitors power data in the power network;

[0007] The power operation data processed by the power data operation terminal is acquired by active collection;

[0008] The power monitoring module obtains the terminal information of the power data operation terminal, and obtains the identity information and IP information of the power data operation terminal by parsing the terminal information.

[0009] Preferably, the power monitoring module adopts multi-threaded monitoring of the power data operation terminal, and after obtaining the keywords of the power operation data, the identity information and IP information of the power data operation terminal, uses a multi-threaded communication method;

[0010] The power monitoring module retrieves keywords from power operation data and identifies complete power data through keywords;

[0011] Retrieve comparison information from the database, perform identity comparison and IP information matching, and monitor whether the identity authority information of the power data operation terminal meets the power data operation authority.

[0012] Preferably, the power monitoring module retrieves the operation log of the power data operation terminal to obtain a complete power data operation log, and performs operations on the address information of the operated device, the port information of the operated device, and the status data of the operated device in the obtained power data operation log;

[0013] The power monitoring module sets power information filtering conditions, excludes power data that does not meet the power information filtering conditions, and finds power data that meets the power information filtering conditions for monitoring.

[0014] Preferably, the power monitoring module performs keyword recognition and power equipment image recognition on the collected power data;

[0015] The identified power data keyword is matched with the corresponding power data in the database to match the corresponding power data, and it is determined whether the power data meets the operation requirements at that time and whether the power data operation terminal has the operation authority.

[0016] Preferably, the power equipment image recognition can use the Mean shift algorithm to match the comparison features in the power equipment image, thereby identifying the state of the power equipment in the image;

[0017] It can also identify whether the operation process of the power data operation terminal on the power equipment image meets the preset requirements.

[0018] Preferably, extracting key elements in the power equipment image: identifying key elements with scale characteristics and color characteristics in the power equipment image by using a Gaussian differential function;

[0019] Locate key elements and determine characteristic status;

[0020] By comparing the characteristic states of each key element, several matching pairs of feature points are found, the states of the feature points are identified and compared, and the state of the power equipment is monitored.

[0021] Preferably, it also includes: a query module, which provides a query interface to the user; including the search and query of power data, data verification and statistics, and the operating status of power equipment.

[0022] Preferably, the power server obtains and stores the power data operation log and the monitoring log of the power monitoring module through the log message queue;

[0023] The power server performs anomaly analysis on the power data operation log and the monitoring log of the power monitoring module;

[0024] When abnormal data occurs, analyze the characteristic parameters of the power data operation log and the monitoring log of the power monitoring module;

[0025] Determine the type of abnormal data, abnormal state and preset normal state based on the standard characteristic parameters stored in the characteristic parameter library;

[0026] After analyzing the abnormal data, it is detected in the power network whether there is abnormal data with the same abnormal data and abnormal state, and an early warning is issued.

[0027] Preferably, the power server collects power data operation logs and monitoring logs of the power monitoring module, and transmits the collected logs to the log message intermediate module through the communication pipeline, and then transmits them to the distributed document database through the communication pipeline;

[0028] After receiving the power data operation log and the monitoring log of the power monitoring module, the log is automatically transmitted to the configured distributed document database. The obtained log is stored in the distributed document database for a preset period of time.

[0029] Preferably, the distributed document database is used to provide data storage for a preset period of time, and an interface for querying the data stored in the distributed document database;

[0030] The power monitoring module monitors the log data in real time to determine whether there is any abnormality in the power data. If abnormality is found, the module analyzes the characteristics of the abnormal power data and finds the corresponding fault point or fault location based on the abnormal power data feature library.

[0031] It can be seen from the above technical solutions that the present invention has the following advantages:

[0032] The method disclosed by the present invention adopts multi-threaded monitoring of an electric power data operation terminal to improve the efficiency of electric power monitoring. After obtaining keywords of electric power operation data, identity information and IP information of the electric power data operation terminal, the method retrieves comparison information from a database through a multi-threaded communication mode, performs identity comparison and IP information matching, and simultaneously monitors whether the identity authority information of the electric power data operation terminal satisfies the electric power data operation authority. While ensuring the timeliness of data processing, the monitoring accuracy is guaranteed to avoid omissions.

[0033] The full-boundary power network data analysis method provided by the present invention collects power data information, uses keyword recognition and image recognition, realizes the monitoring of power data, monitors the power data operation terminal, effectively solves the problem of difficulty in power data monitoring, enhances the stability of power operation, provides the reliability of the power system, and improves the communication efficiency and stability through multi-threaded communication.

[0034] The full-boundary power network data analysis method provided by the present invention is error-free and anti-interference in the entire data transmission process, ensuring that logs are transmitted to the power server and then to the distributed document database without log data backlog in the entire process.

[0035] The full-boundary power network data analysis method of the present invention processes and partitions large amounts of power data within the power network, ensuring real-time processing, storage, and querying of power data. This facilitates the rapid processing and diagnosis of power failures or anomalies. It also extracts key features from power data operation logs and monitoring logs of power monitoring modules, allowing for the tracing of past power data and power operation processes.

[0036] The system layout can be a distributed deployment, with different power equipment, power data operation terminals, and power monitoring modules deployed in different power supply areas. When a fault occurs or data exceeds a threshold, it can be displayed in the power data log, locating the specific location and device, improving the efficiency of locating power faults and abnormal power conditions. Based on the characteristic points of power faults and abnormal power conditions, large-scale monitoring and troubleshooting can be carried out within the power grid to reduce grid failures.

[0037] The full-boundary power network data analysis method disclosed in this invention collects operational and communication data from all modules, including power equipment, power data operation terminals, power monitoring modules, and power operation software. The system analyzes abnormal conditions based on large amounts of power data and, based on abnormality monitoring in power data logs, locates specific locations and devices, improving the efficiency of locating power faults and abnormal conditions and displaying them. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 A flow chart of the full-boundary power network data analysis method based on artificial intelligence technology;

[0040] Figure 2 A flow chart of an embodiment of a method for analyzing full-boundary power network data;

[0041] Figure 3 The flowchart of an embodiment of a method for analyzing full-boundary power network data is shown in FIG. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] The units and algorithm steps of each example described in the embodiment disclosed in the full-boundary power network data analysis method based on artificial intelligence technology provided by the present invention can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0044] The block diagrams shown in the accompanying drawings of the artificial intelligence-based full-boundary power network data analysis method provided by the present invention are merely functional entities and do not necessarily correspond to physically separate entities. Specifically, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0045] In the full-boundary power network data analysis method based on artificial intelligence technology provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0046] The method involves a multi-system architecture that may include a power server, a network, a database, and a power data operation terminal. The power server is equipped with a power monitoring module. The network enables data communication within the system and may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0047] The power data operation terminal may include mobile terminals such as mobile phones, smart phones, laptop computers, digital broadcast receivers, personal digital assistants (PDAs), tablet computers (PADs), portable multimedia players (PMPs), navigation devices, and the like, as well as fixed terminals such as digital TVs, desktop computers, and the like. Below, it is assumed that the terminal is a mobile terminal. However, it will be understood by those skilled in the art that, in addition to elements specifically for mobile purposes, the configuration according to an embodiment of the present invention can also be applied to fixed-type terminals.

[0048] In the method provided by the present invention,

[0049] S101, the power monitoring module collects and monitors power data;

[0050] S102, acquiring the power operation data processed by the power data operation terminal by actively collecting the data;

[0051] S103: The power monitoring module can obtain terminal information of the power data operation terminal, and obtain the identity information and IP information of the power data operation terminal by parsing the terminal information. The power monitoring module retrieves keywords of the power operation data and identifies complete power data through the keywords.

[0052] The power data operation terminal registers its identity information in the power server, and the power server allocates the IP information of the power data operation terminal.

[0053] The power monitoring module uses multi-threaded monitoring of power data operation terminals to improve power monitoring efficiency. After obtaining the keywords of power operation data, the identity information and IP information of the power data operation terminal, it retrieves the comparison information from the database through multi-threaded communication, performs identity comparison and IP information matching, and monitors whether the identity authority information of the power data operation terminal meets the power data operation authority. While ensuring the timeliness of data processing, it ensures accurate monitoring and avoids omissions.

[0054] S201, the power monitoring module retrieves the operation log of the power data operation terminal to obtain a complete power data operation log, and obtains the address information of the operated device, the port information of the operated device, and the status data of the operated device in the obtained power data operation log;

[0055] S202, the power monitoring module sets power information filtering conditions, excludes power data that does not meet the power information filtering conditions, and finds power data that meets the power information filtering conditions for monitoring.

[0056] For example, if the location information of power personnel does not need to be monitored, the location information of power personnel can be set as the power information filtering condition. When the power monitoring module obtains the location information of power personnel from the power data operation terminal, it will be excluded.

[0057] For another example, the power information of the power data operation terminal is filtered and set as a filtering condition. Then, when the power monitoring module obtains the power information of the power data operation terminal, it is excluded and not used as monitoring data.

[0058] The power monitoring module performs keyword recognition and power equipment image recognition on the collected power data; specifically, it can match the recognized power data keywords with the corresponding power data in the database, match the corresponding power data, determine whether the power data meets the operation requirements at that time, and whether the power data operation terminal has operation authority.

[0059] For example, when operating the distribution switch or the power supply switch on and off, whether the current operating status of the power equipment and the power grid operating status meet the operating requirements, and whether the power data operation terminal has the operating authority.

[0060] Alternatively, it can match the corresponding power data, determine the current operating status of the power grid based on the power data, and take appropriate action, such as displaying it or analyzing and determining whether a fault has occurred.

[0061] The power monitoring module uses multi-threading to collect power data and improve monitoring efficiency.

[0062] In the embodiments provided by the present invention, power equipment image recognition can use a mean shift algorithm to match features in power equipment images, thereby identifying the state of the power equipment in the image. It can also identify whether the operation process of the power data operation terminal on the power equipment image meets preset requirements.

[0063] Specifically, the key elements in the power equipment image are extracted: the key elements with scale and color characteristics in the power equipment image are identified through the Gaussian differential function; the key elements are located and the characteristic status is determined; by comparing the characteristic status of each key element, several matching pairs of feature points are found, the status of the feature points is identified and compared, and the status of the power equipment is monitored.

[0064] The power monitoring module stores monitoring data. It filters the data, collects keywords and images, and provides a monitoring interface for monitoring power data. It filters the operational data from the power data operation terminal, monitors only those that meet the requirements, collects the data required for monitoring, and then performs keyword and image recognition on the data.

[0065] It also includes: a query module, which provides a query interface to users; it includes the search and query of power data, data verification and statistics, and the operating status of power equipment.

[0066] The full-boundary power network data analysis method provided by the present invention collects power data information, uses keyword recognition and image recognition, realizes the monitoring of power data, monitors the power data operation terminal, effectively solves the problem of difficulty in power data monitoring, enhances the stability of power operation, provides the reliability of the power system, and improves the communication efficiency and stability through multi-threaded communication.

[0067] S301, the power server obtains and stores the power data operation log and the monitoring log of the power monitoring module through the log message queue;

[0068] S302, the power server performs an anomaly analysis on the power data operation log and the monitoring log of the power monitoring module;

[0069] S303, when abnormal data appears, analyzing characteristic parameters of the power data operation log and the monitoring log of the power monitoring module;

[0070] S304, determining the type of abnormal data, abnormal state, and preset normal state based on the standard characteristic parameters stored in the characteristic parameter library;

[0071] S305: After analyzing the abnormal data, detect whether there is abnormal data with the same abnormal data and abnormal state in the power network, and issue an early warning.

[0072] Furthermore, the power server collects power data operation logs and monitoring logs of the power monitoring module, and transmits the collected logs to the log message intermediate module through the communication pipeline, and then transmits them to the distributed document database through the communication pipeline.

[0073] The entire data transmission process is error-free and interference-resistant, ensuring that logs are transmitted to the power server and then to the distributed document database without any log data backlog throughout the process.

[0074] When extracting and analyzing log data from a distributed document database, you use the distributed document database interface. You can call log data based on the provided interface, customize log data reports, and display them. You can also periodically send log data emails or provide information prompts.

[0075] After receiving the power data operation log and the monitoring log of the power monitoring module, the log is automatically transmitted to the configured distributed document database. The obtained log is stored in the distributed document database for a preset period of time. The power monitoring module on the power server is used to collect logs through the communication channel; when the log is transmitted to the log message intermediate module, the log is divided according to the log type, log source, or log time period;

[0076] The distributed document database is used to provide data storage for a preset duration and interface query. The data stored in the distributed document database is the data after the log is divided;

[0077] The power monitoring module monitors the log data in real time to determine whether there is any abnormality in the power data. If abnormality is found, the module analyzes the characteristics of the abnormal power data and finds the corresponding fault point or fault location based on the abnormal power data feature library.

[0078] The full-boundary power network data analysis method of the present invention processes and partitions large amounts of power data within the power network, ensuring real-time processing, storage, and querying of power data. This facilitates the rapid processing and diagnosis of power failures or anomalies. It also extracts key features from power data operation logs and monitoring logs of power monitoring modules, allowing for the tracing of past power data and power operation processes.

[0079] In the present invention, the operation process of device modules such as power equipment, power data operation terminal, power monitoring module, etc. will generate logs, and the system will automatically transmit the generated logs to the distributed document database, which stores the collected logs.

[0080] The system layout can be a distributed deployment, with different power equipment, power data operation terminals, and power monitoring modules deployed in different power supply areas. When a fault occurs or data exceeds a threshold, it can be displayed in the power data log, locating the specific location and device, improving the efficiency of locating power faults and abnormal power conditions. Based on the characteristic points of power faults and abnormal power conditions, large-scale monitoring and troubleshooting can be carried out within the power grid to reduce grid failures.

[0081] The full-boundary power network data analysis method disclosed in this invention collects operational and communication data from all modules, including power equipment, power data operation terminals, power monitoring modules, and power operation software. The system analyzes abnormal conditions based on large amounts of power data and, based on abnormality monitoring in power data logs, locates specific locations and devices, improving the efficiency of locating power faults and abnormal conditions and displaying them.

[0082] The full-boundary power network data analysis method based on artificial intelligence technology provided by the present invention is a combination of the units and algorithm steps of each example described in the embodiments disclosed herein, and can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0083] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A full-boundary power network data analysis method based on artificial intelligence technology, characterized in that: The method includes: a power monitoring module; The power monitoring module collects and monitors power data in the power network; The power operation data processed by the power data operation terminal is acquired by active collection; The power monitoring module obtains the terminal information of the power data operation terminal, and obtains the identity information and IP information of the power data operation terminal by parsing the terminal information; The power monitoring module performs keyword recognition and power equipment image recognition on the collected power data; Match the identified power data keywords with the corresponding power data in the database, match the corresponding power data, determine whether the power data meets the operation requirements at that time, and whether the power data operation terminal has the operation authority; Power equipment image recognition uses the Mean shift algorithm to match the comparison features in the power equipment image, thereby identifying the status of the power equipment in the image; identifying whether the operation process of the power data operation terminal on the power equipment image meets the preset requirements; Extract key elements from power equipment images: Identify key elements with scale and color characteristics in power equipment images through Gaussian differential functions; locate key elements and determine their characteristic states; find matching pairs of feature points by comparing the characteristic states of each key element, identify and compare the states of the feature points, and monitor the status of power equipment.

2. The power network data analysis method according to claim 1, characterized in that: The power monitoring module uses multi-threaded monitoring of the power data operation terminal. After obtaining the keywords of the power operation data, the identity information and IP information of the power data operation terminal, it uses multi-threaded communication; The power monitoring module retrieves keywords from power operation data and identifies complete power data through keywords; Retrieve comparison information from the database, perform identity comparison and IP information matching, and monitor whether the identity authority information of the power data operation terminal meets the power data operation authority.

3. The power network data analysis method according to claim 1, characterized in that: The power monitoring module retrieves the operation log of the power data operation terminal to obtain the complete power data operation log, and obtains the address information of the operated device, the port information of the operated device, and the status data of the operated device in the obtained power data operation log; The power monitoring module sets power information filtering conditions, excludes power data that does not meet the power information filtering conditions, and finds power data that meets the power information filtering conditions for monitoring.

4. The power network data analysis method according to claim 1, characterized in that: It also includes: a query module, which provides a query interface to users; it includes the search and query of power data, data verification and statistics, and the operating status of power equipment.

5. The power network data analysis method according to claim 1, characterized in that: The power server obtains and stores the power data operation log and the monitoring log of the power monitoring module through the log message queue; The power server performs anomaly analysis on the power data operation log and the monitoring log of the power monitoring module; When abnormal data occurs, analyze the characteristic parameters of the power data operation log and the monitoring log of the power monitoring module; Determine the type of abnormal data, abnormal state and preset normal state based on the standard characteristic parameters stored in the characteristic parameter library; After analyzing the abnormal data, it is detected in the power network whether there is abnormal data with the same abnormal data and abnormal state, and an early warning is issued.

6. The power network data analysis method according to claim 1, characterized in that: The power server collects power data operation logs and monitoring logs of the power monitoring module, and transmits the collected logs to the log message intermediate module through the communication pipeline, and then transmits them to the distributed document database through the communication pipeline; After receiving the power data operation log and the monitoring log of the power monitoring module, the log is automatically transmitted to the configured distributed document database. The obtained log is stored in the distributed document database for a preset period of time.

7. The power network data analysis method according to claim 1, characterized in that: The distributed document database is used to provide data storage for a preset period of time and interface query for the data stored in the distributed document database; The power monitoring module monitors the log data in real time to determine whether there is any abnormality in the power data. If abnormality is found, the module analyzes the characteristics of the abnormal power data and finds the corresponding fault point or fault location based on the abnormal power data feature library.

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