High-speed multi-node AMI communication method, system and equipment

Abstract

The invention provides a high-speed multi-node AMI communication method, system and device. The method is applied to a control device of an AMI communication system. Comprising the following steps: establishing a high-speed communication network with each electric power inspection equipment and intelligent metering equipment; acquiring equipment operation information corresponding to each electric power inspection equipment and working state information measured by the corresponding intelligent metering equipment through a high-speed communication network; the equipment operation information at least comprises an equipment working route and an equipment type; acquiring environment map information of an inspection environment corresponding to the electric power inspection equipment; determining at least one piece of risk position information corresponding to each equipment working route according to the environment map information and the equipment operation information and working state information corresponding to the plurality of pieces of power inspection equipment; and updating an equipment working route corresponding to the electric power inspection equipment according to the risk position information, generating AMI communication information according to the updated equipment working route, and controlling the electric power inspection equipment to complete inspection according to the AMI communication information.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent control, and in particular to a high-speed multi-node AMI communication method, system and device. BACKGROUND

[0002] The safe and stable operation of the power system is crucial to modern society. The traditional power inspection method mainly relies on manual work, and technicians need to conduct on-site inspection of substations, transmission lines, distribution equipment, etc. on a regular basis to ensure the normal operation of the equipment and timely detection of potential problems. However, the traditional inspection method has the following problems:

[0003] 1. High labor cost: The power inspection covers a wide range and has many types of equipment, requiring a large number of technical personnel and time investment.

[0004] 2. Low inspection efficiency: Manual inspection is greatly affected by weather, terrain and other factors, making it difficult to achieve high frequency and comprehensive coverage.

[0005] 3. High risk: Power equipment is usually in a high-voltage environment, and manual inspection poses certain safety hazards.

[0006] 4. Data management lag: Traditional inspection records mainly rely on paper or simple electronic spreadsheets, and data management is not timely, making it difficult to analyze and predict equipment status in real time.

[0007] Therefore, a high-speed multi-node AMI communication method is needed to solve at least one of the above problems. SUMMARY

[0008] The present application provides a high-speed multi-node AMI communication method, system and device, aiming to solve the following problems of the traditional inspection method: 1. High labor cost: The power inspection covers a wide range and has many types of equipment, requiring a large number of technical personnel and time investment. 2. Low inspection efficiency: Manual inspection is greatly affected by weather, terrain and other factors, making it difficult to achieve high frequency and comprehensive coverage. 3. High risk: Power equipment is usually in a high-voltage environment, and manual inspection poses certain safety hazards. 4. Data management lag: Traditional inspection records mainly rely on paper or simple electronic spreadsheets, and data management is not timely, making it difficult to analyze and predict equipment status in real time. The method provided by the present application builds a high-speed communication network between each power inspection device and the intelligent metering device and control device, and each power inspection device, intelligent metering device can serve as a node for communication with the control device, thereby enabling more complex and intelligent control.

[0009] In a first aspect, this application provides a high-speed multi-node AMI communication method applied to a control device of an AMI communication system, wherein the AMI communication system further includes multiple power inspection devices and at least one intelligent metering device mounted on the power inspection devices; the method includes:

[0010] Establish a high-speed communication network with each of the power inspection devices and the smart metering devices; the high-speed communication network includes a 5G communication network and / or an IoT communication network.

[0011] The high-speed communication network is used to acquire the equipment operation information corresponding to each of the power inspection devices and the working status information measured by the corresponding smart metering devices; the equipment operation information includes at least the equipment working route and equipment type.

[0012] Obtain the environmental map information of the inspection environment corresponding to the power inspection equipment;

[0013] Based on the environmental map information, the equipment operation information and working status information corresponding to the multiple power inspection devices, at least one risk location information corresponding to the working route of each device is determined;

[0014] The working route of the power inspection equipment is updated according to the risk location information, and AMI communication information is generated according to the updated working route, so as to control the power inspection equipment to complete the inspection according to the AMI communication information.

[0015] In some embodiments, establishing a high-speed communication network with each of the power inspection devices and the smart metering devices includes: obtaining the average power consumption value of each of the power inspection devices; identifying power inspection devices with an average power consumption value greater than a preset power consumption value as high-power devices; identifying power inspection devices with an average power consumption value less than or equal to the preset power consumption value as low-power devices; establishing the IoT communication network with the low-power devices and the smart metering devices; and establishing the 5G communication network with the high-power devices.

[0016] In some embodiments, the inspection environment includes multiple environmental sensors, which are set at environmental points within the inspection environment. Acquiring the environmental map information of the inspection environment corresponding to the power inspection equipment includes: acquiring satellite data information / or aerial image information corresponding to the inspection environment; performing feature extraction on the satellite data information / or aerial image information to obtain environmental feature information; the environmental feature information includes at least terrain information and vegetation cover information; acquiring real-time environmental parameters measured by the environmental sensors; and performing feature fusion based on the environmental feature information and the real-time environmental parameters to generate the environmental map information.

[0017] In some embodiments, determining at least one risk location information corresponding to each working route of the power inspection equipment based on the environmental map information, equipment operation information, and working status information corresponding to the multiple power inspection equipment includes: calculating environmental risk information, power system status risk information, traffic risk information, and network security risk information corresponding to each working route based on the environmental map information, equipment operation information, and working status information corresponding to the multiple power inspection equipment; calculating a risk threshold corresponding to each point in the working route based on the environmental risk information, power system status risk information, traffic risk information, and network security risk information; determining points with risk thresholds greater than a preset threshold as target points; and generating the risk location information based on the location of the target points.

[0018] For example, the step of calculating environmental risk information, power system status risk information, traffic risk information, and network security risk information corresponding to each work route based on the environmental map information, equipment operation information, and working status information corresponding to multiple power inspection devices includes: obtaining historical disaster information corresponding to the inspection environment; the historical disaster information includes historical disaster types and historical map information; calculating the environmental risk information based on the historical disaster types, historical map information, and environmental map information; obtaining historical fault information corresponding to each power inspection device; the historical fault information includes historical working status and historical operation information; calculating the power system status risk information based on the historical working status, historical operation information, working status information, and equipment operation information; obtaining the equipment travel speed corresponding to multiple power devices; calculating the traffic risk information corresponding to each power device based on the equipment travel speed and work route corresponding to multiple power devices; obtaining the network stability information of the power devices; and calculating the network security risk information based on the network stability information.

[0019] For example, the step of calculating the risk threshold corresponding to each point in the work route based on the environmental risk information, power system status risk information, traffic risk information, and network security risk information includes: obtaining weather forecast information corresponding to the inspection environment and equipment maintenance information corresponding to the power inspection equipment; if the weather forecast information determines that the weather within a preset time period is preset severe weather, generating a first penalty coefficient; if the equipment maintenance information determines that the power inspection equipment needs maintenance, generating a second penalty coefficient; calculating the risk threshold corresponding to each point in the work route based on the first penalty coefficient, the second penalty coefficient, environmental risk information, power system status risk information, traffic risk information, and network security risk information; the expression for the risk threshold includes:

[0020] ;

[0021] in, For the first on the work route The risk threshold corresponding to each location These are the first penalty coefficient and the second penalty coefficient, respectively. , , and These are respectively the environmental risk information, power system status risk information, traffic risk information, and cybersecurity risk information.

[0022] In some embodiments, updating the working route of the power inspection equipment according to the risk location information includes: parsing the risk location information to obtain the coordinates of the risk point and the cause of the risk; the cause of the risk includes any one of physical obstacles, environmental conditions, equipment failure, and human activities; calculating the hazard level corresponding to the risk cause; and updating the working route of the equipment according to the hazard level; wherein, if the hazard level is greater than a preset hazard level, the coordinates of the risk point are not adjusted in the working route; if the hazard level is greater than or equal to the preset hazard level, the working route is adjusted to bypass the coordinates of the risk point.

[0023] In some embodiments, the AMI communication information includes route direction information, route speed information, and route operation information; the step of generating AMI communication information based on the updated device working route, and controlling the power inspection equipment to complete the inspection based on the AMI communication information, includes: parsing the updated working route to obtain the route direction information, route speed information, and route operation information; and controlling the power inspection equipment to complete the inspection based on the route direction information, route speed information, and route operation information.

[0024] Secondly, this application provides an AMI communication device, applied to a control device of an AMI communication system, wherein the AMI communication system further includes multiple power inspection devices and at least one smart metering device mounted on the power inspection devices; the device includes:

[0025] A communication establishment unit is used to establish a high-speed communication network with each of the power inspection devices and the smart metering devices; the high-speed communication network includes a 5G communication network and / or an IoT communication network.

[0026] The information acquisition unit is used to acquire, through the high-speed communication network, the equipment operation information corresponding to each of the power inspection devices and the working status information measured by the corresponding smart metering devices; the equipment operation information includes at least the equipment working route and equipment type.

[0027] The map acquisition unit is used to acquire environmental map information of the inspection environment corresponding to the power inspection equipment.

[0028] The location acquisition unit is used to determine at least one risk location information corresponding to the working route of each of the multiple power inspection devices based on the environmental map information, the equipment operation information and working status information corresponding to the multiple power inspection devices;

[0029] The drive completion unit is used to update the equipment working route corresponding to the power inspection equipment according to the risk location information, generate AMI communication information according to the updated equipment working route, and control the power inspection equipment to complete the inspection according to the AMI communication information.

[0030] Thirdly, this application also provides an AMI communication system, comprising:

[0031] Multiple power inspection devices;

[0032] Multiple intelligent metering devices, with each of the power inspection devices equipped with at least one of the intelligent metering devices;

[0033] A control device is provided, which establishes a high-speed communication network with each of the power inspection devices and the smart metering devices. The high-speed communication network includes a 5G communication network and / or an IoT communication network. The control device obtains equipment operation information corresponding to each power inspection device and the corresponding working status information measured by the smart metering device through the high-speed communication network. The equipment operation information includes at least the equipment working route and equipment type. The control device obtains environmental map information of the inspection environment corresponding to each power inspection device. Based on the environmental map information, the equipment operation information, and the working status information of multiple power inspection devices, the control device determines at least one risk location information corresponding to the working route of each device. The control device updates the working route of the power inspection device based on the risk location information and generates AMI communication information based on the updated working route, so as to control the power inspection device to complete the inspection based on the AMI communication information.

[0034] Fourthly, embodiments of this application provide a control device, the control device including a memory and a processor, the memory for storing a computer program, and the processor for executing the computer program and implementing the method as provided in any embodiment of this application when executing the computer program.

[0035] This application provides a high-speed multi-node AMI (Advanced Metering Infrastructure) communication method, system, and device. This method, based on a high-speed communication-driven AMI approach, aims to optimize the power inspection process. Its main technical contents include the following steps: The method requires constructing a high-speed communication network covering all power inspection equipment and smart metering devices. To achieve real-time and efficient data transmission, the network can choose a 5G communication network or an IoT communication network, or a combination thereof. The operating information of each power inspection device, such as its working route and equipment type, is collected through the high-speed communication network. Simultaneously, environmental map information of the inspection environment where the power inspection equipment is located is acquired, including terrain, weather conditions, and the distribution of surrounding buildings. Using the acquired data, especially the environmental map information and the operating information of the power inspection equipment, potential risk locations on the inspection route are analyzed and identified. These risks may be caused by factors such as high-voltage environments, high-risk areas, and special terrain. Based on the identified risk locations, the working route of the power inspection equipment is adjusted to avoid high-risk or difficult-to-handle environments. Based on the optimized route, AMI communication information is generated to control the power inspection equipment to perform inspections according to the new route.

[0036] Furthermore, the provided method has at least the following beneficial effects:

[0037] 1. Reduce labor costs: By automating inspection route planning and equipment management, the need for technical personnel is reduced, thereby lowering labor costs.

[0038] 2. Improve inspection efficiency: The real-time data support provided by the high-speed communication network enables power inspections to be carried out without being limited by factors such as weather and terrain, and to cover the inspection area frequently and comprehensively.

[0039] 3. Reduce safety risks: The program can identify potential risk points during the inspection process and plan the inspection route in advance, avoiding high-voltage environments and high-risk areas as much as possible, thus reducing the safety risks for power inspection personnel.

[0040] 4. Strengthen data management: The introduction of intelligent metering equipment and high-speed communication networks enables inspection records and data management to be unrestricted by time and location, allowing for timely and accurate data recording, real-time data analysis and status prediction, thereby increasing the value of inspection data.

[0041] In summary, this high-speed multi-node AMI communication method can significantly improve the automation level of power inspection systems, solve some common problems in the industry through technological innovation, and bring more convenience to the maintenance and management of power systems.

[0042] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic block diagram of the structure of an AMI communication system provided in an embodiment of this application;

[0045] Figure 2 This is a schematic flowchart illustrating the steps of a high-speed multi-node AMI communication method provided in an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of the structure of an AMI communication device provided in an embodiment of this application;

[0047] Figure 4 This is a schematic block diagram of the structure of a control device provided in an embodiment of this application.

[0048] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0049] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0051] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0052] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0053] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0054] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0055] The safe and stable operation of power systems is crucial to modern society. Traditional power inspection methods rely primarily on manual labor, requiring technicians to regularly conduct on-site inspections of substations, transmission lines, and distribution equipment to ensure normal equipment operation and promptly identify potential problems. However, traditional inspection methods have the following drawbacks:

[0056] 1. High labor costs: Power line inspection covers a wide area and involves a variety of equipment, requiring a large number of technical personnel and time investment.

[0057] 2. Low inspection efficiency: Manual inspections are greatly affected by factors such as weather and terrain, making it difficult to achieve high frequency and comprehensive coverage.

[0058] 3. High risk: Power equipment is usually in a high-voltage environment, and manual inspection poses certain safety hazards.

[0059] 4. Lagging data management: Traditional inspection records mainly rely on paper or simple spreadsheets, resulting in untimely data management and difficulty in real-time analysis and prediction of equipment status.

[0060] Therefore, a high-speed multi-node AMI communication method is urgently needed to solve at least one of the above problems.

[0061] To resolve the above issues, please refer to [link / reference]. Figure 1This application provides an AMI (Automatic Inspection Machine) communication system, including: multiple power inspection devices; multiple smart metering devices, each power inspection device carrying at least one smart metering device; and a control device for establishing a high-speed communication network with each power inspection device and the smart metering device. The high-speed communication network includes a 5G communication network and / or an IoT communication network. The control device obtains equipment operation information corresponding to each power inspection device and working status information measured by the corresponding smart metering device through the high-speed communication network. The equipment operation information includes at least the equipment working route and equipment type. The control device obtains environmental map information of the inspection environment corresponding to the power inspection device. The control device determines at least one risk location information corresponding to the working route of each device based on the environmental map information, the equipment operation information and working status information of the multiple power inspection devices. The control device updates the working route of the power inspection device according to the risk location information and generates AMI communication information based on the updated working route to control the power inspection device to complete the inspection.

[0062] Specifically, the power inspection equipment assumes the use of drones and autonomous robots. These devices can cover complex terrain and high-altitude environments, making them suitable for a wide range of power inspection tasks. Smart metering devices, such as high-definition cameras, infrared sensors, and vibration sensors, are mounted on the drones and autonomous robots to monitor the operating status of power equipment in real time. The control unit is a central processing unit, which can be a cloud server or a high-performance computer within a local area network. It is responsible for establishing communication links with all power inspection equipment and their onboard smart metering devices, and processing the received data.

[0063] The operational information of the inspection equipment includes its working route (e.g., the flight path of a drone) and equipment type (e.g., autonomous robot or drone). The operational status information includes real-time measurements of the equipment's status by its onboard smart metering devices, such as whether there is damage or overheating. This information is transmitted to the control equipment via 5G or IoT networks.

[0064] Environmental map information: A detailed environmental map is generated using remote sensing images transmitted via 5G / IoT network. This map marks various factors that may affect the inspection, such as terrain and weather conditions.

[0065] Risk location information combines environmental maps and operational status information reported by inspection equipment to identify potentially risky locations using pre-defined rules or algorithm models. For example, it predicts areas with high equipment failure rates based on historical maintenance records, or uses deep learning algorithms to identify anomalies in images. The inspection equipment's work routes are optimized based on this risk location information, prioritizing inspection tasks. This optimization can involve strategies such as avoiding high-risk areas or increasing the frequency of inspections in high-risk areas. The optimized inspection routes generate AMI (Automatic Management Interface) communication information, used to remotely command the power inspection equipment. For example, this is flight control commands for drones; for robots, it's movement path or action commands. Data collected by the inspection equipment and its onboard intelligent metering devices is immediately transmitted to the control equipment, which performs real-time data analysis via a high-speed communication network. The system not only detects current equipment problems but also uses historical data to predict potential future faults, enabling preventative maintenance. Automated inspections and data analysis significantly reduce hidden faults and maintenance delays, resulting in substantial reductions in operating costs.

[0066] For example, suppose this system is deployed in a comprehensive power grid including high-voltage transmission lines, substations, and distribution rooms. It is equipped with drones and robots suitable for different inspection environments, all equipped with high-definition cameras and infrared sensors. The control equipment generates inspection tasks based on the operating status of the power inspection equipment and the inspection environment information. For example, if a section of a transmission line is frequently covered by heavy fog, identifying a section prone to short-circuit faults, a task to increase the inspection frequency is issued. Drones and robots fly to the inspection location, high-definition cameras capture high-resolution images or videos, and infrared sensors detect the temperature of the equipment. This data is transmitted to the control equipment in real time via a 5G network. The control equipment remotely analyzes the acquired data, guiding the inspection equipment to adjust the task sequence or path. When equipment damage or malfunction is detected and identified in the images, or when abnormal temperatures are observed, the control equipment generates alarm information and location coordinates, and sends this information to the power maintenance department via the communication network. Non-destructive testing methods can be utilized as much as possible to identify anomalies during battery inspections.

[0067] This AMI communication system, based on high-speed communication and automated inspection, can effectively improve the safety and efficiency of power inspection, reduce overall maintenance costs, and provide solid data support for preventive maintenance.

[0068] Please refer to Figure 2This application provides a schematic flowchart of a high-speed multi-node AMI communication method. This high-speed multi-node AMI communication method can be implemented by a control device of the AMI communication system as provided in any embodiment of this application. The control device can be deployed on a single server or a server cluster. It can also be deployed on a handheld terminal, control board, laptop computer, wearable device, or robot, etc.

[0069] It should be noted that the acquisition of any information mentioned in the provided methods (such as device operation information and working status information) is in compliance with relevant regulations and with the consent of the relevant users, and will not infringe on user privacy or violate relevant laws and regulations.

[0070] To solve the above problem, please refer to Figure 1 Specifically, such as Figure 1 As shown, the provided high-speed multi-node AMI communication method includes steps S101 to S105. Details are as follows:

[0071] Step S101. Establish a high-speed communication network with each power inspection device and smart metering device; the high-speed communication network includes a 5G communication network and / or an IoT communication network.

[0072] Specifically, 5G communication networks utilize fifth-generation mobile communication technology, featuring high speed, low latency, and high reliability. IoT communication networks leverage Internet of Things (IoT) technologies, including Wi-Fi, Bluetooth, and LoRa, to establish efficient, low-power communication networks over wide areas. This is achieved by deploying antenna base stations, routers, and other necessary equipment in the inspection environment to support 5G and IoT network coverage. For example, 5G cellular base stations can be used to provide comprehensive coverage of areas where power equipment and inspection devices are located. LoRa technology is used to establish low-power communication networks in remote areas or areas with dense IoT devices, ensuring the stability and reliability of data transmission. Furthermore, each power inspection device and smart metering device can act as a node in the high-speed communication network, enabling high-speed multi-node communication between control equipment and multiple inspection and smart metering devices. This ensures that inspection and smart metering devices can transmit and receive data in real time, improving the response speed of inspection tasks and allowing control equipment to promptly obtain equipment status and environmental information. It also guarantees the stability of the communication network, ensuring reliable data transmission even in complex environments.

[0073] Step S102. Obtain the equipment operation information and the working status information measured by the corresponding smart metering equipment for each power inspection device through a high-speed communication network; the equipment operation information includes at least the equipment working route and equipment type.

[0074] Specifically, smart metering devices (such as cameras, infrared sensors, vibration sensors, etc.) collect real-time status data of power equipment. The collected data is transmitted to control equipment via 5G or IoT networks. The control equipment receives and parses the data, extracting key information such as equipment type, operating route, temperature, and vibration status.

[0075] For example, cameras mounted on drones can capture real-time images of power transmission lines during flight and transmit the image data to control equipment. Infrared sensors on robots can monitor temperature changes in power distribution equipment and send temperature data to control equipment in real time.

[0076] The above steps enable real-time monitoring of power equipment status and timely detection of potential problems. Multi-dimensional data collection from multiple smart metering devices provides comprehensive status information. High-precision data acquisition and transmission technologies ensure the accuracy of the information.

[0077] Step S103. Obtain the environmental map information of the inspection environment corresponding to the power inspection equipment.

[0078] Specifically, environmental maps are generated using satellite remote sensing imagery, drone aerial imagery, or other remote sensing data. Information such as terrain, weather conditions, and obstacles is integrated into the environmental map. Geographic Information System (GIS) technology is used to process and generate detailed environmental maps, facilitating the identification and optimization of inspection routes.

[0079] For example, detailed topographic maps of power line inspection areas can be generated using satellite imagery. Alternatively, aerial images captured in real-time by drones can be fused with topographic maps to create real-time environmental maps. Based on these detailed environmental maps, optimal inspection routes can be planned. Inspection routes can be dynamically adjusted according to real-time environmental data to cope with changes in weather and terrain. Dangerous areas and unfavorable terrain can be identified using environmental maps to prevent inspection equipment from entering these areas.

[0080] Step S104. Based on the environmental map information, the equipment operation information and working status information corresponding to multiple power inspection devices, determine at least one risk location information corresponding to the working route of each device.

[0081] Specifically, risk analysis is conducted using historical fault data, weather models, and terrain models. By combining inspection equipment operation information, environmental maps, and status data, machine learning and deep learning algorithms are used to identify risky locations. These risky locations are then classified and graded, such as high-risk, medium-risk, and low-risk areas.

[0082] For example, training models can predict areas where high-voltage transmission lines are prone to short circuits in foggy weather. Image recognition technology can be used to identify obstructions captured by high-altitude inspection drones under strong sunlight, confirming these areas as potential risk zones. Advanced algorithms and historical data can be used to accurately identify high-risk locations along inspection paths. Risk locations can be dynamically adjusted based on real-time environmental changes to optimize inspection strategies. This helps inspection equipment avoid known high-risk areas, improving inspection safety.

[0083] Step S105. Update the working route of the power inspection equipment according to the risk location information, generate AMI communication information according to the updated working route, and control the power inspection equipment to complete the inspection according to the AMI communication information.

[0084] Specifically, the inspection route is adjusted based on risk location information, focusing on high-risk areas and reducing the number of inspections in low-risk areas. Detailed AMI communication information, including control commands and path parameters, is generated and sent to the inspection equipment. The inspection equipment then executes the corresponding inspection tasks based on the AMI communication information, completing the inspection of the power equipment.

[0085] Based on risk location information, the replanned flight paths of drones increased the frequency of inspections in key high-risk areas and avoided complex terrain. Control equipment generated flight trajectories and control commands, which the drones followed to fly to designated locations for image capture and data collection.

[0086] The above steps update the inspection route based on risk location information, improving the targeting and efficiency of the inspection. The inspection equipment can autonomously complete inspection tasks based on AMI communication information, reducing the need for manual intervention. The inspection equipment can also adjust itself during the inspection process based on real-time communication information, adapting to dynamic environmental changes. Through these steps, the high-speed multi-node AMI communication method can significantly improve the safety and efficiency of power system inspections, reduce maintenance costs, and provide a solid guarantee for the safe and stable operation of the power system.

[0087] In some embodiments, establishing a high-speed communication network with each power inspection device and smart metering device includes: obtaining the average power consumption value of each power inspection device; identifying power inspection devices with an average power consumption value greater than a preset power consumption value as high-power devices; identifying power inspection devices with an average power consumption value less than or equal to a preset power consumption value as low-power devices; establishing an IoT communication network with the low-power devices and smart metering devices; and establishing a 5G communication network with the high-power devices.

[0088] By integrating a power consumption monitoring module onto the motherboard of the inspection equipment, energy consumption data is recorded periodically. The control equipment periodically collects and processes this data, calculating the average power consumption of each device. Data analysis software or algorithms are used to statistically analyze the energy consumption data and generate detailed energy consumption reports. Power inspection equipment with an average power consumption value exceeding a preset value is identified as high-power equipment. Reasonable average power consumption thresholds are set based on equipment type and inspection task requirements. The control equipment compares the collected average power consumption value with the preset value, marking devices exceeding the threshold as high-power equipment. Power inspection equipment with an average power consumption value less than or equal to the preset value is identified as low-power equipment. The control equipment compares the collected average power consumption value with the preset value, marking devices not exceeding the threshold as low-power equipment. Suitable communication technologies for low-power devices, such as LoRa, Wi-Fi, or Zigbee, are selected. IoT base stations are deployed in the power inspection area to ensure stable data transmission between low-power devices and smart metering devices. Suitable communication technologies for high-power devices, such as 5G cellular communication modules, are selected. 5G base stations are deployed in the power inspection area to ensure high-speed and stable data transmission for high-power devices.

[0089] For example, suppose there are multiple drones and autonomous robots in a power grid. Some of these drones carry more sensors and consume more energy, while some of the simpler robots consume less energy.

[0090] Power consumption monitoring and calculation:

[0091] High-power drone (D1): According to the power consumption monitoring module, the average power consumption of D1 is 15 watts.

[0092] Low-power robot (R1): According to the power consumption monitoring module, the average power consumption of R1 is 5 watts.

[0093] Preset power consumption threshold: set to 10 watts.

[0094] Equipment Classification:

[0095] High power consumption device: D1 (15 watts > 10 watts).

[0096] Low power devices: R1 (5 watts ≤ 10 watts).

[0097] Network setup:

[0098] IoT communication network: Deploy LoRa base stations to establish stable low-power communication links with low-power devices such as R1.

[0099] 5G communication network: Deploy 5G base stations to establish high-speed, low-latency communication links with high-power devices such as D1.

[0100] Internet of Things (IoT) networks: Utilizing low-power communication technologies such as LoRa, these are suitable for low-power inspection devices, reducing communication costs and extending device battery life. Fifth-generation (5G) networks: Employing high-speed communication technologies, these are suitable for high-power inspection devices, providing a fast and reliable data transmission channel to ensure real-time performance. Selecting the appropriate communication technology based on the power consumption of different devices avoids unnecessary cost waste caused by using 5G for all devices. The low-power characteristics of IoT communication technologies enable low-power devices to reduce battery consumption and extend their lifespan.

[0101] High-power devices transmit high-quality images, videos, and other high-volume data via 5G networks, ensuring data real-time performance and integrity. The low latency of 5G networks enables instant monitoring and response to high-risk areas, improving inspection effectiveness. Dynamically adjusting the communication network based on device power consumption allows the system to operate efficiently in various environments, enhancing overall system stability and reliability.

[0102] Through the above embodiments, the high-speed multi-node AMI communication method can realize the construction of targeted communication networks for different types of power inspection equipment and smart metering equipment, which not only ensures the efficiency and real-time performance of data transmission, but also optimizes communication costs and resource allocation, further improving the security and reliability of the power inspection system.

[0103] In some embodiments, the inspection environment includes multiple environmental sensors, which are set at environmental points in the inspection environment; acquiring environmental map information of the inspection environment corresponding to the power inspection equipment includes: acquiring satellite data information / or aerial image information corresponding to the inspection environment; extracting features from the satellite data information / or aerial image information to acquire environmental feature information; the environmental feature information includes at least terrain information and vegetation cover information; acquiring real-time environmental parameters measured by the environmental sensors; and performing feature fusion based on the environmental feature information and real-time environmental parameters to generate environmental map information.

[0104] Common environmental sensors include temperature sensors, humidity sensors, light sensors, and barometric pressure sensors. These sensors are installed at key locations in the inspection environment, such as substations, power distribution rooms, and along power transmission lines. Satellite data is obtained through satellite remote sensing technology, acquiring high-resolution images or geographic information of the inspection environment. Aerial imagery is obtained by using drones or other aerial equipment to capture high-definition images or videos of the inspection area. Image processing techniques (such as image segmentation and deep learning) are used to extract terrain information, such as plains, mountains, and hills, from satellite data or aerial images. Image analysis techniques are used to identify and extract vegetation cover information, such as tree density and vegetation type. Environmental sensors collect various parameters in the inspection environment in real time, such as temperature, humidity, and light intensity. Sensor data is transmitted to control equipment via IoT or other communication networks. Environmental feature information extracted from satellite data / aerial images is integrated with real-time environmental parameters provided by environmental sensors. GIS technology or related algorithms are used to process and analyze the integrated data to generate detailed environmental map information.

[0105] For example, suppose a power line inspection environment includes transmission lines and substations. Temperature and humidity sensors are installed around the substations to monitor ambient temperature and humidity. Light sensors are installed along the transmission lines to monitor light intensity and environmental changes. Topographic and vegetation cover information of the power area is acquired through high-resolution satellite remote sensing imagery. High-resolution images of the transmission lines are captured by drones to aid in further analysis of topographic and vegetation information. Deep learning algorithms (such as convolutional neural networks) are used to segment and classify satellite and aerial images to extract topographic and vegetation cover information. Temperature and humidity sensors report the latest data every hour. Light intensity data is uploaded every 10 minutes. The topographic and vegetation information extracted from satellites and aerial images is fused with real-time environmental data from the sensors (such as temperature, humidity, and light intensity). GIS technology is used to generate detailed environmental maps from the integrated data, including topography, vegetation distribution, current temperature, and humidity.

[0106] By providing highly detailed information on terrain and vegetation cover, it helps identify potential risk locations. Real-time monitoring of parameters such as temperature, humidity, and light intensity ensures the timeliness and accuracy of environmental information.

[0107] By fusing multi-source data, accurate environmental map information is generated, providing more refined support for inspection route planning. The inspection route is dynamically adjusted based on real-time environmental parameters, improving inspection efficiency and safety.

[0108] In summary, combining the above embodiments, the high-speed multi-node AMI communication method can achieve multi-level and comprehensive monitoring and analysis of the inspection environment, generating high-precision environmental map information. This allows for more effective optimization of inspection paths, improved inspection accuracy, and enhanced overall system reliability. This approach helps improve the safety and efficiency of power line inspections while reducing maintenance costs.

[0109] In some embodiments, determining at least one risk location information corresponding to each device's working route based on environmental map information, equipment operation information, and working status information corresponding to multiple power inspection devices includes: calculating environmental risk information, power system status risk information, traffic risk information, and network security risk information corresponding to each working route based on environmental map information, equipment operation information, and working status information corresponding to multiple power inspection devices; calculating a risk threshold corresponding to each point in the working route based on the environmental risk information, power system status risk information, traffic risk information, and network security risk information; determining points with risk thresholds greater than a preset threshold as target points; and generating risk location information based on the location of the target points.

[0110] Specifically, environmental map information includes terrain, vegetation cover, and historical fault data. Equipment operation information includes the equipment's operating route, equipment type, and equipment status. The smart metering equipment measures operational status information: real-time collected data such as temperature, humidity, and vibration. Terrain information is analyzed, such as identifying high-risk terrain like mountains and cliffs. Vegetation cover is analyzed, as densely wooded areas may impact power equipment. The probability of equipment failure is calculated based on historical fault data. The current health status of the equipment is assessed using data from the smart metering equipment. The equipment's workload is analyzed to identify potential risks from overload or excessive load conditions. Traffic flow in the area is analyzed, as densely trafficked areas may interfere with drone inspections. Road conditions along the inspection route are assessed to identify potential obstacles or hazardous areas, such as potholes and construction zones. Potential cybersecurity threats to the inspection equipment are identified, such as signal interference in hotspot areas. Data transmission security is assessed to ensure data is not intercepted or tampered with. Combining the above environmental, power system status, traffic, and cybersecurity risk information, the risk at each location is comprehensively assessed. Based on multiple factors, a risk threshold is calculated for each location to identify those with higher risks. A preset risk threshold is set; locations exceeding this threshold are considered to require special attention. Locations with risk thresholds greater than the preset threshold are identified as target locations requiring focused monitoring. Based on the location information of these target locations, these high-risk locations are marked on an environmental map. This risk location information is then sent to control equipment for subsequent inspection route optimization.

[0111] For example, suppose a power inspection environment includes transmission lines and substations.

[0112] Topographic and vegetation cover information is obtained through satellite imagery and drone aerial photography.

[0113] Equipment operation information: The flight path of the inspection drone and the equipment type are drones.

[0114] Operating status information: The sensors installed on the drone collect data such as temperature, humidity, and wind speed.

[0115] Environmental risk information includes: Topographic risk: identifying mountainous areas and potential landslide zones. Vegetation risk: identifying densely wooded areas and areas prone to fire.

[0116] Power system status risk information includes: Fault history: analyzing the number and location of faults at substations over the past month. Health status: calculating the health status of substations and transmission lines using sensor data.

[0117] Traffic risk information includes: Traffic flow: Obtaining traffic flow data along bus routes. Vehicle information: Identifying vehicles the drone may encounter during flight or whether it will encounter other drones.

[0118] Cybersecurity risk information includes: Signal interference: detecting network signal interference in hotspot areas. Data security: ensuring encrypted data transmission to prevent interception.

[0119] Based on the risk information above, calculate the comprehensive risk threshold for each location. For example, if the risk threshold is set to 3, locations exceeding 3 are considered high-risk areas. Identify locations with a comprehensive risk threshold greater than 3, such as landslide areas in mountainous regions, densely wooded areas, areas along past bus routes, and internet hotspots. Mark these high-risk locations on the environmental map to generate risk location information. Send this risk location information to the control equipment for subsequent inspection route optimization, ensuring that the inspection equipment prioritizes these high-risk locations.

[0120] By combining risk assessments from multiple perspectives, including environment, power system status, transportation, and cybersecurity, more comprehensive risk information is provided. High-risk locations are identified more accurately by precisely calculating risk thresholds for each point. Prioritizing inspections of high-risk areas ensures the efficiency and effectiveness of inspection tasks. The number of inspections of low-risk areas is reduced, optimizing the time and resource allocation for inspection equipment. Risk location information is dynamically updated based on real-time data changes, enabling inspection paths to adapt to environmental changes. Inspection equipment can instantly adjust its path based on updated risk location information, improving safety.

[0121] In summary, the provided method, combined with the above embodiments, can perform comprehensive risk assessment, generate accurate risk location information, thereby optimizing inspection paths, improving inspection efficiency and safety, and ensuring the stable operation of the power system. This approach is applicable to various complex and changing inspection environments and can flexibly address different risk challenges.

[0122] For example, based on environmental map information, equipment operation information, and working status information corresponding to multiple power inspection devices, environmental risk information, power system status risk information, traffic risk information, and network security risk information corresponding to each working route are calculated. This includes: obtaining historical disaster information corresponding to the inspection environment; historical disaster information includes historical disaster types and historical map information; calculating environmental risk information based on historical disaster types, historical map information, and environmental map information; obtaining historical fault information corresponding to each power inspection device; historical fault information includes historical working status and historical operation information; calculating power system status risk information based on historical working status, historical operation information, working status information, and equipment operation information; obtaining the equipment travel speed corresponding to multiple power devices; calculating traffic risk information corresponding to each power device based on the equipment travel speed and working route corresponding to multiple power devices; obtaining network stability information of the power devices; and calculating network security risk information based on the network stability information.

[0123] Through the technical content and implementation of the above examples, the high-speed multi-node AMI communication method can conduct comprehensive and multi-dimensional risk assessments, generate accurate risk location information, thereby optimizing inspection paths, improving inspection safety and efficiency, and ensuring the stable operation of the power system. This approach is applicable to various complex inspection environments and can flexibly respond to different risk challenges.

[0124] For example, the risk threshold corresponding to each point in the work route is calculated based on environmental risk information, power system status risk information, traffic risk information, and cybersecurity risk information. This includes: obtaining weather forecast information corresponding to the inspection environment and equipment maintenance information corresponding to the power inspection equipment; if the weather forecast information determines that the weather within a preset time period is preset severe weather, a first penalty coefficient is generated; if the equipment maintenance information determines that the power inspection equipment needs maintenance, a second penalty coefficient is generated; the risk threshold corresponding to each point in the work route is calculated based on the first penalty coefficient, the second penalty coefficient, environmental risk information, power system status risk information, traffic risk information, and cybersecurity risk information; the expression for the risk threshold includes:

[0125] ;

[0126] in, For the first on the work route The risk threshold corresponding to each location These are the first penalty coefficient and the second penalty coefficient, respectively. , , and These are environmental risk information, power system status risk information, traffic risk information, and cybersecurity risk information.

[0127] The system obtains weather information for a future period through meteorological forecasting services, including rainfall, wind speed, and temperature. It presets severe weather criteria: defining which weather conditions qualify as severe weather, such as strong winds (wind speed exceeding 20 m / s) and heavy rain (rainfall exceeding 20 mm / hour). Based on the weather forecast information, it determines whether the weather within the preset time period is severe. If the weather within the preset time period is determined to be severe, a first penalty coefficient is generated (e.g., ...). This indicates that severe weather has a greater impact on the inspection route; otherwise, Obtain equipment maintenance information for power inspection equipment: Retrieve the maintenance schedule for each power inspection device through the equipment maintenance system, including whether maintenance is required, the type of maintenance, and the maintenance time. Combine the current operating status of the equipment with historical maintenance records to assess whether maintenance is necessary. Determine whether the power inspection equipment requires maintenance based on the equipment maintenance information. For example, if maintenance is required, generate a second penalty coefficient (e.g., ...). =2), indicating that equipment maintenance needs have a greater impact on the inspection route; otherwise... =1. By combining the device status... Assign values ​​to ensure that maintenance needs are fully considered.

[0128] Through the technical content and implementation of the above examples, the high-speed multi-node AMI communication method can perform accurate risk assessments, dynamically adjust inspection paths, improve the safety and efficiency of inspections, and ensure the stable operation of the power system. This method is applicable to various complex inspection environments, can flexibly respond to different risk challenges, and ensures that inspection tasks are completed efficiently and safely.

[0129] In some embodiments, updating the working route of the power inspection equipment according to the risk location information includes: parsing the risk location information to obtain the coordinates of the risk point and the cause of the risk; the cause of the risk includes any one of physical obstacles, environmental conditions, equipment failure, and human activities; calculating the hazard level corresponding to the risk cause; and updating the working route of the equipment according to the hazard level; wherein, if the hazard level is greater than a preset hazard level, the coordinates of the risk point are not adjusted in the working route; if the hazard level is greater than or equal to the preset hazard level, the working route is adjusted to bypass the coordinates of the risk point.

[0130] By extracting specific geographic location information (latitude and longitude coordinates) from the risk location information, the specific causes of hazards at each risk point are determined, such as physical obstacles (trees, power line towers), environmental conditions (flooded areas), equipment failures (areas where failures have occurred before), and human activities (construction areas, human activities), etc.

[0131] Using a predefined hazard assessment model or weighting system, the hazard level of each risk point is calculated based on the different types and severity of the risk causes. Building upon the above embodiments, the hazard level can be divided into multiple levels according to actual needs, such as Level 1 (low risk), Level 2 (medium risk), and Level 3 (high risk). If the hazard level is greater than the preset hazard level: the risk point coordinates are not adjusted, ensuring that the inspection equipment passes through these points for detailed inspection. If the hazard level is greater than or equal to the preset hazard level: the work route is adjusted to bypass the risk point coordinates, avoiding unforeseen dangers to the inspection equipment. For low to medium hazard levels: the route is adjusted or low-risk points are ignored based on the specific situation, completing the update of the work route.

[0132] By avoiding risk points with a hazard level greater than or equal to a preset hazard level, the likelihood of inspected equipment encountering danger is reduced. Detailed inspections are conducted at critical points with a hazard level greater than the preset hazard level to ensure these areas are in good condition. The working route of the inspection equipment is dynamically updated based on real-time risk location information and hazard levels, improving inspection efficiency and safety. It can flexibly respond to different inspection environments and risk situations, adjusting routes to avoid unforeseen hazardous areas. Detailed inspections are conducted at high-hazard-level critical points to ensure data accuracy and completeness. Through precise risk assessment and route adjustment, errors caused by environmental changes or equipment malfunctions during inspections are reduced, improving inspection quality.

[0133] In some embodiments, the AMI communication information includes route direction information, route speed information, and route operation information; the step of generating AMI communication information based on the updated device working route, and controlling the power inspection equipment to complete the inspection based on the AMI communication information, includes: parsing the updated working route to obtain the route direction information, route speed information, and route operation information; and controlling the power inspection equipment to complete the inspection based on the route direction information, route speed information, and route operation information.

[0134] Obtain detailed information about the updated work route. Determine the direction of travel for the inspection equipment (e.g., forward, reverse, counter-clockwise, etc.). Route speed information is used to determine the specific speed of the inspection equipment on different road sections (e.g., low speed, medium speed, high speed). Route operation information includes operational instructions for the equipment at specific points (e.g., taking photos, recording data, detouring, etc.).

[0135] The generated AMI communication information is transmitted to the power line inspection equipment. The inspection equipment performs inspection work based on route direction information, route speed information, and route operation information, such as reducing speed when passing through high-risk areas or detouring around construction areas.

[0136] For example, suppose the inspection equipment includes a drone D1 and a ground robot R1, and the inspection route is L1.

[0137] Assume the risk point is located at coordinate A1 (latitude and longitude coordinates are (34.123, -118.456)), and the risk is caused by dense vegetation cover (physical obstacle).

[0138] The risk point is located at coordinates A2 (latitude and longitude coordinates are (34.125, -118.458)), and the risk is caused by historical equipment failure records (equipment failure).

[0139] The risk point is located at coordinates A3 (latitude and longitude coordinates are (34.127, -118.460)), and the risk is caused by heavy rain (environmental conditions).

[0140] Assume the preset hazard level is 2. Dense vegetation cover in A1 is assessed as a Level 1 hazard. Historical equipment malfunction records in A2 are assessed as a Level 3 hazard. Heavy rain in A3 is assessed as a Level 2 hazard.

[0141] A1 (Level 1 Hazard): Drone D1 can maintain its original route and slow down appropriately.

[0142] A2 (Level 3 Hazard): Drone D1 will not adjust its route coordinates, but will conduct a detailed inspection at that location.

[0143] A3 (Level 2 Hazard): Drone D1 adjusts its route to avoid the rainstorm area and chooses a safer route.

[0144] The updated route includes drone D1 starting from the origin, slowing down and taking more photos at A1, conducting a detailed inspection at A2, and bypassing the heavy rain area at A3. The direction information indicates positive flight. The speed information is 15 m / s from the origin to A1, 10 m / s from A1 to A2, and 20 m / s for the entire route after bypassing A3. The operational instructions are to take more photos at A1 and conduct a detailed inspection at A2. Through AMI communication, control drone D1 to complete the inspection mission along the updated route. Slow down and take more photos at A1, conduct a detailed inspection at A2, and fly at a safe speed on the route after bypassing A3.

[0145] Through the technical content and implementation of the above embodiments, the high-speed multi-node AMI communication method can dynamically update the inspection path based on real-time risk location information and hazard level, and generate accurate AMI communication information, thereby controlling power inspection equipment to complete inspection tasks efficiently and safely. This method is applicable to various complex and changing inspection environments, can flexibly respond to different risk challenges, and ensure that inspection tasks are completed efficiently and accurately.

[0146] Please see Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of the AMI communication device 200 provided in the embodiments of this application. The AMI communication device 200 is used to execute the steps of the high-speed multi-node AMI communication method shown in the above embodiments. The AMI communication device 200 can be a single server or a server cluster, or the AMI communication device 200 can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0147] like Figure 3 As shown, the AMI communication device 200 includes:

[0148] The communication establishment unit 201 is used to establish a high-speed communication network with each of the power inspection devices and the smart metering devices; the high-speed communication network includes a 5G communication network and / or an IoT communication network.

[0149] The information acquisition unit 202 is used to acquire the equipment operation information and the working status information measured by the corresponding smart metering device for each power inspection device through the high-speed communication network; the equipment operation information includes at least the equipment working route and equipment type.

[0150] The map acquisition unit 203 is used to acquire environmental map information of the inspection environment corresponding to the power inspection equipment.

[0151] The location acquisition unit 204 is used to determine at least one risk location information corresponding to the working route of each of the multiple power inspection devices based on the environmental map information, the device operation information and working status information corresponding to the multiple power inspection devices.

[0152] The drive completion unit 205 is used to update the equipment working route corresponding to the power inspection equipment according to the risk location information, generate AMI communication information according to the updated equipment working route, and control the power inspection equipment to complete the inspection according to the AMI communication information.

[0153] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the AMI communication device and its modules described above can be referred to the corresponding processes in the embodiments of the high-speed multi-node AMI communication method described above, and will not be repeated here.

[0154] The high-speed multi-node AMI communication method described above can be implemented as a computer program, which can be used in, for example... Figure 3 It runs on the device shown.

[0155] Please see Figure 4 , Figure 4 This is a schematic block diagram of the control device provided in an embodiment of this application. The control device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0156] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any high-speed multi-node AMI communication method.

[0157] The processor provides computing and control capabilities to support the operation of the entire control device.

[0158] Internal memory provides an environment for the execution of computer programs stored in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any high-speed multi-node AMI communication method.

[0159] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific control devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0160] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0161] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0162] Establish a high-speed communication network with each of the power inspection devices and the smart metering devices; the high-speed communication network includes a 5G communication network and / or an IoT communication network.

[0163] The high-speed communication network is used to acquire the equipment operation information corresponding to each of the power inspection devices and the working status information measured by the corresponding smart metering devices; the equipment operation information includes at least the equipment working route and equipment type.

[0164] Obtain the environmental map information of the inspection environment corresponding to the power inspection equipment;

[0165] Based on the environmental map information, the equipment operation information and working status information corresponding to the multiple power inspection devices, at least one risk location information corresponding to the working route of each device is determined;

[0166] The working route of the power inspection equipment is updated according to the risk location information, and AMI communication information is generated according to the updated working route, so as to control the power inspection equipment to complete the inspection according to the AMI communication information.

[0167] In some embodiments, establishing a high-speed communication network with each of the power inspection devices and the smart metering devices includes: obtaining the average power consumption value of each of the power inspection devices; identifying power inspection devices with an average power consumption value greater than a preset power consumption value as high-power devices; identifying power inspection devices with an average power consumption value less than or equal to the preset power consumption value as low-power devices; establishing the IoT communication network with the low-power devices and the smart metering devices; and establishing the 5G communication network with the high-power devices.

[0168] In some embodiments, the inspection environment includes multiple environmental sensors, which are set at environmental points within the inspection environment. Acquiring the environmental map information of the inspection environment corresponding to the power inspection equipment includes: acquiring satellite data information / or aerial image information corresponding to the inspection environment; performing feature extraction on the satellite data information / or aerial image information to obtain environmental feature information; the environmental feature information includes at least terrain information and vegetation cover information; acquiring real-time environmental parameters measured by the environmental sensors; and performing feature fusion based on the environmental feature information and the real-time environmental parameters to generate the environmental map information.

[0169] In some embodiments, determining at least one risk location information corresponding to each working route of the power inspection equipment based on the environmental map information, equipment operation information, and working status information corresponding to the multiple power inspection equipment includes: calculating environmental risk information, power system status risk information, traffic risk information, and network security risk information corresponding to each working route based on the environmental map information, equipment operation information, and working status information corresponding to the multiple power inspection equipment; calculating a risk threshold corresponding to each point in the working route based on the environmental risk information, power system status risk information, traffic risk information, and network security risk information; determining points with risk thresholds greater than a preset threshold as target points; and generating the risk location information based on the location of the target points.

[0170] For example, the step of calculating environmental risk information, power system status risk information, traffic risk information, and network security risk information corresponding to each work route based on the environmental map information, equipment operation information, and working status information corresponding to multiple power inspection devices includes: obtaining historical disaster information corresponding to the inspection environment; the historical disaster information includes historical disaster types and historical map information; calculating the environmental risk information based on the historical disaster types, historical map information, and environmental map information; obtaining historical fault information corresponding to each power inspection device; the historical fault information includes historical working status and historical operation information; calculating the power system status risk information based on the historical working status, historical operation information, working status information, and equipment operation information; obtaining the equipment travel speed corresponding to multiple power devices; calculating the traffic risk information corresponding to each power device based on the equipment travel speed and work route corresponding to multiple power devices; obtaining the network stability information of the power devices; and calculating the network security risk information based on the network stability information.

[0171] For example, the step of calculating the risk threshold corresponding to each point in the work route based on the environmental risk information, power system status risk information, traffic risk information, and network security risk information includes: obtaining weather forecast information corresponding to the inspection environment and equipment maintenance information corresponding to the power inspection equipment; if the weather forecast information determines that the weather within a preset time period is preset severe weather, generating a first penalty coefficient; if the equipment maintenance information determines that the power inspection equipment needs maintenance, generating a second penalty coefficient; calculating the risk threshold corresponding to each point in the work route based on the first penalty coefficient, the second penalty coefficient, environmental risk information, power system status risk information, traffic risk information, and network security risk information; the expression for the risk threshold includes:

[0172] ;

[0173] in, For the first on the work route The risk threshold corresponding to each location These are the first penalty coefficient and the second penalty coefficient, respectively. , , and These are respectively the environmental risk information, power system status risk information, traffic risk information, and cybersecurity risk information.

[0174] In some embodiments, updating the working route of the power inspection equipment according to the risk location information includes: parsing the risk location information to obtain the coordinates of the risk point and the cause of the risk; the cause of the risk includes any one of physical obstacles, environmental conditions, equipment failure, and human activities; calculating the hazard level corresponding to the risk cause; and updating the working route of the equipment according to the hazard level; wherein, if the hazard level is greater than a preset hazard level, the coordinates of the risk point are not adjusted in the working route; if the hazard level is greater than or equal to the preset hazard level, the working route is adjusted to bypass the coordinates of the risk point.

[0175] In some embodiments, the AMI communication information includes route direction information, route speed information, and route operation information; the step of generating AMI communication information based on the updated device working route, and controlling the power inspection equipment to complete the inspection based on the AMI communication information, includes: parsing the updated working route to obtain the route direction information, route speed information, and route operation information; and controlling the power inspection equipment to complete the inspection based on the route direction information, route speed information, and route operation information.

[0176] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the high-speed multi-node AMI communication method provided in any embodiment of this application.

[0177] The computer-readable storage medium can be an internal storage unit of the control device described in the foregoing embodiments, such as the hard disk or memory of the control device. Alternatively, the computer-readable storage medium can be an external storage device of the control device, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the control device.

[0178] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A high speed multi-node AMI communication method, characterized by, The application relates to a control device applied to an AMI communication system, wherein the AMI communication system further comprises a plurality of power inspection devices and at least one intelligent metering device carried on the power inspection devices; the method comprises the following steps: a high-speed communication network is established with each of the power inspection devices and the intelligent metering device; the high-speed communication network comprises a 5G communication network and / or an IOT communication network; device operation information corresponding to each of the power inspection devices and working state information measured by the corresponding intelligent metering device are acquired through the high-speed communication network; the device operation information at least comprises device working routes and device types; environmental map information of a corresponding inspection environment of the power inspection device is acquired; at least one risk position information corresponding to each of the device working routes is determined according to the environmental map information, the device operation information and the working state information of the plurality of power inspection devices; the device working routes of the power inspection devices are updated according to the risk position information, AMI communication information is generated according to the updated device working routes, and the power inspection devices are controlled to complete the inspection according to the AMI communication information.

2. The method of claim 1, wherein, The establishment of the high-speed communication network with each of the power inspection devices and the intelligent metering device comprises the following steps: average power consumption values of each of the power inspection devices are acquired; the power inspection devices with the average power consumption values greater than a preset power consumption value are determined as high-power-consumption devices; the power inspection devices with the average power consumption values less than or equal to the preset power consumption value are determined as low-power-consumption devices; the IOT communication network is established with the low-power-consumption devices and the intelligent metering device; the 5G communication network is established with the high-power-consumption devices.

3. The method of claim 1, wherein, The inspection environment comprises a plurality of environmental sensors, the environmental sensors are arranged at environmental points in the inspection environment; the acquisition of the environmental map information of the corresponding inspection environment of the power inspection device comprises the following steps: satellite data information and / or aerial image information corresponding to the inspection environment are acquired; environmental feature information is acquired by performing feature extraction on the satellite data information and / or the aerial image information; the environmental feature information at least comprises terrain information and vegetation coverage information; real-time environmental parameters measured by the environmental sensors are acquired; the environmental map information is generated by performing feature fusion according to the environmental feature information and the real-time environmental parameters.

4. The method of claim 1, wherein, The determination of at least one risk position information corresponding to each of the device working routes according to the environmental map information, the device operation information and the working state information of the plurality of power inspection devices comprises the following steps: environmental risk information, power system state risk information, traffic risk information and network security risk information corresponding to each of the working routes are calculated according to the environmental map information, the device operation information and the working state information of the plurality of power inspection devices; risk threshold values corresponding to each of the points in the working routes are calculated according to the environmental risk information, the power system state risk information, the traffic risk information and the network security risk information. The point position with the risk threshold value greater than the preset threshold value is determined as a target point position, and the risk position information is generated according to the target point position.

5. The method of claim 4, wherein, The environment risk information, the power system state risk information, the traffic risk information and the network security risk information corresponding to each of the work routes are calculated according to the environment map information, the device operation information and the work state information corresponding to the plurality of power inspection devices, and the calculation comprises: Obtaining historical disaster information corresponding to the inspection environment; the historical disaster information comprises a historical disaster type and historical map information; The environment risk information is calculated according to the historical disaster type, the historical map information and the environment map information; Obtaining historical fault information corresponding to each of the power inspection devices; the historical fault information comprises a historical work state and historical operation information; The power system state risk information is calculated according to the historical work state, the historical operation information, the work state information and the device operation information; Obtaining device travel speeds corresponding to the plurality of power devices; The traffic risk information corresponding to each of the power devices is calculated according to the device travel speeds corresponding to the plurality of power devices and the work route; Obtaining network stability information of the power device, and calculating the network security risk information according to the network stability information.

6. The method of claim 4, wherein, The risk threshold value corresponding to each of the point positions in the work route is calculated according to the environment risk information, the power system state risk information, the traffic risk information and the network security risk information, and the calculation comprises: Obtaining weather prediction information corresponding to the inspection environment and device maintenance information corresponding to the power inspection device; If it is determined according to the weather prediction information that the weather in a preset time period is preset severe weather, a first penalty coefficient is generated; If it is determined according to the device maintenance information that the power inspection device needs to be maintained, a second penalty coefficient is generated; The risk threshold value corresponding to each of the point positions in the work route is calculated according to the first penalty coefficient, the second penalty coefficient, the environment risk information, the power system state risk information, the traffic risk information and the network security risk information; and an expression of the risk threshold value comprises: ； wherein, is a risk threshold corresponding to a point on the work route, are the first and second penalty coefficients, respectively, , , and are the environmental risk information, power system state risk information, traffic risk information, and network security risk information, respectively.​ 7. The method of claim 1, wherein, The device work route corresponding to the power inspection device is updated according to the risk position information, and the updating comprises: Analyzing the risk position information to obtain a risk point position coordinate and a risk cause; the risk cause comprises any one of a physical obstacle, an environmental condition, a device fault and human activity; A harm level corresponding to the risk cause is calculated; The device work route is updated according to the harm level; wherein, if the harm level is greater than a preset harm level, the risk point position coordinate is not adjusted in the work route; and if the harm level is greater than or equal to the preset harm level, the work route is adjusted to bypass the risk point position coordinate.

8. The method of claim 1, wherein, The AMI communication information comprises route direction information, route speed information and route operation information; The AMI communication information is generated according to the updated device work route, so that the power inspection device completes the inspection according to the AMI communication information, and the generating comprises: Analyzing the updated work route to obtain route direction information, route speed information and route operation information; Controlling the electric power inspection equipment to complete the inspection according to the route direction information, route speed information and route operation information.

9. An AMI communication system characterized by, Comprise: A plurality of electric power inspection equipment; A plurality of intelligent metering devices, at least one of which is mounted on each of the electric power inspection equipment; A control device for establishing a high-speed communication network with each of the electric power inspection equipment and the intelligent metering device; the high-speed communication network comprises a 5G communication network and / or an IOT communication network; the control device obtains the corresponding device operation information of each of the electric power inspection equipment and the corresponding working state information measured by the intelligent metering device through the high-speed communication network; The device operation information at least includes device work route and device type; The control device obtains the environment map information of the inspection environment corresponding to the electric power inspection equipment; The control device determines at least one risk position information corresponding to each of the device work routes according to the environment map information, the device operation information and the working state information of a plurality of electric power inspection equipment; The control device updates the device work route corresponding to the electric power inspection equipment according to the risk position information, generates AMI communication information according to the updated device work route, and controls the electric power inspection equipment to complete the inspection according to the AMI communication information.

10. A control device, characterized by The control device comprises a memory and a processor, the memory is used to store computer programs, and the processor is used to execute the computer programs and realize the method as claimed in any one of claims 1 to 7 when executing the computer programs.

Images