A power supply and distribution switching system based on power emergency

Abstract

The present invention relates to the field of power emergency, and in particular to a power supply and distribution switching system based on power emergency, comprising: a satellite acquisition module for acquiring satellite remote sensing images monitored by artificial earth satellites, a fire monitoring module for preprocessing the satellite remote sensing images, determining the fire area, forming corresponding fire point preprocessing data, and generating a fire dynamic monitoring map, a matching module for analyzing the fire dynamic monitoring map, calculating the corresponding real-time fire area, and comparing it with the fire area threshold, an emergency switching module for determining the corresponding power supply emergency plan, switching the power supply station, and clearly understanding the operation sequence of the power supply station in an emergency by identifying and analyzing fire characteristics to protect the power infrastructure and ensure the stability of the power supply, thereby achieving a rapid response to the fire and dynamic adjustment of the power supply, and optimizing the real-time monitoring of the fire and the intelligent management of the power supply.

Description

Technical Field

[0001] The present invention relates to the field of power emergency, and in particular to a power supply and distribution switching system based on power emergency. Background Art

[0002] The satellite super-convergent interoperable digital communication emergency command system realizes multi-system and cross-platform interconnection and unified equipment management through integrated emergency communication command platforms such as "satellite portable base station", "satellite vehicle-mounted base station" and "emergency command portable box", and builds a reliable, high-throughput and feature-rich emergency communication dedicated network to meet the requirements of power emergency command work.

[0003] Chinese Patent Application Publication No. CN115350423A discloses a fire prevention and control method, apparatus, equipment, system, and storage medium. The method comprises: transmitting a first signal from one end of an overhead conductor to the other, and receiving a second signal transmitted back from the other end; wherein each end of the overhead conductor is provided with a modem; the first and second signals are both power carrier signals; determining whether the second signal transmitted back from the other end is interrupted; if so, determining that the overhead conductor is disconnected, and sending a trip command to the circuit breaker corresponding to the overhead conductor. This application utilizes power carrier signals to identify disconnection faults, which has higher recognition accuracy and faster recognition speed than traditional distribution automation equipment electrical signal recognition. Furthermore, this application directs image and video monitoring devices, satellites, and drones to conduct automatic inspections based on installation location and fire conditions, enabling rapid feedback of fire information, timely and effective differentiated on-site disposal, and enhancing the energy efficiency of post-fire disaster response.

[0004] Chinese patent application publication number: CN116819652A discloses an early warning device for monitoring lightning, which includes a client and a server. The server includes a lightning monitoring device, a data transmission module, a server, a positioning module, a lightning early warning information generation module and a data publishing module. The lightning monitoring device is used to realize lightning monitoring in the vicinity of the early warning device and send the monitored data to the server via the data transmission module. The server analyzes and processes the received data and sends the processed data to the lightning early warning information generation module. The lightning early warning information generation module determines whether a preset lightning early warning threshold is reached. The invention has considerable practical significance and economic benefits in the inspection of lightning obstacle points in the power system, early warning of lightning conditions near rocket and satellite launch sites, fixed-point monitoring of forest lightning fire zones, lightning early warning of oil field systems, and lightning monitoring and early warning near airports, as well as the monitoring and accurate positioning of lightning.

[0005] However, the above method has the following problems: it is impossible to monitor the location and spread speed of the fire in real time, estimate the fire trend, and take corresponding emergency measures in a timely manner. Summary of the Invention

[0006] To this end, the present invention provides a power supply and distribution switching system based on power emergency to overcome the problem in the prior art that it is impossible to monitor the location and spread speed of fire in real time, estimate the fire trend, and take corresponding emergency measures in a timely manner.

[0007] To achieve the above objectives, the present invention provides a power supply and distribution switching system based on power emergency, comprising:

[0008] A satellite acquisition module is used to acquire satellite remote sensing images monitored by artificial earth satellites;

[0009] A fire monitoring module, connected to the satellite acquisition module, is used to pre-process the satellite remote sensing images, determine the fire area, form corresponding fire point pre-processing data, and generate a fire dynamic monitoring map;

[0010] a matching module connected to the fire monitoring module and configured to analyze the fire dynamic monitoring map, including calculating the corresponding real-time fire area and comparing it with a fire area threshold to form a corresponding comparison result;

[0011] an emergency switching module, connected to the matching module, for determining a corresponding power supply emergency plan according to the comparison result and switching the power supply station;

[0012] Wherein, the fire monitoring module is provided with a fire dynamic learning model for learning the fire point preprocessing data and generating a corresponding fire dynamic monitoring map;

[0013] The fire area threshold is the maximum value of the total area of ​​the fire burning area, and is related to the maximum distance between the fire location and the power supply station.

[0014] Furthermore, the satellite acquisition module includes:

[0015] A timing device for sending collection instructions at preset time intervals;

[0016] Accessing a cloud, which is connected to the timing device, to receive the acquisition instruction and query the artificial earth satellite to obtain the satellite remote sensing image in real time;

[0017] The acquisition device is connected to the access cloud and is used to store the satellite remote sensing images and sort the satellite remote sensing images according to time sequence.

[0018] Furthermore, the fire monitoring module includes:

[0019] A fitter is used to extract and filter the image pixels of the satellite remote sensing image and generate corresponding fire point fitting data, wherein:

[0020] The fitter is provided with a pixel threshold for performing pixel filtering on the satellite remote sensing image;

[0021] A preprocessor, connected to the fitter, for preprocessing the fire point fitting data and generating corresponding fire point preprocessing data;

[0022] The learner is connected to the preprocessor and is used to select a number of indicator features and learn the fire point preprocessing data, and generate a corresponding fire dynamic monitoring map.

[0023] Furthermore, the matching module includes:

[0024] A renderer, configured to render the fire area according to the fire dynamic monitoring map;

[0025] a meter connected to the renderer, for calculating the real-time fire area according to the fire area;

[0026] An analyzer is connected to the meter and is used to compare the real-time fire area with the fire area threshold to form a corresponding comparison result, and determine a power supply emergency plan based on the comparison result.

[0027] Furthermore, when the image pixel is smaller than the pixel threshold, the fitter filters the corresponding satellite remote sensing image; when the image pixel is larger than the pixel threshold, the fitter retains the corresponding satellite remote sensing image and forms corresponding fire point fitting data.

[0028] Furthermore, the preprocessor cuts the fire point fitting data according to the sampling rate to form a plurality of fire point preprocessing data with a standard sampling rate, selects a plurality of indicator features based on the fire point preprocessing data, and the fire point preprocessing data enters the fire dynamic learning model for learning, and generates a corresponding fire dynamic monitoring map.

[0029] The standard sampling rate is a sampling rate that can be recognized by the fire dynamics learning model, and the standard sampling rate remains unchanged during the learning process of the fire dynamics learning model;

[0030] The indicator features include location information, spreading speed and / or duration of the fire area.

[0031] Furthermore, the renderer locks the edge of the fire dynamic monitoring image for rendering to form a corresponding fire area, and the meter divides the fire area into several sub-areas and calculates the number of sub-pixels in each sub-area to form the corresponding real-time fire area, wherein,

[0032] A single sub-pixel represents a unit area, and the number of sub-pixels is the area of ​​the real-time fire area.

[0033] Furthermore, the emergency switching module sorts and numbers the power supply stations from near to far based on the geometric center of the fire area, marks the power supply station closest to the geometric center as an emergency power supply station, and calculates the spread speed based on the fire dynamic monitoring map.

[0034] Furthermore, the analyzer compares the real-time fire area with the fire area threshold. When the real-time fire area is smaller than the fire area threshold, the emergency switching module switches the emergency power supply station to supply power to the fire area.

[0035] Furthermore, when the real-time fire area is greater than the fire area threshold, the emergency switching module calculates the real-time fire area corresponding to the preset rescue time according to the spread speed, marks the corresponding emergency power supply station, and switches it to supply power to the fire area.

[0036] Compared with the prior art, the present invention utilizes a satellite acquisition module to collect satellite remote sensing images monitored by artificial earth satellites, a fire monitoring module to pre-process satellite remote sensing images, determine the fire area, form corresponding fire point pre-processing data, and generate a fire dynamic monitoring map, a matching module to analyze the fire dynamic monitoring map, calculate the corresponding real-time fire area, and compare it with the fire area threshold, an emergency switching module to determine the corresponding power supply emergency plan, switch the power supply station, and clearly understand the operating sequence of the power supply station in an emergency by identifying and analyzing fire characteristics to protect the power infrastructure and ensure the stability of the power supply, thereby achieving rapid response to fires and dynamic adjustment of power supply, and optimizing real-time monitoring of fires and intelligent management of power supply.

[0037] Furthermore, the coordinated work of timing devices, cloud access, and data collection devices ensures the integrity and traceability of image data, enabling automated collection and storage of satellite remote sensing imagery, providing real-time data support for subsequent fire monitoring and emergency response. This enables the system to rapidly respond to emergencies like fires and promptly adjust power supply and distribution strategies to ensure the safety and stability of the power system.

[0038] Furthermore, the fitter extracts image pixels from satellite remote sensing images and filters them using pixel thresholds to generate fire point fitting data. The preprocessor further preprocesses the fire point fitting data, providing a basis for the subsequent generation of fire dynamic monitoring maps. The learner generates fire dynamic monitoring maps by learning the fire point preprocessing data, which can intuitively display the location, scope and possible development trends of the fire area. Through automated image processing and machine learning technology, the efficiency and accuracy of fire monitoring can be improved, so that emergency measures can be taken quickly when a fire occurs to protect power facilities and people's lives and property.

[0039] Furthermore, the renderer renders the specific fire area based on the fire dynamic monitoring map, and the meter calculates the area of ​​the rendered fire area, providing a basis for subsequent comparison and decision-making. Through precise image rendering, area measurement and intelligent analysis, it can ensure that when a fire occurs, the power system can quickly take appropriate emergency measures to protect power facilities and ensure the stability of power supply.

[0040] Furthermore, by setting the pixel threshold, the image area corresponding to the retained pixels will be used by the fitter to generate fire point fitting data. The fitter can effectively screen out possible fire areas from a large amount of satellite remote sensing image data, providing key input data for the fire monitoring module, improving the accuracy and response speed of fire monitoring, which is crucial for early fire detection and rapid response.

[0041] Furthermore, the fire point fitting data is cut according to the sampling rate through the preprocessor, and the fire point preprocessing data is passed to the fire dynamic learning model for learning, which ensures the consistency of the data and the accuracy of the model. After learning, the fire dynamic learning model can generate fire dynamic monitoring maps based on the real-time fire point preprocessing data. These monitoring maps show key information such as the location, spread speed and duration of the fire area, providing decision makers with an intuitive fire development situation. Through this detailed preprocessing and learning process, the fire monitoring module can provide accurate fire monitoring and prediction, and provide key decision support for the power emergency supply and distribution switching system, which helps to improve the efficiency and effectiveness of fire emergency response and reduce the potential damage to people and property caused by fire.

[0042] Furthermore, by sorting all power supply stations by their distance from the geometric center of the fire zone, from closest to farthest, the analyzer compares the real-time fire area with a preset fire area threshold to ensure that the fire area's basic power needs are met. This dynamic emergency response strategy based on the real-time fire area and spread speed allows the emergency switching module to flexibly adjust power supply to adapt to the different stages and scales of fire development. This strategy helps minimize damage to power infrastructure and supports fire rescue and post-disaster recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a structural diagram of a power supply and distribution switching system based on power emergency according to the present invention;

[0044] Figure 2 This is a schematic structural diagram of the satellite acquisition module of the present invention;

[0045] Figure 3 This is a structural diagram of a fire monitoring module of the present invention;

[0046] Figure 4 This is a structural diagram of the matching module of the present invention. DETAILED DESCRIPTION

[0047] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0048] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0050] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0051] See also Figure 1 As shown in FIG, it is a structural diagram of a power supply and distribution switching system based on power emergency of the present invention, comprising:

[0052] A satellite acquisition module is used to acquire satellite remote sensing images monitored by artificial earth satellites;

[0053] The fire monitoring module is connected to the satellite acquisition module to pre-process satellite remote sensing images, determine the fire area, form corresponding fire point pre-processing data, and generate a fire dynamic monitoring map;

[0054] A matching module, connected to the fire monitoring module, is used to analyze the fire dynamic monitoring map, including calculating the corresponding real-time fire area and comparing it with the fire area threshold to form a corresponding comparison result;

[0055] An emergency switching module, which is connected to the matching module, is used to determine the corresponding power supply emergency plan based on the comparison result and switch the power supply station;

[0056] Among them, the fire monitoring module is equipped with a fire dynamic learning model to learn the fire point preprocessing data and generate the corresponding fire dynamic monitoring map;

[0057] The fire area threshold is the maximum value of the total area of ​​the fire burning area, and is related to the maximum distance between the fire location and the power supply station.

[0058] See also Figure 2 As shown in FIG, it is a schematic diagram of the structure of the satellite acquisition module of the present invention, including:

[0059] A timing device for sending collection instructions at preset time intervals;

[0060] Access the cloud, which is connected to the timing device to receive acquisition instructions and query the artificial earth satellite to obtain satellite remote sensing images in real time;

[0061] The acquisition device is connected to the access cloud and is used to store satellite remote sensing images and sort the satellite remote sensing images according to chronological order.

[0062] In specific implementations, the timing device is responsible for sending acquisition instructions at preset intervals. This preset time can be set according to actual monitoring needs and the satellite transit period to ensure that the latest satellite remote sensing images can be obtained in a timely manner. The access cloud module is connected to the timing device and is used to receive acquisition instructions and query artificial earth satellites to obtain satellite remote sensing images in real time. This step involves an interface with the satellite communication system and requires specific hardware and software support to achieve real-time transmission and reception of data. The acquisition device is connected to the access cloud module and is responsible for receiving and storing satellite remote sensing images obtained from the access cloud module. These images contain real-time status information of key power infrastructure such as power grids, transmission lines, and distribution facilities, which are crucial for monitoring and evaluating power grid conditions. This requires the acquisition device to have sufficient storage space and efficient data management capabilities to ensure the integrity and traceability of image data.

[0063] Time-ordered sorting: The acquisition device not only stores the images but also sorts them according to the time they were acquired. This sorting ensures the temporal nature of the image data, enabling the system to track changes in grid status and analyze the development of events along a timeline.

[0064] The coordinated work of timing devices, cloud access, and data collection devices ensures the integrity and traceability of image data, enabling automated collection and storage of satellite remote sensing imagery, providing real-time data support for subsequent fire monitoring and emergency response. This enables the system to rapidly respond to emergencies like fires and promptly adjust power supply and distribution strategies to ensure the safety and stability of the power system.

[0065] See also Figure 3 As shown in FIG, it is a schematic diagram of the structure of the fire monitoring module of the present invention, including:

[0066] A fitter is used to extract and filter image pixels of satellite remote sensing images and generate corresponding fire point fitting data, wherein,

[0067] A pixel threshold is set in the fitter to perform pixel filtering on satellite remote sensing images;

[0068] A preprocessor, connected to the fitter, for preprocessing the fire point fitting data and generating corresponding fire point preprocessing data;

[0069] The learner is connected to the preprocessor and is used to select several indicator features and learn the fire point preprocessing data, and generate the corresponding fire dynamic monitoring map.

[0070] In the implementation, the fitter uses a pixel threshold, a key parameter for pixel filtering of satellite remote sensing imagery. This threshold is based on fire characteristics, such as temperature, brightness, or reflectance in specific wavelengths, to identify possible fire spots. The learner uses machine learning or deep learning algorithms, such as support vector machines (SVMs), random forests, and convolutional neural networks (CNNs), to identify fire characteristics and patterns.

[0071] The fitter extracts image pixels from satellite remote sensing images and filters them using pixel thresholds to generate fire point fitting data. The preprocessor further preprocesses the fire point fitting data, providing a basis for the subsequent generation of fire dynamic monitoring maps. The learner generates fire dynamic monitoring maps by learning the fire point preprocessing data, which can intuitively display the location, scope and possible development trends of the fire area. Through automated image processing and machine learning technology, the efficiency and accuracy of fire monitoring can be improved, so that emergency measures can be taken quickly when a fire occurs to protect power facilities and people's lives and property.

[0072] See also Figure 4 As shown in FIG, it is a schematic structural diagram of the satellite matching module of the present invention, including:

[0073] A renderer, which is used to render the fire area according to the fire dynamic monitoring map;

[0074] A meter, which is connected to the renderer and is used to calculate the real-time fire area based on the fire area;

[0075] The analyzer is connected to the meter and is used to compare the real-time fire area with the fire area threshold to form a corresponding comparison result, and determine the power supply emergency plan according to the comparison result.

[0076] The fire area threshold is related to the ground environment, air humidity, and ground vegetation. These factors will affect the spread speed and scope of the fire, not only affecting the combustion characteristics of the fire, but also directly related to the formulation of fire prevention and emergency response strategies.

[0077] In specific implementations, the renderer's function is to render specific fire areas based on the dynamic fire monitoring map. This involves image processing technology, converting fire characteristics (such as temperature anomalies and smoke) in the monitoring map into visual images of the fire area. The renderer uses color coding or other visual markers to distinguish fire areas, making them more prominent in the monitoring map. The meter, connected to the renderer, is tasked with calculating the area of ​​the rendered fire area. This step involves image analysis techniques, such as pixel counting or geometric analysis, to determine the size of the fire area. The meter outputs quantitative data on the fire area, providing a basis for subsequent comparison and decision-making. The analyzer, connected to the meter, compares the real-time fire area calculated by the meter with a preset fire area threshold. The fire area threshold is a key parameter, set based on historical data, safety standards, or expert experience, to determine the severity of the fire. The analyzer generates a corresponding comparison result based on the comparison and determines a power supply emergency plan. If the real-time fire area exceeds the threshold, the analyzer may trigger emergency measures, such as adjusting power distribution to ensure power supply to critical facilities.

[0078] The renderer renders the specific fire area based on the dynamic fire monitoring map, and the meter calculates the area of ​​the rendered fire area, providing a basis for subsequent comparison and decision-making. Through precise image rendering, area measurement and intelligent analysis, it can ensure that when a fire occurs, the power system can quickly take appropriate emergency measures to protect power facilities and ensure the stability of power supply.

[0079] Specifically, when the image pixel is smaller than the pixel threshold, the fitter filters the corresponding satellite remote sensing image; when the image pixel is larger than the pixel threshold, the corresponding satellite remote sensing image is retained and the corresponding fire point fitting data is formed.

[0080] In some possible implementations, the pixel threshold is set to 1000. When the image pixel of satellite remote sensing image A is 800, the image pixel is less than the pixel threshold, and the fitter filters satellite remote sensing image A. When the image pixel of satellite remote sensing image B is 1200, the image pixel is greater than the pixel threshold, and satellite remote sensing image B is retained to form corresponding fire point fitting data.

[0081] In a specific implementation, the pixel threshold is a preset parameter used to distinguish between normal conditions and possible fire conditions. This threshold is usually based on fire characteristics, such as a specific temperature range, brightness level, or radiation intensity in a specific band.

[0082] The fitter analyzes each pixel in the satellite remote sensing image and compares it with a preset pixel threshold.

[0083] The pixel size in satellite remote sensing images is 1m×1m, also known as ground resolution, which refers to the actual area represented by each pixel on the ground.

[0084] When the value of an image pixel is less than the pixel threshold, the fitter considers these pixels as non-fire areas and therefore does not include them in the fire point fitting data. This means that the image areas corresponding to these pixels are filtered out and not considered as potential fire areas.

[0085] When the value of an image pixel is greater than the pixel threshold, the fitter considers these pixels as possible fire areas. These pixels are retained and used to form the fire point fitting data.

[0086] The image areas corresponding to the retained pixels will be used by the fitter to generate fire point fitting data. This data includes the location, intensity and other relevant features of the pixels, which are the basis for subsequent fire monitoring and analysis.

[0087] The preprocessed data will be passed to the learner, which will use this data to train a fire dynamics learning model to identify and monitor fire areas.

[0088] By setting the pixel threshold, the image area corresponding to the retained pixels will be used by the fitter to generate fire point fitting data. The fitter can effectively screen out possible fire areas from a large amount of satellite remote sensing image data, providing key input data for the fire monitoring module, improving the accuracy and response speed of fire monitoring, which is crucial for early fire detection and rapid response.

[0089] Specifically, the preprocessor cuts the fire point fitting data according to the sampling rate to form a number of fire point preprocessing data with a standard sampling rate, and selects a number of indicator features based on the fire point preprocessing data. The fire point preprocessing data enters the fire dynamic learning model for learning and generates the corresponding fire dynamic monitoring map.

[0090] The standard sampling rate is a sampling rate that can be recognized by the fire dynamics learning model, and the standard sampling rate remains unchanged during the learning process of the fire dynamics learning model;

[0091] Indicator characteristics include location information of the fire area, spread rate and / or duration.

[0092] In a specific implementation, when the standard sampling rate is 160 / second to 180 / second, the fire dynamics learning model has the best learning effect on the fire point preprocessing data. Preferably, the standard sampling rate is 170 / second.

[0093] The preprocessor segments the fire point fitting data according to a specific sampling rate to form fire point preprocessed data that conforms to the standard sampling rate. The sampling rate refers to the frequency of data acquisition within a certain period of time. For image data, it refers to the number of pixels collected per unit area or per unit time. The preprocessor segments the fire point fitting data according to the standard sampling rate to form several fire point preprocessed data blocks that conform to the standard sampling rate. This step ensures that the data size and format match the input requirements of the model.

[0094] The standard sampling rate is a specific sampling rate that the fire dynamics learning model can recognize and process. This means that the model uses the same sampling rate both during training and in actual application to ensure data consistency and model accuracy.

[0095] After learning, the fire dynamics learning model can generate fire dynamics maps based on real-time preprocessed fire point data. These maps display key information such as the location, spread rate, and duration of the fire area, providing decision makers with an intuitive understanding of the fire's development. Maintaining a constant standard sampling rate during the learning process of the fire dynamics learning model is crucial, as this ensures consistency in the model input and helps the model learn stable and reliable fire characteristics.

[0096] The fire point fitting data is cut according to the sampling rate through the preprocessor, and the fire point preprocessing data is passed to the fire dynamic learning model for learning, which ensures the consistency of the data and the accuracy of the model. After learning, the fire dynamic learning model can generate fire dynamic monitoring maps based on the real-time fire point preprocessing data. These monitoring maps show key information such as the location, spread speed and duration of the fire area, providing decision makers with an intuitive understanding of the fire development situation. Through this detailed preprocessing and learning process, the fire monitoring module can provide accurate fire monitoring and prediction, and provide key decision support for the power emergency supply and distribution switching system, which helps to improve the efficiency and effectiveness of fire emergency response and reduce the potential damage to people and property caused by fire.

[0097] Specifically, the renderer locks the edge of the fire dynamic monitoring image for rendering to form the corresponding fire area. The meter divides the fire area into several sub-areas and calculates the number of sub-pixels in each sub-area to form the corresponding real-time fire area.

[0098] A single sub-pixel represents a unit area, and the number of sub-pixels is the real-time fire area.

[0099] In some possible implementations, the fire area is divided into three sub-areas, and the number of sub-pixels in each sub-area is 10, so the fire area is 30.

[0100] Specifically, the emergency switching module uses the geometric center of the fire area as a reference, sorts and numbers the power supply stations from near to far, marks the power supply station closest to the geometric center as the emergency power supply station, and calculates the spread speed based on the fire dynamic monitoring map.

[0101] Specifically, the analyzer compares the real-time fire area with the fire area threshold. When the real-time fire area is smaller than the fire area threshold, the emergency switching module switches the emergency power supply station to supply power to the fire area.

[0102] Specifically, when the real-time fire area is larger than the fire area threshold, the emergency switching module calculates the real-time fire area corresponding to the preset rescue time based on the spread speed, marks the corresponding emergency power supply station, and switches it to supply power to the fire area.

[0103] In specific implementation, the emergency switching module first determines the location of the fire area based on the geometric center of the fire area, sorts all power supply stations from near to far according to the distance from the geometric center of the fire area, and numbers the sorted power supply stations to ensure that each power supply station has a unique identifier. The power supply station closest to the geometric center is marked as an emergency power supply station, which will be considered first for power supply when a fire occurs.

[0104] If the real-time fire area is smaller than the fire area threshold, indicating a small fire, the emergency switching module will directly switch power to the emergency power supply station to ensure that the fire area's basic power needs are met. If the real-time fire area is larger than the fire area threshold, indicating a large fire, a more complex emergency response strategy is required. The emergency switching module will calculate the area that the fire may cover within the preset rescue time based on the spread rate. Based on the calculation results, it will mark emergency power supply stations that will not be affected by the fire within the preset rescue time. The emergency switching module will switch power to these marked emergency power supply stations to the fire area, ensuring that the power supply to critical areas is not affected during the fire and rescue process.

[0105] By sorting all power supply stations by their distance from the geometric center of the fire zone, from closest to farthest, the analyzer compares the real-time fire area with a preset fire area threshold to ensure that the fire area's basic power needs are met. This dynamic emergency response strategy based on the real-time fire area and spread speed allows the emergency switching module to flexibly adjust power supply to adapt to the different stages and scales of fire development. This strategy helps minimize damage to power infrastructure and supports fire rescue and post-disaster recovery.

[0106] In its specific implementation, the satellite hyper-convergent interoperable digital communication emergency command system realizes multi-system and cross-platform interconnection and unified equipment management through integrated emergency communication command platforms such as "satellite portable base station", "satellite vehicle-mounted base station" and "emergency command portable box", and builds a reliable, high-throughput and feature-rich emergency communication dedicated network with voice intercom, two-way audio and video calls, positioning, monitoring, Internet, 4G / 5G network to meet the requirements of power emergency command work.

[0107] The Satellite Super-Converged Interconnected Digital Communications Emergency Command System integrates data and services within the cloud audio and video domain across platforms currently used by the company's emergency command center, including the "Infrastructure Online Monitoring System," "5G Smart Safety Helmet," "Satellite Conferencing System," "Substation Auxiliary Control and Detection System," "Transmission Line Online Detection System," "Early Warning Platform," and "Fire Online Detection System." This system will further expand the application scenarios of video surveillance and video conferencing. Furthermore, it will enable the integration of various surveillance cameras, including smart terminals and drones, to conduct inspection, monitoring, dispatch, and command of mobile targets, emergencies, and targets in remote areas. This will enable more comprehensive and in-depth data analysis and application, leading to more efficient production methods.

[0108] The Satellite + 4G All-Netcom Satellite Portable Station is the first fully automatic backpack-type satellite portable station in China that can simultaneously provide 4G signals from China Unicom, China Mobile, and China Telecom. The entire device is lightweight and portable, easy to operate, and service activation can be completed within 5 minutes after one-click startup. It does not require professional personnel to use and can quickly provide 4G network coverage of the three operators through satellite links. It is suitable for emergency rescue, outdoor inspections and other fields.

[0109] The satellite + 4G all-network satellite portable station product fully leverages the advantages of satellite communication, such as wide coverage, no geographical restrictions, high mobility, and stable performance. It can provide normal call and Internet access functions for remote areas that are not covered by ground networks, such as remote mountainous areas, field operations, emergencies, and natural disaster sites, solving the problem of communication failure in special scenarios.

[0110] The working principles and technical characteristics of the mobile public network, Tiantong-1 satellite mobile communication system, and BeiDou-3 satellite system were studied, and the transformation of the system's air interface was studied, including the physical layer design, terminal access design, and improvement of the communication protocol; the characteristics of the BeiDou-3 system and the previous two generations of systems were investigated and compared, and the communication of short messages of the BeiDou-3 system and the high-precision positioning service function of the BeiDou-3 system were studied, and emergency communication systems suitable for power applications were studied.

[0111] This study investigates the framework and implementation principles of a multi-network integrated power inspection and emergency communications system. Based on this framework, the system platform's front-end hardware infrastructure is constructed, including a mobile public network with a Tiantong communication terminal, Beidou emergency communication terminals, a front-end server, a central station server, and an emergency communications service platform. The emergency communications service platform provides functions such as search and rescue information query, task management, emergency communications command and dispatch, and emergency repair work command. The platform monitors and displays the location of personnel and vehicles, manages work progress, and displays content. Research is underway to utilize Beidou-3's single-frequency RTK high-precision positioning service to achieve regular location reporting, ensuring that power workers and vehicles remain within a safe and controllable range. The system also supports tracking of worker location information, dispatching work tasks, summarizing work content and tasks, monitoring on-site emergency repair locations, and providing route navigation. Software interfaces are provided, allowing it to be integrated as a subsystem into existing emergency command or repair systems.

[0112] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0113] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A power supply and distribution switching system based on power emergency, characterized in that: include: A satellite acquisition module is used to acquire satellite remote sensing images monitored by artificial earth satellites; A fire monitoring module, connected to the satellite acquisition module, is used to pre-process the satellite remote sensing images, determine the fire area, form corresponding fire point pre-processing data, and generate a fire dynamic monitoring map; A matching module, which is connected to the fire monitoring module and is used to analyze the fire dynamic monitoring diagram, Including calculating the corresponding real-time fire area and comparing it with the fire area threshold to form a corresponding comparison result; an emergency switching module, connected to the matching module, for determining a corresponding power supply emergency plan according to the comparison result and switching the power supply station; Wherein, the fire monitoring module is provided with a fire dynamic learning model for learning the fire point preprocessing data and generating a corresponding fire dynamic monitoring map; The fire area threshold is the maximum value of the total area of ​​the fire burning area, and is related to the maximum distance between the fire location and the power supply station; The analyzer compares the real-time fire area with the fire area threshold. When the real-time fire area is smaller than the fire area threshold, the emergency switching module switches the emergency power supply station to supply power to the fire area. When the real-time fire area is larger than the fire area threshold, the emergency switching module calculates the real-time fire area corresponding to the preset rescue time according to the spreading speed, marks the corresponding emergency power supply station, and switches it to supply power to the fire area.

2. The power supply and distribution switching system based on power emergency according to claim 1 is characterized in that: The satellite acquisition module includes: A timing device for sending collection instructions at preset time intervals; Accessing a cloud, which is connected to the timing device, to receive the acquisition instruction and query the artificial earth satellite to obtain the satellite remote sensing image in real time; The acquisition device is connected to the access cloud and is used to store the satellite remote sensing images and sort the satellite remote sensing images according to time sequence.

3. The power supply and distribution switching system based on power emergency according to claim 2 is characterized in that: The fire monitoring module comprises: A fitter is used to extract and filter the image pixels of the satellite remote sensing image and generate corresponding fire point fitting data, wherein: The fitter is provided with a pixel threshold for performing pixel filtering on the satellite remote sensing image; A preprocessor, connected to the fitter, for preprocessing the fire point fitting data and generating corresponding fire point preprocessing data; The learner is connected to the preprocessor and is used to select a number of indicator features and learn the fire point preprocessing data, and generate a corresponding fire dynamic monitoring map.

4. The power supply and distribution switching system based on power emergency according to claim 3 is characterized in that: The matching module includes: A renderer, configured to render the fire area according to the fire dynamic monitoring map; a meter connected to the renderer, for calculating the real-time fire area according to the fire area; An analyzer is connected to the meter and is used to compare the real-time fire area with the fire area threshold to form a corresponding comparison result, and determine a power supply emergency plan based on the comparison result.

5. The power supply and distribution switching system based on power emergency according to claim 4 is characterized in that: When the image pixel is smaller than the pixel threshold, the fitter filters the corresponding satellite remote sensing image; when the image pixel is larger than the pixel threshold, the fitter retains the corresponding satellite remote sensing image and forms corresponding fire point fitting data.

6. The power supply and distribution switching system based on power emergency according to claim 5, characterized in that: The preprocessor cuts the fire point fitting data according to the sampling rate to form a plurality of fire point preprocessing data with a standard sampling rate, selects a plurality of index features based on the fire point preprocessing data, and the fire point preprocessing data enters the fire dynamic learning model for learning, and generates a corresponding fire dynamic monitoring map. The standard sampling rate is a sampling rate that can be recognized by the fire dynamics learning model, and the standard sampling rate remains unchanged during the learning process of the fire dynamics learning model; The indicator features include location information, spreading speed and / or duration of the fire area.

7. The power supply and distribution switching system based on power emergency according to claim 6, characterized in that: The renderer locks the edge of the fire dynamic monitoring image for rendering to form a corresponding fire area. The meter divides the fire area into several sub-areas and calculates the number of sub-pixels in each sub-area to form the corresponding real-time fire area. A single sub-pixel represents a unit area, and the number of sub-pixels is the area of ​​the real-time fire area.

8. The power supply and distribution switching system based on power emergency according to claim 7, characterized in that: The emergency switching module uses the geometric center of the fire area as a reference, sorts and numbers the power supply stations from near to far, marks the power supply station closest to the geometric center as an emergency power supply station, and calculates the spread speed based on the fire dynamic monitoring map.

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