Power supply and distribution switching system based on power emergency

Abstract

The invention relates to the field of electric power emergency, in particular to an electric power supply and distribution switching system based on electric power emergency, which comprises a satellite acquisition module for acquiring a satellite remote sensing image monitored by an artificial earth satellite, and a fire monitoring module for preprocessing the satellite remote sensing image, determining a fire area, forming corresponding fire point preprocessing data, and sending the fire point preprocessing data to a server. The system comprises a fire hazard monitoring module used for monitoring the fire hazard and generating a fire hazard dynamic monitoring chart, a matching module used for analyzing the fire hazard dynamic monitoring chart, calculating a corresponding real-time fire hazard area and comparing the fire hazard area with a fire hazard area threshold value, and an emergency switching module used for determining a corresponding power supply emergency scheme, switching a power supply station, and identifying and analyzing fire hazard characteristics. The operation sequence of the power supply station can be clearly known under the emergency condition, so that the power infrastructure can be protected, the stability of power supply can be ensured, the quick response to the fire and the dynamic adjustment of the power supply can be realized, and the real-time monitoring of the fire and the intelligent management of the power supply can be optimized.

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-fusion interoperable digital communication emergency command system is an emergency communication command platform that integrates "satellite portable base stations", "satellite vehicle-mounted base stations", "emergency command portable boxes", etc., to achieve multi-system and cross-platform interoperability, unified device management, and build a dedicated emergency communication network with strong reliability, high throughput, and rich functions to meet the requirements of power emergency command work.

[0003] Chinese Patent Application Publication No.: CN115350423A discloses a fire prevention and control method, device, equipment, system, and storage medium. The method includes: transmitting a first signal from one end of an overhead wire to the other end and receiving a second signal returned from the opposite end; wherein, modulation and demodulation devices are respectively provided at both ends of the overhead wire; both the first signal and the second signal are power line carrier signals; determining whether the second signal returned from the opposite end is interrupted, and if so, determining that the overhead wire is broken and sending a tripping instruction to the circuit breaker corresponding to the overhead wire. By using power line carrier signals to identify breakage faults, this application has the characteristics of high identification accuracy and fast identification speed compared with traditional distribution automation equipment for electrical signal identification; at the same time, this application can carry out automatic inspections through command image and video monitoring devices, satellites, and drones according to the installation location and fire situation, realize rapid fire situation feedback, carry out differential on-site disposal in a timely and effective manner, and enhance the energy efficiency of post-fire handling of fires.

[0004] Chinese Patent Application Publication No.: CN116819652A discloses a lightning warning device for monitoring lightning. The lightning warning device includes a client and a server. The server includes a lightning monitoring device, a data transmission module, a server, a positioning module, a lightning warning information generation module, and a data publishing module; the lightning monitoring device is used to monitor lightning in the area near the warning device and send the monitored data to the server through the data transmission module. The server analyzes and processes the received data and sends the processed data to the lightning warning information generation module, and the lightning warning information generation module determines whether the lightning warning threshold set in advance is reached; this invention has quite high practical significance and economic benefits for the investigation of lightning strike obstacle points in the power system, the lightning situation near the rocket and satellite launch sites, the fixed-point monitoring of forest lightning strike fire areas, the lightning warning of the oilfield system, and the lightning monitoring and warning near airports.

[0005] However, the above methods have the following problems: They cannot monitor the location and spread speed of a 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, which is used to overcome the problems in the prior art that the location and spread speed of a fire cannot be monitored in real time, the fire trend cannot be estimated, and corresponding emergency measures cannot be taken in a timely manner.

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

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

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

[0010] A matching module, which is connected to the fire monitoring module, used to analyze the fire dynamic monitoring map, including calculating the area of 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, which is connected to the matching module, used to determine a corresponding power supply emergency plan according to the comparison result and switch the power supply station;

[0012] Wherein, a fire dynamic learning model is set in the fire monitoring module, which is used to learn the fire point preprocessing data and generate a corresponding fire dynamic monitoring map;

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

[0014] Further, the satellite acquisition module includes:

[0015] A timing device, which is used to send acquisition instructions at preset time intervals;

[0016] Access to the cloud, which is connected to the timing device, used to receive the acquisition instructions and query the artificial earth satellite to obtain the satellite remote sensing images in real time;

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

[0018] Further, the fire monitoring module includes:

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

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

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

[0022] A learner, connected to the preprocessor, for selecting a number of index features and learning the preprocessed fire point data to generate a corresponding fire dynamic monitoring map.

[0023] Further, the matching module includes:

[0024] A renderer for rendering the fire area according to the fire dynamic monitoring map;

[0025] A meter, connected to the renderer, for calculating the real-time fire area based on the fire area;

[0026] An analyzer, connected to the meter, for comparing the real-time fire area with the fire area threshold to form a corresponding comparison result, and determining a power supply emergency plan according to the comparison result.

[0027] Further, when the image pixel is less than the pixel threshold, the fitter filters the corresponding satellite remote sensing image, and when the image pixel is greater than the pixel threshold, the corresponding satellite remote sensing image is retained and corresponding fire point fitting data is formed.

[0028] Further, the preprocessor cuts the fire point fitting data according to the sampling rate to form a number of preprocessed fire point data with the sampling rate being the standard sampling rate, selects a number of index features according to the preprocessed fire point data, and the preprocessed fire point data enters the fire dynamic learning model for learning to generate a corresponding fire dynamic monitoring map.

[0029] Wherein, the standard sampling rate is the sampling rate that the fire dynamic learning model can recognize, and during the learning process of the fire dynamic learning model, the standard sampling rate remains unchanged;

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

[0031] Further, the renderer locks the edge of the fire dynamic monitoring map for rendering to form a corresponding fire area, and the meter divides the fire area into a number of sub-areas and calculates the number of sub-pixels of each sub-area to form a corresponding real-time fire area. Among them,

[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] Further, based on the geometric center of the fire area, the emergency switching module 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 according to the fire dynamic monitoring map.

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

[0035] Further, when the area of the real-time fire area is greater than the fire area threshold, the emergency switching module calculates the area of 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 uses a set of satellite acquisition modules to collect satellite remote sensing images monitored by artificial earth satellites, a fire monitoring module to preprocess the satellite remote sensing images to determine the fire area, form corresponding fire point preprocessing data, and generate a fire dynamic monitoring map, a matching module to analyze the fire dynamic monitoring map, calculate the corresponding area of the real-time fire area, and compare it with the fire area threshold, and an emergency switching module to determine the corresponding power supply emergency plan and switch the power supply station, so as to clearly understand the operation sequence of the power supply station in an emergency by identifying and analyzing fire characteristics, protect the power infrastructure and ensure the stability of power supply, realize the rapid response to fires and the dynamic adjustment of power supply, and optimize the real-time monitoring of fires and the intelligent management of power supply.

[0037] Further, through the collaborative work of the timing device, accessing the cloud, and the acquisition device, the integrity and traceability of image data can be ensured, the automatic acquisition and storage of satellite remote sensing images can be realized, and real-time data support can be provided for subsequent fire monitoring and emergency response. In this way, the system can quickly respond to emergencies such as fires and timely adjust the power supply and distribution strategy to ensure the safety and stability of the power system.

[0038] Furthermore, an imager extracts image pixels from satellite remote sensing images, and uses pixel thresholds for filtering to generate hotspot fitting data. A preprocessor further preprocesses the hotspot fitting data, providing a basis for the subsequent generation of fire dynamic monitoring maps. A learner generates fire dynamic monitoring maps by learning the preprocessed hotspot data, which can intuitively display the location, scope, and possible development trends of the fire area. Through automated image processing and machine learning techniques, the efficiency and accuracy of fire monitoring can be improved, enabling rapid emergency measures to be taken in the event of a fire to protect power facilities and the safety of people's lives and property.

[0039] Furthermore, a renderer renders the specific fire area based on the fire dynamic monitoring map, and a 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 be ensured that in the event of a fire, the power system can quickly take appropriate emergency measures to protect power facilities and ensure the stability of power supply.

[0040] Furthermore, by setting pixel thresholds, the image areas corresponding to the retained pixels will be used by the imager to generate hotspot fitting data. The imager 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 preprocessor cuts the hotspot fitting data according to the sampling rate and inputs the preprocessed hotspot data into the fire dynamic learning model for learning, ensuring data consistency and model accuracy. After learning, the fire dynamic learning model can generate fire dynamic monitoring maps based on real-time preprocessed hotspot data. These monitoring maps display key information such as the location, spread speed, and duration of the fire area, providing decision-makers with an intuitive view of the fire development. Through this detailed preprocessing and learning process, the fire monitoring module can provide accurate fire monitoring and prediction, providing key decision-making support for the power emergency power supply and distribution switching system, helping to improve the efficiency and effectiveness of fire emergency response, and reducing the potential damage of fires to people and property.

[0042] Further, by sorting all power supply stations in ascending order of their distances from the geometric center of the fire area, the analyzer compares the real-time fire area with a preset fire area threshold to ensure that the basic power demand of the fire area is met. Through this dynamic emergency response strategy based on the real-time fire area and spread speed, the emergency switching module can flexibly adjust the power supply to adapt to different stages and scales of fire development. This strategy helps to minimize the damage to the power infrastructure caused by the fire and provides support for fire rescue and post-disaster recovery. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0044] Figure 2 Schematic structural diagram of the satellite acquisition module of the present invention;

[0045] Figure 3 Schematic structural diagram of the fire monitoring module of the present invention;

[0046] Figure 4 Schematic structural diagram of the matching module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0047] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

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

[0049] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for 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, and therefore should not be construed as a limitation of the present invention.

[0050] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0051] Please refer to Figure 1 as shown in the figure, which is a schematic structural diagram of a power supply and distribution switching system based on power emergency of the present invention, including:

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

[0053] A fire monitoring module, which is connected to the satellite acquisition module, used to preprocess the satellite remote sensing images, determine the fire area, form corresponding fire point preprocessing data, and generate a fire dynamic monitoring map;

[0054] A matching module, which is connected to the fire monitoring module, used to analyze the fire dynamic monitoring map, including calculating the area of 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, used to determine the corresponding power supply emergency plan according to the comparison result and switch the power supply station;

[0056] Among them, a fire dynamic learning model is set in the fire monitoring module, which is used 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] Please refer to Figure 2 as shown in the figure, which is a schematic structural diagram of the satellite acquisition module of the present invention, including:

[0059] A timing device, which is used to send acquisition instructions at preset time intervals;

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

[0061] An acquisition device, which is connected to the access to the cloud, used to store the satellite remote sensing images and sort the satellite remote sensing images according to the time sequence.

[0062] In a specific implementation, the timing device is responsible for sending acquisition instructions at preset intervals. This preset time can be set according to actual monitoring requirements and satellite transit cycles 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 the acquisition instructions and query the artificial earth satellite 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 data transmission and reception. The acquisition device is connected to the access cloud module and is responsible for receiving the satellite remote sensing images obtained from the access cloud module and storing them. These images contain real-time status information of key power infrastructure such as power grids, transmission lines, and distribution facilities, which is crucial for monitoring and evaluating the grid status. This requires the acquisition device to have sufficient storage space and efficient data management capabilities to ensure the integrity and traceability of the image data.

[0063] Time sequence sorting: The acquisition device not only stores the images but also sorts them according to the time sequence of obtaining the images. This sorting ensures the timeliness of the image data, enables the system to track changes in the grid status, and allows for analyzing the development of events along the time line.

[0064] Through the collaborative work of the timing device, access cloud, and acquisition device, the integrity and traceability of the image data can be ensured, the automated acquisition and storage of satellite remote sensing images can be achieved, and real-time data support can be provided for subsequent fire monitoring and emergency response. In this way, the system can quickly respond to emergencies such as fires and timely adjust the power supply and distribution strategies to ensure the safety and stability of the power system.

[0065] Please refer to Figure 3 as shown, which is a schematic structural diagram of the fire monitoring module of the present invention, including:

[0066] A fitter, which is used to extract the image pixels of the satellite remote sensing image, filter them, and generate corresponding fire point fitting data. Among them,

[0067] A pixel threshold is set in the fitter to filter the pixels of the satellite remote sensing image;

[0068] A preprocessor, which is connected to the fitter and is used to preprocess the fire point fitting data and generate corresponding fire point preprocessing data;

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

[0070] In specific implementation, a pixel threshold is set in the fitter, which is a key parameter for pixel filtering of satellite remote sensing images. The setting of the pixel threshold is based on fire characteristics such as temperature, brightness, or reflectivity of specific bands to identify potential fire points. The learner uses machine learning or deep learning algorithms such as support vector machine (SVM), random forest, convolutional neural network (CNN), etc. to identify fire characteristics and patterns.

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

[0072] Please refer to Figure 4 as shown, which is a schematic structural diagram of the satellite matching module of the present invention, including:

[0073] A renderer for rendering the fire area according to the fire dynamic monitoring map;

[0074] A meter connected to the renderer for calculating the real-time fire area based on the fire area;

[0075] An analyzer connected to the meter for comparing the real-time fire area with the fire area threshold to form a corresponding comparison result and determining a power supply emergency plan based on the comparison result.

[0076] The fire area threshold is related to the ground environment, air humidity, and ground vegetation. These factors 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 a specific implementation, the function of the renderer is to render a specific fire area based on the fire dynamic monitoring map. This involves image processing technology to convert the fire characteristics (such as temperature anomalies, smoke, etc.) in the monitoring map into a visual fire area image. The renderer uses color coding or other visual markers to distinguish the fire area, making it more obvious in the monitoring map. The meter is connected to the renderer, and its task is to calculate the area of the rendered fire area. This step involves image analysis techniques, such as pixel counting or geometric shape analysis, to determine the size of the fire area. The output of the meter is the quantified data of the fire area, providing a basis for subsequent comparison and decision-making. The analyzer is connected to the meter, and its role is to compare the real-time fire area calculated by the meter with a preset fire area threshold. The fire area threshold is a key parameter, which is set based on historical data, safety standards, or expert experience and is used to judge the severity of the fire. The analyzer forms corresponding comparison results based on the comparison results and determines the power supply emergency plan accordingly. If the real-time fire area exceeds the threshold, the analyzer may trigger emergency measures, such as adjusting the power distribution to ensure the power supply of critical facilities.

[0078] The renderer renders a 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.

[0079] Specifically, when the image pixels are less than the pixel threshold, the fitter filters the corresponding satellite remote sensing image. When the image pixels are greater 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 pixels of Satellite A remote sensing image are 800, the image pixels are less than the pixel threshold, and the fitter filters the Satellite A remote sensing image. When the image pixels of Satellite B remote sensing image are 1200, the image pixels are greater than the pixel threshold, and the Satellite B remote sensing image is retained and the corresponding fire point fitting data is formed.

[0081] In a specific implementation, the pixel threshold is a preset parameter used to distinguish normal situations from possible fire situations. 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 the preset pixel threshold.

[0083] The pixel size in satellite remote sensing images is 1m × 1m, also known as the 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 will consider these pixels as non-fire areas and thus will 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 potential fire areas.

[0085] When the value of an image pixel is greater than the pixel threshold, the fitter will consider these pixels as possible fire areas. These pixels will be 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 the fire point fitting data. This data includes the positions, intensities, 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 the fire dynamics learning model to identify and monitor fire areas.

[0088] By setting the pixel threshold, the image areas corresponding to the retained pixels will be used by the fitter to generate the 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, and being crucial for early fire detection and rapid response.

[0089] Specifically, the preprocessor cuts the fire point fitting data at the sampling rate to form several pieces of fire point preprocessed data with the sampling rate being the standard sampling rate, selects several index features based on the fire point preprocessed data, and the fire point preprocessed data enters the fire dynamics learning model for learning and generates the corresponding fire dynamics monitoring map.

[0090] Among them, the standard sampling rate is the sampling rate that the fire dynamics learning model can recognize, and during the learning process of the fire dynamics learning model, the standard sampling rate remains unchanged.

[0091] The index features include the position information, spread speed, and / or duration of the fire area.

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

[0093] The preprocessor cuts the fire point fitting data at a certain sampling rate to form fire point preprocessed data that meets the standard sampling rate. The sampling rate refers to the frequency of data collection within a certain period of time. For image data, it refers to the number of pixel points collected per unit area or per unit time. The preprocessor cuts the fire point fitting data according to the standard sampling rate to form several fire point preprocessed data blocks that meet the standard sampling rate. This step ensures that the size and format of the data match the input requirements of the model.

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

[0095] After learning, the fire dynamics learning model can generate fire dynamics monitoring maps based on real-time fire point preprocessed 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. During the learning process of the fire dynamics learning model, it is important to keep the standard sampling rate unchanged because this ensures the consistency of the model input and helps the model learn stable and reliable fire characteristics.

[0096] The preprocessor cuts the fire point fitting data according to the sampling rate and inputs the fire point preprocessed data into the fire dynamics learning model for learning, ensuring data consistency and model accuracy. After learning, the fire dynamics learning model can generate fire dynamics monitoring maps based on real-time fire point preprocessed 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. Through this detailed preprocessing and learning process, the fire monitoring module can provide accurate fire monitoring and prediction, providing key decision support for the power emergency power supply and distribution switching system, helping to improve the efficiency and effectiveness of fire emergency response, and reducing the potential damage of fires to people and property.

[0097] Specifically, the renderer locks the edges of the fire dynamics monitoring map 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 area. Among them,

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

[0099] In some possible implementations, the fire area is divided into 3 sub-areas, and the number of sub-pixels in each sub-area is 10, then the fire area is 30.

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

[0101] Specifically, the analyzer compares the real-time fire area with the fire area threshold. When the real-time fire area is less 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 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.

[0103] In the specific implementation, the emergency switching module first takes the geometric center of the fire area as a reference to determine the location of the fire area, sorts all the power supply stations according to the distance from the geometric center of the fire area from near to far, numbers the sorted power supply stations to ensure that each power supply station has a unique identifier, and marks the power supply station closest to the geometric center as the emergency power supply station. This station will be considered first for power supply when a fire occurs.

[0104] If the real-time fire area is less than the fire area threshold, it indicates that the fire scale is small. The emergency switching module will directly switch the emergency power supply station to supply power to the fire area to ensure that the basic power demand of the fire area is met. If the real-time fire area is greater than the fire area threshold, it indicates that the fire scale is large and a more complex emergency response strategy is needed. The emergency switching module will calculate the area that the fire may cover within the preset rescue time according to the spread speed. According to the calculation result, it marks the emergency power supply stations that will not be affected by the fire within the preset rescue time. The emergency switching module will switch the marked emergency power supply stations to supply power to the fire area to ensure that the power supply in the critical area is not affected during the fire occurrence and rescue process.

[0105] By sorting all the power supply stations according to the distance from the geometric center of the fire area from near to far, the analyzer compares the real-time fire area with the preset fire area threshold to ensure that the basic power demand of the fire area is met. Through this dynamic emergency response strategy based on the real-time fire area and spread speed, the emergency switching module can flexibly adjust the power supply to adapt to different stages and scales of the fire development. This strategy helps to minimize the damage of the fire to the power infrastructure and provides support for fire rescue and post-disaster recovery.

[0106] In specific implementation, the satellite ultra-fusion interoperable digital communication emergency command system realizes multi-system and cross-platform interoperability and unified device management through emergency communication command platforms such as "satellite portable base stations", "satellite vehicle-mounted base stations", and "emergency command portable boxes", and constructs a dedicated emergency communication network with strong reliability, high throughput, and rich functions. It has functions such as voice intercom, two-way audio and video calls, positioning, monitoring, Internet, and 4G / 5G networks, meeting the requirements of power emergency command work.

[0107] The satellite ultra-fusion interoperable digital communication emergency command system integrates data and fuses services in the cloud audio and video field for platforms such as the "infrastructure online monitoring system", "5G intelligent safety helmets", "satellite conference system", "substation auxiliary control detection system", "transmission line online detection system", "early warning platform", and "fire online detection system" currently used in the company's emergency command center. The application scenarios of the integration of video monitoring and video conferencing will also be further expanded. In addition, various monitoring cameras such as smart terminals and drones can be used to join the conference for scenarios such as patrol, monitoring, dispatching, and command of moving targets, emergencies, or targets in remote areas, realizing more comprehensive and in-depth data analysis and applications, and bringing a more efficient production method.

[0108] The satellite + 4G full-network-communication satellite portable station is the first backpack-type satellite portable station in China that can work automatically and provide 4G signals for the three major operators of China Unicom, China Mobile, and China Telecom at the same time. The whole machine is light and portable, easy to operate, and can complete service activation within 5 minutes after one-key startup. It can be used without professional personnel and can quickly provide 4G network coverage for the three major operators through satellite links, and is suitable for fields such as emergency rescue and outdoor inspection.

[0109] The satellite + 4G full-network-communication satellite portable station gives full play to the advantages of satellite communication such as wide coverage, no geographical restrictions, high mobility, and stable performance, and can provide normal call and Internet functions for remote areas where ground network coverage is unavailable, such as remote mountainous areas, field operations, emergencies, and natural disaster sites, solving the problem of communication failure in special scenarios.

[0110] Studied the working principles and technical characteristics of the mobile public network full-network-communication network, Tiantong-1 satellite mobile communication system, and Beidou-3 satellite system, studied the transformation of the system air interface, including physical layer design, terminal access design, and improvement of communication protocols; investigated and compared the characteristics of the Beidou-3 system with the previous two generations of systems, studied the short message communication of the Beidou-3 system and the high-precision positioning service function of the Beidou-3 system, and studied the emergency communication system applicable to power applications.

[0111] Study the composition framework and implementation principle of the power inspection and emergency communication system for multi-network integrated communication, navigation and positioning. Build the front-end hardware facilities of the system platform according to the composition: including a mobile public network full-network communication network + Tiantong communication terminal, Beidou emergency communication terminal, front-end server, central station server and emergency communication service platform. The emergency communication service platform has functions such as search and rescue information query, task management, emergency communication command and dispatch, and emergency repair work command. At the platform end, the position monitoring and display of personnel and vehicles, work progress management and content display can be realized. Study the single-frequency RTK high-precision positioning service function based on Beidou-3 to achieve timed reporting of positions and ensure that power operation personnel and vehicles are within a safe and controllable range. Query the recorded trajectory of the position information of operation personnel, dispatch command work tasks, summarize and query the operation content, summarize and query tasks, monitor the position and provide route navigation for the on-site emergency repair location, and provide software interfaces, which can be integrated into the existing emergency command system or emergency repair system as a subsystem.

[0112] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0113] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope 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, which is used to acquire satellite remote sensing images monitored by artificial earth satellites; A fire monitoring module, which is connected to the satellite acquisition module, is used to pre-process the satellite remote sensing image, 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 emergency power supply plan according to the comparison result and switching the power supply station; Wherein, a fire dynamics learning model is provided in the fire monitoring module to learn the fire point preprocessing data and generate a corresponding fire dynamics monitoring diagram; 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.

2. The power supply and distribution switching system based on power emergency according to claim 1 is characterized in that: The satellite acquisition module comprises: A timing device for sending collection instructions at preset time intervals; Accessing the 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; A collection 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 comprises: A renderer, used for rendering 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 according to 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, and when the image pixel is larger than the pixel threshold, the corresponding satellite remote sensing image is retained and corresponding fire point fitting data is formed.

6. The power supply and distribution switching system based on power emergency according to claim 5 is 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 sampling rate of a standard sampling rate, and selects a plurality of index features according to the fire point preprocessing data. The fire point preprocessing data enters the fire dynamic learning model for learning, and generates a corresponding fire dynamic monitoring diagram. Wherein, the standard sampling rate is a sampling rate that can be recognized by the fire dynamics learning model, and during the learning process of the fire dynamics learning model, the standard sampling rate remains unchanged; 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 is characterized in that: The renderer locks the edge of the fire dynamic monitoring map for rendering to form a corresponding fire area. The meter divides the fire area into a plurality of sub-areas and calculates the number of sub-pixels in each sub-area to form a corresponding real-time fire area. A single sub-pixel represents a unit area, and the number of sub-pixels is the real-time fire area area.

8. The power supply and distribution switching system based on power emergency according to claim 7 is 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 diagram.

9. The power supply and distribution switching system based on power emergency according to claim 8 is characterized in that: The analyzer compares the real-time fire area with the fire area threshold, and 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.

10. The power supply and distribution switching system based on power emergency according to claim 9, characterized in that: 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 spreading speed, marks the corresponding emergency power supply station, and switches it to supply power to the fire area.

Images