Satellite Internet-based power mountain fire disaster prevention emergency resource optimization dispatching method and system

Abstract

The present invention provides a method and system for optimizing and dispatching power mountain fire disaster prevention emergency resources based on satellite Internet, which relates to the field of power disaster prevention emergency technology, and obtains multivariate data through a sky-ground integrated monitoring system; extracts and fuses multivariate data based on a convolutional neural network to obtain processing results; evaluates the processing results in combination with the hierarchical analysis method to generate weight factors and determine the evaluation quantification results; establishes an objective function based on the sky-ground integrated power mountain fire disaster prevention emergency resource dispatching system, the evaluation quantification results and the weight factors; establishes the constraint conditions of the sky-ground integrated power mountain fire disaster prevention emergency resource dispatching system; and determines the optimization plan for power mountain fire disaster prevention emergency resource dispatching according to the constraint conditions and the objective function. It can not only realize fire monitoring and information transmission from multiple dimensions, but also improve the utilization rate of communication resources in the disaster area and reduce the overall firefighting time of power mountain fires.

Description

Technical Field

[0001] The present application relates to the field of power disaster prevention and emergency technology, and in particular to a method and system for optimizing and scheduling power wildfire disaster prevention and emergency resources based on satellite Internet. Background Art

[0002] The frequent occurrence of extreme weather events such as drought and strong winds has led to a significant increase in the incidence of wildfires (especially forest fires), resulting in damage to key power facilities such as transmission lines, substations, and distribution equipment, triggering large-scale power outages, and seriously threatening the safe operation of power infrastructure. Power transmission corridors are mostly located in remote areas such as mountainous areas and dense forests, and traditional communication and monitoring methods are often difficult to cover. After the fire, emergency response and resource scheduling are delayed. At present, although some power grid companies have adopted ground wireless communication technology and limited satellite communication methods for emergency management, when the scale of the fire is expanded, the geographical location is complex, or the ground facilities are severely damaged, traditional communication methods are often difficult to meet the needs of overall control and precise scheduling, which brings unprecedented challenges to the rapid scheduling of emergency resources.

[0003] The existing power wildfire monitoring mainly combines the monitoring of meteorological satellites and the inspection of drones and maintenance personnel for data collection. It monitors and warns wildfires by monitoring hot spot information, fire spread and other data, and relies on ground communication networks for data transmission and decision-making information. The resource dispatch for power wildfires currently relies on the dispatcher's experience, expert knowledge base, etc. to allocate and command resources. In view of the above-mentioned single emergency communication means, backward decision-making means and resource dispatch methods, the current research on the dispatch method of power wildfire emergency resources still has the following defects: (1) The ground communication network has many blind spots and uneven service performance. Wildfire disasters often cause single-point failure of the ground communication network. The communication means in disaster areas and remote areas are difficult to ensure the real-time transmission of collected data information. (2) The current sky-ground integrated monitoring network does not realize cross-layer transmission of data information, lacks the mutual connection between space-based, air-based and ground-based, and relies on meteorological satellites for data collection in air-based systems. There is a lack of research on the application of satellite remote sensing, satellite navigation and satellite communication in power wildfires. (3) Traditional manual decision-making and dispatching are slow, with delayed information acquisition and are easily affected by the dispatcher’s experience and subjective factors. They are difficult to adapt to complex and dynamically changing fire conditions and lack flexibility. Summary of the invention

[0004] In order to solve the above problems, the present application provides a method and system for optimizing and scheduling power wildfire disaster prevention emergency resources based on satellite Internet.

[0005] In the first aspect, the present application provides a method for optimizing and dispatching power mountain fire disaster prevention emergency resources based on satellite Internet, which adopts the following technical solutions:

[0006] A satellite internet-based power mountain fire disaster prevention emergency resource optimization dispatching method, comprising:

[0007] Acquire multivariate data through the integrated sky-ground monitoring system;

[0008] Extracting and fusing the multivariate data based on a convolutional neural network to obtain a processing result;

[0009] Performing an evaluation based on the analytic hierarchy process in combination with the processing results to generate weight factors and determine an evaluation quantitative result;

[0010] Establishing an objective function based on the sky-ground integrated electric power wildfire disaster prevention emergency resource dispatching system, the evaluation quantification result and the weight factor;

[0011] Establishing constraints for the sky-ground integrated electric power mountain fire disaster prevention emergency resource dispatching system;

[0012] An optimization plan for dispatching power wildfire disaster prevention emergency resources is determined based on the constraint conditions and the objective function.

[0013] Optionally, the step of acquiring multivariate data through the integrated sky-ground monitoring system further includes:

[0014] Acquiring satellite remote sensing data through the space-based system in the integrated sky-ground monitoring system;

[0015] Acquiring drone data through the air-based system in the air-ground integrated monitoring system;

[0016] Acquiring ground monitoring system data through the ground-based system in the integrated sky-ground monitoring system;

[0017] Multivariate data is generated based on the satellite remote sensing data, the drone data, and the ground monitoring system data.

[0018] Optionally, the multivariate data includes: temperature, wind speed, vegetation density, fire source density, fire truck accessibility and drone fire extinguishing efficiency.

[0019] Optionally, the step of performing evaluation according to the analytic hierarchy process in combination with the processing result to generate a weight factor and determine the evaluation quantitative result includes:

[0020] Obtain information of each factor in the criterion layer and construct a judgment matrix;

[0021] Obtain a target reference data set from a preset database;

[0022] performing pairwise comparison on the information of each element in the judgment matrix in combination with the target reference data set to generate a weight factor;

[0023] In the solution layer, the corresponding evaluation quantification result is generated according to the weight factor.

[0024] Optionally, the step of establishing an objective function based on the sky-ground integrated electric power wildfire disaster prevention emergency resource dispatching system, the evaluation quantification result and the weight factor includes:

[0025] Using random geometry theory, satellites, communication base stations, drones, and fire trucks are abstracted into points. Corresponding points And use three-dimensional coordinates Determine, and assume that the point distribution obeys the strength Uniform Poisson point process, represented by Φ, the distance between the fire-fighting equipment and the communication base station or satellite Obey the probability density function:

[0026]

[0027]

[0028] in, represents the base of natural logarithms;

[0029] The mathematical relationship between signal-to-noise ratio and distance can be expressed as:

[0030]

[0031] in, Indicates the transmit power, represents the noise power, which is set as a constant;

[0032] To ensure the maximum signal-to-noise ratio, the objective function can be expressed as:

[0033]

[0034] Among them, M represents all devices gather, represents the indicator function, when the ground network is active , when the ground network fails, , and Respectively represent the signal-to-noise ratio of the terrestrial communication network and the satellite communication network;

[0035] Combine the weight factors to maximize the overall signal-to-noise ratio of the communication system ;

[0036] ;

[0037] Combining the weight factors to represent the objective function of fire extinguishing resource scheduling corresponding to the shortest fire extinguishing time;

[0038]

[0039] in, Indicates The wildfire assessment weight of each disaster area. The higher the weight, the more serious the disaster area. The drone and fire truck worked together to handle the The time required for a fire to The details are as follows:

[0040]

[0041] in, Indicates the disaster area The fire area, and Represent the fire extinguishing efficiency of drones and fire trucks, and Respectively represent the time it takes for the drone and fire truck to reach the fire area.

[0042] Optionally, the step of establishing constraint conditions for the sky-ground integrated electric power mountain fire disaster prevention emergency resource dispatching system includes:

[0043] Set the signal-to-noise ratio of the terrestrial network and the satellite network to meet the preset minimum value of the network signal-to-noise ratio:

[0044]

[0045]

[0046] in, They represent the minimum values ​​of the signal-to-noise ratio required for the ground network and the satellite network respectively;

[0047] Set the signal-to-noise ratio of each disaster area to meet the preset minimum signal-to-noise ratio of the disaster area:

[0048]

[0049] in, Indicates the minimum signal-to-noise ratio of the preset disaster area;

[0050] When switching between the terrestrial network and the satellite network, the maximum signal-to-noise ratio difference after switching is set to not exceed the preset switching threshold:

[0051]

[0052] in, represents the preset switching threshold;

[0053] Shared drone, Fire trucks, and each drone or fire truck can only handle one fire area:

[0054]

[0055]

[0056] in, is a binary variable. If a drone or a fire truck is assigned to If firefighting is carried out in the disaster area, or , otherwise 0;

[0057] It is assumed that either drones or fire trucks are allowed to work in the disaster area, expressed by the following constraints:

[0058]

[0059] The total working time of each drone should not exceed the maximum endurance time:

[0060]

[0061] in, Drones in disaster areas Fire extinguishing time, is the maximum flight time of the drone;

[0062] The total working time of each fire truck should not exceed its maximum endurance time:

[0063]

[0064] in, For fire trucks in disaster areas Fire extinguishing time, The maximum cruising time of a fire truck.

[0065] Optionally, the step of determining the optimization scheme for dispatching power wildfire disaster prevention emergency resources according to the constraint conditions and the objective function includes:

[0066] Determine the optimization problem according to the constraint conditions and the objective function

[0067]

[0068] Processing integer variables of the optimization problem by multi-objective discrete binary particle swarm optimization algorithm and combining particle swarm optimization algorithm to process continuous variables in the optimization problem to determine maximum communication performance and shortest fire rescue time;

[0069] An optimization plan for dispatching power wildfire disaster prevention emergency resources is determined based on the maximum communication performance and the shortest fire rescue time.

[0070] In a second aspect, the present application provides a satellite Internet-based power mountain fire disaster prevention emergency resource optimization and scheduling system, wherein the satellite Internet-based power mountain fire disaster prevention emergency resource optimization and scheduling system includes:

[0071] A data acquisition module, used to acquire multivariate data through the sky-ground integrated monitoring system;

[0072] A data processing module, used for extracting and fusing features of the multivariate data based on a convolutional neural network to obtain a processing result;

[0073] An evaluation module, for evaluating according to the analytic hierarchy process in combination with the processing results to generate weight factors and determine evaluation quantitative results;

[0074] An objective function module is used to establish an objective function based on the sky-ground integrated electric power wildfire disaster prevention emergency resource dispatching system, the evaluation quantification result and the weight factor;

[0075] A constraint condition module, used to establish the constraint conditions of the sky-ground integrated electric power mountain fire disaster prevention emergency resource dispatching system;

[0076] A scheme generation module is used to determine the optimization scheme for dispatching power wildfire disaster prevention emergency resources according to the constraint conditions and the objective function.

[0077] In the third aspect, the present application provides a satellite Internet-based power wildfire disaster prevention emergency resource optimization and scheduling device, the device comprising: a memory, a processor, and the processor executes the method described above when running the satellite Internet-based power wildfire disaster prevention emergency resource optimization and scheduling program stored in the memory.

[0078] In a fourth aspect, the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0079] In summary, the present application includes the following beneficial technical effects:

[0080] This application obtains multivariate data through the sky-ground integrated monitoring system; extracts and fuses multivariate data based on convolutional neural networks to obtain processing results; evaluates the processing results according to the hierarchical analysis method to generate weight factors and determine the evaluation quantification results; establishes an objective function based on the sky-ground integrated power mountain fire disaster prevention emergency resource scheduling system, the evaluation quantification results and the weight factors; establishes the constraints of the sky-ground integrated power mountain fire disaster prevention emergency resource scheduling system; determines the optimization plan for power mountain fire disaster prevention emergency resource scheduling based on the constraints and the objective function. It can not only realize fire monitoring and information transmission from multiple dimensions, but also improve the utilization rate of communication resources in the disaster area and reduce the overall firefighting time of power mountain fires. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic diagram of the structure of a satellite Internet-based power mountain fire disaster prevention emergency resource optimization and dispatching device in the hardware operating environment involved in the embodiment of the present application;

[0082] Figure 2 It is a flow chart of a method for optimizing and dispatching electric power wildfire disaster prevention emergency resources based on satellite Internet in an embodiment of the present application;

[0083] Figure 3 It is a hierarchical structure of a power wildfire disaster assessment model of a method for optimizing and dispatching power wildfire disaster prevention emergency resources based on satellite Internet in an embodiment of the present application;

[0084] Figure 4 It is a schematic diagram of the operation of the sky-ground three-layer system of the power wildfire disaster prevention emergency resource optimization scheduling method based on satellite Internet in a wildfire disaster prevention emergency scenario in an embodiment of the present application;

[0085] Figure 5 A model framework diagram of the method for optimizing and dispatching electric power wildfire disaster prevention emergency resources based on satellite Internet in an embodiment of the present application;

[0086] Figure 6 It is a structural block diagram of the electric power wildfire disaster prevention emergency resource optimization and dispatching system based on satellite Internet in an embodiment of the present application. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below through the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0088] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a satellite Internet-based power wildfire disaster prevention emergency resource optimization and dispatching device in the hardware operating environment involved in the embodiment of the present application.

[0089] like Figure 1 As shown, the satellite Internet-based power wildfire disaster prevention emergency resource optimization and dispatching device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage system independent of the aforementioned processor 1001.

[0090] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the satellite Internet-based power wildfire disaster prevention emergency resource optimization and dispatching equipment, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0091] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a satellite Internet-based power wildfire disaster prevention emergency resource optimization scheduling program.

[0092] exist Figure 1 In the satellite Internet-based electric power wildfire disaster prevention emergency resource optimization and scheduling device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the present application can be arranged in the satellite Internet-based electric power wildfire disaster prevention emergency resource optimization and scheduling device, and the satellite Internet-based electric power wildfire disaster prevention emergency resource optimization and scheduling device calls the satellite Internet-based electric power wildfire disaster prevention emergency resource optimization and scheduling program stored in the memory 1005 through the processor 1001, and executes the satellite Internet-based electric power wildfire disaster prevention emergency resource optimization and scheduling method provided in the embodiment of the present application.

[0093] The present application embodiment provides a method for optimizing and dispatching power mountain fire disaster prevention emergency resources based on satellite Internet, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of the method for optimizing and dispatching power wildfire disaster prevention emergency resources based on satellite Internet in this application.

[0094] Step S10: Acquire multivariate data through the integrated sky-ground monitoring system.

[0095] It should be noted that the step of obtaining multivariate data through the integrated sky-ground monitoring system also includes: obtaining satellite remote sensing data through the space-based system in the integrated sky-ground monitoring system; obtaining drone data through the air-based system in the integrated sky-ground monitoring system; obtaining ground monitoring system data through the ground-based system in the integrated sky-ground monitoring system; and generating multivariate data based on the satellite remote sensing data, the drone data and the ground monitoring system data.

[0096] It can be understood that the multivariate data include: temperature, wind speed, vegetation density, fire source density, fire truck accessibility and drone fire extinguishing efficiency.

[0097] In the specific implementation, Figure 3 As shown, Figure 3 This is the hierarchical structure of the electric power wildfire disaster assessment model involved in the present invention. By using convolutional neural networks, deep feature extraction is performed on the multivariate monitoring data provided by satellite remote sensing, drones, and ground monitoring systems, such as the temperature in the disaster area, wind speed, vegetation density, fire source density, and other data information closely related to electric power wildfires. Based on the judgment of the overall fire situation by satellite remote sensing, satellite navigation performs high-precision positioning and tracking of the specific location of the fire source, the smoothness of ground roads, and the location information of drones and fire trucks. At the same time, satellite remote sensing, drones and other monitoring systems adjust the monitoring range according to the specific location of the fire point located by satellite navigation, ensuring that the monitoring range of satellite remote sensing can cover the entire fire area. In the face of communication interruptions caused by fire or geographical location, satellite communications will provide a stable data transmission channel to ensure that the data information collected by satellite remote sensing, drones, ground monitoring systems and satellite navigation can be transmitted to the integrated sky-ground emergency command center. The command platform will generate a fire situation map based on the data information, divide the entire disaster area into N small areas to construct a plan layer, and finally use the hierarchical analysis method to evaluate the severity of the fire in the N disaster areas, and use the final evaluation results as the weight factor in the integrated sky-ground power mountain fire disaster prevention emergency resource scheduling model.

[0098] It should be noted that the space-based system mainly includes satellite communications, satellite navigation and satellite remote sensing. With the technical support of satellite Internet, the remote sensing information granularity, navigation positioning accuracy, communication coverage and service capabilities are improved through multi-satellite networking and space-ground interconnection technology. Satellite remote sensing carries different types of payloads such as optics, radar, infrared, microwave, etc., and uses infrared, thermal imaging and other technologies to observe the surface of the wildfire area, and provide high-altitude fire hotspot information, especially in remote areas that are difficult to monitor manually, and judge the spread trend of the fire through data analysis. Satellite navigation helps drones accurately locate target points at the wildfire site through precise orbit calculation and navigation technology, while ensuring that fire trucks maintain stable positioning in more remote or signal-blind areas, assisting fire trucks in choosing the best fire-fighting route, thereby improving fire-fighting efficiency and reducing the risk of fire spread. Satellite communication uses radio to use communication satellites as relay stations to forward information at high speed between ground stations. When ground communication facilities are damaged or communication cannot be covered due to wildfire disasters, satellite communication can achieve cross-regional communication, ensuring real-time communication between the sky-ground integrated emergency command center and front-line firefighting equipment and personnel.

[0099] It should be noted that the main equipment of the air-based system is the drone. The drone is equipped with a 3D modeling acquisition module for geological exploration and an infrared detector for fire point identification. Through 3D model structure analysis technology and fire point image recognition technology, it can assess the damage of wildfire disasters, and quickly establish a closed loop of emergency communication network through communication networks such as satellite communication and ground communication. By observing the fire situation at close range, taking high-definition pictures and transmitting them to the integrated sky-ground emergency command center in real time. The drone will carry fire-fighting bombs, spray fire-fighting agents and other fire-fighting materials to perform fire-fighting tasks in mountainous or forest areas that are difficult to access by ground transportation.

[0100] It is understandable that the main equipment of the ground-based system includes an integrated sky-ground emergency command center, communication base stations and fire trucks. The integrated sky-ground emergency command center integrates a comprehensive emergency command platform based on space, air and ground. It is equipped with satellite phones and data terminals, portable satellite communication equipment, navigation positioning analysis platforms, GPS / Beidou receiving terminals, remote sensing data receiving stations, remote sensing data analysis systems, visualization terminals, video conferencing systems, artificial intelligence analysis terminals, big data platforms, etc. It integrates multiple information such as satellites and drones to monitor mountain fires in real time and grasp the direction and intensity of fire spread. By integrating space-based, air-based and ground-based information flows, scientific decisions can be made quickly, fire-fighting strategies can be optimized, and the efficiency and accuracy of fire-fighting can be improved. The communication base station can be connected with the integrated sky-ground emergency command center and satellite communications to expand the communication coverage and provide stable communication guarantees for fire-fighting equipment such as drones and fire trucks. Fire trucks are equipped with fire-fighting resources such as water and foam. They are the main fire-fighting equipment on the ground and are used to directly extinguish fires and block the spread of fires.

[0101] In specific implementation, the three-layer system of sky, ground and air can realize information sharing and linkage, forming a closed loop of information flow, material flow and command flow. The data fusion platform of the integrated sky, ground and air emergency command center is used to integrate and process data at different levels in real time. Each level in the system has a two-way real-time feedback mechanism. The ground-based system feeds back the latest dynamics and environment of the fire scene to the space-based and air-based systems, and transmits it to the command center to update the fire situation map and navigation information. The three layers of sky, ground and air can be coordinated and dispatched across systems. When the sky-based satellite finds a fire hotspot, the air-based drone can be directly dispatched through the communication link to conduct close-range reconnaissance, obtain detailed fire images and data, and transmit this information to the ground-based command center in real time to assist the dispatch of ground fire trucks and fire-fighting equipment. At the same time, satellite communications, drone relays and ground communication base stations are used to form an adaptive, cross-platform communication network. When a part of the communication network fails due to fire damage or terrain restrictions, other systems can automatically take over to ensure the reliability of the emergency communication network. Specifically, Figure 4 As shown, Figure 4 This is a schematic diagram of the working of the sky-ground three-layer system in a wildfire disaster prevention emergency scenario.

[0102] Step S20: extracting and fusing features of multivariate data based on a convolutional neural network to obtain a processing result.

[0103] Step S30: Perform evaluation based on the analytic hierarchy process in combination with the processing results to generate weight factors and determine the evaluation quantification results.

[0104] It should be noted that the steps of evaluating and generating weight factors according to the analytic hierarchy process combined with the processing results include: obtaining the information of each element in the criterion layer and constructing a judgment matrix; obtaining the target reference data set from the preset database; performing pairwise comparison of the information of each element in the judgment matrix combined with the target reference data set to generate weight factors; and generating corresponding evaluation quantitative results according to the weight factors in the scheme layer.

[0105] In the specific implementation, by comparing the various elements of the criterion layer, the judgment matrix A is constructed, and the importance of each criterion is compared in pairs in combination with expert opinions or historical data, that is:

[0106]

[0107] in, Represents the factor importance scale.

[0108] Step S40: Establishing an objective function based on the sky-ground integrated electric power wildfire disaster prevention emergency resource dispatching system, the evaluation quantification result and the weight factor.

[0109] It should be noted that in this embodiment, a sky-ground integrated power mountain fire disaster prevention emergency resource rapid dispatch system is constructed. In this system, the sky-based, air-based and ground-based systems have different equipment and functions, and a complete fire fighting and resource dispatch system is formed through the sky-ground integration. In order to ensure the feasibility of the constructed sky-ground integrated power mountain fire disaster prevention emergency resource rapid dispatch system, the following contents are simplified by engineering:

[0110] Simplification 1: Considering the possibility of failure of air-based and ground-based emergency equipment during the dispatching process, the failure rate is set to 0;

[0111] Simplification 2: Consider the wildfire time scale and the information processing and decision-making time scale of the air-ground integrated command system, and set this time to 0;

[0112] Simplification 3: The fire extinguishing materials of drones and fire trucks are sufficient;

[0113] Simplification 4: Considering that the communication network established by satellites, base stations, drones and other equipment will be interfered by factors such as smoke, trees, and terrain, the interference factor is set to 0;

[0114] Simplification 5: Considering the data fusion problem from space-based, air-based and ground-based systems, the multivariate data fusion rate is taken as 100%.

[0115] In the specific implementation, the steps of establishing the objective function based on the sky-ground integrated power wildfire disaster prevention emergency resource dispatching system and weight factors include: using random geometry theory to abstract satellites, communication base stations, drones and fire trucks as points, equipment Corresponding points And use three-dimensional coordinates Determine, and assume that the point distribution obeys the strength Uniform Poisson point process, represented by Φ, mobile firefighting equipment (drones, fire trucks) always matches the nearest communication base station or satellite, and the distance between the firefighting equipment and the communication base station or satellite is Obey the probability density function:

[0116]

[0117] in, represents the base of natural logarithms;

[0118] The mathematical relationship between signal-to-noise ratio and distance can be expressed as:

[0119]

[0120] in, Indicates the transmit power, represents the noise power, which is set as a constant;

[0121] The longer the communication distance, the greater the path loss and the lower the signal-to-noise ratio. To ensure the maximum signal-to-noise ratio, the objective function can be expressed as:

[0122]

[0123] Among them, M represents all devices gather, represents the indicator function, when the ground network is active , when the ground network fails , and Respectively represent the signal-to-noise ratio of the terrestrial communication network and the satellite communication network;

[0124] Combined with weight factors to maximize the overall signal-to-noise ratio of the communication system ;

[0125] ;

[0126] The fire area with high priority weight should be controlled faster, and the weight factor is combined to express the objective function of fire-fighting resource scheduling corresponding to the shortest fire-fighting time.

[0127]

[0128] in, Indicates The wildfire assessment weight of each disaster area. The higher the weight, the more serious the disaster area. The drone and fire truck worked together to handle the The time required for a fire to The details are as follows:

[0129]

[0130] in, Indicates the disaster area The fire area, and Represent the fire extinguishing efficiency of drones and fire trucks, and Respectively represent the time it takes for the drone and fire truck to reach the fire area.

[0131] It should be noted that when establishing the objective function of communication resource scheduling, the sky-ground integrated emergency command center will receive data information monitored by satellite remote sensing, navigation, drones and other equipment in real time through the ground terminal carried by it, and send the data information to each communication base station in real time through the cloud server, forming a satellite-ground fusion mode of communication to send data information to drones and fire trucks moving on the ground. When the ground communication network fails due to wildfires, satellite communications can be seamlessly accessed and provide reliable signal quality, and can directly transmit the data information collected by satellite remote sensing and satellite navigation to drones and fire trucks.

[0132] Step S50: Establishing constraint conditions for the sky-ground integrated electric power mountain fire disaster prevention emergency resource dispatching system.

[0133] In a specific implementation, the step of establishing the constraint conditions of the sky-ground integrated electric power wildfire disaster prevention emergency resource dispatching system includes: setting the signal-to-noise ratio of the ground network and the satellite network to meet the preset network signal-to-noise ratio minimum value:

[0134]

[0135]

[0136] in, They represent the minimum values ​​of the signal-to-noise ratio required for the ground network and the satellite network respectively;

[0137] Set the signal-to-noise ratio of each disaster area to meet the preset minimum signal-to-noise ratio of the disaster area:

[0138]

[0139] in, Indicates the minimum signal-to-noise ratio of the preset disaster area;

[0140] When switching between the terrestrial network and the satellite network, the maximum signal-to-noise ratio difference after switching is set to not exceed the preset switching threshold:

[0141]

[0142] in, represents the preset switching threshold;

[0143] Shared drone, Fire trucks, and each drone or fire truck can only handle one fire area:

[0144]

[0145]

[0146] in, is a binary variable. If a drone or a fire truck is assigned to If firefighting is carried out in the disaster area, or , otherwise 0;

[0147] It is assumed that either drones or fire trucks are allowed to work in the disaster area, expressed by the following constraints:

[0148]

[0149] The total working time of each drone should not exceed the maximum endurance time:

[0150]

[0151] in, Drones in disaster areas Fire extinguishing time, is the maximum flight time of the drone;

[0152] The total working time of each fire truck should not exceed its maximum endurance time:

[0153]

[0154] in, For fire trucks in disaster areas Fire extinguishing time, The maximum cruising time of a fire truck.

[0155] Step S60: Determine an optimization plan for dispatching power wildfire disaster prevention emergency resources according to the constraints and the objective function.

[0156] In the specific implementation, the steps of determining the optimization plan for the dispatch of power wildfire disaster prevention emergency resources according to the constraints and the objective function include:

[0157] Determine the optimization problem based on constraints and objective function

[0158]

[0159] The integer variables of the optimization problem are processed by a multi-objective discrete binary particle swarm optimization algorithm, and the continuous variables in the optimization problem are processed by the particle swarm algorithm to determine the maximum communication performance and the shortest fire rescue time; the optimization plan for the dispatch of power wildfire disaster prevention emergency resources is determined based on the maximum communication performance and the shortest fire rescue time.

[0160] In the specific implementation, the optimization problem of this embodiment belongs to multi-objective mixed integer linear programming, the integer variables are 0-1, and and There are different dimensions. In multi-objective optimization, there is no unique optimal solution, but a Pareto optimal solution set. These solutions are not optimal on a certain goal, that is, no solution can be better than another solution on all goals at the same time. The multi-objective discrete binary particle swarm optimization algorithm is suitable for processing discrete and combinatorial optimization problems. It can optimize multiple objectives at the same time and generate a set of Pareto optimal solutions. Therefore, this embodiment introduces a multi-objective discrete binary particle swarm algorithm to solve the above-mentioned multi-objective mixed integer linear programming problem, processes integer variables through binary representation, and combines the traditional particle swarm algorithm to process continuous variables, and solves the maximum communication performance and the shortest fire rescue time.

[0161] It can be understood that, in this embodiment, Figure 5 As shown, Figure 5 The model framework diagram is as follows: Step 1) constructs a power wildfire disaster assessment model under the satellite-ground fusion mode, uses space-based, air-based, and ground-based platforms to extract and fuse the features of multivariate data information closely related to power wildfires, and obtains a power wildfire disaster assessment model;

[0162] Step 2) Build a space-ground integrated rapid dispatch system for power mountain fire disaster prevention emergency resources. Under simplified conditions, use different equipment and functions of space-based, air-based and ground-based systems to form a space-ground integrated firefighting and resource dispatch system.

[0163] Step 3) An integrated sky-ground power wildfire disaster prevention emergency resource scheduling model is established. The objective function of the integrated sky-ground power wildfire disaster prevention emergency resource scheduling model is established from the two aspects of the overall signal-to-noise ratio of the communication system and the fire rescue time. Constraints such as signal-to-noise ratio, number of fire-fighting equipment, and endurance are established. Finally, a solution method for the multi-objective optimization problem is given.

[0164] It can be understood that the satellite Internet-based power mountain fire disaster prevention emergency resource optimization dispatching method provided in this embodiment, with the help of the satellite Internet's wide coverage, low latency, high bandwidth, low cost and all-weather, all-day communication, navigation, remote sensing comprehensive information service capabilities, can achieve the maximum overall signal-to-noise ratio of the communication system and the minimum fire rescue time in the dispatch of power mountain fire emergency resources, and reduce the threat of mountain fires to power infrastructure. It effectively overcomes the problems of single point failure of communication, lagging decision-making means, and lack of flexibility in traditional power emergency resource dispatching.

[0165] This embodiment obtains multivariate data through the sky-ground integrated monitoring system; extracts and fuses multivariate data based on convolutional neural networks to obtain processing results; evaluates the processing results according to the hierarchical analysis method to generate weight factors and determine the evaluation quantification results; establishes an objective function based on the sky-ground integrated power mountain fire disaster prevention emergency resource scheduling system, the evaluation quantification results and the weight factors; establishes the constraints of the sky-ground integrated power mountain fire disaster prevention emergency resource scheduling system; and determines the power mountain fire disaster prevention emergency resource scheduling optimization plan based on the constraints and the objective function. It can not only realize fire monitoring and information transmission from multiple dimensions, but also improve the utilization rate of communication resources in the disaster area and reduce the overall firefighting time of power mountain fires.

[0166] In addition, an embodiment of the present application also proposes a computer-readable storage medium, on which is stored a program for optimizing and scheduling emergency resources for electric power wildfire disaster prevention based on satellite Internet. When the program for optimizing and scheduling emergency resources for electric power wildfire disaster prevention based on satellite Internet is executed by a processor, the steps of the method for optimizing and scheduling emergency resources for electric power wildfire disaster prevention based on satellite Internet as described above are implemented.

[0167] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the satellite Internet-based power wildfire disaster prevention emergency resource optimization and dispatching system of the present application.

[0168] like Figure 6 As shown, the satellite Internet-based power mountain fire disaster prevention emergency resource optimization and dispatching system proposed in the embodiment of the present application includes:

[0169] A data acquisition module 10, used to acquire multivariate data through the sky-ground integrated monitoring system;

[0170] A data processing module 20, used for extracting and fusing features of the multivariate data based on a convolutional neural network to obtain a processing result;

[0171] An evaluation module 30, for evaluating according to the hierarchical analysis method in combination with the processing result to generate a weight factor and determine an evaluation quantitative result;

[0172] An objective function module 40 is used to establish an objective function based on the sky-ground integrated electric power wildfire disaster prevention emergency resource dispatching system, the evaluation quantification result and the weight factor;

[0173] Constraint condition module 50, used to establish the constraint conditions of the sky-ground integrated electric power mountain fire disaster prevention emergency resource dispatching system;

[0174] The scheme generation module 60 is used to determine the optimization scheme for dispatching power wildfire disaster prevention emergency resources according to the constraint conditions and the objective function.

[0175] This embodiment obtains multivariate data through the sky-ground integrated monitoring system; extracts and fuses multivariate data based on convolutional neural networks to obtain processing results; evaluates the processing results according to the hierarchical analysis method to generate weight factors and determine the evaluation quantification results; establishes an objective function based on the sky-ground integrated power mountain fire disaster prevention emergency resource scheduling system, the evaluation quantification results and the weight factors; establishes the constraints of the sky-ground integrated power mountain fire disaster prevention emergency resource scheduling system; and determines the power mountain fire disaster prevention emergency resource scheduling optimization plan based on the constraints and the objective function. It can not only realize fire monitoring and information transmission from multiple dimensions, but also improve the utilization rate of communication resources in the disaster area and reduce the overall firefighting time of power mountain fires.

[0176] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present application. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0177] In addition, for technical details not described in detail in this embodiment, please refer to the method for optimizing and scheduling power wildfire disaster prevention emergency resources based on satellite Internet provided in any embodiment of the present application, which will not be repeated here.

[0178] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0179] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0180] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0181] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for optimizing and dispatching power mountain fire disaster prevention emergency resources based on satellite Internet, characterized in that: include: Acquire multivariate data through the integrated sky-ground monitoring system; Extracting and fusing the multivariate data based on a convolutional neural network to obtain a processing result; Performing an evaluation based on the analytic hierarchy process in combination with the processing results to generate weight factors and determine an evaluation quantitative result; Establishing an objective function based on the sky-ground integrated electric power wildfire disaster prevention emergency resource dispatching system, the evaluation quantification result and the weight factor; Establishing constraints for the sky-ground integrated electric power mountain fire disaster prevention emergency resource dispatching system; Determine an optimization plan for dispatching power wildfire disaster prevention emergency resources according to the constraint conditions and the objective function; The step of evaluating the processing results according to the hierarchical analysis method to generate weight factors and determine the evaluation quantitative results includes: Obtain information of each factor in the criterion layer and construct a judgment matrix; Obtain a target reference data set from a preset database; performing pairwise comparison on the information of each element in the judgment matrix in combination with the target reference data set to generate a weight factor; Generating corresponding evaluation quantification results according to the weight factors in the solution layer; The step of establishing an objective function based on the sky-ground integrated electric power mountain fire disaster prevention emergency resource dispatching system, the evaluation quantification result and the weight factor includes: Using random geometry theory, satellites, communication base stations, drones, and fire trucks are abstracted into points. Corresponding points And use three-dimensional coordinates Determine, and assume that the point distribution obeys the strength Uniform Poisson point process, represented by Φ, the distance between the fire-fighting equipment and the communication base station or satellite Obey the probability density function: in, represents the base of natural logarithms; The mathematical relationship between signal-to-noise ratio and distance is expressed as: in, Indicates the transmit power, represents the noise power, which is set as a constant; Combine the weight factors to maximize the overall signal-to-noise ratio of the communication system ; ； Among them, M represents all devices gather, represents the indicator function, when the ground network is active , when the ground network fails, , and Respectively represent the signal-to-noise ratio of the terrestrial communication network and the satellite communication network; Combining the weight factors to represent the objective function of fire extinguishing resource scheduling corresponding to the shortest fire extinguishing time; in, Indicates The wildfire assessment weight of each disaster area. The higher the weight, the more serious the current disaster area is. The drone and fire truck worked together to handle the The time required for a fire to The details are as follows: in, Indicates the disaster area The fire area, and Represent the fire extinguishing efficiency of drones and fire trucks, and Respectively represent the time it takes for the drone and fire truck to reach the fire area; The step of establishing the constraint conditions of the sky-ground integrated electric power mountain fire disaster prevention emergency resource dispatching system includes: Set the signal-to-noise ratio of the terrestrial network and the satellite network to meet the preset minimum value of the network signal-to-noise ratio: in, They represent the minimum values ​​of the signal-to-noise ratio required for the ground network and the satellite network respectively; Set the signal-to-noise ratio of each disaster area to meet the preset minimum signal-to-noise ratio of the disaster area: in, Indicates the minimum signal-to-noise ratio of the preset disaster area; When switching between the terrestrial network and the satellite network, the maximum signal-to-noise ratio difference after switching is set to not exceed the preset switching threshold: in, represents the preset switching threshold; Shared drone, Fire trucks, and each drone or fire truck can only handle one fire area: in, is a binary variable. If a drone or a fire truck is assigned to If firefighting is carried out in the disaster area, or , otherwise 0; It is assumed that either drones or fire trucks are allowed to work in the disaster area, expressed by the following constraints: The total working time of each drone should not exceed the maximum endurance time: in, Drones in disaster areas Fire extinguishing time, is the maximum flight time of the drone; The total working time of each fire truck should not exceed its maximum endurance time: in, For fire trucks in disaster areas Fire extinguishing time, The maximum cruising time of the fire truck; The step of determining the optimization scheme for dispatching power wildfire disaster prevention emergency resources according to the constraint conditions and the objective function includes: Determine the optimization problem according to the constraint conditions and the objective function: Processing integer variables of the optimization problem by multi-objective discrete binary particle swarm optimization algorithm and combining particle swarm optimization algorithm to process continuous variables in the optimization problem to determine maximum communication performance and shortest fire rescue time; An optimization plan for dispatching power wildfire disaster prevention emergency resources is determined based on the maximum communication performance and the shortest fire rescue time.

2. The satellite Internet-based power mountain fire disaster prevention emergency resource optimization scheduling method according to claim 1 is characterized in that: The step of acquiring multivariate data through the integrated sky-ground monitoring system also includes: Acquiring satellite remote sensing data through the space-based system in the integrated sky-ground monitoring system; Acquiring drone data through the air-based system in the air-ground integrated monitoring system; Acquiring ground monitoring system data through the ground-based system in the integrated sky-ground monitoring system; Multivariate data is generated based on the satellite remote sensing data, the drone data, and the ground monitoring system data.

3. The satellite Internet-based power mountain fire disaster prevention emergency resource optimization scheduling method according to claim 1 is characterized in that: The multivariate data include: temperature, wind speed, vegetation density, fire source density, fire truck accessibility and drone firefighting efficiency.

4. A satellite Internet-based power mountain fire disaster prevention emergency resource optimization and dispatching system, characterized in that: Executing the method according to claim 1, the satellite Internet-based power wildfire disaster prevention emergency resource optimization scheduling includes: A data acquisition module, used to acquire multivariate data through the sky-ground integrated monitoring system; A data processing module, used for extracting and fusing features of the multivariate data based on a convolutional neural network to obtain a processing result; An evaluation module, for evaluating according to the analytic hierarchy process in combination with the processing results to generate weight factors and determine evaluation quantitative results; An objective function module is used to establish an objective function based on the sky-ground integrated electric power wildfire disaster prevention emergency resource dispatching system, the evaluation quantification result and the weight factor; A constraint condition module, used to establish the constraint conditions of the sky-ground integrated electric power mountain fire disaster prevention emergency resource dispatching system; A scheme generation module is used to determine the optimization scheme for dispatching power wildfire disaster prevention emergency resources according to the constraint conditions and the objective function.

5. A satellite Internet-based power mountain fire disaster prevention emergency resource optimization and dispatching device, characterized in that: The device includes: a memory and a processor. When the processor runs the satellite Internet-based power wildfire disaster prevention emergency resource optimization scheduling program stored in the memory, the processor executes the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The method comprises instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 3.

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