A multi-spectral satellite fire prevention method and system based on data recognition

Abstract

The present invention discloses a multi-spectral satellite fire prevention method and system based on data identification. The multi-spectral satellite optical sensor array scans the target area for 30 minutes, obtains data with high-precision attitude system and formula to ensure the accuracy of data acquisition, and guarantees the quality through adaptive filtering and correction combined with multi-source data. The fire characteristics are extracted by dynamic threshold and wavelet transform to construct vectors, and the fusion model is input to distinguish the fire in real time. The warning information including location and fire intensity is transmitted to the ground command center within 8 minutes. The data is updated every 10 minutes in case of fire. The fire intensity is evaluated according to multiple indicators, the strategy is adjusted and the data is stored by combining GIS and time series model. The present invention collects and processes data quickly, with 8-minute warning and an accuracy rate of over 92%. The fire intensity is predicted 15 minutes in advance. The system is stable, data is processed efficiently, and the ecological and life and property safety are protected.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite fire prevention, and in particular to a system and method for multi-spectral satellite fire prevention based on data recognition. Background Art

[0002] In recent years, with the change of global climate and the continuous expansion of human activities, the frequency and severity of fires have shown a sharp increase. Once large areas such as forests, grasslands, and mountains are hit by fires, it will not only cause a devastating blow to the local ecological environment, causing a large number of animals and plants to lose their habitats and a sharp decline in biodiversity, but also seriously threaten the lives and property of surrounding residents and trigger a series of social problems.

[0003] Traditional fire monitoring methods are unable to cope with these challenges. Ground observation towers are a relatively primitive monitoring method that requires long hours of manpower. The field of vision of observers is easily limited by factors such as terrain undulations and mountain obstructions, making it difficult to achieve comprehensive coverage of large areas. Moreover, in the event of severe weather such as heavy fog, heavy rain, and sandstorms, the observation effect is almost zero, and there is a great lag in the detection of fires.

[0004] Although aerial patrols have expanded the monitoring field to a certain extent, the cost remains high. Each dispatch of an aircraft requires a lot of manpower, material and financial resources. At the same time, the flight of aircraft is strictly restricted by airspace control, and it is impossible to respond at any time and patrol on demand. In addition, complex natural environments, such as dense forests and deep canyons, will interfere with pilots' accurate positioning of the fire source and their judgment of the direction of fire spread.

[0005] Ground sensor networks have a certain role in local areas. For example, smoke sensors and temperature sensors can sense fires at close range. However, the deployment range of such sensors is very limited. They are mainly concentrated in densely populated areas or key facilities such as towns and factories. It is difficult to extend them to vast nature reserves and remote forest areas. In addition, sensors are exposed to the outside for a long time and are easily eroded by the natural environment and damaged by wild animals. The maintenance cost is quite high, and it is also difficult to integrate and analyze the data collected by different sensors.

[0006] Early satellite monitoring technology also had flaws. Optical satellites rely on visible light band imaging, which makes it difficult to detect early fires and smoldering fires hidden under vegetation, because the smoke and weak heat signals produced by these hidden fires are easily overlooked in a single band. Although infrared satellites are sensitive to temperature changes, they lack the ability to comprehensively consider multiple factors at the fire scene, and misjudgment of the scale and spread of fires often occurs. Faced with the increasingly severe fire prevention situation, there is an urgent need for a new fire prevention technology based on multispectral satellites that can accurately, quickly, and comprehensively monitor fires. Summary of the invention

[0007] The present invention proposes a multi-spectral satellite fire prevention system and method based on data recognition to solve the problems mentioned in the above-mentioned prior art.

[0008] In order to achieve the above object, the present invention adopts the following technical solution: a multi-spectral satellite fire prevention method based on data recognition, comprising:

[0009] Data collection: The optical sensor array carried by the spectral satellite has a spectral channel from visible light to thermal infrared through a CCD detector. It scans the target area once every 30 minutes. The satellite relies on the attitude stabilization system of the combination of star sensors and gyroscopes to ensure that the satellite attitude accuracy is stably controlled within 0.1° during the collection process. Assuming the satellite orbit height is H, the sensor field of view angle is θ, and the ground coverage width is W, according to the formula Plan the collection scope;

[0010] Data preprocessing: After receiving the original spectral data, an adaptive filtering algorithm is used. The algorithm has a built-in transformation module to analyze the spectral characteristics of the band noise and automatically update the filtering parameters every 5 minutes. Suppose the original signal is , the noise is , the filtered signal , the filter coefficient a is adjusted through the feedback control system to meet ;

[0011] Feature extraction: accurately extract features closely related to fire from preprocessed spectral data, use dynamic threshold segmentation method, threshold adjustment algorithm combined with historical temperature data and real-time meteorological data of the region, extract smoke texture features through small band transformation, according to the change of smoke concentration, adaptively select small band basis function and decomposition layer number through built-in concentration small band parameter mapping table, calculate vegetation burning index by comparing the time series change of normalized vegetation index, set the current , the moment before , vegetation burn index ;

[0012] Fire identification and warning: The extracted feature vector is input into a deep learning model that integrates the convolutional neural network (CNN) and the memory network (LSTM). CNN uses 3-5 convolutional layers, with a convolution kernel size of 3×3 or 5×5 and a step size of 1-2, focusing on extracting spatial features. LSTM contains 2-3 hidden layers and 64-128 neurons. It mainly captures time series features and makes real-time judgments on the fire situation in the target area. If there are signs of fire, the warning information is sent to the ground command center via a satellite communication link. The warning information covers the fire location, preliminary fire estimate, spread direction, and expected impact range. Assume that the probability of fire is , obtained by weighted summation of the values ​​of each dimension of the model output feature vector, with the weight being , the eigenvector is ,but , determine the fire risk situation;

[0013] Fire monitoring and dynamic assessment: When a fire occurs, the spectral satellite will increase the monitoring frequency. According to the growth rate of the burning area, the temperature change trend, and the smoke diffusion range indicators, a fire assessment model based on time series analysis is used. The model uses the ARIMA model combined with the Kalman filter algorithm to filter and predict the indicators in real time, estimate the fire development trend in advance, and gain preparation time for fire fighting and rescue. Combined with geographic information system (GIS) technology, the changes in the fire are displayed on the electronic map to provide information support for fire fighting and rescue command.

[0014] Furthermore, when collecting data, the optical sensor array adopts a modular design concept, and each module is equipped with a temperature control system, which facilitates rapid replacement and maintenance in a space environment.

[0015] Furthermore, during data preprocessing, the adaptive filtering algorithm adjusts the filtering parameters according to the noise spectrum, combined with the satellite orbit altitude, sunlight angle and satellite attitude to optimize the denoising effect.

[0016] Furthermore, during feature extraction, the dynamic threshold segmentation method is used to identify the temperature anomaly areas with reference to the surrounding population density, building distribution and socioeconomic factors, and an adaptive threshold strategy is adopted according to the terrain.

[0017] Furthermore, during fire identification and warning, deep learning model training uses transfer learning, introducing regional or seasonal,fire data for pre-training, and fine-tuning combined with samples from the target area.

[0018] Furthermore, during fire monitoring and dynamic assessment, the fire assessment model based on time series analysis uses the Kalman filter algorithm to filter the indicators in real time, estimate the fire in advance, and optimize the fire spread prediction model in combination with wind direction and wind speed.

[0019] Furthermore, a system for implementing the multi-spectral satellite fire prevention method based on data recognition includes:

[0020] Satellite data acquisition subsystem: It is composed of a spectral satellite platform, an optical sensor array, and an advanced attitude stabilization system. The satellite platform uses radiation-resistant reinforced electronic components and has autonomous fault diagnosis and repair capabilities.

[0021] Data processing and analysis subsystem: Integrates data preprocessing, feature extraction, fire identification and warning, fire monitoring and dynamic evaluation functional modules, and calculates real-time data through the processor;

[0022] Communication subsystem: Build a high-speed, two-way communication link between the satellite and the ground command center. The communication frequency bands cover Ka-band and X-band, and automatically switch according to channel quality and data volume;

[0023] Ground command center subsystem: equipped with 8K resolution screen, data storage server, and decision support system. The system provides fire fighting strategy suggestions based on knowledge base and system learning algorithm according to satellite data;

[0024] Cloud storage and management subsystem: It adopts a distributed cloud storage architecture to store satellite collected data, analysis results, and model parameters. It has data backup, disaster recovery, and data retrieval functions, providing data support for system optimization and historical backtracking.

[0025] Furthermore, in the satellite data acquisition subsystem, the design of the optical sensor array takes space radiation into consideration, uses shielding materials, and combines anti-radiation reinforcement technology and radiation-resistant materials to ensure the stability of the sensor's performance in space.

[0026] Furthermore, in the data processing and analysis subsystem, the feature extraction module supports the parallelization of feature extraction algorithms and automatically switches algorithms based on scheduling algorithms and task requirements;

[0027] Furthermore, the communication subsystem uses laser communication technology to shorten the data transmission time when the satellite passes over the ground command center.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] On the one hand, the timeliness of fire warnings has been greatly improved. Multi-spectral satellites collect data quickly and, combined with advanced deep learning algorithms, can detect signs of fire within 8 minutes at the fastest. The warning accuracy rate exceeds 92%, greatly reducing the probability of false alarms and missed alarms, and buying critical time for firefighters to act in advance.

[0030] In terms of fire monitoring, satellites periodically update data and use professional time series analysis models to integrate meteorological factors such as wind direction and wind speed to accurately predict the direction of the fire 15 minutes in advance, providing scientific guidance for fire-fighting strategy planning and improving the targetedness and success rate of fire-fighting operations.

[0031] Furthermore, from the perspective of system stability, the satellite platform has its own fault diagnosis and repair functions, and can operate stably in space for more than 5 years. The communication link is stable and reliable, and the bit error rate is controlled at an extremely low 0.01%. It is not afraid of complex space environments and bad weather, ensuring uninterrupted data transmission.

[0032] Finally, efficient data processing capabilities should not be underestimated. Multi-core processors and GPU clusters allow massive amounts of data to be processed instantly, with a throughput of over 100GB per second. Feature extraction algorithms can be switched intelligently, and cloud storage facilitates backtracking and optimization, all of which improve fire prevention and control capabilities and protect the safety of nature and humans. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic block diagram of a multi-spectral satellite fire prevention system based on data recognition proposed by the present invention;

[0034] Figure 2 This is a schematic block diagram of a multi-spectral satellite fire prevention method based on data recognition proposed by the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0037] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" 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 it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0038] Reference Figure 1-2 :A multi-spectral satellite fire prevention method based on data recognition, comprising:

[0039] Data collection: The optical sensor array carried by the multispectral satellite uses a highly sensitive CCD detector with multiple spectral channels from visible light to thermal infrared. It scans the target area once every 30 minutes, and the spatial resolution of the acquired multi-band spectral data is better than 30 meters. The satellite relies on a high-precision star sensor and gyro combination attitude stabilization system. The star sensor accuracy can reach 0.05°, and the gyro drift rate is less than 0.01° / hour, ensuring that the satellite attitude accuracy is stably controlled within 0.1° during the acquisition process. Assume that the satellite orbit height is H, the sensor field of view angle is θ, and the ground coverage width is W. According to the formula Accurately plan the collection scope to ensure that key fire prevention areas are covered without omissions.

[0040] Data preprocessing: After receiving the original spectral data, an adaptive filtering algorithm is used. The algorithm has a built-in fast Fourier transform module, which can analyze the spectral characteristics of noise in different bands within 5 seconds, and automatically update the filtering parameters every 5 minutes. , the noise is , the filtered signal , the filter coefficient a is adjusted through the feedback control system to meet , which improves the signal-to-noise ratio of the denoised signal by more than 40%. At the same time, based on the satellite's built-in blackbody radiation source, its blackbody emissivity is stable at more than 0.99, and radiation correction is performed with ground-synchronized measurement data, with a correction accuracy of 0.5%. The image registration algorithm based on SURF feature points is used to complete geometric correction. After correction, the geometric distortion of the image is controlled within 1%, allowing the data to meet the needs of subsequent precise analysis.

[0041] Feature extraction: Accurately extract features closely related to fire from pre-processed multispectral data. For example, the dynamic threshold segmentation method is used. Its threshold adjustment algorithm is dynamically adjusted by combining historical temperature data of the region for more than 3 years, real-time meteorological data (obtained through satellite-mounted meteorological sensors with an accuracy of 0.1°C and wind speed accuracy of 0.5m / s, etc.) and vegetation coverage types (classified and labeled in advance based on high-resolution satellite images); the wavelet transform is used to extract smoke texture features. According to the changes in smoke concentration, the wavelet basis function and the number of decomposition layers are adaptively selected through the built-in concentration-wavelet parameter mapping table to ensure that the accuracy of smoke feature extraction is not less than 85%; the vegetation burning index is calculated by comparing the time series changes of the normalized vegetation index, and the current , the moment before , vegetation burn index , construct a multi-dimensional feature vector.

[0042] Fire identification and warning: The extracted feature vector is input into a deep learning model that integrates a convolutional neural network (CNN) and a long short-term memory network (LSTM). CNN uses 3-5 convolutional layers, with a kernel size of 3×3 or 5×5 and a step size of 1-2, focusing on extracting spatial features. LSTM contains 2-3 hidden layers and 64-128 neurons, focusing on capturing time series features and making real-time judgments on the fire situation in the target area. Once it is determined that there are signs of fire, the warning information will be pushed to the ground command center within 8 minutes by means of a satellite communication link, taking into account factors such as the possibility of fire and the rate of development. The warning information covers the precise location of the fire (based on a satellite positioning system with an accuracy of less than 100 meters), a preliminary estimate of the fire (based on the combustion index and the size of the temperature abnormality area), possible direction of spread (combined with wind direction, topography, and GIS analysis) and the expected impact range. The warning accuracy rate is over 92%. Assume that the probability of fire is , obtained by weighted summation of the values ​​of each dimension of the model output feature vector, with the weight being , the eigenvector is ,but , and judge the degree of fire risk accordingly.

[0043] Fire monitoring and dynamic assessment: During a fire, multispectral satellites increase the monitoring frequency and update data every 10 minutes. Based on key indicators such as the growth rate of the burning area (accurately calculated through image segmentation algorithms, with an error of less than 5%), temperature change trends (using thermal infrared band data, with a temperature measurement accuracy of 0.5°C), and smoke diffusion range (based on smoke texture feature tracking algorithms), a fire assessment model based on time series analysis is used. This model uses the ARIMA model combined with the Kalman filter algorithm to filter and predict key indicators in real time, and estimates the fire development trend 15 minutes in advance, so as to gain more preparation time for fire fighting and rescue. Combined with geographic information system (GIS) technology, the changes in the fire are intuitively displayed on electronic maps, providing visual support for fire fighting and rescue command, while adjusting early warning information and response strategies in real time. All monitoring data and assessment results are automatically stored in the cloud database for subsequent review and analysis.

[0044] In the present invention, during data collection, the optical sensor array adopts a modular design concept. Each module has an independent temperature control system with a temperature control accuracy of ±0.5°C and a dust and moisture resistance level of IP67, which is convenient for rapid replacement and maintenance in a space environment. The sensor surface is coated with 7-9 layers of anti-reflection film. Through the optimization of the film system design, the transmittance of light in each band is increased by more than 30%, thereby enhancing the light collection efficiency.

[0045] In the present invention, during data preprocessing, the adaptive filtering algorithm not only adjusts the filtering parameters based on the noise spectrum, but also combines the satellite orbit altitude, the sun's illumination angle and the satellite attitude change to optimize the denoising effect. The radiation correction introduces the lunar radiation data to assist in the calibration. The lunar radiation data is collected by a dedicated radiometer carried by the satellite with an acquisition accuracy of 0.05W / m², which improves the accuracy and stability of the calibration.

[0046] In the present invention, when extracting features, the dynamic threshold segmentation method identifies temperature anomaly areas and also refers to socioeconomic factors such as surrounding population density (obtained through geographic information data) and building distribution (identified based on high-resolution images) to avoid misjudgment and unnecessary panic. For complex terrain such as mountainous areas and canyons, a local adaptive threshold strategy is adopted to dynamically adjust the threshold calculation window size according to the terrain undulations and vegetation occlusion to improve recognition accuracy.

[0047] In the present invention, during fire identification and early warning, deep learning model training uses transfer learning, and introduces fire data from other similar regions or seasons (similar climate conditions and vegetation types) for pre-training. The amount of pre-training data accounts for 60%-70% of the total training data. Combined with a small number of samples in the target area for fine-tuning, the training cycle is shortened by more than 30%, and the generalization ability of complex environments is improved. During fine-tuning, a small batch gradient descent algorithm is used, with a batch size of 32-64 and a learning rate of 0.001-0.01, to ensure that the model converges quickly and has stable performance.

[0048] In the present invention, during fire monitoring and dynamic assessment, the fire assessment model based on time series analysis uses the Kalman filter algorithm to filter and predict key indicators in real time, estimates the fire 15 minutes in advance, and optimizes the fire spread prediction model in combination with wind direction and wind speed. Among them, the wind direction and wind speed data are obtained through a wind field measuring instrument carried by a satellite, with an accuracy of ±5° and ±0.3m / s respectively. The fluid mechanics simulation algorithm is used to quantify the impact of the wind field on the spread of the fire, thereby improving the prediction accuracy.

[0049] The present invention also discloses a multi-spectral satellite fire prevention system based on data recognition, comprising:

[0050] Satellite data acquisition subsystem: It consists of a multi-spectral satellite platform, a high-precision optical sensor array, and an advanced attitude stabilization system. The satellite platform uses radiation-hardened electronic components and can withstand a total radiation dose of 100 krad (Si). It has autonomous fault diagnosis and repair functions, with a fault diagnosis accuracy of more than 95%. It can operate continuously in space for more than 5 years, ensuring stable and efficient collection of spectral data in the target area.

[0051] Data processing and analysis subsystem: It integrates data preprocessing, feature extraction, fire identification and warning, fire monitoring and dynamic assessment functional modules, adopts multi-core high-performance processors with 32-64 cores, and is matched with large-scale GPU clusters with 8-16 GPUs to accelerate computing. The data processing throughput is more than 100GB per second, meeting real-time requirements.

[0052] Communication subsystem: Build a high-speed, two-way communication link between the satellite and the ground command center. The communication frequency band covers Ka band and X band, and automatically switches according to channel quality and data volume. LDPC coding is used as an advanced channel coding technology with a coding gain of 3-5dB. Combined with the AES-256 encryption algorithm, the data transmission bit error rate is controlled within 0.01%, ensuring information security and fast transmission.

[0053] Ground command center subsystem: equipped with a large visual screen with a size of 5-8 meters and a resolution of 4K-8K, a high-performance data storage server with a storage capacity of no less than 100TB, and an intelligent decision-making assistance system. The system is based on an expert knowledge base and machine learning algorithms, and provides fire-fighting strategy recommendations based on satellite data to assist commanders in making decisions.

[0054] Cloud storage and management subsystem: It uses a distributed cloud storage architecture to store data, analysis results, and model parameters collected by satellites over a long period of time. The data storage capacity can be expanded to PB level as needed. It has data backup, disaster recovery, and fast retrieval functions to provide support for system optimization and historical backtracking. It uses erasure coding technology to ensure data redundancy, with a redundancy of 30%-50%, to ensure data reliability.

[0055] In the present invention, in the satellite data acquisition subsystem, the optical sensor array is designed with space radiation in mind, using multi-layer shielding materials with a shielding effectiveness of 30-50dB, combined with anti-radiation reinforcement technology and radiation-resistant materials to ensure the stability of the sensor's space performance and extend its life by more than 20%.

[0056] In the present invention, in the data processing and analysis subsystem, the feature extraction module supports multiple feature extraction algorithms in parallel. Through the intelligent scheduling algorithm, the optimal algorithm is automatically switched within 1 second according to the data characteristics and task requirements. For example, the algorithm based on the vegetation index is preferentially enabled in the dense vegetation area, thereby improving the efficiency and pertinence of feature extraction.

[0057] In the present invention, the communication subsystem adopts laser communication technology to supplement when the satellite passes over the ground command center. The laser communication terminal has a transmission power of 10-20W and a receiving sensitivity of -30dBm, which increases the communication bandwidth to more than 10Gbps, shortens the transmission time of large amounts of data, and meets emergency transmission needs.

[0058] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A multi-spectral satellite fire prevention method based on data recognition, characterized in that: include: Data collection: The optical sensor array carried by the spectral satellite has a spectral channel from visible light to thermal infrared through a CCD detector. It scans the target area once every 30 minutes. The satellite relies on the attitude stabilization system of the combination of star sensors and gyroscopes to ensure that the satellite attitude accuracy is stably controlled within 0.1° during the collection process. Assuming the satellite orbit height is H, the sensor field of view angle is θ, and the ground coverage width is W, according to the formula Plan the collection scope; Data preprocessing: After receiving the original spectral data, an adaptive filtering algorithm is used. The algorithm has a built-in transformation module to analyze the spectral characteristics of the band noise and automatically update the filtering parameters every 5 minutes. Suppose the original signal is , the noise is , the filtered signal , the filter coefficient a is adjusted through the feedback control system to meet ; Feature extraction: Extract fire-related features from preprocessed spectral data, use dynamic threshold segmentation method, threshold adjustment algorithm combined with historical temperature data and real-time meteorological data of the region, extract smoke texture features through small band transformation, according to the change of smoke concentration, adaptively select small band basis function and decomposition layer number through built-in concentration small band parameter mapping table, calculate vegetation burning index by comparing the time series change of normalized vegetation index, set the current , the moment before , vegetation burn index , construct a multi-dimensional feature vector; Fire identification and warning: The extracted feature vectors are input into a deep learning model that integrates convolutional neural network (CNN) and memory network (LSTM). CNN uses 3-5 convolutional layers, with a convolution kernel size of 3×3 or 5×5 and a step size of 1-2, focusing on extracting spatial features. LSTM contains 2-3 hidden layers and 64-128 neurons to capture time series features and make real-time judgments on the fire situation in the target area. If there are signs of fire, the warning information is sent to the ground command center via a satellite communication link. The warning information covers the location of the fire, the preliminary estimate of the fire intensity, the direction of spread, and the expected impact range. Assume that the probability of fire is , obtained by weighted summation of the values ​​of each dimension of the model output feature vector, with the weight being , the eigenvector is ,but , determine the fire risk situation; Fire monitoring and dynamic assessment: When a fire occurs, the spectral satellite will intensify the monitoring frequency. According to the growth rate of the burning area, the temperature change trend, and the smoke diffusion range indicators, a fire assessment model based on time series analysis is used. The model uses the ARIMA model combined with the Kalman filter algorithm to perform real-time filtering and prediction of indicators. Combined with the geographic information system (GIS) technology, the changes in the fire are displayed on the electronic map, providing information support for fire fighting and rescue command.

2. The method of multi-spectral satellite fire prevention based on data recognition according to claim 1 is characterized in that: When collecting data, the optical sensor array adopts a modular design concept, and each module is equipped with a temperature control system.

3. The method of multi-spectral satellite fire prevention based on data recognition according to claim 1 is characterized in that: During data preprocessing, the adaptive filtering algorithm adjusts the filtering parameters based on the noise spectrum, satellite orbit altitude, sunlight angle, and satellite attitude to optimize the denoising effect.

4. The method of multi-spectral satellite fire prevention based on data recognition according to claim 1 is characterized in that: During feature extraction, the dynamic threshold segmentation method was used to identify the temperature anomaly areas with reference to the surrounding population density, building distribution and socioeconomic factors, and an adaptive threshold strategy was adopted according to the terrain.

5. The method of multi-spectral satellite fire prevention based on data recognition according to claim 1 is characterized in that: When identifying and warning fires, deep learning models are trained using transfer learning, introducing regional or seasonal fire data for pre-training and fine-tuning with samples from the target area.

6. The method of multi-spectral satellite fire prevention based on data recognition according to claim 1 is characterized in that: During fire monitoring and dynamic assessment, the fire assessment model based on time series analysis uses the Kalman filter algorithm to filter indicators in real time, estimate the fire in advance, and optimize the fire spread prediction model based on wind direction and wind speed.

7. A system for implementing a multi-spectral satellite fire prevention method based on data recognition as described in any one of claims 1 to 6, characterized in that: include: Satellite data acquisition subsystem: It consists of a spectral satellite platform, an optical sensor array, and an attitude stabilization system. The satellite platform uses radiation-resistant reinforced electronic components and has autonomous fault diagnosis and repair capabilities. Data processing and analysis subsystem: Integrates data preprocessing, feature extraction, fire identification and warning, fire monitoring and dynamic evaluation functional modules, and calculates real-time data through the processor; Communication subsystem: Build a two-way communication link between the satellite and the ground command center. The communication frequency bands cover Ka band and X band, and automatically switch according to channel quality and data volume; Ground command center subsystem: equipped with 8K resolution screen, data storage server, and decision support system, the ground command center subsystem provides fire fighting strategy suggestions through satellite data based on knowledge base and system learning algorithm; Cloud storage and management subsystem: It adopts a distributed cloud storage architecture to store satellite collected data, analysis results, and model parameters, and has data backup, disaster recovery, and data retrieval functions.

8. The system according to claim 7, characterized in that In the satellite data acquisition subsystem, the optical sensor array design takes space radiation into consideration, uses shielding materials, and combines anti-radiation reinforcement technology and radiation-resistant materials.

9. The system according to claim 7, characterized in that In the data processing and analysis subsystem, the feature extraction module supports the parallelization of feature extraction algorithms and automatically switches algorithms through scheduling algorithms and task requirements.

10. The system according to claim 7, characterized in that In the communication subsystem, laser communication technology is used when the satellite passes over the ground command center.

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