Forest grassland fire prevention comprehensive research and judgment monitoring and early warning method and related equipment

Abstract

The application discloses a forest and grassland fire prevention comprehensive analysis and judgment monitoring and early warning method and related equipment. The method comprises the following steps: acquiring monitoring plan preset information and specific monitoring point information to perform monitoring; based on a monitoring standard database and a weight strategy, real-time analysis and judgment are performed, and monitoring and weighted data are written into a monitoring plan log; the monitoring standard database is determined based on sensor information and remote sensing data source data; the monitoring plan log is acquired in real time, the weighted values in the monitoring data of the monitoring points meeting the plan log are grouped and summed, and are sorted in descending order to determine a fire prevention key monitoring area. The method can solve the problems that the early fire point state cannot be captured, the best fire extinguishing opportunity may be missed, a single remote sensing data needs to be interpreted and analyzed by professionals, needs to be judged by professionals, there is a risk of misjudgment, the monitoring range is limited, the influence of the terrain is large, and complete fire characteristics and environmental background cannot be provided.

Description

Technical Field

[0001] This application relates to the field of remote sensing monitoring, and more specifically, to a comprehensive assessment, monitoring and early warning method and related equipment for forest and grassland fire prevention. Background Technology

[0002] Forest fire early warning technology involves predicting and assessing the likelihood of forest fires, and taking preventative measures to mitigate their impact or losses. With increasing global climate change and human activity, forest fires are becoming more frequent, causing enormous economic losses and ecological disasters. Therefore, researching and developing effective forest fire early warning technologies is of significant practical importance.

[0003] Existing forest fire early warning technologies mainly include the following:

[0004] Remote sensing monitoring: This method utilizes remote sensing technologies such as satellites and drones to monitor forest fires in real time, promptly detecting fire sources and providing a basis for firefighting decisions. This method has advantages such as wide coverage and high monitoring accuracy, but the equipment cost is relatively high. Because it relies on remote sensing data for analysis, the resolution of remote sensing images may not be able to capture the early stages of a fire, potentially missing the optimal firefighting opportunity. Furthermore, single remote sensing data requires professional interpretation and analysis, necessitating expert judgment, and there is a risk of misjudgment.

[0005] Ground-based monitoring: By setting up ground-based monitoring equipment such as fire detectors and smoke sensors, forest fire sources and smoke conditions are monitored in real time, providing a basis for fire prevention decisions. This method is simple to implement and low in cost, but the monitoring range is limited, it is greatly affected by terrain, and it cannot provide complete fire characteristics and environmental background. Summary of the Invention

[0006] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0007] To address the issues of failing to capture early fire ignition points, potentially missing optimal firefighting opportunities, and the need for specialized interpretation and analysis of single remote sensing data, which carries the risk of misjudgment, limited monitoring range, and significant influence from terrain, thus failing to provide complete fire characteristics and environmental context, this invention proposes a comprehensive assessment, monitoring, and early warning method for forest and grassland fire prevention. This method includes:

[0008] Obtain the preset information of the monitoring plan and the specific monitoring point information to execute monitoring;

[0009] Based on the monitoring standard database and weighting strategy, real-time analysis is performed and the monitoring and weighted data are written to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data sources.

[0010] The monitoring plan log is acquired in real time. The weighted values ​​of the monitoring data of the monitoring points that meet the plan log are grouped, summed, and sorted from largest to smallest to determine the key fire prevention monitoring areas.

[0011] Optionally, the method further includes:

[0012] Acquire sensor information and remote sensing data source data, and write them to the sensor data raw table and the remote sensing data raw table respectively;

[0013] The two original tables are parsed based on sensor parameters and remote sensing technology parameters, and the parsed results are written into the monitoring standard database.

[0014] Optionally, the step of parsing the two original tables based on sensor parameters and remote sensing technology parameters, and writing the parsed results into the monitoring standard database, includes:

[0015] Based on the correspondence between remote sensing technology and parameters, the original remote sensing data table is parsed to convert the data in the original remote sensing data table into monitoring standard data;

[0016] Based on the correspondence between sensors and parameters, the original sensor data table is parsed to convert the data in the original sensor data table into monitoring standard data.

[0017] Optionally, the storage path for the remote sensing data source is configured to support both plain file format and hosted GIS server mode.

[0018] Optionally, the remote sensing data source includes monitoring and assessment indices of at least one of the following: land cover type, vegetation index, topographic elevation, meteorological parameters, land use change, and water resources.

[0019] Optional, also includes:

[0020] The weight influence mutation factor is determined based on the types of remote sensing monitoring data currently being monitored. The weight influence mutation factor is used to indicate the weight strategy mutation parameter of a single monitoring and evaluation index caused by the simultaneous occurrence of monitoring and evaluation indices.

[0021] If the types of remote sensing data currently monitored indicate that there are weight-affecting factors in the current weighting strategy, the monitoring and evaluation index of the remote sensing data source is adjusted based on the weight-affecting factors.

[0022] Optionally, determining the weighted influence of the mutation factor based on the types of currently monitored remote sensing data includes:

[0023] Based on the types of remote sensing data currently being monitored, determine the possible combinations of the types of monitoring data currently being monitored;

[0024] Each of the aforementioned combinations is matched with a preset weighted combination of mutation factors to determine the weighted mutation factor.

[0025] Secondly, this invention also proposes a comprehensive forest and grassland fire prevention assessment, monitoring, and early warning device, comprising:

[0026] The acquisition unit is used to acquire preset information of the monitoring plan and specific monitoring point information to perform monitoring;

[0027] The analysis unit is used to perform real-time analysis based on the monitoring standard database and weighting strategy, and write the monitoring and weighted data to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data source data.

[0028] The determination unit is used to acquire the monitoring plan log in real time, group and sum the weighted values ​​of the monitoring data of the monitoring points that meet the plan log, and sort them from largest to smallest to determine the key fire prevention monitoring areas.

[0029] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the forest and grassland fire prevention comprehensive assessment, monitoring, and early warning method as described in any of the first aspects above.

[0030] Fourthly, the present invention also proposes a computer-readable storage medium storing a computer program thereon, wherein when the computer program is executed by a processor, it implements the forest and grassland fire prevention comprehensive assessment, monitoring and early warning method of any one of the above claims in the first aspect.

[0031] In summary, the comprehensive assessment, monitoring, and early warning method for forest and grassland fire prevention proposed in this application executes monitoring by acquiring preset information from the monitoring plan and specific monitoring point information; based on a monitoring standard database and weighting strategy, it performs real-time assessment and writes the monitoring and weighted data to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data sources; the monitoring plan log is acquired in real-time, and the weighted values ​​of the monitoring data from monitoring points that meet the plan log are grouped, summed, and sorted from largest to smallest to determine key fire prevention monitoring areas. Thus, monitoring points formed by various technical solutions are comprehensively weighted and sorted, and key fire monitoring points can be generated in real-time based on the weighted data of monitoring points formed at different times. The monitoring values ​​from sensors and remote sensing data are matched and parsed in the form of parameter groups, and a custom strategy weight is set for each parameter group. Then, the real-time acquired monitoring data is assigned different weighted values ​​according to the set weighting strategy, and all monitoring points in the entire monitoring area are comprehensively evaluated according to their weights. This process can effectively optimize monitoring efficiency and accuracy.

[0032] The forest and grassland fire prevention comprehensive assessment, monitoring and early warning method of the present invention, other advantages, objectives and features of the present invention will be partly apparent from the following description, and partly understood by those skilled in the art through research and practice of the present invention. Attached Figure Description

[0033] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0034] Figure 1 A schematic diagram of a comprehensive assessment, monitoring and early warning method for forest and grassland fire prevention provided in this application embodiment;

[0035] Figure 2 A schematic diagram illustrating the logic for determining key fire monitoring areas provided in this application embodiment;

[0036] Figure 3 A schematic diagram of a comprehensive forest and grassland fire prevention assessment, monitoring, and early warning device provided in this application embodiment;

[0037] Figure 4 This is a schematic diagram of the structure of an electronic device for comprehensive assessment, monitoring and early warning of forest and grassland fire prevention, provided in an embodiment of this application. Detailed Implementation

[0038] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0039] To address the issue of failing to capture the early stages of a fire, potentially missing the optimal firefighting window, single-source remote sensing data requires professional interpretation and analysis. This necessitates expert judgment and carries the risk of misjudgment. Furthermore, monitoring range may be limited, significantly influenced by terrain, and unable to provide complete fire characteristics and environmental context. Please refer to [further details needed]. Figure 1 This is a schematic diagram of a comprehensive assessment, monitoring and early warning method for forest and grassland fire prevention provided in an embodiment of this application, which may specifically include steps S110 to S140.

[0040] S110: Obtain the preset information of the monitoring plan and the specific monitoring point information to perform monitoring.

[0041] For example, the system acquires preset information from the monitoring plan, including the area to be monitored, the monitoring frequency, and the monitoring targets. It also determines specific monitoring point information, such as the location and coverage area of ​​sensors or remote sensing equipment. This information will serve as the basis for subsequent real-time monitoring and analysis.

[0042] S120, based on the monitoring standard database and weighting strategy, performs real-time analysis and writes the monitoring and weighted data to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data source data.

[0043] For example, based on the monitoring point locations and monitoring targets determined in the preset information, the system uses sensor information and remote sensing data stored in the monitoring standard database to perform real-time monitoring and analysis. Simultaneously, according to a pre-set weighting strategy, the monitoring data is weighted to comprehensively assess the fire risk at each monitoring point. This real-time monitoring and weighted data will be recorded in the monitoring plan log for subsequent analysis and processing.

[0044] S130: Real-time acquisition of monitoring plan logs; summing of weighted values ​​in the monitoring data of monitoring points that meet the plan logs; sorting by value from largest to smallest to determine key fire prevention monitoring areas.

[0045] For example, the system acquires the monitoring and weighted data recorded in the monitoring plan log in real time. Then, according to the preset fire prevention monitoring area rules, the weighted values ​​of the monitoring data of the monitoring points that meet the plan log are grouped and summed, and then sorted according to the weighted values ​​from largest to smallest. The sorting results will be used to determine the key fire prevention monitoring areas, that is, those areas with a high fire risk that require special attention and monitoring.

[0046] In summary, the comprehensive forest and grassland fire prevention assessment, monitoring, and early warning method provided in this application executes monitoring by acquiring preset information of the monitoring plan and specific monitoring point information; based on a monitoring standard database and weighting strategy, it performs real-time assessment and writes the monitoring and weighted data to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data sources; the monitoring plan log is acquired in real-time, and the weighted values ​​in the monitoring data of monitoring points that meet the plan log are grouped, summed, and sorted from largest to smallest to determine key fire prevention monitoring areas. Thus, monitoring points formed by various technical solutions are comprehensively weighted and sorted, and key fire monitoring points can be generated in real-time based on the weighted data of monitoring points formed at different times. The monitoring values ​​of sensor and remote sensing data are matched and parsed in the form of parameter groups, and a custom strategy weight is set for each parameter group. Then, the real-time acquired monitoring data is assigned different weighted values ​​according to the set weighting strategy, and all monitoring points in the entire monitoring area are comprehensively evaluated according to their weights. This process can effectively optimize monitoring efficiency and accuracy.

[0047] According to some embodiments, the method further includes:

[0048] Acquire sensor information and remote sensing data source data, and write them to the sensor data raw table and the remote sensing data raw table respectively;

[0049] The two original tables are parsed based on sensor parameters and remote sensing technology parameters, and the parsed results are written into the monitoring standard database.

[0050] For example, this method is implemented through some program modules in the system, such as a background management module within the basic settings and monitoring settings. The basic settings include sensor registration and maintenance, remote sensing technology solution management, monitoring points, and parameter group settings to define various sensor parameters. In sensor maintenance management, basic information about the sensor and monitoring points is linked, including the sensor model and name. The system also includes remote sensing technology solution management; various remote sensing technologies exist, and each technology produces different parameters and values ​​for a given area. In monitoring point management, this system refers to forest fire monitoring points. In real life, a monitoring point might be an area or a building, both possessing latitude and longitude coordinates. The monitoring points in this system also have latitude and longitude information. In parameter management, parameter group management and parameter definition are included. Parameter management is primarily used for monitoring point data generated by sensors and remote sensing technologies. Each sensor has a different parameter structure for this data. To better support monitoring point data generated by various sensors and remote sensing technologies, parameter group information and specific parameters need to be configured. It may also include a front-end display module, which displays monitoring logs. These logs allow users to view monitoring data generated by various sensors and remote sensing technologies, and filter data by time and monitoring point dimensions. For fire prevention key monitoring, a priority monitoring area sorting program is used, based on the weighted sum of grouped monitoring plan logs, and sorted from largest to smallest. It may also include a back-end service program, specifically a data acquisition program. Remote sensing data is stored in a fixed server directory. This program acquires remote sensing data sources according to certain rules and timeframes. Remote sensing data mainly includes monitoring point information and specific remote sensing indicator analysis data. The acquired data will be stored in the raw remote sensing data table. Sensor data acquisition is performed through a common sensor communication protocol to obtain raw sensor monitoring data. The acquired information will be stored in the raw sensor data table. For the data conversion program, there are two types: remote sensing data conversion and sensor data conversion. The conversion program first parses the raw remote sensing data table according to the correspondence between remote sensing technologies and parameters, converting the data into monitoring standard data. Similarly, the conversion program also parses the raw sensor data table according to the correspondence between sensors and parameters, converting the information into monitoring standard data. For the planned monitoring execution procedure, this procedure is executed according to a predetermined monitoring plan. Monitoring plan information typically includes the plan name, execution period, status, start time, and end time, and may include specific monitoring point information. The procedure scans and executes the monitoring plan information line by line. For the real-time comprehensive analysis procedure, this procedure generates monitoring plan log data based on standard monitoring data and the definition of a weighted strategy.The generated log data mainly includes important information such as monitoring time, monitoring value, weighted value, source type (sensor or remote sensing technology solution), technology solution ID, and sensor ID. For the key monitoring area sorting program, this program groups and sums the weighted values ​​based on the monitoring plan log data, arranges them from largest to smallest, and filters out the focus field monitoring points and weighted values ​​to form key fire prevention monitoring areas.

[0051] Based on the above system, the above method can be implemented in the following steps: S1: Maintain and add monitoring point information in the monitoring point module of the basic settings of the backend management module. S2: Maintain and add sensor information in the sensor module of the basic settings of the backend management module. S3: Maintain and add remote sensing technology solution information in the remote sensing technology solution module of the basic settings of the backend management module. S4: Define parameter groups in the parameter groups of the basic settings of the backend management module. S5: Define parameters in the parameter group sub-function of the basic settings of the backend management module. S6: Bind sensors and parameter groups in the sensor module of the basic settings of the backend management module. S7: Bind remote sensing technology solutions and parameter groups in the remote sensing technology solution of the basic settings of the backend management module. S8: Define weights and bind parameter groups in the strategy weight function of the monitoring settings of the backend management module. S9: Configure weights and parameter details in the strategy weight function of the monitoring settings of the backend management module. S10: Create a monitoring plan in the monitoring plan of the monitoring settings of the backend management module. S11: Access the monitoring plan function in the monitoring settings of the backend management module to set the specific monitoring points for the monitoring plan. S12: Start the data acquisition program to acquire raw data in real time from sensor components and remote sensing data sources and store it in the remote sensing data raw table and the sensor data raw table. S13: Start the data conversion program to read raw data from the remote sensing data raw table and the sensor data raw table, combine it with sensor parameters and remote sensing technical parameters for analysis, and form a monitoring standard database. S14: Start the planned monitoring execution program to activate the monitoring plan based on the planned data and specific monitoring point information. S15: Start the comprehensive analysis program to perform weighted calculations based on the standard monitoring database and weighted strategy data to form the monitoring plan log. S16: Start the key monitoring area sorting program to group and sum the weighted values ​​based on the monitoring plan log and sort them from largest to smallest to form key fire prevention monitoring areas.

[0052] For example, when designing a weighted strategy for real-time assessment of forest fire prevention, the following factors can be considered:

[0053] Fire risk factor weighting: Considering the risk factors of forest fires, weights can be assigned to the risk factors at different monitoring points. For example, factors such as forest dryness, temperature, wind direction, and wind speed may affect the occurrence and spread of fires.

[0054] Data reliability weighting: Monitoring data from different monitoring points may come from different sources or have varying degrees of reliability. More reliable data should be assigned a higher weight. For example, satellite remote sensing data may be more comprehensive and reliable than data from local sensor monitoring.

[0055] Geographic location weighting: Some areas may be more susceptible to fire than others, so geographic location can be considered, and monitoring points in different locations can be assigned weights. For example, monitoring points near areas of human activity or flammable vegetation may require higher weights.

[0056] Historical fire data weighting: Past fire data can provide valuable information because some areas may be more prone to future fires. Therefore, historical fire data can be considered as one of the weighting factors.

[0057] Based on the above weighting factors, a weighting strategy can be designed, for example:

[0058] Dryness weighting: 30%

[0059] Reliability weight of monitoring data: 25%

[0060] Geographic location weight: 20%

[0061] Historical fire data weight: 25%

[0062] During the real-time analysis, these weighted factors are calculated based on the relevant data from each monitoring point to obtain a comprehensive weighted value.

[0063] For example, if there are two monitoring points A and B, their weighted values ​​are as follows:

[0064] Weighted value for monitoring point A: 0.3*0.8 + 0.25*0.9 + 0.2*0.7 + 0.25*0.6 = 0.775

[0065] Weighted value for monitoring point B: 0.3*0.7 + 0.25*0.8 + 0.2*0.6 + 0.25*0.5 = 0.71

[0066] Then, these weighted values ​​are sorted, and the results are written into the key fire prevention monitoring areas. In this example, monitoring point A has a higher weighted value, so it will be given priority and written into the key monitoring area.

[0067] According to some embodiments, the storage path of the remote sensing data source is configured to support both plain file format and hosted GIS server mode.

[0068] For example, the storage path of the data source mainly specifies the fixed location for storing remote sensing data. It should support plain file format and hosted GIS server mode so that the system can easily call and retrieve it.

[0069] According to some embodiments, the remote sensing data source includes monitoring and assessment indices of at least one of land cover type, vegetation index, topographic elevation, meteorological parameters, land use change, and water resources.

[0070] According to some embodiments, it also includes:

[0071] The weight influence mutation factor is determined based on the types of remote sensing monitoring data currently being monitored. The weight influence mutation factor is used to indicate the weight strategy mutation parameter of a single monitoring and evaluation index caused by the simultaneous occurrence of monitoring and evaluation indices.

[0072] If the types of remote sensing data currently monitored indicate that there are weight-affecting factors in the current weighting strategy, the monitoring and evaluation index of the remote sensing data source is adjusted based on the weight-affecting factors.

[0073] According to some embodiments, determining the weighted influence of the mutation factor based on the type of currently monitored remote sensing data includes:

[0074] Based on the types of remote sensing data currently being monitored, determine the possible combinations of the types of monitoring data currently being monitored;

[0075] Each of the aforementioned combinations is matched with a preset weighted combination of mutation factors to determine the weighted mutation factor.

[0076] For example, in some cases, an index alone may have a positive impact on the fire monitoring zone rating, but when combined with another index, it may lead to a weighting abrupt change or a negative impact. Combinations of factors that abruptly change the weighting can include vegetation indices and land cover type: a high vegetation index typically indicates dense vegetation, which can have a positive impact on the fire monitoring zone rating because dense vegetation may mean a slower fire spread. However, if the vegetation index is high but the land cover type remains flammable vegetation, such as dry grassland or shrubland, then once a fire occurs, it may spread rapidly, leading to a weighting abrupt change or a negative impact. Another example is meteorological parameters and topographic elevation: when meteorological parameters are favorable (e.g., low wind speed, high humidity) and the topographic elevation is low, this usually has a positive impact on the fire monitoring zone rating because these conditions are unfavorable for fire spread. However, if meteorological parameters are favorable and the topographic elevation is high, this combination may lead to an increased fire spread rate because winds in high-altitude areas can exacerbate fire spread, resulting in a weighting abrupt change or a negative impact. When the combination method matches the preset weighted mutation factor combination method, the weighted mutation factor can be determined. For example, the index of positive influence can be adjusted to the index of negative influence or the weight of the index can be changed by the weighted mutation factor.

[0077] Please see Figure 3 One embodiment of the forest and grassland fire prevention comprehensive assessment, monitoring and early warning device in this application may include:

[0078] Acquisition unit 21 is used to acquire preset information of the monitoring plan and specific monitoring point information to perform monitoring;

[0079] The analysis unit 22 is used to perform real-time analysis based on the monitoring standard database and weighting strategy, and write the monitoring and weighted data to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data source data.

[0080] Unit 23 is used to acquire the monitoring plan log in real time, group and sum the weighted values ​​in the monitoring data of the monitoring points that meet the plan log, and sort them from largest to smallest to determine the key fire prevention monitoring areas.

[0081] In summary, the forest and grassland fire prevention comprehensive assessment, monitoring, and early warning device provided in this application executes monitoring by acquiring preset monitoring plan information and specific monitoring point information; based on a monitoring standard database and weighting strategy, it performs real-time assessment and writes monitoring and weighted data to a monitoring plan log, whereby the monitoring standard database is determined based on sensor information and remote sensing data sources; it acquires the monitoring plan log in real time, groups and sums the weighted values ​​in the monitoring data of monitoring points that meet the plan log, and sorts them from largest to smallest to determine key fire prevention monitoring areas. Thus, it comprehensively integrates monitoring points formed by various technical solutions, sorts them by weight, and can generate key fire monitoring points in real time based on the weighted data of monitoring points formed within different time periods. The monitoring values ​​of sensor and remote sensing data are matched and parsed in the form of parameter groups, and a custom strategy weight is set for each parameter group. Then, the real-time acquired monitoring data is assigned different weighted values ​​according to the set weighting strategy, comprehensively evaluating all monitoring points in the entire monitoring area according to their weights. This process can effectively optimize monitoring efficiency and accuracy.

[0082] like Figure 4 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-mentioned methods for comprehensive assessment, monitoring, and early warning of forest and grassland fire prevention:

[0083] Obtain the preset information of the monitoring plan and the specific monitoring point information to execute monitoring;

[0084] Based on the monitoring standard database and weighting strategy, real-time analysis is performed and the monitoring and weighted data are written to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data sources.

[0085] The monitoring plan log is acquired in real time. The weighted values ​​of the monitoring data of the monitoring points that meet the plan log are grouped, summed, and sorted from largest to smallest to determine the key fire prevention monitoring areas.

[0086] Optionally, the method further includes:

[0087] Acquire sensor information and remote sensing data source data, and write them to the sensor data raw table and the remote sensing data raw table respectively;

[0088] The two original tables are parsed based on sensor parameters and remote sensing technology parameters, and the parsed results are written into the monitoring standard database.

[0089] Optionally, the step of parsing the two original tables based on sensor parameters and remote sensing technology parameters, and writing the parsed results into the monitoring standard database, includes:

[0090] Based on the correspondence between remote sensing technology and parameters, the original remote sensing data table is parsed to convert the data in the original remote sensing data table into monitoring standard data;

[0091] Based on the correspondence between sensors and parameters, the original sensor data table is parsed to convert the data in the original sensor data table into monitoring standard data.

[0092] Optionally, the storage path for the remote sensing data source is configured to support both plain file format and hosted GIS server mode.

[0093] Optionally, the remote sensing data source includes monitoring and assessment indices of at least one of the following: land cover type, vegetation index, topographic elevation, meteorological parameters, land use change, and water resources.

[0094] Optional, also includes:

[0095] The weight influence mutation factor is determined based on the types of remote sensing monitoring data currently being monitored. The weight influence mutation factor is used to indicate the weight strategy mutation parameter of a single monitoring and evaluation index caused by the simultaneous occurrence of monitoring and evaluation indices.

[0096] If the types of remote sensing data currently monitored indicate that there are weight-affecting factors in the current weighting strategy, the monitoring and evaluation index of the remote sensing data source is adjusted based on the weight-affecting factors.

[0097] Optionally, determining the weighted influence of the mutation factor based on the types of currently monitored remote sensing data includes:

[0098] Based on the types of remote sensing data currently being monitored, determine the possible combinations of the types of monitoring data currently being monitored;

[0099] Each of the aforementioned combinations is matched with a preset weighted combination of mutation factors to determine the weighted mutation factor.

[0100] Since the electronic device described in this embodiment is the device used to implement the forest and grassland fire prevention comprehensive assessment, monitoring and early warning device in this application embodiment, those skilled in the art can understand the specific implementation method and its various variations of the electronic device in this embodiment based on the method described in this application embodiment. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. As long as those skilled in the art implement the method in this application embodiment, the device used is within the scope of protection of this application.

[0101] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1Any of the implementation methods in the corresponding embodiments:

[0102] Obtain the preset information of the monitoring plan and the specific monitoring point information to execute monitoring;

[0103] Based on the monitoring standard database and weighting strategy, real-time analysis is performed and the monitoring and weighted data are written to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data sources.

[0104] The monitoring plan log is acquired in real time. The weighted values ​​of the monitoring data of the monitoring points that meet the plan log are grouped, summed, and sorted from largest to smallest to determine the key fire prevention monitoring areas.

[0105] Optionally, the method further includes:

[0106] Acquire sensor information and remote sensing data source data, and write them to the sensor data raw table and the remote sensing data raw table respectively;

[0107] The two original tables are parsed based on sensor parameters and remote sensing technology parameters, and the parsed results are written into the monitoring standard database.

[0108] Optionally, the step of parsing the two original tables based on sensor parameters and remote sensing technology parameters, and writing the parsed results into the monitoring standard database, includes:

[0109] Based on the correspondence between remote sensing technology and parameters, the original remote sensing data table is parsed to convert the data in the original remote sensing data table into monitoring standard data;

[0110] Based on the correspondence between sensors and parameters, the original sensor data table is parsed to convert the data in the original sensor data table into monitoring standard data.

[0111] Optionally, the storage path for the remote sensing data source is configured to support both plain file format and hosted GIS server mode.

[0112] Optionally, the remote sensing data source includes monitoring and assessment indices of at least one of the following: land cover type, vegetation index, topographic elevation, meteorological parameters, land use change, and water resources.

[0113] Optional, also includes:

[0114] The weight influence mutation factor is determined based on the types of remote sensing monitoring data currently being monitored. The weight influence mutation factor is used to indicate the weight strategy mutation parameter of a single monitoring and evaluation index caused by the simultaneous occurrence of monitoring and evaluation indices.

[0115] If the types of remote sensing data currently monitored indicate that there are weight-affecting factors in the current weighting strategy, the monitoring and evaluation index of the remote sensing data source is adjusted based on the weight-affecting factors.

[0116] Optionally, determining the weighted influence of the mutation factor based on the types of currently monitored remote sensing data includes:

[0117] Based on the types of remote sensing data currently being monitored, determine the possible combinations of the types of monitoring data currently being monitored;

[0118] Each of the aforementioned combinations is matched with a preset weighted combination of mutation factors to determine the weighted mutation factor.

[0119] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The corresponding embodiment describes the process for comprehensive assessment, monitoring, and early warning of forest and grassland fire prevention.

[0125] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A comprehensive assessment, monitoring, and early warning method for forest and grassland fire prevention, characterized in that, include: Obtain the preset information of the monitoring plan and the specific monitoring point information to execute monitoring; Based on the monitoring standard database and weighting strategy, real-time analysis is performed and the monitoring and weighted data are written to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data sources. Real-time acquisition of monitoring plan logs; weighted summation of monitoring data from monitoring points that meet the plan logs; sorting by size from largest to smallest to determine key fire prevention monitoring areas. The method further includes: Based on the types of remote sensing data currently being monitored, determine the possible combinations of the types of monitoring data currently being monitored; Each of the aforementioned combination methods is matched with a preset weight influence mutation factor combination method to determine the weight influence mutation factor. The weight influence mutation factor is used to indicate the weight strategy mutation parameter of a single monitoring and evaluation index caused by the simultaneous occurrence of the monitoring and evaluation index combination. If the types of remote sensing data currently monitored indicate that there are weight-affecting mutation factors in the current weighting strategy, the monitoring and evaluation index of the remote sensing data source is adjusted based on the weight-affecting mutation factors, including adjusting the index of positive impact to the index of negative impact through the weight-affecting mutation factors. The preset weighted combination of mutation factors includes vegetation index and land cover type: when the currently monitored remote sensing data indicates a high vegetation index and the land cover type is flammable vegetation, the weight coefficient of the vegetation index in the weighting strategy is adjusted from a positive value to a negative value; and / or, The preset weighted combination of abrupt change factors includes meteorological parameters and terrain elevation. When the meteorological parameters indicate low wind speed and high humidity, and the terrain elevation is high, the weight coefficient of the monitoring and evaluation index related to the terrain elevation in the weighting strategy is adjusted from a positive value to a negative value.

2. The method as described in claim 1, characterized in that, The method further includes: Acquire sensor information and remote sensing data source data, and write them to the sensor data raw table and the remote sensing data raw table respectively; The two original tables are parsed based on sensor parameters and remote sensing technology parameters, and the parsed results are written into the monitoring standard database.

3. The method as described in claim 2, characterized in that, The process of parsing the two original tables based on sensor parameters and remote sensing technology parameters, and then writing the parsed results into the monitoring standard database, includes: Based on the correspondence between remote sensing technology and parameters, the original remote sensing data table is parsed to convert the data in the original remote sensing data table into monitoring standard data; Based on the correspondence between sensors and parameters, the original sensor data table is parsed to convert the data in the original sensor data table into monitoring standard data.

4. The method as described in claim 2, characterized in that, The storage path for the remote sensing data source is configured to support both plain file format and hosted GIS server mode.

5. The method as described in claim 2, characterized in that, The remote sensing data sources include monitoring and assessment indices for at least one of the following: land cover type, vegetation index, topographic elevation, meteorological parameters, land use change, and water resources.

6. A comprehensive assessment, monitoring, and early warning device for forest and grassland fire prevention, characterized in that, The apparatus comprising, using the method as described in any one of claims 1 to 5, includes: The acquisition unit is used to acquire preset information of the monitoring plan and specific monitoring point information to perform monitoring; The analysis unit is used to perform real-time analysis based on the monitoring standard database and weighting strategy, and write the monitoring and weighted data to the monitoring plan log. The monitoring standard database is determined based on sensor information and remote sensing data source data. The determination unit is used to acquire the monitoring plan log in real time, group and sum the weighted values ​​of the monitoring data of the monitoring points that meet the plan log, and sort them from largest to smallest to determine the key fire prevention monitoring areas.

7. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program stored in the memory to implement the steps of the forest and grassland fire prevention comprehensive assessment, monitoring, and early warning method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the comprehensive assessment, monitoring and early warning method for forest and grassland fire prevention as described in any one of claims 1-5.

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