Fire alarm system, fire point identification method, electronic equipment and storage medium

Abstract

The invention provides a fire alarm system, a fire point identification method, electronic equipment and a storage medium. The fire alarm system comprises an algorithm analysis module, a fire behavior analysis module and a map interaction module. Compared with the prior art, according to the embodiment of the invention, various satellite data are acquired through the algorithm analysis module, the fire point identification adaptive algorithm is used for processing, and the advantages and characteristics of different satellites are fully utilized, so that the possible limitation of a single satellite data source can be overcome, the accuracy and reliability of fire point identification can be improved, and the accuracy and reliability of fire point identification can be improved. The probability of false alarm and missing alarm is reduced, so that potential fire hazards can be found more timely and accurately; after the fire analysis module receives the fire point identification result, whether a fire occurs can be accurately judged based on the fire point identification result, and unnecessary resource waste or rescue opportunity delay caused by misjudgment is avoided; and the map interaction module marks the fire point position according to the position information in the fire information and displays the related fire information, so that the transmission efficiency and operability of the information can be enhanced.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a fire alarm system, a method for identifying a fire point, an electronic device, and a storage medium. Background Art

[0002] In the existing technical system, the monitoring of forest and grassland fires mainly adopts traditional smoke fire detectors, fixed-position monitoring probes, and manual observation of watchtowers. However, these methods are obviously insufficient in large-scale, complex terrain mountain forest and grassland areas, and it is difficult to meet the actual needs of current disaster monitoring. In addition, although the insurance industry has certain needs for fire monitoring, the single-type satellite remote sensing monitoring technology used in the past has problems such as low monitoring cycle frequency, low timeliness, and poor accuracy. When a fire occurs, the early warning mechanism is inefficient, and it is difficult for relevant stakeholders to respond quickly and make timely disaster prevention and loss reduction measures. In particular, when using geostationary orbit satellites or low-orbit satellites for monitoring, geostationary orbit satellites are easily affected by cloud cover, and the monitoring frequency of low-orbit satellites is relatively low, which is prone to monitoring blind spots. Therefore, there is an urgent need for a forest and grassland fire monitoring and early warning system that can overcome the defects of existing technologies and improve the monitoring timeliness, accuracy and breadth. Summary of the invention

[0003] The present invention provides a fire alarm system, a method for identifying a fire point, an electronic device and a storage medium, the main purpose of which is to solve the current problem of low timeliness and poor accuracy of fire monitoring.

[0004] According to a first aspect of the present disclosure, a forest fire warning system is provided, comprising: an algorithm analysis module, a fire situation analysis module, and a map interaction module;

[0005] The algorithm analysis module is connected to the fire situation analysis module, and is used to obtain a variety of satellite data, use a fire point recognition adaptive algorithm to perform fire point recognition on the multiple satellite data, and send the fire point recognition result to the fire situation analysis module;

[0006] The fire situation analysis module is connected to the map interaction module, and is used to determine whether a fire occurs according to the fire point identification result; after determining that a fire occurs, fire information is generated according to the fire point identification result, and the fire information is sent to the map interaction module;

[0007] The map interaction module is used to mark the monitored fire point location according to the fire location information in the fire information, and display the fire related information in the fire information.

[0008] In some embodiments, the system further includes: an alarm push module;

[0009] The alarm push module is connected to the fire situation analysis module, and is used to respond to the fire information sent by the fire situation analysis module, generate fire alarm information according to the fire information; and send the fire alarm information to the user terminal.

[0010] In some embodiments, the system further comprises: a data management module;

[0011] The data management module is connected to the algorithm analysis module, the fire situation analysis module, and the map interaction module, and the data management module is used to store the various satellite data, the fire point identification results, the fire information, and map data.

[0012] In some embodiments, the map interaction module includes: a navigation submodule and a layer switching submodule;

[0013] The navigation submodule is used to generate navigation information from the user terminal to the fire location based on the fire information, and send the navigation information to the user terminal;

[0014] The layer switching submodule is used to respond to a layer switching instruction, obtain map data and switch the map layer corresponding to the layer switching instruction.

[0015] In some embodiments, before generating the fire point identification result, the fire situation analysis module is also used to obtain vegetation spectral data at the location of the multiple satellite data; and use the vegetation spectral data to compare the initial fire point identification result to determine whether a fire has occurred.

[0016] According to a second aspect of the present disclosure, a method for identifying a fire point is provided, comprising:

[0017] Acquire multiple satellite data, and identify fire points using a fire point identification adaptive algorithm;

[0018] Determining whether a fire occurs according to the fire point identification result;

[0019] After determining that a fire has occurred, fire information is generated according to the fire point identification result.

[0020] In some embodiments, judging whether a fire occurs according to the fire point identification result includes:

[0021] Acquiring vegetation spectral data at locations where the multiple satellite data are located;

[0022] The vegetation spectral data is used to compare the initial fire point identification results to determine whether a fire has occurred.

[0023] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0024] at least one processor; and

[0025] a memory communicatively connected to the at least one processor; wherein,

[0026] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the second aspect.

[0027] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the second aspect.

[0028] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the second aspect above.

[0029] The present disclosure provides a fire alarm system, a method for identifying a fire point, an electronic device and a storage medium, comprising: an algorithm analysis module, a fire condition analysis module and a map interaction module; the algorithm analysis module is connected to the fire condition analysis module, and is used to obtain a variety of satellite data, identify fire points on the various satellite data using a fire point identification adaptive algorithm, and send the fire point identification results to the fire condition analysis module; the fire condition analysis module is connected to the map interaction module, and is used to determine whether a fire has occurred based on the fire point identification results; after determining that a fire has occurred, fire information is generated based on the fire point identification results, and the fire information is sent to the map interaction module; the map interaction module is used to mark the monitored fire point location based on the fire location information in the fire information, and display the fire related information in the fire information. Compared with the related art, the embodiment of the present disclosure obtains multiple satellite data through the algorithm analysis module, and processes them using the fire point identification adaptive algorithm, making full use of the advantages and characteristics of different satellites, which helps to overcome the limitations that may exist in a single satellite data source, improve the accuracy and reliability of fire point identification, and reduce the probability of false alarms and missed alarms, so as to more timely and accurately discover potential fire hazards; after receiving the fire point identification results, the fire situation analysis module can accurately judge whether a fire has occurred based on this, avoiding unnecessary waste of resources or delays in rescue opportunities due to misjudgment; the map interaction module marks the fire point location according to the location information in the fire information, and displays related fire information, which can enhance the efficiency and operability of information transmission.

[0030] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0032] Figure 1 A structural schematic diagram of a fire alarm system provided by an embodiment of the present disclosure;

[0033] Figure 2 A structural schematic diagram of another fire alarm system provided by an embodiment of the present disclosure;

[0034] Figure 3 A flowchart of a method for fire point identification provided by an embodiment of the present disclosure;

[0035] Figure 4 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0037] The fire alarm system, the method for identifying a fire point, the electronic device and the storage medium according to the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0038] Figure 1 A structural diagram of a fire alarm system provided in an embodiment of the present disclosure, the system comprises: an algorithm analysis module 11, a fire situation analysis module 12, and a map interaction module 13.

[0039] The algorithm analysis module 11 is connected to the fire situation analysis module 12, and is used to obtain a variety of satellite data, use a fire point recognition adaptive algorithm to identify fire points in the various satellite data, and send the fire point recognition results to the fire situation analysis module.

[0040] In the embodiment of the present disclosure, the algorithm analysis module 11, as one of the core data processing units of the entire fire alarm system, establishes a close and efficient connection channel with the fire analysis module 12. It has powerful data acquisition and processing capabilities, and can interact with multiple different types of satellites to obtain rich and diverse satellite data resources. These satellite data cover different bands, resolutions, and time series information, providing a comprehensive material basis for subsequent precise analysis.

[0041] After acquiring the above-mentioned multiple satellite data, the algorithm analysis module 11 will immediately start its built-in adaptive algorithm for fire point identification. This algorithm is developed through a large number of experiments and optimizations, and it can automatically adapt the most appropriate analysis strategy according to the characteristics of different satellite data. For example, in view of the fact that some satellite data has a high sensitivity to high-temperature heat sources in a specific band, the algorithm will focus on extracting the data in this band for in-depth analysis; for data with higher resolution, the algorithm will use its fine pixel information to more accurately locate the potential fire point.

[0042] In the specific process of fire point identification, the algorithm will first pre-process the satellite data, including removing noise, correcting data deviations, and other operations to ensure the quality and accuracy of the data. Then, the fire point is identified by comparing the radiance and brightness temperature differences between the mid-infrared and far-infrared channels. This is based on the principle that when a fire occurs, thermal radiation will cause significant changes in these channels. At the same time, for the processing of sub-pixel fire points, the algorithm uses the advanced Dozier algorithm, combined with the Newton iteration method and the dichotomy method to estimate the area and temperature, so that the scale and intensity of the fire point can be judged more accurately.

[0043] After completing the comprehensive analysis of multiple satellite data and fire point identification, the algorithm analysis module 11 will quickly and accurately send the fire point identification results to the fire analysis module 12 through the pre-set communication protocol and data interface. These results include key information such as the location coordinates of the fire point, the confidence level of the suspected fire point, and the temperature range estimation of the fire point, which provide indispensable basic data support for the fire analysis module 12 to conduct subsequent fire judgment and further analysis.

[0044] The fire situation analysis module 12 is connected to the map interaction module 13 and is used to determine whether a fire occurs according to the fire point identification result; after determining that a fire occurs, fire information is generated according to the fire point identification result, and the fire information is sent to the map interaction module 13.

[0045] In the embodiment of the present disclosure, the fire analysis module 12 is responsible for the key decision-making and information integration responsibilities in the entire fire alarm system, and maintains a close and efficient connection relationship with the map interaction module 13. After receiving the fire point identification result from the algorithm analysis module 11, the fire analysis module 12 immediately starts its precise analysis and judgment process. It first conducts a comprehensive evaluation of the various data in the fire point identification result, such as the number of fire points, distribution range, temperature anomaly degree and duration and other key indicators. Through a series of pre-set scientific and reasonable thresholds and judgment logics, it accurately determines whether the current situation has reached the standard for fire occurrence. These thresholds and logics are determined based on a large amount of historical fire data, field monitoring experience and professional fire science research results, ensuring the accuracy and reliability of the judgment. Once a fire is determined to have occurred through rigorous analysis, the fire analysis module 12 quickly enters the fire information generation stage. It will deeply mine the detailed information in the fire point identification result, further accurately calculate the size of the fire, and obtain a more accurate estimate of the burned area by integrating and analyzing the location and area data of multiple fire points. At the same time, combined with the changing trends of fire point data in different time periods, a preliminary prediction of the development trend of the fire is made to determine whether the fire is in the initial spread stage, the rapid development stage or the stable stage. In addition, the fire situation analysis module 12 will also integrate other relevant auxiliary information, such as the geographical environment information around the fire site (including topography, vegetation type, nearby water source distribution, etc.), which is of great guiding significance for subsequent fire fighting and rescue work. After completing the collection and integration of all information, the fire situation analysis module 12 will send the complete fire information to the map interaction module 13 in a timely and accurate manner in accordance with the predetermined data format and communication protocol, providing strong data support for the subsequent intuitive display of fire details on the map and auxiliary decision-making, thereby ensuring that the entire fire alarm system can operate efficiently and orderly, and providing a solid guarantee for fire prevention and control work.

[0046] The map interaction module 13 is used to mark the monitored fire point location according to the fire location information in the fire information, and display the fire related information in the fire information.

[0047] In the embodiment of the present disclosure, the map interaction module 13, as a key visualization component of the fire alarm system, plays an indispensable bridge role in the entire fire prevention and control process. It is mainly responsible for receiving fire information from the fire analysis module 12, and based on this, it carries out a series of important map operations and information display work. When the fire information is transmitted to the map interaction module 13, its built-in high-precision geographic positioning system is quickly started to deeply analyze the fire location information therein. The location information is usually presented in the form of longitude and latitude coordinates. With its coordinate conversion and map matching capabilities, the map interaction module 13 can accurately locate these abstract coordinates on the corresponding map layer. In the process of marking the location of the fire point, the map interaction module 13 uses a variety of eye-catching visual marking methods. For example, it will highlight the specific location of the fire point on the map with a bright red flashing icon, so that the operator can capture the location of the fire at a glance when browsing the map. At the same time, in order to further enhance the visual effect and recognition, a specific warning circle will be drawn around the fire point, and the depth of its color and the size of the radius can be dynamically adjusted according to the scale and degree of danger of the fire. In this way, even in a complex map background, the core area of ​​the fire can be clearly highlighted, providing intuitive and accurate geographical guidance for subsequent decision-making and rescue operations. In addition to accurately marking the location of the fire point, the map interaction module 13 also shoulders the important task of comprehensively displaying fire-related information. It will extract key data such as the fire intensity, the burned area, and the fire development trend from the received fire information, and present these data on the map interface in an intuitive and easy-to-understand manner. For the intensity of the fire, it may be displayed near the fire point in the form of a color gradient bar chart or heat map. The darker the color or the higher the heat value, the stronger the fire, so that users can quickly understand the severity of the fire. The burned area can be represented by digital markings or shaded coverage areas corresponding to the actual geographical area, so that people have a clear concept of the scope of the fire.

[0048] In terms of displaying the development trend of fires, the map interaction module 13 uses dynamic visualization technology. It will draw arrows on the map showing the possible direction of fire spread, based on the forecast information provided by the fire analysis module, and simulate the spread of fire over time in a dynamic form. In this way, decision makers can intuitively observe the future direction of the fire, plan and deploy corresponding firefighting and rescue resources in advance, and effectively improve the scientificity and timeliness of fire response. In addition, the map interaction module 13 also has rich user interaction functions to meet the diverse needs of different users in the process of fire monitoring and response. Users can easily zoom and pan the map through the mouse wheel or touch operation to view the geographical environment details around the fire point in detail, such as nearby rivers, roads, residential areas, forest resource distribution and other information. This surrounding environment information is of vital reference value for formulating firefighting strategies and assessing the potential impact of fire on surrounding areas. At the same time, when the user clicks on the fire point mark or related information area on the map, a detailed information window will pop up, which contains more detailed information such as the specific time of the fire, duration, current meteorological conditions (such as wind speed, wind direction, humidity, etc.) and nearby available firefighting resources (such as the location of the fire station, the number of fire trucks, and the storage point of fire-fighting equipment, etc.). These rich interactive functions and detailed information display make the map interaction module 13 an important link between data and decision-making in the fire alarm system, greatly improving the practicality and effectiveness of the entire system, and providing strong support for rapid response and effective control of fires.

[0049] The present disclosure provides a fire alarm system, including: an algorithm analysis module, a fire situation analysis module, and a map interaction module; the algorithm analysis module is connected to the fire situation analysis module, and is used to obtain a variety of satellite data, use a fire point identification adaptive algorithm to identify fire points in the various satellite data, and send the fire point identification results to the fire situation analysis module; the fire situation analysis module is connected to the map interaction module and the alarm push module, and is used to determine whether a fire has occurred according to the fire point identification results; after determining that a fire has occurred, fire information is generated according to the fire point identification results, and the fire information is sent to the map interaction module; the map interaction module is used to mark the monitored fire point location according to the fire location information in the fire information, and display the fire related information in the fire information. Compared with the related art, the embodiment of the present disclosure obtains multiple satellite data through the algorithm analysis module, and processes them using the fire point identification adaptive algorithm, making full use of the advantages and characteristics of different satellites, which helps to overcome the limitations that may exist in a single satellite data source, improve the accuracy and reliability of fire point identification, and reduce the probability of false alarms and missed alarms, so as to more timely and accurately discover potential fire hazards; after receiving the fire point identification results, the fire situation analysis module can accurately judge whether a fire has occurred based on this, avoiding unnecessary waste of resources or delays in rescue opportunities due to misjudgment; the map interaction module marks the fire point location according to the location information in the fire information, and displays related fire information, which can enhance the efficiency and operability of information transmission.

[0050] Furthermore, in a possible implementation of this embodiment, as Figure 2 As shown, the system further includes: an alarm push module 14;

[0051] The alarm push module 14 is connected to the fire situation analysis module 12, and is used to respond to the fire information sent by the fire situation analysis module 12, generate fire alarm information according to the fire information; and send the fire alarm information to the user terminal.

[0052] Specifically, the alarm push module 14, as a crucial information dissemination hub in the fire alarm system, is closely connected with the fire analysis module 12 to form an efficient information transmission link. After receiving the fire information from the fire analysis module 12, the alarm push module 14 immediately starts its internal complex and orderly processing flow and devotes itself to the generation of fire alarm information. First, the alarm push module 14 will conduct a comprehensive and detailed analysis of the received fire information. It deeply analyzes the key elements such as the location, scale, development trend and surrounding environment of the fire, and converts these raw data into highly targeted and practical fire alarm information based on a set of pre-set scientific and rigorous rules and algorithms. For example, for fire location information, it will accurately extract and convert it into a geocoding format that can be quickly identified and located by the user terminal; for the scale of the fire, it will generate corresponding concise and easy-to-understand text descriptions according to different area ranges and fire intensity levels, such as "small fire", "medium fire", "large fire and fierce fire", etc.; for the development of the fire, it combines time series data and prediction models to present it in dynamic language such as "the fire is spreading rapidly and is expected to affect the surrounding [specific area range] within the next [X] hours", so that the recipient can intuitively feel the urgency and danger of the fire. After completing the careful generation of the fire alarm information, the alarm push module 14 will quickly start a powerful communication mechanism to send these key information to the user terminal in a timely and accurate manner. It has multi-channel push capabilities, whether it is through SMS, mobile phone application push notifications, or data docking with professional fire command systems or related emergency management platforms, it can ensure that the alarm information reaches the target user as quickly as possible. In terms of SMS push, the alarm push module 14 will send the fire alarm information concisely and completely according to the mobile phone number pre-registered by the user in accordance with the standard SMS format specification, ensuring that the user can obtain key information in the first time even without network connection. For mobile application push notifications, it will use push technology to push the alarm information to the user's mobile device in a variety of ways such as eye-catching pop-up windows, vibration and sound reminders, attracting the user's immediate attention and guiding him to view the detailed content.

[0053] At the same time, the alarm push module 14 also fully considers the diversity and personalized needs of different user terminals. It can intelligently adapt the format and content display method of the push information according to the type of user terminal, the operating system version and the user-defined settings. For example, for the large-screen terminal of the fire command center, the alarm push module 14 will display the fire alarm information in a high-definition, large-font graphic form, highlighting the key data and map location information of the fire, so that the command personnel can clearly obtain information and make decisions and deployments at a long distance and in a complex environment; for ordinary mobile phone user terminals, the push information will be more concise and clear, focusing on the core content such as the location, scale and emergency response suggestions of the fire, so as to avoid information overload causing trouble to users.

[0054] In addition, the alarm push module 14 also has a powerful information backup and retransmission mechanism. When encountering abnormal situations such as network failure, server congestion or the user terminal temporarily unable to receive information, it will automatically back up the fire alarm information locally, and after the network returns to normal or the user terminal is detected to be back online, it will immediately start the retransmission program to ensure that important fire alarm information will not be lost or delayed due to technical problems, and to maximize the reliability and integrity of fire information transmission, providing a solid information guarantee foundation for timely response and effective disposal of fires.

[0055] Furthermore, in a possible implementation of this embodiment, as Figure 2 As shown, the system further includes: a data management module 15;

[0056] The data management module 15 is connected to the algorithm analysis module 11, the fire situation analysis module 12, and the map interaction module 13. The data management module 15 is used to store the various satellite data, the fire point identification results, the fire information, and map data.

[0057] Specifically, the data management module 15 is first responsible for properly storing the various satellite data from the algorithm analysis module 11. These satellite data come from a wide range of sources, covering a large amount of information collected by multiple satellites at different times and in different bands. It uses advanced distributed storage technology and high-capacity storage devices to classify and archive these data in an orderly manner. For example, index storage is performed according to multiple dimensions such as satellite name, data collection time, and band type to ensure that it can be quickly located and extracted when it is needed later. At the same time, in order to ensure the integrity and reliability of the data, the data management module 15 will also regularly verify and back up the satellite data to prevent data loss or damage, thereby providing a solid data foundation for the continuous and stable operation of the fire alarm system. After receiving the fire point identification results transmitted by the algorithm analysis module 11, the data management module 15 will also store them safely. These fire point identification results contain key information such as the location coordinates of suspected fire points, area estimates, temperature characteristics, and recognition confidence. The data management module 15 will store it in the form of a structured data table and establish an association relationship with the corresponding satellite data, so that in the subsequent fire analysis and tracing process, it is easy to obtain the original data and identification results for comparison and in-depth research. When the fire analysis module 12 generates fire information, the data management module 15 will immediately incorporate this important information into the storage system. The fire information covers the details of the fire, such as the exact location of the fire, the size of the fire, the development trend, the surrounding environmental impact assessment and other rich content. The data management module 15 adopts efficient data compression and encryption technology to save storage space to the maximum extent while ensuring data security, and through the establishment of an intelligent retrieval mechanism, users can quickly query the relevant information of a specific fire event, whether it is used for fire case analysis or subsequent fire fighting strategy formulation, it can provide strong data support. In addition, the data management module 15 also undertakes the important task of storing map data. These map data include basic geographic information data, such as topography, administrative divisions, road and water system vector data, and high-resolution satellite image map data. The data management module 15 will perform hierarchical storage and management according to the map's attributes such as scale, resolution and geographical area, ensuring that the appropriate map data source can be quickly provided when the map interaction module 13 needs to call it. At the same time, it will also update the map data in real time to reflect the changes in the geographical environment and the latest surveying and mapping results, provide accurate geographical background information for the fire alarm system, and enable the fire point location marking and fire situation analysis to be based on the latest and most accurate map data, thereby improving the reliability and practicality of the entire system.

[0058] Furthermore, in a possible implementation of this embodiment, as Figure 2 As shown, the map interaction module 13 includes: a navigation submodule 131 and a layer switching submodule 132;

[0059] The navigation submodule 131 is used to generate navigation information from the user terminal to the fire location based on the fire information, and send the navigation information to the user terminal;

[0060] The layer switching submodule 132 is used to respond to a layer switching instruction, obtain map data and switch the map layer corresponding to the layer switching instruction.

[0061] Specifically, in the fire alarm system, the navigation submodule 131 relies on the detailed fire information obtained from various links of the system. This information includes important data such as the precise latitude and longitude coordinates of the fire location, the surrounding geographical environment characteristics, and the direction of fire spread. After receiving the task instruction, the navigation submodule 131 quickly starts its built-in advanced path planning algorithm. This algorithm will first accurately locate the current location of the user terminal, and obtain the user's real-time location information by working in conjunction with the satellite positioning system or the user terminal's own positioning function. Subsequently, the road condition information is comprehensively considered. This information comes from the real-time traffic data platform or the locally stored road map database, including road congestion, traffic restrictions, road types (such as highways, ordinary roads, small roads, etc.) and road slopes, curvatures and other geographical attributes. At the same time, the algorithm will also combine the terrain data to determine whether there are natural obstacles such as mountains, rivers, forests, etc. that may affect the route. Based on the above comprehensive analysis, the navigation submodule 131 uses an intelligent computing model to calculate the optimal navigation path from the user terminal to the fire location. This path must not only ensure the shortest distance, but also take into account the safety and timeliness of driving. In the process of generating navigation information, the navigation submodule 131 will carefully organize the detailed description of the route, including key information such as the coordinates of each turning point, driving direction, estimated driving distance, and estimated arrival time. Then, the navigation submodule 131 will quickly and accurately send the generated navigation information to the user terminal through a variety of communication protocols, such as Bluetooth, Wi-Fi, or mobile network data transmission. On the user terminal, whether it is a smartphone, a car navigation device, or a professional fire rescue terminal, the navigation route can be displayed in the form of an intuitive map interface, and at the same time, with the voice prompt function, it provides users with clear driving instructions to ensure that users can quickly and safely reach the fire scene.

[0062] The layer switching submodule 132 is mainly responsible for meeting the diverse needs of users in the process of using maps. When the user issues a layer switching instruction, the layer switching submodule 132 will immediately start the response mechanism. It will first establish a connection with the map data storage unit in the system, and obtain specific map data corresponding to the instruction through efficient data retrieval and extraction technology. For example, if the user needs to view the topographic layer around the fire site, the layer switching submodule 132 will filter out map data containing topographic information such as contour lines, mountains, rivers, lakes, etc. from the storage unit, and load it to the map display interface. If the user switches to the road layer, it will extract detailed road network data, including road names, road grades, intersection locations and other information, and display them clearly. In the process of switching map layers, the layer switching submodule 132 will also ensure smooth transition and seamless connection between layers. It will automatically match and calibrate the scales and coordinate systems of different layers to avoid layer misalignment or unclear display. At the same time, in order to improve the user experience, the layer switching submodule 132 also supports users to customize the display style and properties of the layer, such as color, transparency, annotation font size, etc., so that users can obtain the map view that best suits the actual application scenario according to their own needs and preferences, thereby better assisting fire rescue and related decision-making work.

[0063] Furthermore, in a possible implementation of this embodiment, before generating the fire point identification result, the fire situation analysis module 12 is also used to obtain vegetation spectral data at the location where the multiple satellite data are located; and use the vegetation spectral data to compare the initial fire point identification result to determine whether a fire has occurred.

[0064] Specifically, in the precise operation process of the entire fire alarm system, in the pre-stage of generating the fire point identification result, the fire analysis module 12 will first use a series of advanced satellite data analysis technologies and geographic information positioning algorithms, which can accurately lock the geographic location information corresponding to a variety of satellite data. These satellite data sources are rich and diverse, including massive data collected by multiple satellites in different orbits with different observation capabilities. Each set of satellite data carries detailed information on a specific area, and the vegetation spectral data of these areas is the key element that the fire analysis module 12 urgently needs to obtain at this time.

[0065] In order to accurately obtain the vegetation spectral data of the corresponding position, the fire analysis module 12 searches the database and pre-stores the vegetation spectral characteristic information from different regions and covering various vegetation types. These data are accumulated through long-term field measurements, satellite remote sensing monitoring and a large number of experimental analyses. Whether it is the spectral curve of dense vegetation in tropical rainforest areas or the spectral change characteristics of temperate grassland vegetation in different seasons, they are all recorded in detail in this database. After successfully obtaining the required vegetation spectral data, the fire analysis module 12 immediately carries out comparative analysis with the initial identification results of the fire point. The initial identification results of the fire point are the regional information where the fire point may exist, which is obtained by the algorithm analysis module 11 through preliminary analysis of key indicators such as thermal radiation anomalies and temperature change trends in satellite data. However, this is only a preliminary judgment, and there is a certain uncertainty and risk of misjudgment. In the comparison process, the fire analysis module 12 will focus on the specific changes in vegetation spectral data in several key bands. In the visible light band, normal vegetation usually has specific absorption and reflection patterns. For example, green vegetation has a certain absorption effect on blue and red light, but reflects green light more strongly, giving it a green appearance. However, when a fire occurs, the color of the vegetation may change due to factors such as smoke and ash produced by the burning, as well as the dehydration and carbonization of the vegetation itself, resulting in obvious abnormalities in the reflection and absorption characteristics in the visible light band.

[0066] In the near-infrared band, healthy vegetation usually shows a high reflectivity due to its cell structure and internal moisture content. However, once attacked by fire, the cell structure of the vegetation is destroyed, and a large amount of water evaporates and dissipates, causing its reflectivity in the near-infrared band to drop sharply. At the same time, in the short-wave infrared band, changes in the chemical composition of vegetation caused by fire, such as the combustion and decomposition of lignin, cellulose, etc., will cause the reflectivity of vegetation in this band to increase significantly. The fire analysis module 12 compares the vegetation spectral data of the area involved in the initial identification result of the fire point with the spectral characteristics of normal vegetation in the corresponding area pre-stored in the database one by one. It will use complex data analysis models and statistical methods to calculate the degree of difference between the two and compare it with a pre-set threshold. If the degree of difference exceeds the threshold range, and these differences show a trend consistent with the typical vegetation spectral change law when a fire occurs in multiple key bands, then the fire analysis module 12 will make a judgment that a fire has occurred. On the contrary, if it is found through comparative analysis that although the vegetation spectral data has certain changes in some aspects, these changes do not exceed the reasonable threshold range, or do not match the typical spectral change characteristics caused by fire, then the fire analysis module 12 will re-evaluate the initial fire point identification results. This may involve re-examining the quality of satellite data, checking whether there are deviations in the calculation process of the algorithm analysis module 11, or further collecting other relevant auxiliary information, such as local meteorological data, topographic information, etc., to ensure that the final judgment result is accurate, thereby effectively avoiding unnecessary waste of resources and social panic caused by misjudgment, and providing solid protection for the reliability and stability of the entire fire alarm system.

[0067] Figure 3 A flowchart of a method for fire point identification provided in an embodiment of the present disclosure.

[0068] like Figure 3 As shown, the method comprises the following steps:

[0069] Step 201, acquiring multiple satellite data, and using a fire point identification adaptive algorithm to identify fire points on the multiple satellite data.

[0070] Step 202, determining whether a fire occurs based on the fire point identification result.

[0071] Step 203: after determining that a fire has occurred, generate fire information according to the fire point identification result.

[0072] Specifically, in steps 201 to 203, in order to realize the identification of fire points in different regions, it is necessary to establish stable and efficient connection links with multiple satellite data receiving platforms, which cover a variety of satellite systems with different observation advantages and characteristics. Through the preset communication protocol and data transmission interface, the system can accurately and quickly obtain a variety of satellite data from these satellite data sources. These data contain rich and diverse information, such as radiation intensity values, temperature distribution data, and geographic positioning information in different bands, which together constitute an important basis for subsequent fire point identification. After successfully acquiring a variety of satellite data, the fire point identification adaptive algorithm is immediately started. This algorithm is an advanced technical means developed through a large number of experimental studies and actual application scenario verification. It has strong adaptive capabilities and can automatically adjust analysis strategies and parameter settings according to the characteristics of different satellite data. For example, for satellite data with higher resolution, the algorithm will use its fine pixel information for more accurate local analysis; for satellite data with specific band advantages, such as data with higher sensitivity in the thermal infrared band, the algorithm will focus on the data changes in this band, and identify potential fire points by comparing the radiance and brightness temperature differences between the mid-infrared and far-infrared channels. At the same time, for the identification of sub-pixel fire points, the advanced Dozier algorithm is adopted, and combined with the Newton iteration method and the dichotomy method for accurate area and temperature estimation, thereby comprehensively improving the accuracy and reliability of fire point identification.

[0073] The fire point identification results are analyzed and judged comprehensively and in depth. First, key indicators such as the number of fire points, distribution range, and their intensity characteristics are counted. Then, these indicators are compared with a series of pre-set scientific and reasonable thresholds and judgment criteria. These thresholds and standards are determined based on a large amount of historical fire data, field monitoring experience, and professional fire science research results. For example, if multiple high-intensity fire points appear in a relatively small area, and the distribution of these fire points shows a certain aggregation trend, then it may indicate that a fire has occurred. At the same time, a comprehensive judgment will be made in combination with the topographic data and vegetation coverage information in the geographic information system (GIS). If the fire point is located in an area with dense vegetation, dry and flammable, the possibility of a fire will be greatly increased. Through this multi-dimensional and comprehensive analysis and judgment method, it is possible to accurately determine whether a fire has occurred, and effectively avoid the occurrence of misjudgment and missed judgment.

[0074] Generate comprehensive and valuable fire information based on the detailed information in the fire point identification results. First, the size of the fire will be accurately assessed, and a relatively accurate estimate of the burned area will be obtained by comprehensive calculation and analysis of the area, number and temperature data of the fire points. At the same time, the time series analysis technology is used to make a preliminary prediction of the development trend of the fire. For example, by comparing the changes in the fire point data at different time points, it can be judged whether the fire is in the initial slow development stage, the rapid spread stage or has stabilized. In addition, other relevant auxiliary information will be integrated, such as the geographical environment information around the fire site, including the location and distribution of nearby rivers, lakes, roads, residential areas, etc. This information is of vital guiding significance for subsequent fire fighting and rescue work. After completing the collection and integration of all information, the generated fire information will be sorted and packaged according to the predetermined format and standards so that it can be easily and quickly used by subsequent system modules or relevant personnel, providing strong support and guarantee for fire response and disposal.

[0075] As an implementable manner of the embodiment of the present disclosure, judging whether a fire occurs according to the fire point identification result includes:

[0076] Acquiring vegetation spectral data at locations where the multiple satellite data are located;

[0077] The vegetation spectral data is used to compare the initial fire point identification results to determine whether a fire has occurred.

[0078] Specifically, in the entire fire monitoring system, obtaining vegetation spectral data at the location of multiple satellite data is a crucial and complex task. First, the system will start a deep interaction program with the geographic information database based on the precise geographic location information contained in the acquired satellite data. Through the detailed analysis and matching of these location data, the corresponding geographic area can be quickly located.

[0079] Subsequently, the system will call a special vegetation spectral data acquisition module, which integrates multi-source data acquisition technologies, including satellite remote sensing inversion technology, field spectrometer measurement data, and historical vegetation spectral archives. Using satellite remote sensing inversion technology, the vegetation spectral characteristic information of the target area is extracted from massive satellite image data. This process involves complex spectral analysis algorithms and atmospheric correction models to ensure that the acquired spectral data can accurately reflect the true state of the vegetation. At the same time, for some key areas of concern or areas with data uncertainty, the system will dispatch a field measurement team to conduct field measurements using a high-precision spectrometer to obtain first-hand vegetation spectral data, and upload these data to the system database in a timely manner for updating and supplementation. In addition, the historical vegetation spectral archive also plays an important role. It stores vegetation spectral data from various regions in different seasons and under different climatic conditions over the years, providing a valuable reference for current data analysis.

[0080] After successfully acquiring the vegetation spectral data at the locations of various satellite data, the next step is to use these data to compare and analyze the initial identification results of the fire point, so as to accurately determine whether a fire has occurred. The system will compare the vegetation spectral data of the area involved in the initial identification results of the fire point with the pre-established standard vegetation spectral library one by one. In this standard vegetation spectral library, the spectral characteristic curves of different vegetation types in healthy growth states, different growth stages, and when disturbed by various natural and human factors are recorded in detail.

[0081] During the comparison process, we focused on the spectral changes in several key bands. For example, in the visible light band, normal vegetation usually exhibits specific absorption and reflection patterns. Green vegetation has a certain absorption effect on blue and red light, but a strong reflection of green light, which makes the vegetation appear green to the human eye. However, when a fire occurs, the vegetation may be covered with smoke and ash produced by the combustion, or its own dehydration and carbonization, resulting in significant changes in the reflection and absorption characteristics in the visible light band.

[0082] In the near-infrared band, healthy vegetation generally shows a high reflectivity due to its cell structure and internal water content. However, once it is attacked by a fire, the cell structure of the vegetation is destroyed and a large amount of water is lost, and its reflectivity in the near-infrared band will drop sharply. Similarly, in the short-wave infrared band, changes in the chemical composition of vegetation caused by fire, such as the combustion and decomposition of lignin and cellulose, will significantly increase the reflectivity of vegetation in this band.

[0083] The system uses advanced data analysis algorithms and statistical models to calculate the degree of difference between the vegetation spectral data in the fire area and the standard spectral library data in each key band, and compares it with the pre-set threshold. If the degree of difference in multiple key bands exceeds the threshold, and these differences show a trend consistent with the law of vegetation spectral changes when the fire occurs, then it can be determined with a certainty that a fire has occurred in the area. On the contrary, if the degree of difference is within the threshold range, or although there are certain changes, they do not match the typical spectral change characteristics caused by the fire, then the initial identification results of the fire point need to be re-evaluated and further analyzed. It may be necessary to supplement more relevant data or adopt other auxiliary judgment methods to ensure the accuracy and reliability of the final judgment results, and avoid unnecessary waste of resources and social panic caused by misjudgment.

[0084] It should be noted that the embodiments of the present disclosure may include multiple steps. For the convenience of description, these steps are numbered, but these numbers do not limit the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of the present disclosure do not limit this.

[0085] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0086] Figure 4 A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0087] like Figure 4As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 302 or a computer program loaded from a storage unit 308 to a RAM (Random Access Memory) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0088] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0089] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as a method for fire point identification. For example, in some embodiments, the method for fire point identification may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute the aforementioned fire point identification method in any other appropriate manner (for example, by means of firmware).

[0090] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0092] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0093] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0094] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0095] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0096] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0097] The various numerical numbers such as first and second involved in the present disclosure are only for the convenience of description and are not used to limit the scope of the embodiments of the present disclosure, but also indicate the order of precedence.

[0098] At least one in the present disclosure may also be described as one or more, and a plurality may be two, three, four or more, which is not limited in the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no order of precedence or size between the technical features described by the "first", "second", "third", "A", "B", "C" and "D".

[0099] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0100] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A fire alarm system, characterized in that: The system includes: an algorithm analysis module, a fire situation analysis module, and a map interaction module; The algorithm analysis module is connected to the fire situation analysis module, and is used to obtain a variety of satellite data, use a fire point recognition adaptive algorithm to perform fire point recognition on the multiple satellite data, and send the fire point recognition result to the fire situation analysis module; The fire situation analysis module is connected to the map interaction module, and is used to determine whether a fire occurs according to the fire point identification result; after determining that a fire occurs, fire information is generated according to the fire point identification result, and the fire information is sent to the map interaction module; The map interaction module is used to mark the monitored fire point location according to the fire location information in the fire information, and display the fire related information in the fire information.

2. The system according to claim 1, characterized in that The system also includes: an alarm push module; The alarm push module is connected to the fire situation analysis module, and is used to respond to the fire information sent by the fire situation analysis module, generate fire alarm information according to the fire information; and send the fire alarm information to the user terminal.

3. The system according to claim 1, characterized in that The system further comprises: a data management module; The data management module is connected to the algorithm analysis module, the fire situation analysis module, and the map interaction module, and the data management module is used to store the various satellite data, the fire point identification results, the fire information, and map data.

4. The system according to claim 1, characterized in that The map interaction module includes: a navigation submodule and a layer switching submodule; The navigation submodule is used to generate navigation information from the user terminal to the fire location based on the fire information, and send the navigation information to the user terminal; The layer switching submodule is used to respond to a layer switching instruction, obtain map data and switch the map layer corresponding to the layer switching instruction.

5. The system according to claim 1, characterized in that Before generating the fire point identification result, the fire situation analysis module is further used to obtain vegetation spectral data at the location where the multiple satellite data are located; and use the vegetation spectral data to compare the initial fire point identification result to determine whether a fire has occurred.

6. A method for fire point identification, characterized in that: The method comprises: Acquire multiple satellite data, and identify fire points using a fire point identification adaptive algorithm; Determining whether a fire occurs according to the fire point identification result; After determining that a fire has occurred, fire information is generated according to the fire point identification result.

7. The method according to claim 6, characterized in that The determining whether a fire occurs according to the fire point identification result includes: Acquiring vegetation spectral data at locations where the multiple satellite data are located; The vegetation spectral data is used to compare the initial fire point identification results to determine whether a fire has occurred.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 6 to 7.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 6-7.

10. A computer program product, characterized in that A computer program is included which, when executed by a processor, implements the method according to any one of claims 6-7.

Images