Intelligent fire-fighting data analysis and management system

Abstract

The invention, which relates to the technical field of fire-fighting data management, discloses an intelligent fire-fighting data analysis management system comprising a fire-fighting data monitoring module, a prediction and simulation module, a linkage response module and an information interaction module. The information interaction module is used for connecting a fire-fighting department terminal, a regional management department terminal, a meteorological department terminal and a vehicle information management department terminal; and the prediction and simulation module comprises a regional evaluation unit, a fire behavior simulation unit, a dynamic prediction unit and a decision-making unit. According to the invention, the prediction and simulation module is arranged to predict a fire high-frequency occurrence area and a fire occurrence probability, so that a high-risk area can be accurately found in advance and fire-fighting early warning can be carried out, the occurrence of a fire phenomenon can be avoided, the dynamic prediction unit predicts a fire behavior change state and a fire spreading path, and the decision-making unit accurately outputs an emergency response decision. The fire-fighting effect can be improved, the loss caused by fire disasters can be effectively reduced, and the fire-fighting safety can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fire protection data management, and in particular to an intelligent fire protection data analysis and management system. Background Art

[0002] Fire data analysis and management system refers to a system that uses modern information technology, including big data analysis and artificial intelligence, to efficiently manage fire resources. The main purpose of the system is to reduce the possibility of fire through real-time monitoring, rapid response and intelligent early warning. However, traditional fire data analysis and management systems are difficult to accurately assess the probability of fire and fire dynamics, and thus difficult to output appropriate fire decisions and fire extinguishing instructions, resulting in reduced fire extinguishing effectiveness of fire departments and increased property and safety losses.

[0003] The defects of the existing fire data analysis and management system are:

[0004] 1. Patent document CN112370710B mainly considers how to solve the problem of selecting fire stations when a fire occurs, but does not consider how to conduct risk assessment in urban areas and predict the fire status or spread path;

[0005] 2. Patent document CN115147928B collects fire-fighting action data during the actual fire-fighting process to evaluate the safety of fire-fighting. However, the above patent does not consider the impact of actual fire-fighting training on the safety of firefighters themselves.

[0006] 3. Patent document CN117745083B mainly considers how to select appropriate driving routes for fire trucks through big data, but does not consider how to coordinate multiple departments to manage the fire passages at the fire point. Summary of the invention

[0007] The purpose of the present invention is to provide an intelligent fire data analysis and management system to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: an intelligent fire data analysis and management system, comprising a fire data monitoring module, a prediction and simulation module, a linkage response module and an information interaction module, the fire data analysis and management system is applied to the fire department end, and the information interaction module is used to connect the fire department end, the regional management department end, the meteorological department end and the vehicle information management department end;

[0009] The fire data monitoring module is used to acquire, process and store historical fire data information, which includes historical fire data, historical environmental data, historical climate data and historical alarm data;

[0010] The prediction and simulation module includes a regional assessment unit, a fire simulation unit, a dynamic prediction unit and a decision-making unit;

[0011] The regional assessment unit is used to assess the probability of fire occurrence in each area of ​​the city where the fire station is located, divide the risk area according to the assessed probability, and transmit the calibration results to the decision-making unit;

[0012] The fire simulation unit is used to establish a fire simulation model, generate multiple types of fire scenes, and use multiple types of fire scenes to conduct firefighting training for firefighters;

[0013] The dynamic prediction unit is used to establish a fire prediction model based on historical fire data information. The fire department obtains real-time fire data and predicts the real-time fire change dynamics and real-time fire spread path based on the fire prediction model, and transmits the prediction results to the decision-making unit;

[0014] The decision unit is used to receive the output results of the regional assessment unit, the fire model unit and the dynamic prediction unit, analyze the output results and obtain the fire instructions and output the fire instructions to the linkage response unit.

[0015] Preferably, the historical fire data includes historical fire locations, historical fire occurrence times, historical fire causes, fire affected areas, and historical fire extinguishing process videos. Fires are classified according to historical fire locations, and the fire classification categories are building area fires, forest area fires, and other fires.

[0016] The historical environmental data includes the building types, building structure characteristics and hazard source locations in the historical fire locations of the building area fires. The historical environmental data also includes the forest area topography and forest area vegetation distribution status for the forest area fires. The historical environmental data is obtained by the fire department from the regional management department.

[0017] Historical climate data includes weather conditions, temperature and humidity information, wind direction information, and wind force information corresponding to historical fire locations and historical fire occurrence times. The fire department obtains historical climate data from the meteorological department.

[0018] Historical fire response data includes the number of people who responded to fire calls, the number of vehicles that responded to fire calls, the number of equipment used in fire response calls, the duration of fire fighting, and the mode of fire fighting.

[0019] Preferably, the fire data monitoring module also includes using big data processing technology to clean, analyze and store historical fire data information, and for the storage of historical fire data information, using distributed storage technology to store historical fire data.

[0020] Preferably, the area assessment unit assesses the probability of fire occurrence and divides the risk area into the following steps:

[0021] a1. Divide the city where the fire department is located into large areas according to its own zoning map, and count the number of historical fires, the time points of fire occurrence, and the time intervals between adjacent fires in each large area based on historical fire data. Based on the statistical results, a fire frequency model is established through a machine learning algorithm;

[0022] a2. Re-divide the large area according to the density of the building groups in the large area, divide each large area into multiple groups of small areas, obtain the key feature data in each small area, and use the key feature data combined with the fire frequency model of the corresponding large area to use logistic regression, decision tree, neural network algorithm to establish a fire probability prediction model, where the key feature data includes building category , building density , Emergency fire-fighting equipment deployment density , population density , Climate conditions and average building density in large areas , the calculation method of the average building density in a large area is as follows:

[0023] =

[0024] a3. The regional fire occurrence probability P corresponding to the small area is calculated by the fire occurrence probability prediction model, and the calculation method of P is as follows:

[0025]

[0026] in, For small area The probability of fire occurrence, For small area The fire frequency occurrence model factor corresponding to the large area, , , , , Building categories , building density , Emergency fire-fighting equipment deployment density , population density , Climate conditions The corresponding weight coefficient;

[0027] a4. Divide the fire risk areas according to the calculation results of regional fire probability. The division method is as follows:

[0028]

[0029] And transmit the fire risk area division results to the decision-making unit.

[0030] Preferably, the fire simulation unit includes a scene generation subunit and a recognition training unit. The fire simulation unit establishes a fire simulation model based on historical fire data and uses 3D modeling technology to generate multiple types of fire scenes. The recognition training unit combines VR technology to conduct firefighting training for firefighters in multiple types of fire scenes. During the firefighting training, the firefighting operations of the firefighters are detected by wearable sensor equipment, and a motion recognition algorithm is used to evaluate the accuracy of the firefighting actions and the effectiveness of the firefighting effects.

[0031] Preferably, the dynamic prediction unit comprises the following steps:

[0032] b1. Analyze the fire change status based on historical fire data information, establish a fire prediction model based on the combustion characteristics of objects around the historical fire location, and train the fire prediction model through multi-type fire scene data and fire extinguishing training data transmitted by the fire simulation unit;

[0033] b2. The fire department obtains real-time fire data when a fire occurs, and obtains the fire prediction status through a fire prediction model based on the real-time fire data. The fire prediction status acquisition includes the following steps:

[0034] b21. Forecast the fire status before the fire department dispatches firefighters and fire trucks: obtain the fire location information, fire type information, and fire status information at the time of alarm from the alarm personnel, obtain the building type and building structure information corresponding to the fire location information from the regional management department, and obtain the climate information corresponding to the fire location information from the meteorological department. The climate information includes the current climate information and future climate information at the alarm time. The current climate information and future climate information at the alarm time include wind speed and humidity, and the future climate information takes the alarm time as the starting point. The update is performed at a time interval, and the fire department transmits the fire extinguishing completion information to the meteorological department after the fire extinguishing is completed, and the meteorological department stops transmitting the future climate information to the fire department;

[0035] The fire prediction model is used to predict the dynamic changes of the fire intensity through the acquired information, and the maximum fire intensity state of the ignition point is obtained from the prediction results, and the maximum fire intensity state is transmitted to the decision-making unit;

[0036] b22. Predict the fire status when firefighters and fire trucks arrive at the fire point: obtain the fire scene video information when they arrive at the fire point through the video equipment carried by firefighters and fire trucks, predict the fire spread path in combination with the building type and building structure information and future climate information, and transmit the fire spread path prediction result to the decision-making unit;

[0037] b23. Predict the fire status during the firefighting process by firefighters: During the firefighting process, the fire scene video information, the distance between the flame position and the firefighters, the firefighters' moving distance information and time point information are obtained through the firefighters' portable video equipment and sensor equipment;

[0038] The actual fire spread rate and spread time during the firefighting process are calculated based on the information obtained. The actual fire spread rate during the firefighting process is calculated as follows:

[0039]

[0040] The spread time is calculated as follows:

[0041] in, The actual rate of fire spread during firefighting by firefighters. is the distance that firefighters move at adjacent time points, is the distance between the flame position and the firefighter at the current time point, is the distance between the flame position and the firefighter at the previous time point, To spread time.

[0042] Preferably, the decision unit constructs a fire extinguishing prediction model based on historical fire data, historical climate data and historical alarm data, and the fire extinguishing prediction model is used for the fire department to output fire extinguishing instructions, wherein the output of the fire extinguishing instructions includes the following steps:

[0043] The fire extinguishing prediction model analyzes the dynamic prediction results of fire intensity changes and the maximum fire intensity state prediction results transmitted by the dynamic prediction unit, and outputs the dispatch instructions for firefighters and fire trucks, and the instructions for the use of fire equipment;

[0044] The fire-fighting prediction model is used to analyze the fire spread path prediction results and output instructions for fire-fighting plans, personnel evacuation plans, and personnel rescue plans;

[0045] The fire extinguishing prediction model is used to analyze the actual fire spread rate and spread time, determine the firefighters' own safety status during the fire extinguishing process, and output firefighters' safety action command instructions.

[0046] Preferably, the linkage response module includes a linkage warning unit, a decision response unit and a collaborative implementation unit;

[0047] The linkage warning unit connects the fire department end and the regional management department end through the information interaction module, and transmits the fire risk area division result to the regional management department end. The regional management department end issues a warning prompt to the area under its jurisdiction based on the fire risk division result of the area under its jurisdiction.

[0048] Preferably, the decision response unit receives the fire extinguishing instruction from the decision unit, so that the fire department dispatches and directs the fire brigade to perform fire extinguishing actions according to the fire extinguishing instruction. The fire extinguishing brigade includes firefighters and fire trucks.

[0049] Preferably, the collaborative implementation unit is used to obtain surveillance videos of the fire location and the surrounding area of ​​the fire location through the regional management department end when a fire occurs in a residential area, and transmit them to the fire department end, query whether there are any obstacles on the rescue channel around the fire location based on the surveillance video, and then analyze the status of the rescue channel. When a vehicle is parked on the rescue channel, it is determined that the rescue channel is blocked, and then the license plate information of the parked vehicle is obtained based on the surveillance video, and the license plate management information is transmitted to the vehicle information management department end, the personnel information and contact information corresponding to the license plate information are obtained, and the person to whom the vehicle belongs is notified to move the vehicle in time.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The present invention sets a prediction and simulation module, including a regional simulation unit and a dynamic prediction unit, and evaluates the urban area in combination with multiple influencing factors to predict the high-frequency fire occurrence areas and the future fire occurrence probability of the corresponding areas, so as to facilitate the accurate and early discovery of high-risk areas, achieve the purpose of fire warning for risk areas, and help avoid the occurrence of fire phenomena. The dynamic prediction unit predicts the change state of fire and the fire spread path, and the decision unit accurately outputs emergency response decisions, which is convenient for the reasonable allocation of fire-fighting resources, the selection of reasonable fire-fighting plans, personnel evacuation plans and personnel rescue plans, thereby improving the fire-fighting effect, and can effectively reduce the property losses caused by fire and improve the safety of personnel rescue and firefighters themselves.

[0052] 2. The present invention constructs a fire extinguishing prediction model by setting up a decision unit, which is used for the output results of the regional simulation unit and the dynamic prediction unit, and outputs fire extinguishing instructions that match the prediction results. The fire department is commanded to perform fire extinguishing operations according to the fire instructions, thereby improving the rational utilization of fire fighting resources and the safety during the fire extinguishing process.

[0053] 3. The present invention provides a fire simulation unit to simulate various types of fire scenes of different difficulty levels for firefighting training of firefighters, replaces firefighters in real fire scene training, avoids firefighters from being injured, improves the firefighters' operational capabilities when extinguishing fires at different fire scenes, and improves the personal safety of firefighters and the effectiveness of firefighting to a certain extent. By providing a motion recognition unit to evaluate the accuracy of motions during firefighting training and to provide firefighting reminders, it is beneficial to improve the firefighting operational skills of firefighters.

[0054] 4. The present invention realizes the synergy between multiple departments such as the fire department end, the regional management department end, and the vehicle information management department end by setting up a linkage response module, including a linkage warning module and a collaborative implementation module, realizes resource sharing and data intercommunication among multiple departments, ensures the effective use of fire resources in an emergency, and thus avoids rescue delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is the overall system structure diagram of the present invention;

[0056] Figure 2 It is a diagram of the prediction and simulation module structure of the present invention;

[0057] Figure 3 It is a diagram of the linkage response module of the present invention;

[0058] Figure 4 A diagram of a regional assessment unit method of the present invention;

[0059] Figure 5 This is a diagram of the dynamic prediction unit method of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] See also Figure 1 , an embodiment provided by the present invention: an intelligent fire data analysis and management system, including a fire data monitoring module, a prediction and simulation module, a linkage response module and an information interaction module, the fire data analysis and management system is applied to the fire department end, and the information interaction module is used to connect the fire department end, the regional management department end, the meteorological department end and the vehicle information management department end;

[0062] The fire data monitoring module is used to obtain, process and store historical fire data information, which includes historical fire data, historical environmental data, historical climate data and historical alarm data;

[0063] Historical fire data includes historical fire locations, historical fire occurrence times, historical fire causes, fire affected areas, and historical fire extinguishing process videos. Fires are classified according to historical fire locations, and the fire classification categories are building area fires, forest area fires, and other fires.

[0064] The historical environmental data includes the building types, building structure characteristics and hazard source locations in the historical fire locations of the building area fires. The historical environmental data also includes the forest area topography and forest area vegetation distribution status for the forest area fires. The historical environmental data is obtained by the fire department from the regional management department.

[0065] Historical climate data includes weather conditions, temperature and humidity information, wind direction information, and wind force information corresponding to historical fire locations and historical fire occurrence times. The fire department obtains historical climate data from the meteorological department.

[0066] Historical fire response data include the number of people who responded to fire calls, the number of vehicles that responded to fire calls, the number of equipment used in fire response calls, the duration of fire fighting, and the mode of fire fighting. The fire data monitoring module also includes the use of big data processing technology to clean, analyze and store historical fire data information, and for the storage of historical fire data information, uses distributed storage technology to store historical fire data.

[0067] Furthermore, the use of distributed storage technology in the fire data monitoring module to store historical fire data is conducive to improving the scalability and high availability of the data monitoring module, thereby facilitating the storage of more historical fire data. The historical fire data provides strong data support for the establishment of models in the prediction and simulation module and the linkage response module, thereby improving the reliability of the intelligent fire data analysis and management system. At the same time, distributed data storage is conducive to improving the convenience of later data queries.

[0068] See also Figure 2 and Figure 4 , an embodiment provided by the present invention: an intelligent fire data analysis and management system, including a prediction and simulation module, the prediction and simulation module includes a regional assessment unit, a fire simulation unit, a dynamic prediction unit and a decision unit;

[0069] The regional assessment unit is used to assess the probability of fire occurrence in each area of ​​the city where the fire station is located, divide the risk area according to the assessed probability, and transmit the calibration results to the decision-making unit;

[0070] The regional assessment unit evaluates the probability of fire and divides the risk area into the following steps:

[0071] a1. Divide the city where the fire department is located into large areas according to its own zoning map, and count the number of historical fires, the time points of fire occurrence, and the time intervals between adjacent fires in each large area based on historical fire data. Based on the statistical results, a fire frequency model is established through a machine learning algorithm;

[0072] a2. Re-divide the large area according to the density of the building groups in the large area, and divide each large area into multiple groups of small areas, where each group of small areas has a corresponding regional management department, and multiple groups of regional management parts belong to the regional management department of the city where the fire department is located. Obtain the key feature data in each small area, and use the key feature data combined with the fire frequency occurrence model of the corresponding large area to use logistic regression\decision tree\neural network algorithm to establish a fire probability prediction model, where the key feature data includes building category , building density , Emergency fire-fighting equipment deployment density , population density , Climate conditions and average building density in large areas , the calculation method of the average building density in a large area is as follows:

[0073] =

[0074] a3. The regional fire occurrence probability P corresponding to the small area is calculated by the fire occurrence probability prediction model, and the calculation method of P is as follows:

[0075]

[0076] in, For small area The probability of fire occurrence, For small area The fire frequency occurrence model factor corresponding to the large area, , , , , Building categories , building density , Emergency fire-fighting equipment deployment density , population density , Climate conditions The corresponding weight coefficient;

[0077] a4. Divide the fire risk areas according to the calculation results of regional fire probability. The division method is as follows:

[0078]

[0079] The fire risk area division result is transmitted to the decision-making unit. The decision-making unit receives the fire risk area division result. The decision-making unit sets different warning methods according to the fire risk area division result. For high-risk fire occurrence areas, a prompt warning method and a supervision warning method are set. The supervision warning method includes regularly conducting fire inspections on high-risk fire occurrence areas through the fire department end, and setting prompt warning methods for medium-risk fire occurrence areas and low-risk fire occurrence areas;

[0080] The linkage warning unit connects the fire department and the regional management department through the information interaction module, and transmits the fire risk area division results to the regional management department. The regional management department issues early warning prompts to the area under its jurisdiction based on the fire risk division results and corresponding early warning methods. For high-risk fire areas, the regional management department implements corresponding prompt warning methods and building warning methods, such as sending fire warnings and safety fire reminders to residents in the area under its jurisdiction, and issuing prompts to the buildings in the area under its jurisdiction through broadcasting and smart screens. For medium-risk fire areas and low-risk fire areas, the regional management department implements corresponding prompt warning methods to prompt people and buildings in the area under its jurisdiction.

[0081] Furthermore, by setting up regional assessment units to assess the risk areas of the city, predict the risk of occurrence in small areas, and predict the high-frequency fire occurrence areas and the future probability of fire in the corresponding areas, the purpose of discovering high-risk areas in advance and issuing fire warnings for risk areas can be achieved, which is conducive to avoiding the occurrence of fire and reducing fire losses.

[0082] See also Figure 1 , an embodiment provided by the present invention: an intelligent fire data analysis and management system, comprising a fire simulation unit, the fire simulation unit is used to establish a fire simulation model, generate multiple types of fire scenes, and use the multiple types of fire scenes to conduct fire fighting training for firefighters;

[0083] The fire simulation unit includes a scene generation subunit and an identification training unit. The fire simulation unit establishes a fire simulation model based on historical fire data and uses 3D modeling technology to generate multiple types of fire scenes, including building fires, forest fires, electrical fires and other types of fire scenes. It also generates fire scenes of different difficulty levels by adjusting various influencing factors such as climate impact, building structure, building type, terrain and vegetation growth status. The identification training unit combines VR technology to conduct firefighting training for firefighters in multiple types of fire scenes. During the firefighting training, the firefighting operations of firefighters are detected through wearable sensor equipment, and motion recognition algorithms are used to evaluate the accuracy of firefighting actions and the effectiveness of firefighting effects. During the firefighting training, firefighting reminders can be given to firefighters based on the firefighting effects.

[0084] Furthermore, by setting up virtual scenes, firefighters can be replaced in real fire scene training to avoid injuries to firefighters. The fire simulation unit simulates multiple types of fire scenes with different difficulties for firefighting training of firefighters, which improves the operational capabilities of firefighters when extinguishing fires at different fire scenes, and to a certain extent improves the personal safety of firefighters and the effectiveness of firefighting. By setting up a motion recognition unit to evaluate the accuracy of actions during firefighting training and provide fire warnings, it is beneficial to improve the firefighting operational skills of firefighters.

[0085] See also Figure 1 , Figure 2 and Figure 5 , an embodiment provided by the present invention: an intelligent fire data analysis and management system, including a dynamic prediction unit, the dynamic prediction unit is used to establish a fire prediction model based on historical fire data information, the fire department obtains real-time fire data, and predicts the real-time fire change dynamics and the real-time fire spread path based on the fire prediction model, and transmits the prediction result to the decision unit;

[0086] The dynamic prediction unit includes the following steps:

[0087] b1. Analyze the fire change status based on historical fire data information, establish a fire prediction model based on the combustion characteristics of objects around the historical fire location, and train the fire prediction model through multi-type fire scene data and fire extinguishing training data transmitted by the fire simulation unit;

[0088] b2. The fire department obtains real-time fire data when a fire occurs, and obtains the fire prediction status through a fire prediction model based on the real-time fire data. The fire prediction status acquisition includes the following steps:

[0089] b21. Forecast the fire status before the fire department dispatches firefighters and fire trucks: The alarm personnel provide the fire location information, fire type information, and fire status information at the time of alarm, where the fire status information at the time of alarm includes whether there is a visible flame and the size of the fire; obtain the building type and building structure information corresponding to the fire location information from the regional management department; obtain the climate information corresponding to the fire location information from the meteorological department, and the climate information includes the current climate information and future climate information at the alarm time. The current climate information and future climate information at the alarm time include wind speed and humidity, and the future climate information takes the alarm time as the starting point. The update is performed at a time interval, and the fire department transmits the fire extinguishing completion information to the meteorological department after the fire extinguishing is completed, and the meteorological department stops transmitting the future climate information to the fire department;

[0090] The fire prediction model is used to predict the dynamic changes of the fire intensity through the acquired information, and the maximum fire intensity state of the ignition point is obtained from the prediction results, and the maximum fire intensity state is transmitted to the decision-making unit;

[0091] b22. Predict the fire status when firefighters and fire trucks arrive at the fire point: obtain the fire scene video information when they arrive at the fire point through the video equipment carried by firefighters and fire trucks, predict the fire spread path in combination with the building type and building structure information and future climate information, and transmit the fire spread path prediction result to the decision-making unit;

[0092] b23. Predict the fire status during the firefighting process by firefighters: During the firefighting process, the fire scene video information, the distance between the flame position and the firefighters, the firefighters' moving distance information and time point information are obtained through the firefighters' portable video equipment and sensor equipment;

[0093] The actual fire spread rate and spread time during the firefighting process are calculated based on the information obtained. The actual fire spread rate during the firefighting process is calculated as follows:

[0094]

[0095] The spread time is calculated as follows:

[0096] in, The actual rate of fire spread during firefighting by firefighters. is the distance that firefighters move at adjacent time points, is the distance between the flame position and the firefighter at the current time point, is the distance between the flame position and the firefighter at the previous time point, For the spreading time;

[0097] The decision-making unit builds a fire extinguishing prediction model based on historical fire data, historical climate data, and historical alarm data. The fire extinguishing prediction model is used by the fire department to output fire extinguishing instructions, where the output of fire extinguishing instructions includes the following steps:

[0098] The fire extinguishing prediction model analyzes the dynamic prediction results of fire changes and the maximum fire state prediction results transmitted by the dynamic prediction unit, and outputs the dispatch instructions for firefighters and fire trucks, and the use instructions for firefighting equipment, including the number of firefighters dispatched ;

[0099] The fire-fighting prediction model is used to analyze the fire spread path prediction results and output instructions for fire-fighting plans, personnel evacuation plans, and personnel rescue plans;

[0100] The fire extinguishing prediction model is used to analyze the actual fire spread rate and spread time, determine the firefighters' own safety status during the fire extinguishing process, and calculate the safe time for firefighters to extinguish the fire at a certain location by combining the flame position, firefighter position, flame spread speed and flame spread time. The safe time is the difference between the flame spread time and the firefighters' predicted escape time. Before the safe time arrives, the fire department sends a retreat order to the firefighters to ensure their own safety.

[0101] The decision response unit receives the fire-fighting instruction from the decision unit, so that the fire department dispatches and directs the fire brigade to perform fire-fighting actions according to the fire-fighting instruction. The fire-fighting brigade includes firefighters and fire trucks.

[0102] Furthermore, the dynamic prediction unit predicts the dynamic changes of fire and the path of fire spread based on real-time fire data and environmental data, which makes it easier for the decision-making unit to accurately output emergency response decisions and reasonably allocate fire-fighting resources to avoid insufficient resources for effective fire-fighting or excessive use of resources. It improves the fire-fighting efficiency to a certain extent and facilitates the selection of reasonable fire-fighting plans, personnel evacuation plans and personnel rescue plans, which can effectively reduce the property losses caused by fire and improve the safety of personnel rescue and firefighters themselves.

[0103] See also Figure 1 and Figure 3The present invention provides an embodiment: an intelligent fire data analysis and management system, including a linkage response module, the linkage response module includes a linkage warning unit, a decision response unit and a collaborative implementation unit, the collaborative implementation unit is used to obtain the fire location and the monitoring video around the fire location through the regional management department end when a fire occurs in a residential area, and transmit it to the fire department end, query whether there are obstacles on the rescue channel around the fire location according to the monitoring video, and then analyze the state of the rescue channel. When a vehicle is parked on the rescue channel, it is determined that the rescue channel is blocked, and then the license plate information of the parked vehicle is obtained according to the monitoring video, and the license plate management information is transmitted to the vehicle information management department end, and the personnel information and contact information corresponding to the license plate information are obtained, and the personnel to whom the vehicle belongs are notified to move the vehicle in time to avoid delays in fire rescue, thereby reducing the impact range of the fire and reducing the fire loss.

[0104] Furthermore, by setting up a linkage response module, when a fire occurs, the management terminals of multiple departments can work together to ensure the smooth progress of the fire-fighting operation.

[0105] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent fire data analysis and management system, characterized by: It includes a fire data monitoring module, a prediction and simulation module, a linkage response module and an information interaction module. The fire data analysis and management system is applied to the fire department end. The information interaction module is used to connect the fire department end, the regional management department end, the meteorological department end and the vehicle information management department end; The fire data monitoring module is used to acquire, process and store historical fire data information, which includes historical fire data, historical environmental data, historical climate data and historical alarm data; The prediction and simulation module includes a regional assessment unit, a fire simulation unit, a dynamic prediction unit and a decision-making unit; The regional assessment unit is used to assess the probability of fire occurrence in each area of ​​the city where the fire station is located, divide the risk area according to the assessed probability, and transmit the calibration results to the decision-making unit; The fire simulation unit is used to establish a fire simulation model, generate multiple types of fire scenes, and use multiple types of fire scenes to conduct firefighting training for firefighters; The dynamic prediction unit is used to establish a fire prediction model based on historical fire data information. The fire department obtains real-time fire data and predicts the real-time fire change dynamics and real-time fire spread path based on the fire prediction model, and transmits the prediction results to the decision-making unit; The decision unit is used to receive the output results of the regional assessment unit, the fire model unit and the dynamic prediction unit, analyze the output results and obtain the fire instructions and output the fire instructions to the linkage response unit.

2. The intelligent fire data analysis and management system according to claim 1, characterized in that: The historical fire data includes the locations of historical fires, the time of historical fires, the causes of historical fires, the areas affected by fires, and videos of historical fire extinguishing processes. Fires are classified according to the locations of historical fires, and the fire classification categories are building area fires, forest area fires, and other fires. The historical environmental data includes the building types, building structure characteristics and hazard source locations in the historical fire locations of the building area fires. The historical environmental data also includes the forest area topography and forest area vegetation distribution status for the forest area fires. The historical environmental data is obtained by the fire department from the regional management department. Historical climate data includes weather conditions, temperature and humidity information, wind direction information, and wind force information corresponding to historical fire locations and historical fire occurrence times. The fire department obtains historical climate data from the meteorological department. Historical fire response data includes the number of people who responded to fire calls, the number of vehicles that responded to fire calls, the number of equipment used in fire response calls, the duration of fire fighting, and the mode of fire fighting.

3. The intelligent fire data analysis and management system according to claim 1, characterized in that: The fire data monitoring module also includes using big data processing technology to clean, analyze and store historical fire data information, and for the storage of historical fire data information, using distributed storage technology to store historical fire data.

4. The intelligent fire data analysis and management system according to claim 1, characterized in that: The area assessment unit assesses the probability of fire occurrence and divides the risk area into the following steps: a1. Divide the city where the fire department is located into large areas according to its own zoning map, and count the number of historical fires, the time points of fire occurrence, and the time intervals between adjacent fires in each large area based on historical fire data. Based on the statistical results, a fire frequency model is established through a machine learning algorithm; a2. Re-divide the large area according to the density of the building groups in the large area, divide each large area into multiple groups of small areas, obtain the key feature data in each small area, and use the key feature data combined with the fire frequency model of the corresponding large area to use logistic regression, decision tree, neural network algorithm to establish a fire probability prediction model, where the key feature data includes building category , building density , Emergency fire-fighting equipment deployment density , population density , Climate conditions and average building density in large areas , the calculation method of the average building density in a large area is as follows: = a3. The regional fire occurrence probability P corresponding to the small area is calculated by the fire occurrence probability prediction model, and the calculation method of P is as follows: in, For small area The probability of fire occurrence, For small area The fire frequency occurrence model factor corresponding to the large area, , , , , Building categories , building density , Emergency fire-fighting equipment deployment density , population density , Climate conditions The corresponding weight coefficient; a4. Divide the fire risk areas according to the calculation results of regional fire probability. The division method is as follows: And transmit the fire risk area division results to the decision-making unit.

5. The intelligent fire data analysis and management system according to claim 2, characterized in that: The fire simulation unit includes a scene generation subunit and an identification training unit. The fire simulation unit establishes a fire simulation model according to historical fire data and generates multiple types of fire scenes by using 3D modeling technology. The identification training unit combines VR technology to conduct firefighting training for firefighters in multiple types of fire scenes. During the firefighting training, the firefighting operations of the firefighters are detected by wearable sensor equipment, and the action recognition algorithm is used to evaluate the accuracy of the firefighting actions and the effectiveness of the firefighting effects.

6. The intelligent fire data analysis and management system according to claim 5, characterized in that: The dynamic prediction unit comprises the following steps: b1. Analyze the fire change status based on historical fire data information, establish a fire prediction model based on the combustion characteristics of objects around the historical fire location, and train the fire prediction model through multi-type fire scene data and fire extinguishing training data transmitted by the fire simulation unit; b2. The fire department obtains real-time fire data when a fire occurs, and obtains the fire prediction status through the fire prediction model based on the real-time fire data. The fire prediction status acquisition includes the following steps: b21. Forecast the fire status before the fire department dispatches firefighters and fire trucks: obtain the fire location information, fire type information, and fire status information at the time of alarm from the alarm personnel, obtain the building type and building structure information corresponding to the fire location information from the regional management department, and obtain the climate information corresponding to the fire location information from the meteorological department. The climate information includes the current climate information and future climate information at the alarm time. The current climate information and future climate information at the alarm time include wind speed and humidity, and the future climate information takes the alarm time as the starting point. The update is performed at a time interval, and the fire department transmits the fire extinguishing completion information to the meteorological department after the fire extinguishing is completed, and the meteorological department stops transmitting the future climate information to the fire department; The fire prediction model is used to predict the dynamic changes of the fire intensity through the acquired information, and the maximum fire intensity state of the ignition point is obtained from the prediction results, and the maximum fire intensity state is transmitted to the decision-making unit; b22. Predict the fire status when firefighters and fire trucks arrive at the fire point: obtain the fire scene video information when they arrive at the fire point through the video equipment carried by firefighters and fire trucks, predict the fire spread path in combination with the building type and building structure information and future climate information, and transmit the fire spread path prediction result to the decision-making unit; b23. Predict the fire status during the firefighting process by firefighters: During the firefighting process, the fire scene video information, the distance between the flame position and the firefighters, the firefighters' moving distance information and time point information are obtained through the firefighters' portable video equipment and sensor equipment; The actual fire spread rate and spread time during the firefighting process are calculated based on the information obtained. The actual fire spread rate during the firefighting process is calculated as follows: The spread time is calculated as follows: in, The actual rate of fire spread during firefighting by firefighters. is the distance that firefighters move at adjacent time points, is the distance between the flame position and the firefighter at the current time point, is the distance between the flame position and the firefighter at the previous time point, For spreading time.

7. The intelligent fire data analysis and management system according to claim 6, characterized in that: The decision unit constructs a fire extinguishing prediction model based on historical fire data, historical climate data and historical alarm data. The fire extinguishing prediction model is used for the fire department to output fire extinguishing instructions, wherein the output of the fire extinguishing instructions includes the following steps: The fire extinguishing prediction model analyzes the dynamic prediction results of fire intensity changes and the maximum fire intensity state prediction results transmitted by the dynamic prediction unit, and outputs the dispatch instructions for firefighters and fire trucks, and the instructions for the use of fire equipment; The fire-fighting prediction model is used to analyze the fire spread path prediction results and output instructions for fire-fighting plans, personnel evacuation plans, and personnel rescue plans; The fire extinguishing prediction model is used to analyze the actual fire spread rate and spread time, determine the firefighters' own safety status during the fire extinguishing process, and output firefighters' safety action command instructions.

8. The intelligent fire data analysis and management system according to claim 4, characterized in that: The linkage response module includes a linkage warning unit, a decision response unit and a collaborative implementation unit; The linkage warning unit connects the fire department end and the regional management department end through the information interaction module, and transmits the fire risk area division result to the regional management department end. The regional management department end issues a warning prompt to the area under its jurisdiction based on the fire risk division result of the area under its jurisdiction.

9. The intelligent fire data analysis and management system according to claim 8, characterized in that: The decision response unit receives the fire extinguishing instruction from the decision unit, so that the fire department can dispatch and command the fire brigade to perform fire extinguishing actions according to the fire extinguishing instruction. The fire extinguishing brigade includes firefighters and fire trucks.

10. The intelligent fire data analysis and management system according to claim 8, characterized in that: The collaborative implementation unit is used to obtain the surveillance video of the fire location and the surrounding area of ​​the fire location through the regional management department when a fire occurs in a residential area, and transmit it to the fire department. It queries whether there are any obstacles on the rescue channel around the fire location based on the surveillance video, and then analyzes the status of the rescue channel. When a vehicle is parked on the rescue channel, it is determined that the rescue channel is blocked. The license plate information of the parked vehicle is obtained based on the surveillance video, and the license plate management information is transmitted to the vehicle information management department to obtain the personnel information and contact information corresponding to the license plate information, and notify the person to whom the vehicle belongs to move the vehicle in time.

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