A visual fire rescue personnel and vehicle statistical analysis and display system

Abstract

The present invention relates to the field of electronic digital data processing technology, specifically a visual fire rescue personnel and vehicle statistical analysis and display system, comprising: a basic data acquisition module, an initial resource allocation module, an on-site resource allocation module, an equipment responsibility analysis module, and a statistical analysis and display module; first, the vehicle status data and on-duty firefighter data of the fire station are acquired; then, data processing is performed to construct a dynamic rescue force demand model, and at the same time, a comprehensive matching degree is calculated to construct a rescue force allocation plan; then, the actual rescue force data and the regional standard rescue force data of each rescue area are acquired, and rescue forces are allocated between regions based on regional force deviations; then, an equipment flow directed graph is constructed and edge weights and node responsibilities are calculated; finally, data from each module are counted and visualized for on-site resource allocation and result traceability. The present invention can provide real-time data processing and scientific decision support in dynamic rescue scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, in particular to a visual firefighting and rescue personnel and vehicle statistical analysis and display system. Background Art

[0002] With the continuous advancement of computer and data processing technologies, the firefighting and rescue field is gradually transitioning towards intelligent systems. Currently, firefighting and rescue operations face increasingly complex fire situations and diverse disaster types, such as high-rise building fires, hazardous chemical explosions, and fires in large commercial complexes. These accidents are sudden, carry high casualty risks, and are challenging to handle. The resulting complex data is characterized by dynamic temporal and spatial changes. Traditional firefighting and rescue command and decision-making relies primarily on the commander's experience and intuitive judgment, lacking scientific and data-driven support. This makes it difficult to rationally deploy rescue forces and evenly distribute resources in a volatile rescue environment.

[0003] In recent years, significant progress has been made in the Internet of Things (IoT), big data analysis, and visualization technologies, providing technical support for data processing in firefighting and rescue operations. However, the current application of these technologies in firefighting and rescue operations primarily focuses on static data recording and simple statistical analysis, lacking effective dynamic data analysis. This leads to inefficient resource allocation and insufficient support for collaborative decision-making in complex and changing rescue scenarios, impacting rescue efficiency and success rates.

[0004] Therefore, a visual fire rescue personnel and vehicle statistical analysis and display system is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a visual fire rescue personnel and vehicle statistical analysis and display system, including: a basic data acquisition module, an initial resource allocation module, an on-site resource allocation module, an equipment responsibility analysis module and a statistical analysis and display module. First, the vehicle status data and on-duty firefighter data of the fire station are obtained; then, data processing is performed to construct a dynamic rescue force demand model, and at the same time, the comprehensive matching degree is calculated to construct a rescue force allocation plan; then, the actual rescue force data and the regional standard rescue force data of each rescue area are obtained, and the rescue force is allocated between regions according to the regional force deviation; then, an equipment flow directed graph is constructed and the edge weights and node responsibilities are calculated; finally, the data of each module are counted and visualized for on-site resource allocation and result traceability. The present invention can provide real-time data analysis and scientific decision support in dynamic rescue scenarios.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A visual fire rescue personnel and vehicle statistical analysis and display system, including:

[0008] Basic data acquisition module, which obtains vehicle status data and on-duty firefighter data of the fire station;

[0009] The initial resource allocation module builds a dynamic rescue force demand model based on the historical rescue database, extracts the characteristics of fire alarm information data to calculate the initial rescue force demand data, calculates the comprehensive matching degree based on the on-duty firefighter data, and builds a rescue force allocation plan;

[0010] On-site resource allocation module, which obtains the actual rescue force data of each area in real time, calculates the regional standard rescue force data, and allocates rescue forces between regions based on regional force deviation;

[0011] Equipment responsibility analysis module records equipment handover data, constructs a directed graph of equipment flow, and calculates edge weights and node responsibility;

[0012] The statistical analysis display module collects data from the statistical basic data acquisition module, the initial resource allocation module, the on-site resource allocation module, and the equipment responsibility analysis module, and renders a visual interface based on real-time data streams, performing on-site resource allocation and result traceability through data analysis.

[0013] Preferably, constructing the dynamic rescue force demand model based on the historical rescue database includes: calculating case feature similarity through data processing based on the historical rescue database, and selecting similar historical cases according to a preset similarity threshold; constructing the dynamic rescue force demand model based on the case feature similarity of the similar historical cases; before dispatching, calculating the initial rescue force demand data based on the fire alarm information data features and the dynamic rescue force demand model; the dynamic rescue force demand model expression is:

[0014] ;

[0015] in, is the dynamic rescue force demand data at time t, is the number of cumulative time windows, is the weight coefficient, is the number of similar historical cases in the k-th time window, is the fire alarm information data feature at time t, for The fire alarm information data features of the i-th similar historical case among similar historical cases, for and The case feature similarity of is calculated using cosine similarity. The rescue force invested in the i-th similar historical case.

[0016] Preferably, constructing a rescue force allocation plan includes: obtaining the on-duty firefighter data, constructing a personal capability vector for each firefighter, constructing a rescue team capability matrix based on the personal capability vector, and calculating the team professional complementarity according to the rescue team capability matrix; extracting rescue team information characteristics and fire alarm information data characteristics to calculate task fitness, and calculating a comprehensive matching degree based on the team professional complementarity and the task fitness; and selecting m rescue teams through an integer programming algorithm based on the comprehensive matching degree.

[0017] Preferably, the initial resource allocation module also includes a reinforcement judgment unit for judging whether reinforcement is needed, specifically: after arriving at the scene, the dynamic rescue force demand data at the current moment is calculated based on the fire alarm information data characteristics at the current moment and the dynamic rescue force demand model; the overall force deviation between the dynamic rescue force demand data and the actual rescue force data on the scene is calculated, and if the overall force deviation exceeds the first deviation threshold, reinforcement is needed.

[0018] Preferably, the allocation of rescue forces among regions includes: counting the actual rescue force data of each region; calculating the real-time situation score of each region according to the on-site situation; calculating the regional standard rescue force data according to the real-time situation score; calculating the regional force deviation according to the regional standard rescue force data and the regional actual rescue force data; constituting the regions where the regional force deviation is greater than the second deviation threshold into a set of supported regions; for regions where the regional force deviation is less than or equal to the second deviation threshold, if the regional force deviation is less than or equal to the third deviation threshold, they constitute a support region set, otherwise they are not used as support regions.

[0019] Preferably, constructing the equipment flow directed graph includes: collecting equipment handover data and performing data preprocessing; the equipment handover data includes equipment data, handover firefighter data, receiving firefighter data, handover time data and handover location coordinate data; constructing the equipment flow directed graph based on the equipment handover data , is a set of nodes, representing firefighters with equipment, is an edge set, representing the equipment handover record. After the rescue is completed, the responsibility of each piece of equipment is traced according to the equipment flow directed graph. The edge weight and node responsibility calculation formula are:

[0020] ;

[0021] ;

[0022] in, is the edge weight from node i to j, 、 and are the holding time, handover frequency and responsibility level of node i, is the weight coefficient, 、 and These are the maximum reference values ​​for holding time, handover frequency, and responsibility level; Equipment for node i degree of responsibility, For equipment The equipment flow directed graph, for In the edge where node i is the equipment handover party, For the edge The edge weights of for The number of edges, is the attenuation coefficient.

[0023] Preferably, the statistical analysis and display module includes a rescue data display unit and an equipment responsibility tracing unit; the rescue data display unit is used to visualize the data of the basic data acquisition module, the initial resource allocation module and the on-site resource allocation module in real time; the equipment responsibility tracing unit is used to visualize the equipment flow directed graph and perform equipment responsibility tracing.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. The present invention establishes a dynamic rescue force demand model based on a historical rescue database. By calculating the similarity of case features, it screens out historical cases similar to the current fire situation, and constructs a rescue force demand model suitable for the current scenario based on the rescue force input of these similar historical cases. Its dynamic and adaptive nature enables the allocation of rescue forces to be dynamically adjusted over time, which is more in line with the uncertainty of the development of the fire during the actual rescue process. At the same time, it can quickly calculate the initial rescue force demand data before dispatching, providing a scientific basis for the first-time rescue force allocation. During the rescue process, it can calculate the dynamic rescue force demand data in real time based on the current fire information data, and promptly determine whether reinforcements are needed. It effectively solves the problem of inaccurate resource allocation caused by the lack of real-time data analysis in dynamic rescue scenarios, and significantly improves the utilization efficiency of rescue resources.

[0026] 2. This invention proposes a rescue team optimization mechanism. By constructing each firefighter's individual capability vector, a rescue team capability matrix is ​​formed, and the team's professional complementarity is calculated. Simultaneously, task fitness is calculated based on rescue team information and fire alarm data, the team's overall matching degree is evaluated, and an optimization algorithm is used to select the optimal rescue team combination, ensuring that the selected team has the highest overall matching degree while meeting the constraints of the initial rescue force demand data. This allows the formation of rescue forces to move beyond simple matching of personnel and vehicle numbers, and instead considers the matching relationship between the professional skills of rescue firefighters and specific rescue tasks. This improves the professionalism and effectiveness of rescue operations, provides a scientific means for precise firefighter deployment in dynamic rescue scenarios, and effectively enhances the quality of rescue decision-making.

[0027] 3. The present invention constructs a dynamic collaborative resource allocation mechanism for inter-regional rescue forces. Through real-time evaluation of multi-dimensional on-site information, the real-time situation score of each rescue area is calculated, and regional standard rescue force data is calculated based on the situation score, and regional force deviation is calculated, thereby identifying areas in need of assistance and areas that can provide support. In addition, the present invention also constructs an equipment flow directed graph through equipment handover data, realizes the precise traceability of equipment responsibility, quantifies the degree of responsibility of each firefighter for specific equipment, and provides an objective basis for subsequent equipment management and accountability. Through real-time data analysis and visual display, cross-regional optimized scheduling of rescue forces and precise traceability of equipment responsibility are achieved, effectively solving the problems of unbalanced resource allocation and unclear responsibility definition in dynamic rescue scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a schematic diagram of the structure of a visual fire rescue personnel and vehicle statistical analysis and display system of the present invention;

[0029] Figure 2 This is a schematic diagram of the inter-regional rescue force dispatching process of the present invention;

[0030] Figure 3 This is a schematic diagram of the statistical analysis display module structure of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by firefighters of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0032] See also Figures 1 to 3 The present invention provides a visual fire rescue personnel and vehicle statistical analysis and display system, the technical solution is as follows:

[0033] Example 1:

[0034] A visual fire rescue personnel and vehicle statistical analysis and display system, including:

[0035] Basic data acquisition module, which obtains vehicle status data and on-duty firefighter data of the fire station;

[0036] The initial resource allocation module builds a dynamic rescue force demand model based on the historical rescue database, extracts the characteristics of fire alarm information data to calculate the initial rescue force demand data, calculates the comprehensive matching degree based on the on-duty firefighter data, and builds a rescue force allocation plan;

[0037] On-site resource allocation module, which obtains the actual rescue force data of each area in real time, calculates the regional standard rescue force data, and allocates rescue forces between regions based on regional force deviation;

[0038] Equipment responsibility analysis module records equipment handover data, constructs a directed graph of equipment flow, and calculates edge weights and node responsibility;

[0039] The statistical analysis display module collects data from the statistical basic data acquisition module, the initial resource allocation module, the on-site resource allocation module, and the equipment responsibility analysis module, and renders a visual interface based on real-time data streams, performing on-site resource allocation and result traceability through data analysis.

[0040] Specifically, the vehicle status data includes vehicle RFID tag data, vehicle positioning data, vehicle equipment list, equipment RFID tag data and equipment positioning data; the on-duty firefighter data includes firefighter RFID tag data, firefighter positioning data and work data;

[0041] The present invention uses 2.4GHz active RFID technology to embed active tags into firefighter uniforms, inside vehicles, and on the casings of key equipment. Active base stations are installed in vehicles and at camp access points. Vehicle positioning data is obtained through on-board GPS, equipment positioning data and firefighter positioning data are obtained through ultra-wideband positioning equipment, and tag data is read simultaneously through active RFID technology. The active base station has an identification range of up to 30 meters and can penetrate walls, ensuring accurate identification in complex environments. When firefighters board vehicles or enter or exit camp access points, the corresponding base station can read the tag information in real time and automatically count personnel and vehicle information.

[0042] Based on the real-time monitoring function of the active RFID base station, when it detects that the RFID tag of a piece of equipment and the RFID tag of a firefighter are in close contact for a specific period of time, and the equipment was previously associated with another firefighter's tag, the system automatically generates equipment handover data, including the equipment ID, equipment type, handing over firefighter ID, receiving firefighter ID, and handover time. In addition, the ultra-wideband positioning device can provide the equipment's location coordinate data to record the specific location of the equipment handover.

[0043] When combatants arrive at the rescue site, real-time data is collected through RFID tags, active base stations and positioning devices and transmitted to the system's central server via wireless communication. The 2.4G frequency communication protocol is used to achieve stable signal transmission, with strong anti-interference capabilities and suitable for complex radio network environments. The server eliminates positioning noise through Kalman filtering and aligns multi-source data through a time synchronization protocol. The pre-processed data is analyzed and calculated through the initial resource allocation module, on-site resource allocation module and equipment responsibility analysis module, and input into the statistical analysis and display module.

[0044] The visual firefighting and rescue personnel and vehicle statistical analysis and display system proposed in this invention constructs a dynamic rescue force demand model based on historical data, optimizes the formation of rescue teams according to the matching relationship between firefighters' professional skills and rescue tasks, realizes the dynamic allocation of forces in the on-site rescue area, and realizes responsibility traceability by recording the equipment flow path through equipment labels. Ultimately, all analysis results are visualized to provide all-round support for command decision-making. This invention realizes the intelligent allocation of rescue forces throughout the entire process, from pre-deployment preparation to on-site rescue and then to post-event evaluation. It helps to improve the scientific nature and accuracy of rescue force deployment, enhance the coordination and efficiency of on-site rescue, and strengthen the traceability of resource allocation, thereby significantly improving the overall level of firefighting and rescue, and providing strong technical support for safeguarding public safety.

[0045] Furthermore, constructing the dynamic rescue force demand model based on the historical rescue database includes: calculating case feature similarity through data processing based on the historical rescue database, and selecting similar historical cases according to a preset similarity threshold; constructing the dynamic rescue force demand model based on the case feature similarity of the similar historical cases; before dispatching, calculating the initial rescue force demand data based on the fire alarm information data features and the dynamic rescue force demand model; the dynamic rescue force demand model is expressed as:

[0046] ;

[0047] in, is the dynamic rescue force demand data at time t, is the number of cumulative time windows, is the weight coefficient, is the number of similar historical cases in the k-th time window, is the fire alarm information data feature at time t, for The fire alarm information data features of the i-th similar historical case among similar historical cases, for and The case feature similarity of is calculated using cosine similarity. The rescue force invested in the i-th similar historical case.

[0048] Specifically, the fire alarm information data in the historical rescue database is classified, and each type of fire alarm information data includes the required types of fire trucks and rescue equipment. When calculating the dynamic rescue force demand data, the number of firefighters, the number of each type of fire trucks, and the number of each type of equipment are calculated separately.

[0049] Calculating the current rescue force requirements based on historical experience avoids shortages or surpluses and improves resource utilization efficiency. Furthermore, the dynamic nature of the dynamic rescue force demand model enables firefighters to adjust rescue strategies as the fire develops, achieving optimal resource allocation and providing data support for scientific decision-making.

[0050] Furthermore, constructing a rescue force allocation plan includes: obtaining the on-duty firefighter data, constructing a personal capability vector for each firefighter, constructing a rescue team capability matrix based on the personal capability vector, and calculating the team professional complementarity according to the rescue team capability matrix; extracting rescue team information characteristics and fire alarm information data characteristics to calculate task fitness, and calculating a comprehensive matching degree according to the team professional complementarity and the task fitness; and selecting m rescue teams through an integer programming algorithm according to the comprehensive matching degree.

[0051] Specifically, the weighted sum of the team's professional complementarity and task adaptability is used to obtain the comprehensive matching degree. The objective function is to maximize the sum of the comprehensive matching degrees of the selected teams. The number of firefighters, the number of each type of fire trucks, and the number of each type of equipment that meet the initial rescue force demand data are used as constraints. m rescue teams are selected through the integer programming optimization algorithm. The calculation formulas for the team's professional complementarity and task adaptability are:

[0052] ;

[0053] ;

[0054] in, To ensure the professional complementarity of the group, is the length of the personal ability vector, each component in the personal ability vector represents a professional skill evaluation value, is the variance of the i-th professional skill evaluation value within the group, is the mean of the professional skill evaluation value of the i-th person in the group; is the group task adaptability, is the collective capability vector of the group, defined as the maximum evaluation value of all group members on each professional skill, is the capability requirement vector generated based on the fire alarm information data, is the magnitude of the vector.

[0055] This method, which calculates team professional complementarity based on the rescue team capability matrix and selects the optimal rescue team based on task adaptability, helps build highly complementary and professional rescue teams, avoiding the extensive deployment model used in traditional rescue operations based solely on the number of personnel and vehicles. Using an integer programming optimization algorithm, the team combination with the highest overall matching degree is selected, ensuring that rescue forces meet demand while maximizing the matching of rescue team professional capabilities, thereby improving rescue efficiency and success rates.

[0056] Furthermore, the initial resource allocation module also includes a reinforcement judgment unit for judging whether reinforcement is needed. Specifically, after arriving at the scene, the dynamic rescue force demand data at the current moment is calculated based on the fire alarm information data characteristics at the current moment and the dynamic rescue force demand model; the overall force deviation between the dynamic rescue force demand data and the actual rescue force data on the scene is calculated. If the overall force deviation exceeds the first deviation threshold, reinforcement is required.

[0057] The overall force deviation The calculation method is ,in, To obtain dynamic rescue force demand data, The data of the actual rescue force on the scene. Specifically, the number of firefighters, the number of each type of fire trucks and the number of each type of equipment are respectively Calculate the mean of all results as the overall strength deviation.

[0058] Calculate the deviation between the dynamic rescue force demand data at the current moment and the overall force of the actual rescue force on site, and scientifically judge whether reinforcement is needed, avoiding the subjectivity and lag of reinforcement decisions, and making reinforcement decisions based on real-time data analysis. It can not only replenish insufficient rescue forces in a timely manner, but also avoid unnecessary waste of resources, and improve the accuracy and timeliness of emergency response.

[0059] Further, see Figure 2The allocation of rescue forces among regions includes: counting the actual rescue force data of each region; calculating the real-time situation score of each region according to the on-site situation; calculating the regional standard rescue force data according to the real-time situation score; calculating the regional force deviation according to the regional standard rescue force data and the regional actual rescue force data; constituting the regions where the regional force deviation is greater than the second deviation threshold into a set of supported regions; for the regions where the regional force deviation is less than or equal to the second deviation threshold, if the regional force deviation is less than or equal to the third deviation threshold, they constitute a support region set, otherwise they are not used as support regions.

[0060] Specifically, at each time window, the designated firefighters on site will conduct a fire spread survey of the area they are responsible for. , the situation of trapped firefighters , the extent of building damage , dangerous goods threats and rescue progress Scores are given (0 to 10 points) and transmitted to the platform system through mobile terminal voice input to calculate the real-time situation score; the calculation formula for the real-time situation score and regional standard rescue force data is:

[0061] ;

[0062] ;

[0063] ;

[0064] in, For real-time situation score, and is the weight coefficient, For urgency, Scores are given for fire spread, trapped firefighters, building damage, and hazardous materials threats. is the weight coefficient; It is the regional standard rescue force data. is the weight coefficient, The force is configured as the benchmark; the calculation of regional standard rescue force data is based on the number categories of fire trucks and fire equipment in the initial rescue force demand data, and the number of firefighters, the number of fire trucks of each category, and the number of equipment of each category are calculated respectively.

[0065] Regional power bias The calculation method is ,in, It is the regional standard rescue force data. The actual rescue force data for the region. Specifically, the number of firefighters, the number of each type of fire trucks and the number of each type of equipment are calculated by Calculate and then take the mean of all the results as the regional force deviation.

[0066] By using labels to count the actual rescue force data of the region, and combining the on-site situation to calculate the real-time situation score and regional standard rescue force data, the assistance and support status of each region can be judged. The inter-regional force coordination allocation mechanism based on real-time data is beneficial to optimizing the allocation of on-site rescue resources, ensuring that each region has rescue forces that match its level of urgency, avoiding the situation where some regions have excess forces and other regions have insufficient forces, and improving the overall rescue efficiency and coordinated combat capabilities.

[0067] Furthermore, constructing the equipment flow directed graph includes: collecting equipment handover data and performing data preprocessing; the equipment handover data includes equipment data, handover firefighter data, receiving firefighter data, handover time data and handover location coordinate data; constructing the equipment flow directed graph based on the equipment handover data , is a set of nodes, representing firefighters with equipment, is an edge set, representing the equipment handover record. After the rescue is completed, the responsibility of each piece of equipment is traced according to the equipment flow directed graph. The edge weight and node responsibility calculation formula are:

[0068] ;

[0069] ;

[0070] in, is the edge weight from node i to j, 、 and are the holding time, handover frequency and responsibility level of node i, respectively. The responsibility level is determined according to the position of the firefighter. is the weight coefficient, 、 and These are the maximum reference values ​​for holding time, handover frequency, and responsibility level; Equipment for node i degree of responsibility, For equipment The equipment flow directed graph, for In the edge where node i is the equipment handover party, For the edge The edge weights of for The number of edges, is the attenuation coefficient.

[0071] Based on equipment handover data, a directed equipment flow graph is constructed to enable equipment accountability traceability. This helps clarify the equipment flow path and accountability during the rescue process, resolving issues of chaotic equipment management and unclear accountability during the rescue process. By quantifying accountability, it provides an objective basis for equipment management and subsequent accountability, promoting standardized equipment management and the implementation of a responsibility system.

[0072] Further, see Figure 3 The statistical analysis and display module includes a rescue data display unit and an equipment responsibility tracing unit; the rescue data display unit is used to visualize the data of the basic data acquisition module, the initial resource allocation module and the on-site resource allocation module in real time; the equipment responsibility tracing unit is used to visualize the equipment flow directed graph and perform equipment responsibility tracing.

[0073] Specifically, the statistical analysis and display module receives data streams from various modules in real time through the central server's message middleware. The rescue data display unit includes: based on positioning data, a Gaussian kernel density estimation algorithm is used to generate a dynamic thermal distribution, and HSV color gamut mapping is used to obtain a visual real-time rescue force heat map, with the thermal intensity value updated every 500ms; a data visualization library is used to draw a dual-axis line chart, with the left axis showing real-time rescue force demand (rescue force demand data or regional standard rescue force data), and the right axis showing force deviation (overall force deviation or regional force deviation). The data window length is 15 minutes and is smoothly refreshed every 3 seconds; the equipment responsibility traceability unit includes: a dynamically laid out topological structure of the equipment flow directed graph, with the node radius positively correlated with the firefighter's responsibility, and the edge width scaled logarithmically by the handover frequency. Clicking on a node supports viewing the holding time and handover data details, which is conducive to equipment responsibility traceability after the rescue is completed.

[0074] The statistical analysis and display module helps improve the intuitiveness and convenience of command and decision-making, enabling firefighters to quickly access key information and make informed decisions. Furthermore, the visual traceability of equipment responsibility provides an intuitive basis for post-event evaluation and improvement, promoting the continuous improvement and optimization of rescue efforts and enhancing the overall level of firefighting and rescue operations.

[0075] Example 2:

[0076] To achieve force deployment and collaborative management of fire rescue in high-rise residential buildings, this embodiment uses a visual fire rescue personnel and vehicle statistics analysis and display system, including:

[0077] Basic data acquisition module, which obtains vehicle status data and on-duty firefighter data of the fire station;

[0078] The initial resource allocation module builds a dynamic rescue force demand model based on the historical rescue database, extracts the characteristics of fire alarm information data to calculate the initial rescue force demand data, calculates the comprehensive matching degree based on the on-duty firefighter data, and builds a rescue force allocation plan;

[0079] On-site resource allocation module, which obtains the actual rescue force data of each area in real time, calculates the regional standard rescue force data, and allocates rescue forces between regions based on regional force deviation;

[0080] Equipment responsibility analysis module records equipment handover data, constructs a directed graph of equipment flow, and calculates edge weights and node responsibility;

[0081] The statistical analysis display module collects data from the statistical basic data acquisition module, the initial resource allocation module, the on-site resource allocation module, and the equipment responsibility analysis module, and renders a visual interface based on real-time data streams, performing on-site resource allocation and result traceability through data analysis.

[0082] Furthermore, constructing the dynamic rescue force demand model based on the historical rescue database includes: calculating case feature similarity through data processing based on the historical rescue database, and selecting similar historical cases according to a preset similarity threshold; constructing the dynamic rescue force demand model based on the case feature similarity of the similar historical cases; before dispatching, calculating the initial rescue force demand data based on the fire alarm information data features and the dynamic rescue force demand model; the dynamic rescue force demand model is expressed as:

[0083] ;

[0084] in, is the dynamic rescue force demand data at time t, is the number of cumulative time windows, is the weight coefficient, is the number of similar historical cases in the k-th time window, is the fire alarm information data feature at time t, for The fire alarm information data features of the i-th similar historical case among similar historical cases, for and The case feature similarity of is calculated using cosine similarity. The rescue force invested in the i-th similar historical case.

[0085] Table 1 shows the fire alarm information data of current high-rise residential fires and similar historical cases; Table 2 shows an example of initial rescue force demand data.

[0086] Table 1. Fire alarm information data of similar historical cases

[0087]

[0088] Table 2. Example of initial rescue force requirements

[0089]

[0090] Furthermore, constructing a rescue force allocation plan includes: obtaining the on-duty firefighter data, constructing a personal capability vector for each firefighter, constructing a rescue team capability matrix based on the personal capability vector, and calculating the team professional complementarity according to the rescue team capability matrix; extracting rescue team information characteristics and fire alarm information data characteristics to calculate task fitness, and calculating a comprehensive matching degree according to the team professional complementarity and the task fitness; and selecting m rescue teams through an integer programming algorithm according to the comprehensive matching degree.

[0091] Table 3. Group professional complementarity and task adaptability

[0092]

[0093] Table 4. Examples of firefighters’ personal competence vectors

[0094]

[0095] Table 3 shows the professional complementarity and task adaptability data of different rescue teams; Table 4 gives examples of the individual ability vectors of firefighters in each team.

[0096] Furthermore, the initial resource allocation module also includes a reinforcement judgment unit for judging whether reinforcement is needed. Specifically, after arriving at the scene, the dynamic rescue force demand data at the current moment is calculated based on the fire alarm information data characteristics at the current moment and the dynamic rescue force demand model; the overall force deviation between the dynamic rescue force demand data and the actual rescue force data on the scene is calculated. If the overall force deviation exceeds the first deviation threshold, reinforcement is required.

[0097] Furthermore, the allocation of rescue forces among regions includes: counting the actual rescue force data of each region; calculating the real-time situation score of each region according to the on-site situation; calculating the regional standard rescue force data according to the real-time situation score; calculating the regional force deviation according to the regional standard rescue force data and the regional actual rescue force data; constituting the regions where the regional force deviation is greater than the second deviation threshold into a set of supported regions; for the regions where the regional force deviation is less than or equal to the second deviation threshold, if the regional force deviation is less than or equal to the third deviation threshold, they constitute a support region set, otherwise they are not used as support regions.

[0098] Furthermore, constructing the equipment flow directed graph includes: collecting equipment handover data and performing data preprocessing; the equipment handover data includes equipment data, handover firefighter data, receiving firefighter data, handover time data and handover location coordinate data; constructing the equipment flow directed graph based on the equipment handover data , is a set of nodes, representing firefighters with equipment, is an edge set, representing the equipment handover record. After the rescue is completed, the responsibility of each piece of equipment is traced according to the equipment flow directed graph. The edge weight and node responsibility calculation formula are:

[0099] ;

[0100] ;

[0101] in, is the edge weight from node i to j, 、 and are the holding time, handover frequency and responsibility level of node i, is the weight coefficient, 、 and These are the maximum reference values ​​for holding time, handover frequency, and responsibility level; Equipment for node i degree of responsibility, For equipment The equipment flow directed graph, for In the edge where node i is the equipment handover party, For the edge The edge weights of for The number of edges, is the attenuation coefficient.

[0102] Furthermore, the statistical analysis and display module includes a rescue data display unit and an equipment responsibility tracing unit; the rescue data display unit is used to visualize the data of the basic data acquisition module, the initial resource allocation module and the on-site resource allocation module in real time; the equipment responsibility tracing unit is used to visualize the equipment flow directed graph and perform equipment responsibility tracing.

[0103] While the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A visual fire rescue personnel and vehicle statistical analysis and display system, characterized by: include: Basic data acquisition module, which obtains vehicle status data and on-duty firefighter data of the fire station; The initial resource allocation module builds a dynamic rescue force demand model based on the historical rescue database, extracts the characteristics of fire alarm information data to calculate the initial rescue force demand data, calculates the comprehensive matching degree based on the on-duty firefighter data, and builds a rescue force allocation plan; On-site resource allocation module, which obtains the actual rescue force data of each area in real time, calculates the regional standard rescue force data, and allocates rescue forces between regions based on regional force deviation; Equipment responsibility analysis module records equipment handover data including equipment data, handover firefighter data, receiving firefighter data, handover time data and handover location coordinate data, and builds an equipment flow directed graph , is a set of nodes, representing firefighters with equipment, is an edge set, representing the equipment handover record; and the responsibility of each piece of equipment is traced. The formula for calculating edge weight and node responsibility is: ； ； in, is the edge weight from node i to j, 、 and are the holding time, handover frequency and responsibility level of node i, is the weight coefficient, 、 and These are the maximum reference values ​​for holding time, handover frequency, and responsibility level; Equipment for node i degree of responsibility, For equipment The equipment flow directed graph, for In the edge where node i is the equipment handover party, For the edge The edge weights of for The number of edges, is the attenuation coefficient; The statistical analysis display module collects data from the statistical basic data acquisition module, the initial resource allocation module, the on-site resource allocation module, and the equipment responsibility analysis module, and renders a visual interface based on real-time data streams, performing on-site resource allocation and result traceability through data analysis.

2. A visual fire rescue personnel and vehicle statistical analysis and display system according to claim 1, characterized in that: Constructing the dynamic rescue force demand model based on the historical rescue database includes: calculating case feature similarity through data processing based on the historical rescue database, and selecting similar historical cases according to a preset similarity threshold; constructing the dynamic rescue force demand model based on the case feature similarity of the similar historical cases; and calculating the initial rescue force demand data based on the fire alarm information data features and the dynamic rescue force demand model before dispatching; the dynamic rescue force demand model is expressed as: ； in, is the dynamic rescue force demand data at time t, is the number of cumulative time windows, is the weight coefficient, is the number of similar historical cases in the k-th time window, is the fire alarm information data feature at time t, for The fire alarm information data features of the i-th similar historical case among similar historical cases, for and The case feature similarity of is calculated using cosine similarity. The rescue force invested in the i-th similar historical case.

3. A visual fire rescue personnel and vehicle statistical analysis and display system according to claim 1, characterized in that: Constructing a rescue force allocation plan includes: obtaining the on-duty firefighter data, constructing a personal capability vector for each firefighter, constructing a rescue team capability matrix based on the personal capability vector, and calculating the team professional complementarity according to the rescue team capability matrix; extracting rescue team information characteristics and fire alarm information data characteristics to calculate task fitness, and calculating a comprehensive matching degree according to the team professional complementarity and the task fitness; and selecting m rescue teams through an integer programming algorithm according to the comprehensive matching degree.

4. A visual fire rescue personnel and vehicle statistical analysis and display system according to claim 1, characterized in that: The initial resource allocation module also includes a reinforcement judgment unit for judging whether reinforcement is needed. Specifically, after arriving at the scene, the dynamic rescue force demand data at the current moment is calculated based on the fire alarm information data characteristics at the current moment and the dynamic rescue force demand model; the overall force deviation between the dynamic rescue force demand data and the actual rescue force data on the scene is calculated. If the overall force deviation exceeds a first deviation threshold, reinforcement is needed.

5. A visual fire rescue personnel and vehicle statistical analysis and display system according to claim 1, characterized in that: The allocation of rescue forces among regions includes: counting the actual rescue force data of each region; calculating the real-time situation score of each region according to the on-site situation; calculating the regional standard rescue force data according to the real-time situation score; calculating the regional force deviation according to the regional standard rescue force data and the regional actual rescue force data; constituting the regions where the regional force deviation is greater than the second deviation threshold into a set of supported regions; for the regions where the regional force deviation is less than or equal to the second deviation threshold, if the regional force deviation is less than or equal to the third deviation threshold, they constitute a support region set, otherwise they are not used as support regions.

6. A visual fire rescue personnel and vehicle statistical analysis and display system according to claim 1, characterized in that: The statistical analysis and display module includes a rescue data display unit and an equipment responsibility tracing unit; the rescue data display unit is used to visualize the data of the basic data acquisition module, the initial resource allocation module and the on-site resource allocation module in real time; The equipment responsibility tracing unit is used to visualize the equipment flow directed graph and perform equipment responsibility tracing.

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