Fire emergency management method for big data education platform of education institution

Abstract

The invention relates to the technical field of fire-fighting emergency processing, and particularly discloses a fire-fighting emergency management method for a big data education platform of an education institution, and the method comprises the steps: patrol management judgment, fire-fighting emergency simulation evaluation and fire-fighting emergency management feedback. Structural data of an area to which an educational institution belongs and patrol management data of a big data fire-fighting education cloud platform are subjected to quantitative comparison, so that a more accurate patrol indication value of the area to which the educational institution belongs can be judged, and fire-fighting emergency simulation is performed on a pre-management educational institution area. And finally, inputting the fire emergency simulation process of the pre-management educational institution area into the big data fire education cloud platform, and performing visual display, so as to improve the emergency capability of coping with a real fire event, and improve the safety of the fire emergency simulation process of the pre-management educational institution area. And meanwhile, the overall fire safety management level of a big data fire-fighting education cloud platform in an education institution is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fire emergency treatment, and specifically to a fire emergency management method for a big data education platform used in educational institutions. Background Art

[0002] Currently, with the continuous expansion of the number and scale of various educational places such as schools and training institutions, they usually have a relatively high population density and complex building structures, thus posing higher requirements for fire safety. However, the existing fire emergency management has the following problems:

[0003] The traditional fire alarm system can only complete the fire extinguishing ability through a pre-set fire emergency process when a fire occurs; moreover, today's fire emergency management plan lacks an effective emergency drill mechanism when predicting the occurrence of a fire. Once a fire occurs, the ability to prevent, reduce, and fight disasters cannot be effectively improved, and ultimately the overall fire safety management level of educational places cannot be enhanced.

[0004] For example, the invention patent with the publication number CN112215564A discloses a smart fire emergency management system and its method. The smart fire emergency management method includes the following steps: Step S1: The smart fire emergency management system guides the user to configure one or more of the basic management module, education and training module, equipment management module, and on-site management module; Step S2: The smart fire emergency management system guides the user to input one or more of the dual prevention module, emergency response module, event management module, operation management module, and target management module. The disclosed smart fire emergency management system and its method help to improve the integration level and control coverage.

[0005] For example, the invention patent with the publication number CN112348729A discloses a smart fire and security integrated big data platform system, including a smart operation big screen module, an Internet of Things device management module, a fire intelligent early warning and monitoring module, a fire safety hazard inspection module, a smart big data application analysis module, a fire publicity and education module, a fire business management module, and a unit management module; the above modules are controlled by a data analysis and control module. This system can, firstly, construct a real-time and effective fire safety supervision network through the collection and analysis of various effective fire information; secondly, construct an accurate urban rapid response system through the immediate joint duty and linkage of various fire forces, thereby improving the fire fighting and rescue combat effectiveness and fire prevention supervision and management level, and further enhancing the urban fire prevention, disaster reduction, and relief capabilities.

[0006] Combined with the above technical solutions, it is found that in the technical solutions for fire emergency management, the previous emergency procedures are still followed, and extensive fire emergency management is carried out, which is not conducive to refined emergency control. At the same time, due to the large fluctuations in the utilization of fire-fighting equipment, there is a possibility that the prediction results are inaccurate, resulting in the inability to carry out efficient fire emergency during the event and the inability to improve the level of fire safety management. Summary of the Invention

[0007] Aiming at the deficiencies of the prior art, the present invention provides a fire emergency management method for a big data education platform for educational institutions, which can effectively solve the problems involved in the above background technology.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: A fire emergency management method for a big data education platform for educational institutions includes: Patrol management determination: Collect the structural data of the area where the educational institution is located and the patrol management data of the big data fire education cloud platform, and thus determine the patrol indication value of the area where the educational institution is located, and compare it with the preset patrol indication threshold; Fire emergency simulation evaluation: Mark the area where the educational institution is located corresponding to the patrol indication value greater than the patrol indication threshold as the pre-management educational institution area, and thus conduct a fire emergency simulation on the pre-management educational institution area to obtain the fire emergency simulation data of the pre-management educational institution area, and evaluate the fire emergency simulation evaluation index of the pre-management educational institution area; Fire emergency management feedback: Compare the fire emergency simulation evaluation index of the pre-management educational institution area with the preset fire emergency simulation evaluation reference index, and finally conduct management feedback on the fire emergency simulation process of the pre-management educational institution area.

[0009] As a further method, the comparison between the patrol indication value of the area where the educational institution is located and the preset patrol indication threshold is specifically as follows:

[0010] If the patrol indication value of the area where the educational institution is located is greater than the preset patrol indication threshold, a fire emergency simulation is carried out on the area where the educational institution is located. If the patrol indication value of the area where the educational institution is located is less than or equal to the preset patrol indication threshold, the patrol indication values of various areas are extracted to obtain the arrangement order of the patrol indication values, which is used as the fire inspection and investigation order, and the fire inspection and investigation order is transmitted to the big data fire education cloud platform for management prompts.

[0011] As a further method, the management feedback on the fire emergency simulation process of the pre-management educational institution area is specifically as follows:

[0012] The fire emergency simulation evaluation index of the pre-managed educational institution area is compared with the preset fire emergency simulation evaluation reference index. If the fire emergency simulation evaluation index of the pre-managed educational institution area is greater than the preset fire emergency simulation evaluation reference index, a fire emergency simulation is performed on the pre-managed educational institution area to extract the fire emergency simulation process of the pre-managed educational institution area. The fire emergency simulation process of the pre-managed educational institution area is input into the big data fire education cloud platform and visualized. If the fire emergency simulation evaluation index of the pre-managed educational institution area is equal to or less than the fire emergency simulation evaluation reference index, management feedback is given to the fire emergency simulation process of the pre-managed educational institution area.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0014] (1) The present invention provides a fire emergency management method for a big data education platform of an educational institution, quantitatively compares the structural data of the area to which the educational institution belongs and the patrol management data of the big data fire education cloud platform, and integrates the data quantitative analysis of fire equipment, sensors and cameras to determine a more accurate patrol index value of the area to which the educational institution belongs, conducts a fire emergency simulation on the pre-managed educational institution area, and evaluates the fire emergency simulation evaluation index of the pre-managed educational institution area, thereby greatly reducing the possibility of inaccurate pre-disaster prediction results. The fire emergency simulation process of the pre-managed educational institution area is compared with a preset fire emergency simulation evaluation reference index, and the fire emergency simulation process of the pre-managed educational institution area is input into the big data fire education cloud platform and visualized for improvement. This improves the emergency response capability to respond to real fire incidents, that is, efficient fire emergency response can be carried out during a fire accident, thereby ultimately improving the overall fire safety management level of the educational institution.

[0015] (2) The present invention collects structural data of the area to which the educational institution belongs and the inspection management data of the big data fire education cloud platform. By conducting all-round inspection and monitoring of the educational institution, that is, conducting targeted and refined inspections of fire sensors, fire cameras and fire equipment, the inspection indicator value of the area to which the educational institution belongs can be obtained, and corresponding measures can be taken in time to solve the fire hazard areas in the educational institution that fail the inspection, thereby improving the accuracy of subsequent fire warnings.

[0016] (3) The present invention conducts a fire emergency simulation for the pre-management education institution area, obtains the fire emergency simulation data of the pre-management education institution area, compares the evaluated fire emergency simulation evaluation index of the pre-management education institution area with the preset fire emergency simulation evaluation reference index, and finally inputs the fire emergency simulation process of the pre-management education institution area into the big data fire education cloud platform for visual display, making the big data fire education cloud platform an efficient and intelligent fire management system. Once an abnormal situation or fire signal is detected, the big data fire education cloud platform can immediately activate the emergency simulation plan and respond to the accident safely and accurately, improving the management intensity of fire emergency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the following drawings.

[0018] Figure 1 It is a schematic flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] Here, the application scenario of the present invention and each step of the method will be described in detail:

[0021] The scenario applicable to the present invention is the field of education institutions in large and dense places. Due to its complex structure and high population density, if a response plan can be made more accurately, efficiently, and safely before an accident occurs, it can ensure the substantial safety of the area and enable efficient fire protection work during the accident. Therefore, the present invention focuses on exploring the pre-fire emergency management intensity and making a series of judgment and analysis as follows:

[0022] Statistical analysis tools (such as SQL, R, Python) built into the big data fire education cloud platform are used to statistically extract the structural data of various regions (i.e., the functional coverage area of the present invention and the building main body volume) from the regional database (the data storage repository inside the big data fire education cloud platform, which will be explained in detail later and will not be elaborated here), and transmit them to the big data fire education cloud platform in real time through the edge computing gateway. At the same time, the big data fire education cloud platform has an integrated intelligent patrol device patrol page to collect data related to patrol management (i.e., the monitoring coverage area of the fire cameras of the present invention, the number of qualified inspections of fire sensors, the operation output inspection data corresponding to the fire cameras, and the historical fault data corresponding to the fire equipment); the big data fire education cloud platform uses the built-in data analysis module to determine the patrol indication value of the region where the educational institution is located based on the real-time received structural data, patrol management data, and historical fault data. If the patrol indication value of the region where the educational institution is located is greater than the preset patrol indication threshold, the big data fire education cloud platform marks this region as the pre-management educational institution region; the big data fire education cloud platform will automatically trigger the corresponding fire emergency simulation system through the cloud notification service, and at the same time synchronously send the structural data, patrol management data, and other associated data required for fire simulation (such as the positions of fire sensors, the positions of fire cameras, online status, etc.) corresponding to the pre-management educational institution region to this fire emergency simulation system. Thus, this fire emergency simulation system can conduct a comprehensive emergency simulation of the pre-management educational institution region by means of simulating fire scenarios, planning evacuation routes for personnel, etc. (This simulation process is based on the structural data, patrol management data, and other associated data required for fire simulation corresponding to the pre-management educational institution region. For example, if the building main body volume of a certain type of regional structure corresponding to the pre-management educational institution region is 100 cubic meters, then the building main body volume simulated and established during the emergency simulation process of this fire emergency simulation system should be 100 cubic meters); during the emergency simulation process, the big data fire education cloud platform uses the real-time monitoring interface of the fire emergency simulation system to obtain the fire emergency simulation data of the pre-management educational institution region in real time, and conducts data processing through the built-in data analysis module, and finally evaluates the fire emergency simulation evaluation index of this pre-management educational institution region; the big data fire education cloud platform compares the evaluated fire emergency simulation evaluation index with the preset fire emergency simulation evaluation reference index. If the fire emergency simulation evaluation index of the pre-management educational institution region is greater than the fire emergency simulation evaluation reference index, the big data fire education cloud platform notifies the fire emergency simulation system, enabling the fire emergency simulation system to automatically upload the simulation process to the big data fire education cloud platform through the data input interface, and the big data fire education cloud platform conducts visual display through visual display tools (such as large screen display, mobile device display).

[0023] Another explanation is made through the above process:

[0024] The big data fire education cloud platform is a comprehensive platform for fire management in the area where the educational institution is located. It has multiple capabilities such as data collection, data processing, signal sending, and signal receiving. Therefore, it can perform operations such as collecting structural data and determining patrol indicator values ​​in the present invention; the role of the edge computing gateway is to perform preliminary data processing and analysis near the data generation point, reduce the bandwidth consumption of transmitting a large amount of raw data to the cloud, and improve the real-time and response speed of data processing. It can integrate multiple communication protocols, support the access of different types of sensor data, and then upload the processed key information to the big data fire education cloud platform for subsequent data analysis.

[0025] The built-in data analysis module of the big data fire education cloud platform integrates advanced technologies such as big data processing, machine learning, and artificial intelligence, enabling the big data fire education cloud platform to provide comprehensive, accurate, and real-time data analysis services, thereby improving the effectiveness of fire education and safety management.

[0026] The big data fire education cloud platform marks the area as a pre-managed education institution area. This mark is usually displayed in the platform's visual interface in the form of a specific color, icon or label to facilitate quick identification.

[0027] The big data fire education cloud platform will automatically trigger the corresponding fire emergency simulation system through the cloud notification service. The conditions for triggering the fire emergency simulation are defined in the background settings of the platform, such as when the patrol indicator value of a certain area exceeds the preset threshold; once the triggering conditions are met, the platform's cloud notification service will automatically start, which is usually achieved through API calls, email services, SMS services or integrated instant messaging tools. For example, the platform can notify the relevant person in charge of security management about the upcoming fire emergency simulation through API calls. At the same time, in order for the fire emergency simulation system to accurately simulate, the platform needs to synchronously send the structural data, patrol management data and other related data that require fire simulation to the system corresponding to the pre-managed education institution area. This can be done in the following ways:

[0028] The platform and the fire emergency simulation system exchange data through the API interface. The API interface allows the platform to extract the required data (such as spatial layout, equipment location, historical inspection records, etc.) from the database and send the data to the fire emergency simulation system in JSON, XML or other formats. After receiving the data, the fire emergency simulation system will verify the data to ensure its integrity and accuracy. Then, the system will build or update the simulation scenario based on the received data and conduct simulated fire drills or training.

[0029] The fire emergency simulation system is a highly simulated and comprehensive training tool that can construct realistic scenarios such as buildings, urban blocks, or the interior of factories (this invention mainly focuses on educational institutions), and can simulate different types of fire occurrences, including incipient fires, rapidly spreading fires, etc. At the same time, it supports the drill of various emergency response strategies, such as evacuating people, using fire-fighting equipment, and implementing fire-extinguishing operations. This fire emergency simulation system provides a realistic and accurate emergency basis for actual rescue work.

[0030] If the fire emergency simulation evaluation index in the pre-managed educational institution area is greater than the fire emergency simulation evaluation reference index, the big data fire education cloud platform will notify the fire emergency simulation system to perform the next operation. The notification method can be that the big data fire education cloud platform docks with the fire emergency simulation system through the API interface. This method does not require manual intervention, and data interaction and instruction execution are directly carried out between systems.

[0031] After the fire emergency simulation system receives the notification, it automatically uploads the simulation process to the big data fire education cloud platform through the data input interface. The fire emergency simulation system sends the data generated during the simulation process (such as the evacuation path, time, equipment usage, emergency response efficiency, etc.) to the cloud platform through the API interface or data transfer protocol (such as FTP, SFTP). In this way, the big data fire education cloud platform can obtain the simulation process of the fire emergency simulation system in real time for subsequent analysis, optimization, and educational purposes.

[0032] Refer to Figure 1 As shown, the present invention provides a fire emergency management method for a big data education platform for educational institutions, including: inspection management determination: collecting the structural data of the area where the educational institution is located and the inspection management data of the big data fire education cloud platform, and thereby determining the inspection indication value of the area where the educational institution is located, and comparing it with the preset inspection indication threshold.

[0033] It should be elaborated that the reason for determining the inspection indication value of the area where the educational institution is located and comparing it with the preset inspection indication threshold is that subsequent fire emergency simulations require a certain amount of simulation resources. If the data input before the simulation deviates too much from the actual situation, it will lead to a largely ineffective simulation process in subsequent emergency simulations, wasting a certain amount of resource utilization and doing ineffective work. Therefore, in order to ensure that subsequent fire simulations have simulation data that is close to and more accurate than the actual situation, it is necessary to perform efficient data determination work before the simulation to ensure that the subsequent fire emergency simulations will not have an unsatisfactory simulation effect due to inaccurate data input.

[0034] Specifically, the comparison process of comparing the inspection indication value of the area where the educational institution is located with the preset inspection indication threshold is as follows:

[0035] If the inspection indication value of the area where the educational institution is located is greater than the preset inspection indication threshold value in the fire emergency simulation database, it indicates that the inspection management work of the educational institution has reached or exceeded the preset fire inspection standards. In other words, the educational institution has performed well in aspects such as fire safety inspection, maintenance of fire-fighting facilities, and ensuring the unobstructed fire passage, meeting or exceeding the specified inspection requirements; the inspection indication threshold value refers to a preset inspection qualification standard or index in the fire emergency simulation database, which is used to measure the management level of the educational institution in fire safety inspection and to judge whether the fire safety inspection of an educational institution has reached the due level; then, a fire emergency simulation is carried out on the area where the educational institution is located. If the inspection indication value of the area where the educational institution is located is less than or equal to the preset inspection indication threshold value, it indicates that there are deficiencies or non-compliance with the specified requirements in aspects such as fire safety inspection, maintenance of fire-fighting facilities, and ensuring the unobstructed fire passage in this area; then, the inspection indication values of various types of areas are extracted, that is, various types of areas corresponding to the inspection indication values less than or equal to the preset inspection indication threshold value are extracted (indicating that the inspection indication values of multiple types of areas are relatively lower than the standard values. Here, the inspection indication values of various types of areas can be equal to the various types of areas divided by the area where the educational institution is located, or less than the various types of areas divided by the area where the educational institution is located, because it is possible that the inspection indication values of some areas are greater than the preset inspection indication threshold value, but there are still some areas where the inspection indication values are less than or equal to the preset inspection indication threshold value), and the arrangement order of the inspection indication values is obtained. This arrangement order is to arrange the inspection indication values of various types of areas in descending order, which is used as the fire inspection and investigation order, and the fire inspection and investigation order is transmitted to the big data fire education cloud platform for management prompts. The specific management process is as follows:

[0036] First, the big data fire education cloud platform, through on-site inspection or other means, clarifies the specific problems and non-conforming points in the inspection management of various types of areas corresponding to the inspection indication values less than or equal to the preset inspection indication threshold value. For the identified problems, detailed improvement measures and schedules are formulated, which may include repairing or replacing equipment, installing fire-fighting equipment in areas not covered by fire emergency, etc.; the improvement plan is implemented (conducting inspection and investigation one by one according to the fire inspection and investigation order to prevent loopholes in the inspection of certain types of areas), and at the same time, a supervision mechanism is established to ensure the effective implementation of various measures, which may involve regular re-inspection, continuous monitoring of the equipment status, etc.; based on the implementation results, the inspection indication values of the above-mentioned inspected areas are determined again, and the inspection management of the area where this educational institution is located is comprehensively judged to ensure high-quality fire safety management.

[0037] Furthermore, the structural data of the area where the educational institution is located is collected, and the specific collection process is as follows:

[0038] The area to which the educational institution belongs is divided according to the functional area type and recorded as various areas, and the structural data of each area is counted. The big data fire education cloud platform uses GIS (geographic information system) to collect geographic information data such as the geographical location, building floor plan, functional zoning map of the educational institution, and at the same time collects the attribute data of the educational institution, such as purpose, area, number of people accommodated, distribution of fire protection facilities and other information; the geographic information data and attribute data are integrated together to form a unified regional database (stored inside the big data fire education cloud platform); the built-in machine learning algorithm (such as cluster analysis) of the big data fire education cloud platform is used to automatically classify according to the attribute characteristics of the educational institution, such as teaching area, experimental area, office area, accommodation area, canteen area, etc., so as to obtain the above-mentioned specific categories divided according to the functional area type; each functional area is assigned a unique identifier (ID); the classification information and structural data (such as area, number of fire protection facilities, number of people accommodated, etc.) of each functional area are stored in the regional database; thus, the built-in statistical analysis tools (such as SQL, R, Python) of the big data fire education cloud platform are used to count and extract the structural data of various areas from the regional database.

[0039] The structural data of each type of area include the functional coverage area of ​​each type of area and the main building volume of each type of area. The functional coverage area represents the area occupied by each type of area. For example, if a certain type of area has one floor, the area occupied is the functional coverage area of ​​one floor. If a certain type of area has five floors, the area occupied is the functional coverage area of ​​five floors. The main building volume represents the total volume of the building. For example, if a certain type of area is a teaching area and has three floors, the main building volume is the total spatial volume of the three-story teaching area, including the internal classroom area, corridor area, and staircase area and other spatial building volumes.

[0040] The structural data of each type of region is used as the structural data of the region to which the educational institution belongs.

[0041] Specifically, the inspection management data of the big data fire education cloud platform refers to the most recent inspection data before the fire emergency simulation, which indicates the length of time spent on fire inspections of educational institutions, usually including the time consumed from the start of the inspection to the completion of all scheduled inspection items; specifically including the monitoring coverage area of ​​fire cameras in various areas and the number of qualified fire sensor inspections in various areas, and also including the operation output inspection data corresponding to each fire camera in various areas. The inspection management data of the big data fire education cloud platform are all obtained from the inspection page of the integrated intelligent inspection equipment of the big data fire education cloud platform. The detailed acquisition process is not explained in this example; and the fire camera monitoring coverage area indicates the area that can be monitored by the fire camera when inspecting the area, and this area is less than or equal to the area covered by the above-mentioned function.

[0042] Among the operation output inspection data corresponding to each fire camera in each type of area, it specifically includes the online duration, memory occupancy, and minimum picture output resolution corresponding to each fire camera in each type of area.

[0043] Furthermore, the process of specifically determining the inspection indication value of the area to which the educational institution belongs is as follows:

[0044] The monitored coverage area of the fire cameras in each type of area is processed by taking the ratio with the functional coverage area of each type of area to obtain the monitored coverage area ratio of each type of area, specifically by dividing the monitored coverage area of the fire camera by the functional coverage area.

[0045] The building main body volume of each type of area is matched with the reference qualified quantity of fire sensors corresponding to each building main body volume interval preset in the fire emergency simulation database to obtain the reference qualified quantity of fire sensors for each type of area. The matching process is as follows:

[0046] Each building main body volume interval corresponds to the reference qualified quantity of fire sensors. Query the building main body volume interval to which the building main body volume of a certain type of area belongs, then the reference qualified quantity of fire sensors corresponding to this building main body volume interval is the reference qualified quantity of fire sensors for this type of area.

[0047] The above-mentioned corresponding reference qualified quantity of fire sensors is obtained by matching the building main body volume intervals. Specifically, because the space that a fire sensor can cover is fixed, but since the fire sensors can be distributed at different positions in the area, there will be a situation where the coverage areas of the sensors overlap. Therefore, when the sizes of the building main body volumes are relatively close, there will be a large degree of the same number of fire sensors. In the fire emergency simulation database, the interval matching rules have been preset in advance. According to different building main body volumes, the building main body volume interval where it is located can be obtained, and thus the reference qualified quantity of fire sensors can be known. The specific interval matching rules are not described in detail in this example. Only a specific example is used for illustration. For example, if the building main body volumes are 80 cubic meters and 90 cubic meters, the reference qualified quantity of fire sensors obtained by matching is 2 each. However, the positions where the fire sensors are placed may be different, and the covered space areas will also be different, but ultimately the fire sensors will completely cover the space.

[0048] Based on the monitored coverage area ratio of each type of area, the qualified quantity of fire sensor inspections in each type of area, and the reference qualified quantity of fire sensors in each type of area, the first inspection characterization index of each type of area is comprehensively processed and determined. The specific expression is as follows:

[0049]

[0050] In the formula, MCR k is the monitoring coverage area ratio of the k-th type of area, specifically referring to the ratio of the area monitored and covered by the fire camera in the area to the fixed floor area of the area building. The larger the ratio, the closer the area covered by the fire camera is to the floor area.

[0051] is the reference monitoring coverage area ratio of the k-th type of area preset in the fire emergency simulation database, specifically referring to the situation where the area covered by the fire camera is equal to the fixed floor area of the area building, that is, the fire camera completely covers the floor area of the area.

[0052] CQA k is the qualified number of fire sensor inspections in the k-th type of area, specifically referring to the inspection quantity corresponding to the volume of the building main body covered by the fire sensors.

[0053] is the reference qualified number of fire sensors in the k-th type of area, specifically referring to the adapted quantity corresponding to the situation where the fire sensors completely cover the volume of the building main body.

[0054] e is the natural constant, k is the number of each type of area, k = 1, 2, 3,..., p, and p is the total number of types of areas.

[0055] is the first inspection characterization index of the k-th type of area. In this example, during the inspection of educational institutions, it is crucial to determine whether fire-fighting equipment (i.e., fire sensors, fire cameras, etc.) can completely cover the areas belonging to educational institutions. Because if there is a large difference between the inspection results and the area building (for example, the fire camera does not cover the entire floor area of the building, or the fire sensor fails to cover the entire building volume), it will not only affect real-time accident monitoring, but also increase the spread of accidents, and will also have a certain negative impact on the subsequent fire emergency simulation in this example. At the same time, both the fire camera and the fire sensor belong to fire-fighting equipment, and there is a great close relationship between them. If the fire camera fails to monitor and cover the entire area, even if the fire sensor detects the occurrence of an accident, the total platform cannot view it in real time through the camera, affecting subsequent rescue work. Similarly, if the fire sensor fails to monitor and cover the entire building volume, even if the fire camera monitors the accident scene, the total platform cannot send a fire extinguishing instruction to the nearby fire sensors, which will also affect subsequent rescue work. To sum up, during the fire inspection, it is necessary to focus on inspecting the emergency management level of fire-fighting equipment to improve the subsequent efficient accident-solving ability.

[0056] Obtain the historical fault data corresponding to each fire-fighting equipment in each type of area, specifically including the number of faults corresponding to each fire-fighting equipment in each type of area and the duration of each fault. The historical fault data corresponding to each fire-fighting equipment in each type of area are all obtained from the integrated intelligent patrol equipment inspection page of the big data fire education cloud platform. The detailed acquisition process is not explained in this example; the fire-fighting equipment includes but is not limited to the fire cameras and fire sensors mentioned above.

[0057] Based on the online time, memory usage, and minimum resolution of the screen output of each fire camera in each area, as well as the number of faults and duration of each fault of each fire equipment in each area, the second inspection characterization index of each area is determined through comprehensive processing. The specific expression is as follows:

[0058]

[0059] Among them, CPI kc It is the patrol representation index corresponding to the c-th fire camera in the k-th area, and is the representation value obtained by comprehensive processing of the online time, memory usage, and minimum resolution of the screen output corresponding to the fire camera.

[0060] B 1 The inspection representation weight factor corresponding to the fire camera preset in the fire emergency simulation database can be directly obtained from the fire emergency simulation database when used, and the corresponding relationship can be a preset mapping relationship. For example, the memory occupancy of the fire camera and the inspection representation weight factor corresponding to the fire camera preset in the fire emergency simulation database form a mapping set, and the real-time fire camera memory occupancy mapping set is used to obtain the inspection representation weight factor corresponding to the fire camera. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0061] FPI ka It is the inspection characterization index corresponding to the a-th fire-fighting equipment in the k-th area, and is the characterization value obtained by comprehensive processing of the number of faults corresponding to the fire-fighting equipment and the duration of each fault.

[0062] B 2The inspection characterization weight factor corresponding to the fire-fighting equipment preset in the fire emergency simulation database can be directly obtained from the fire emergency simulation database during use. The corresponding relationship can be a pre-set mapping relationship. For example, the number of faults occurred corresponding to the fire-fighting equipment forms a mapping set with the inspection characterization weight factor corresponding to the fire-fighting equipment preset in the fire emergency simulation database, and the inspection characterization weight factor corresponding to the fire-fighting equipment is obtained by mapping the real-time mapping set of the number of faults occurred corresponding to the fire-fighting equipment. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0063]

[0064] In the formula, OLT kc is the online duration corresponding to the c-th fire camera in the k-th type of area, which represents the online time length of the fire camera from the start of online to the current time point, excluding offline, fault and other times.

[0065] is the online reference duration corresponding to the c-th fire camera preset in the fire emergency simulation database, which represents a reference online time length set for the fire camera.

[0066] MEM kc is the memory occupancy corresponding to the c-th fire camera in the k-th type of area, which represents the memory occupancy during the real-time video processing of the fire camera during the inspection of the fire camera. The memory occupancy includes video cache and temporary memory required for encoding / decoding.

[0067] is the memory reference occupancy corresponding to the c-th fire camera preset in the fire emergency simulation database, which represents a reference memory occupancy value defined when the fire camera processes video in real time.

[0068] LMR kc is the minimum output resolution of the picture corresponding to the c-th fire camera in the k-th type of area, which represents the minimum resolution of the picture output by the fire camera during the inspection of the fire camera.

[0069] LMR Δ is the defined resolution for picture output corresponding to the fire camera preset in the fire emergency simulation database, which represents a preset minimum resolution of the picture output by the fire camera. If it is lower than this value, the picture output by the fire camera will be blurred, stuck and other phenomena.

[0070] FC ka is the number of faults occurred corresponding to the a-th fire-fighting equipment in the k-th type of area, which represents the total number of faults occurred by the fire-fighting equipment from the start of work to this inspection time.

[0071] The number of permitted fault occurrences corresponding to the ath fire protection device preset for the fire emergency simulation database, which represents the maximum permitted number of faults of the specified fire protection device.

[0072] FD kaq The duration corresponding to the qth fault of the ath fire protection device in the kth type of area, which represents the duration of each fire protection device during a fault. For example, when a fire camera fails, it is the time when the real-time monitoring image is not available.

[0073] It is the second inspection characterization index for the kth type of area. In this example, the fire camera is not only related to the above-mentioned area structure, but its own factors will also affect the inspection results. That is, if the online duration of the fire camera deviates from the specified reference value, the shorter the online duration of the fire camera, the less conducive it is to the alarm behavior during an accident. Similarly, if the online duration of the fire camera is longer, the longer its own usage time, due to environmental and other influencing factors during use, there will be negative phenomena such as a relatively blurred output image, which will increase the possibility of alarm errors. And during the process of outputting the image, due to the blurred image, the memory occupied by the output will fluctuate greatly, that is, the memory occupancy exceeds the defined reference value. At the same time, if the memory occupancy is much lower than the defined reference value, the output image will be quite different from the actual image, and there is a possibility of image loss, that is, the resolution of the output image is lower than the specified minimum value, which is not conducive to subsequent actions such as the overall platform locating the accident location through the transmitted images, increasing the possibility of accident spread.

[0074] During the process of fire inspection of educational institutions, it is not only necessary to strictly inspect the operation of fire cameras, but other fire protection devices will also have a great impact on fire emergency. That is, if a fire protection device has a large number of faults and exceeds the specified number of faults and is not replaced in time, this fire protection device will not be able to play an effective emergency role during an accident. Moreover, the more faults a fire protection device has, the more problems will occur, and the longer the repair time will be. And the repair time is the fault duration, that is, the fault duration will increase with the increase in the number of faults, ultimately being not conducive to the efficient resolution of accidents.

[0075] In summary, the operation and faults of fire-fighting equipment will both affect the inspection results of educational institutions. At the same time, there is also a close relationship between the operation and faults of fire-fighting equipment. If the operation efficiency of fire-fighting equipment is not high (i.e., the online duration deviates from the reference value, the memory occupancy deviates from the reference value, or the screen output resolution is lower than the defined value), it indicates that there are problems in the operation process of this fire-fighting equipment, increasing the risk of the fire-fighting equipment malfunctioning, that is, being unable to operate. And if the number of faults of the fire-fighting equipment exceeds the specified permitted number of faults or the fault time of the fire-fighting equipment is relatively long, maintenance or even replacement is required after inspection, increasing the inspection time, that is, the negative value of the inspection indication value of the area where the educational institution is located in this expression becomes larger and larger, ultimately affecting the subsequent simulated emergency process.

[0076] k is the number of various types of areas, k = 1, 2, 3,..., p, where p is the total number of types of areas; c is the number of each fire camera, c = 1, 2, 3,..., f, where f is the total number of fire cameras; a is the number of each fire-fighting equipment, a = 1, 2, 3,..., i, where i is the total number of fire-fighting equipment; q is the number of each fault, q = 1, 2, 3,..., u, where u is the total number of faults.

[0077] By synthesizing the first inspection characterization index of various types of areas and the second inspection characterization index of various types of areas, the inspection indication value of the area where the educational institution is located is obtained through data processing. The inspection indication value of the area where the educational institution is located is used to represent the management degree of inspecting the area where the educational institution is located, and is used to provide a data basis for fire emergency simulation in the pre-managed area of the educational institution.

[0078] Specifically, the inspection indication value of the area where the educational institution is located has the following specific expression:

[0079]

[0080] In the formula, is the first inspection characterization index of the k-th type of area, is the second inspection characterization index of the k-th type of area.

[0081] A 1 is the weight factor corresponding to the first inspection characterization index preset in the fire emergency simulation database. When used, the weight factor corresponding to the first inspection characterization index can be directly obtained from the fire emergency simulation database, and its corresponding relationship can be a pre-set mapping relationship. For example, the proportion of the monitoring coverage area and the weight factor corresponding to the first inspection characterization index preset in the fire emergency simulation database form a mapping set, and the real-time proportion of the monitoring coverage area is mapped to obtain the weight factor corresponding to the first inspection characterization index. The mapping relationship therein can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0082] A 2 It is the weight factor corresponding to the second inspection characterization index preset for the fire emergency simulation database. When in use, the weight factor corresponding to the second inspection characterization index can be directly obtained from the fire emergency simulation database, and its corresponding relationship can be a preset mapping relationship. For example, the online duration of the fire camera forms a mapping set with the weight factor corresponding to the second inspection characterization index preset in the fire emergency simulation database, and the weight factor corresponding to the second inspection characterization index is obtained by mapping the real-time online duration mapping set of the fire camera. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0083] k is the number of various regions, k = 1, 2, 3,..., p, and p is the total number of types of regions.

[0084] ξ is the inspection indication value of the area where the educational institution is located. In this example, when inspecting an educational institution, it includes multiple factors that need to be inspected. Not only the operation and faults of various fire-fighting equipment need to be inspected, but also the structure of the area where the educational institution is located will affect the inspection results. If we want to ensure the good quality of the inspection results of the educational institution, we need to ensure that the fire-fighting equipment can cover all the structures of the educational institution area, because accidents may occur at various locations in the educational institution area and cannot be quantitatively evaluated. Therefore, when conducting inspections, it is necessary to make the fire-fighting equipment able to monitor various locations in the area, and at the same time ensure that the fire-fighting equipment can perform behaviors such as real-time video transmission and accident alarm. Then, it is necessary to analyze the operation and faults of the fire-fighting equipment, and repair or replace them in time to provide a data basis for the effective utilization of resources in subsequent fire emergency simulations.

[0085] In a specific embodiment, the present invention collects the structural data of the area where the educational institution is located and the inspection management data of the big data fire education cloud platform. By comprehensively inspecting and monitoring the educational institution, that is, conducting targeted and refined inspections on fire sensors, fire cameras, and fire-fighting equipment, the inspection indication value of the area where the educational institution is located can be obtained, and fire hazard areas with unqualified inspections in the educational institution can be discovered in time and corresponding measures can be taken to improve the accuracy of subsequent fire warnings.

[0086] Fire emergency simulation evaluation: The area where the educational institution is located with an inspection indication value greater than the inspection indication threshold is recorded as the pre-management educational institution area. Then, fire emergency simulation is carried out on the pre-management educational institution area to obtain the fire emergency simulation data of the pre-management educational institution area, and the fire emergency simulation evaluation index of the pre-management educational institution area is evaluated.

[0087] It should be elaborated that the above fire emergency simulation of the pre - management education institution area is because the fire emergency simulation system can comprehensively simulate the emergency situation of the pre - management education institution area by means of simulating fire scenes, planning personnel evacuation routes, etc. The specific simulation process is as follows:

[0088] Automatically start the simulation according to specific conditions (such as the sensor detects abnormal smoke and temperature). Once the fire trigger point is determined, the fire emergency simulation system begins to simulate the fire scene, including the following elements:

[0089] The fire emergency simulation system simulates different types of fires (such as electrical fires, chemical fires, oil fires, etc.) and their sources (such as electrical equipment, chemical laboratories). According to factors such as fire type, building materials, and ventilation conditions, the system simulates the spread process of the fire, including the spread speed and direction of flames and smoke, and simulates the temperature change after the fire occurs and its impact on the environment and building structure. While simulating the fire scene, the system begins to simulate and plan the personnel evacuation route:

[0090] According to the layout of the building, the location of the fire, the spread direction of the fire, and the current environmental conditions, the system automatically generates the best evacuation route. Since the specific route generation process is automatically generated by the fire emergency simulation system and the automatic generation process is not the focus of this example, the above - mentioned route generation process will not be explained in detail in this example. Considering the density and movement speed of people in the building (based on different population characteristics, such as students, teachers, people with disabilities, etc.), the system simulates the movement of people during the evacuation process. The system simulates the obstacles (such as collapsed building materials, closed doors) and personnel congestion that may be encountered during the evacuation process, and dynamically adjusts the evacuation route. During the personnel evacuation process, the system also simulates the emergency response measures:

[0091] Simulate the use and effects of fire - fighting equipment such as fire hydrants and fire extinguishers, simulate the arrival time, action route, and fire - fighting operations of firefighters, and simulate the arrival of the first - aid team and the treatment process of the injured. As the emergency response progresses, the system simulates the process of fire control and extinguishment:

[0092] Simulate the use of fire extinguishing agents (such as water, foam, dry powder) and the gradual control of the fire. After the main fire is extinguished, the system assesses whether there is a risk of reignition and potential fire sources that need further treatment, simulates the cooling of the building and the discharge of smoke to ensure safety, and finally deals with the potential fire sources to complete the entire fire emergency simulation process; during this process, the fire emergency simulation system continuously monitors the temperature change after fire extinguishing. If the temperature remains higher than the temperature threshold preset in the fire emergency simulation database (the temperature threshold here refers to the preset temperature value used to judge whether there is a risk of reignition after fire extinguishing) within a certain period after fire extinguishing, it may mean a potential risk of reignition. Therefore, the fire emergency simulation system can determine that the fire has not been completely extinguished.

[0093] As mentioned above, before the fire emergency simulation system conducts simulation, it is necessary to input various data of the educational institution area, including but not limited to:

[0094] The building floor plan, floor distribution map, and room structure diagram of the educational institution, information such as the building material, fire resistance rating, and fire compartment of the building; the distribution of personnel in the educational institution, including the number and location of teaching staff, students, and visitors, information on special groups (such as children, the elderly, and the disabled), and personnel flow characteristics (such as peak time periods and personnel density distribution); the distribution map of fire-fighting facilities, including fire extinguishers, fire hydrants, fire alarms, emergency lighting, etc., the location of safety exits and their accessibility data; environmental data (such as temperature, humidity, wind speed inside and outside the building), historical fire records and emergency event response data of the educational institution; real-time monitoring data, such as camera video streams, the status of the fire alarm system, sensor data (smoke, temperature, etc.), and real-time pedestrian flow monitoring data for dynamically adjusting evacuation routes and strategies.

[0095] Specifically, the fire emergency simulation data of the pre-managed educational institution area specifically includes the total fire simulation duration, evacuation duration, response duration corresponding to each fire-fighting equipment, and the total amount of fire extinguishing agent used. The above fire emergency simulation data are all extracted by the big data fire education cloud platform from the real-time monitoring interface of the fire emergency simulation system; among them, the evacuation duration refers to the time length for all personnel in the area to move away from the accident area and be in a safe position, and the total fire simulation duration refers to the time length from the start time point of the accident to the complete resolution of the accident.

[0096] Furthermore, the process of evaluating the fire emergency simulation evaluation index of the pre-managed educational institution area is as follows:

[0097] Perform a ratio process on the evacuation duration and the total fire simulation duration of the pre-managed educational institution area to obtain the evacuation duration ratio of the pre-managed educational institution area, specifically, the evacuation duration divided by the total fire simulation duration.

[0098] Accumulate and calculate the average value of the response times of each fire protection device in the pre - management education institution area in sequence. Specifically, first accumulate the response times of each fire protection device in the pre - management education institution area to obtain the total response time of the fire protection devices in the pre - management education institution area, and then calculate the average value, that is, divide by the total number of fire protection devices, thereby obtaining the average response time of the fire protection devices in the pre - management education institution area.

[0099] Based on the evacuation time ratio in the pre - management education institution area, the average response time of the fire protection devices in the pre - management education institution area, and the total amount of fire extinguishing agent used in the pre - management education institution area, comprehensively process and evaluate the fire emergency simulation evaluation index of the pre - management education institution area. The fire emergency simulation evaluation index of the pre - management education institution area is used to represent the evaluation degree of the fire emergency simulation of the pre - management education institution area.

[0100] Specifically, the fire emergency simulation evaluation index of the pre - management education institution area has the following specific expression:

[0101]

[0102] In the formula, EPR is the evacuation time ratio in the pre - management education institution area, representing the ratio of the evacuation time in this fire emergency simulation to the total fire protection time.

[0103] ARR is the average response time of the fire protection devices in the pre - management education institution area, representing the average value of the response times of the fire protection devices in this fire emergency simulation. The response time of each fire protection device represents the time length from the accident occurrence time point to the time when the fire protection device alarms.

[0104] TFA is the total amount of fire extinguishing agent used in the pre - management education institution area, representing the total usage amount of various fire extinguishing agents that can extinguish fires, including but not limited to foam fire extinguishing agents, dry powder fire extinguishing agents, carbon dioxide fire extinguishing agents, and metal fire extinguishing agents.

[0105] TFA Δ Is the preset total reference amount of fire extinguishing agent used in the fire emergency simulation database, representing the reference usage amount of the fire extinguishing agent predicted for this fire emergency simulation.

[0106] E 1The influence factor corresponding to the unit value of the evacuation duration ratio preset for the fire emergency simulation database. When in use, the influence factor corresponding to the unit value of the evacuation duration ratio can be directly obtained from the fire emergency simulation database, and its corresponding relationship can be a pre-set mapping relationship. For example, the evacuation duration ratio and the influence factor corresponding to the unit value of the evacuation duration ratio preset in the fire emergency simulation database form a mapping set, and the real-time evacuation duration ratio mapping set is used to obtain the influence factor corresponding to the unit value of the evacuation duration ratio. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0107] E 2 The influence factor corresponding to the unit value of the average response duration of fire-fighting equipment preset for the fire emergency simulation database. When in use, the influence factor corresponding to the unit value of the average response duration of fire-fighting equipment can be directly obtained from the fire emergency simulation database, and its corresponding relationship can be a pre-set mapping relationship. For example, the average response duration of fire-fighting equipment and the influence factor corresponding to the unit value of the average response duration of fire-fighting equipment preset in the fire emergency simulation database form a mapping set, and the real-time average response duration of fire-fighting equipment mapping set is used to obtain the influence factor corresponding to the unit value of the average response duration of fire-fighting equipment. The mapping relationship therein can be a one-to-one or many-to-one relationship. In this example, its value range is [0, 1].

[0108] e is the natural constant.

[0109] ψ is the fire emergency simulation evaluation index for the pre-management education institution area. In this example, in order to determine whether the simulated fire in the education institution area is higher than the specified simulated effective process, it is necessary to conduct a quantitative analysis of the data during the simulation process; if the response duration of the fire-fighting equipment is relatively long when an accident occurs, it will increase the spread of the accident. At the same time, if the personnel receive the instruction to evacuate to a safe area relatively late, there is a possibility that some personnel will not evacuate safely according to the instruction, which will increase the evacuation duration, that is, the proportion of the simulated evacuation duration in the total fire duration is too large. This will not only increase the possibility of personnel being in danger, but also cause the subsequent emergency work to be unable to be carried out effectively, that is, the fire extinguishing agent does not effectively aim at the fire source, and finally the usage amount of the fire extinguishing agent is higher than the predicted reference usage amount, resulting in low efficiency of this fire emergency simulation and reducing the effectiveness of the simulation. And if the usage amount of the fire extinguishing agent is much lower than the predicted reference usage amount, there is a possibility that the fire source will not be completely extinguished, which will also make the efficiency of this fire emergency simulation relatively low; in summary, the response duration of the fire-fighting equipment, the evacuation duration ratio, and the usage amount of the fire extinguishing agent can comprehensively reflect the effect of this fire emergency simulation.

[0110] In a specific embodiment, the present invention conducts a fire emergency simulation on the pre-management education institution area, obtains the fire emergency simulation data of the pre-management education institution area, compares the evaluated fire emergency simulation evaluation index of the pre-management education institution area with the preset fire emergency simulation evaluation reference index, and finally inputs the fire emergency simulation process of the pre-management education institution area into the big data fire education cloud platform for visual display, making the big data fire education cloud platform an efficient and intelligent fire management system. Once an abnormal situation or fire signal is detected, the big data fire education cloud platform can immediately activate the emergency simulation plan and respond to the occurring accident safely and accurately, improving the management strength of fire emergency response.

[0111] Fire emergency management feedback: Compare the fire emergency simulation evaluation index of the pre-management education institution area with the preset fire emergency simulation evaluation reference index, and finally conduct management feedback on the fire emergency simulation process of the pre-management education institution area.

[0112] Specifically, the management feedback on the fire emergency simulation process of the pre-management education institution area is as follows:

[0113] Compare the fire emergency simulation evaluation index of the pre - managed education institution area with the fire emergency simulation evaluation reference index preset in the fire emergency simulation database. The fire emergency simulation evaluation reference index is a benchmark or target value set based on multiple factors such as industry standards, historical accident analysis, and multiple practices. It represents an ideal or expected level of fire emergency response ability and is used to evaluate and compare the fire emergency preparedness of different areas. If the fire emergency simulation evaluation index of the pre - managed education institution area is greater than the fire emergency simulation evaluation reference index preset in the fire emergency simulation database, it indicates that the fire emergency preparedness and response ability of this area have exceeded a certain standard or expectation. In other words, when dealing with fires or other emergencies, the overall performance of this area is more outstanding. For example, it has reached a relatively high level in aspects such as the evacuation speed of personnel, the response time of fire - fighting equipment, and the distribution efficiency of emergency resources. Then, through the fire emergency simulation of the pre - managed education institution area, extract the fire emergency simulation process of the pre - managed education institution area. Then input the fire emergency simulation process of the pre - managed education institution area into the big - data fire education cloud platform and conduct visual display. The purpose is to intuitively and comprehensively present the whole process of fire emergency, including but not limited to multiple links such as fire detection, alarm, personnel evacuation, fire - fighting equipment response, and the dispatching and coordination of the emergency command center. And the purpose of visualization is to remind the relevant personnel of the education institution that in case of a fire, they can carry out corresponding fire emergency work according to the simulated process shown visually. If the fire emergency simulation evaluation index of the pre - managed education institution area is equal to or less than the fire emergency simulation evaluation reference index, it indicates that the fire emergency preparedness and response ability of this area fails to reach the preset standard or expectation, meaning that there are certain defects or inefficiencies in aspects such as fire prevention, alarm response, personnel evacuation, and fire - fighting equipment operation. Then, conduct management feedback on the fire emergency simulation process of the pre - managed education institution area. Here, management feedback is carried out instead of visual display because the fire emergency simulation system requires certain simulation resources to transmit the simulation process, to prevent the ineffective use of the simulation resources of the fire emergency simulation system, and also to avoid misleading the relevant personnel of the education institution. Because if the evaluation is not high or the wrong process with large casualties is visually displayed, some people will mistakenly think that the simulation operation of this process is correct and efficient, which is not conducive to fire emergency in case of an actual accident. The specific feedback process is as follows:

[0114] First, when the big data fire protection education cloud platform senses that the fire emergency simulation evaluation index is equal to or less than the fire emergency simulation evaluation reference index, it directly sends a warning signal that the fire emergency simulation process does not meet the standard to the fire emergency simulation system, causing the fire emergency simulation system to immediately stop the fire simulation operation. At the same time, the big data fire protection education cloud platform determines the reasons for the low fire emergency simulation evaluation index caused by equipment failures, unsmooth processes, etc. According to the explored reasons, specific improvement measures and plans are formulated, which may include equipment maintenance and updates, optimization of emergency processes, strengthening of daily management and supervision, etc. The improvement plan is implemented (i.e., the big data fire protection education cloud platform sends the improvement plan and measures to the fire emergency simulation system to re-conduct the fire emergency simulation in the pre-management education institution area), and the effect is continuously monitored during the simulation process to ensure the effectiveness of the improvement measures. After the fire emergency simulation process is completed, the big data fire protection education cloud platform re-evaluates the fire emergency simulation evaluation index, compares it with the reference value, and confirms the improvement effect for subsequent adjustment and optimization. Through the above series of management feedback processes, the fire emergency response ability in the pre-management education institution area can be effectively improved, ensuring that in the event of an actual fire or other emergencies, it can respond quickly and effectively to protect the safety of personnel's lives.

[0115] In a specific embodiment, the present invention provides a fire emergency management method for a big data education platform of an educational institution. By quantitatively comparing the structural data of the area where the educational institution is located and the inspection management data of the big data fire protection education cloud platform, and integrating the quantitative analysis of the data of fire protection equipment, sensors, and cameras, more accurate inspection indication values for the area where the educational institution is located can be determined. Fire emergency simulation is carried out in the pre-management education institution area, and the fire emergency simulation evaluation index of the pre-management education institution area is evaluated, greatly reducing the possibility of inaccurate pre-disaster prediction results. It is compared with the preset fire emergency simulation evaluation reference index, and the fire emergency simulation process of the pre-management education institution area is input into the big data fire protection education cloud platform and visually displayed to improve the emergency response ability to real fire incidents, that is, to be able to conduct efficient fire emergency response during the fire accident process, and ultimately improve the overall fire safety management level of the educational institution.

[0116] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A fire emergency management method for a big data education platform of an educational institution, characterized in that: include: Inspection management determination: Collect the structural data of the area where the educational institution belongs and the inspection management data of the big data fire education cloud platform, thereby determining the inspection indicator value of the area where the educational institution belongs, and compare it with the preset inspection indicator threshold; Fire emergency simulation evaluation: The area to which the educational institution belongs, where the patrol indicator value is greater than the patrol indicator threshold, is recorded as the pre-management educational institution area, and a fire emergency simulation is performed on the pre-management educational institution area to obtain the fire emergency simulation data of the pre-management educational institution area, and the fire emergency simulation evaluation index of the pre-management educational institution area is evaluated; Fire emergency management feedback: Compare the fire emergency simulation evaluation index of the pre-management education institution area with the preset fire emergency simulation evaluation reference index, and finally provide management feedback on the fire emergency simulation process in the pre-management education institution area.

2. According to claim 1, a fire emergency management method for a big data education platform of an educational institution is characterized in that: The structural data of the region to which the educational institution belongs is collected, and the specific collection process is as follows: The areas to which educational institutions belong are divided according to the functional area types and recorded as various types of areas, and the structural data of each type of area is thus counted; The structural data of each type of area includes the functional coverage area of ​​each type of area and the main building volume of each type of area; The structural data of each type of region is used as the structural data of the region to which the educational institution belongs.

3. According to claim 1, a fire emergency management method for a big data education platform of an educational institution is characterized in that: The inspection management data of the big data fire education cloud platform specifically includes the monitoring coverage area of ​​fire cameras in various areas and the number of fire sensors in various areas that have passed the inspection, and also includes the operation output inspection data corresponding to each fire camera in various areas; The operation output inspection data corresponding to each fire camera in each area includes the online time, memory usage and minimum resolution of the screen output of each fire camera in each area.

4. The fire emergency management method for a big data education platform of an educational institution according to claim 3, characterized in that: The specific determination process of determining the inspection indicator value of the area to which the educational institution belongs is as follows: The monitoring coverage area of ​​the fire cameras in each area is processed by ratio with the functional coverage area of ​​each area to obtain the monitoring coverage area ratio of each area; Matching the building main volume of each type of area with the reference qualified number of fire sensors corresponding to each preset building main volume interval to obtain the reference qualified number of fire sensors in each type of area; Based on the proportion of monitoring coverage area of ​​each type of area, the number of fire sensor inspections that have passed in each type of area, and the number of fire sensor reference inspections that have passed in each type of area, the first inspection characterization index of each type of area is determined through comprehensive processing; Obtain the historical fault data corresponding to each fire-fighting equipment in each area, including the number of faults corresponding to each fire-fighting equipment in each area and the duration of each fault; Based on the online time, memory usage, and minimum resolution of the screen output of each fire camera in each area, as well as the number of faults and duration of each fault of each fire equipment in each area, the second inspection characterization index of each area is determined through comprehensive processing; The inspection index values ​​of the areas to which educational institutions belong are obtained through data processing by combining the first inspection characterization indicators of various types of areas and the second inspection characterization indicators of various types of areas. The inspection index values ​​of the areas to which educational institutions belong are used to characterize the degree of management of inspections of the areas to which educational institutions belong, and to provide data basis for fire emergency simulations for pre-management areas of educational institutions.

5. A fire emergency management method for a big data education platform of an educational institution according to claim 4, characterized in that: The inspection indicator value of the area to which the educational institution belongs is specifically expressed as: Where ξ is the inspection indicator value of the area where the educational institution belongs, is the first inspection characterization indicator of the k-th area, is the second inspection characterization index of the k-th type of area, A1 is the weight factor corresponding to the preset first inspection characterization index, A2 is the weight factor corresponding to the preset second inspection characterization index, k is the number of each type of area, k=1,2,3,...,p, p is the total type of area.

6. The fire emergency management method for a big data education platform of an educational institution according to claim 1, characterized in that: The patrol indicator value of the area to which the educational institution belongs is compared with the preset patrol indicator threshold value. The specific comparison process is as follows: If the inspection indicator value of the area to which the educational institution belongs is greater than the preset inspection indicator threshold, a fire emergency simulation will be carried out on the area to which the educational institution belongs. If the inspection indicator value of the area to which the educational institution belongs is less than or equal to the preset inspection indicator threshold, the inspection indicator values ​​of each type of area will be extracted, and the arrangement order of the inspection indicator values ​​will be obtained as the fire inspection order, and the fire inspection order will be transmitted to the big data fire education cloud platform for management prompts.

7. The fire emergency management method for a big data education platform of an educational institution according to claim 1, characterized in that: The fire emergency simulation data of the pre-managed educational institution area specifically includes the total fire simulation duration, evacuation duration, corresponding response time of each fire-fighting equipment and the total amount of fire extinguishing agent used.

8. A fire emergency management method for a big data education platform of an educational institution according to claim 7, characterized in that: The fire emergency simulation evaluation index of the pre-management education institution area is evaluated, and the specific evaluation process is as follows: The evacuation time of the pre-managed educational institution area is processed by ratio with the total fire simulation time to obtain the evacuation time ratio of the pre-managed educational institution area; The corresponding response time of each fire-fighting equipment in the pre-managed educational institution area is sequentially accumulated and averaged to obtain the average response time of the fire-fighting equipment in the pre-managed educational institution area; Therefore, based on the evacuation time ratio of the pre-managed educational institution area, the average response time of fire-fighting equipment in the pre-managed educational institution area and the total amount of fire extinguishing agent used in the pre-managed educational institution area, a fire emergency simulation evaluation index of the pre-managed educational institution area is comprehensively processed and evaluated. The fire emergency simulation evaluation index of the pre-managed educational institution area is used to characterize the evaluation degree of the fire emergency simulation in the pre-managed educational institution area.

9. A fire emergency management method for a big data education platform of an educational institution according to claim 8, characterized in that: The fire emergency simulation evaluation index of the pre-management educational institution area is specifically expressed as: Where, ψ is the fire emergency simulation evaluation index of the pre-management education institution area, EPR is the evacuation time ratio of the pre-management education institution area, ARR is the average response time of fire equipment in the pre-management education institution area, TFA is the total amount of fire extinguishing agent used in the pre-management education institution area, TFA Δ is the preset reference total amount of fire extinguishing agent used, E1 is the impact factor corresponding to the preset unit value of the evacuation time ratio, E2 is the impact factor corresponding to the preset unit value of the average response time of fire-fighting equipment, and e is a natural constant.

10. The fire emergency management method for a big data education platform of an educational institution according to claim 1, characterized in that: The management feedback of the fire emergency simulation process in the pre-management education institution area is as follows: The fire emergency simulation evaluation index of the pre-managed educational institution area is compared with the preset fire emergency simulation evaluation reference index. If the fire emergency simulation evaluation index of the pre-managed educational institution area is greater than the preset fire emergency simulation evaluation reference index, a fire emergency simulation is performed on the pre-managed educational institution area to extract the fire emergency simulation process of the pre-managed educational institution area. The fire emergency simulation process of the pre-managed educational institution area is input into the big data fire education cloud platform and visualized. If the fire emergency simulation evaluation index of the pre-managed educational institution area is equal to or less than the fire emergency simulation evaluation reference index, management feedback is given to the fire emergency simulation process of the pre-managed educational institution area.

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