Fire alarm decision generation method and device, medium and product

Abstract

The embodiment of the invention provides a fire alarm decision generation method and device, a medium and a product. The method comprises the following steps: acquiring first alarm information of a fire alarm event; generating first decision information according to the first alarm information through a fire-fighting decision generation model; and based on the first decision information, the firefighter arrives at the alarm address according to the police route, and obtains field information of a fire-fighting field of the fire-fighting alarm event in real time. And updating the first decision-making information according to the field information based on the fire-fighting decision-making generation model to obtain second decision-making information updated in real time, so that firefighters can carry out fire-fighting rescue according to the second decision-making information. And first decision information is generated according to the first alarm information through a fire-fighting decision generation model, so that a firefighter can obtain a detailed action guide before the firefighter leaves a police. Second decision information updated in real time is generated by acquiring field information of a fire protection field in real time.

Description

Technical Field

[0001] The present disclosure relates to the technical field of fire dispatch, and particularly to a method, device, medium, and product for generating fire alarm decision-making. Background Art

[0002] With the acceleration of the urbanization process, the complexity and urgency of fire alarms are increasing day by day. In the related art, the fire decision-making method mainly relies on the experience and intuition of firefighters. In the face of complex and changeable fire alarms, it is often difficult to make scientific and reasonable decisions quickly. Especially in the event of an emergency such as a fire, timely and accurate decision-making is crucial for protecting people's lives and property. In the related art, the dispatch of firefighters often mainly relies on the limited information provided by the alarm personnel, resulting in the problem that firefighters may face insufficient information during the dispatch process, affecting the accuracy of decision-making. For example, for the fire disposal in a residential area, the fire scene document usually only contains the address information and does not provide the actual structure information of the residential area, such as the number of building floors, surrounding environment, etc. This makes it impossible for firefighters to quickly judge the building type of the residential area, thereby affecting the formulation of the disposal plan. Also, for example, it is impossible to determine whether the residential area belongs to a multi-story building, and it is impossible to obtain the surrounding road traffic information and available water source information in a timely manner. This leads to the problem that firefighters may face information lag during the dispatch process, affecting the rescue effect. On the other hand, in the related art, after firefighters arrive at the scene, there is often a lack of effective means to obtain and update the on-site information in real time, resulting in the initial decision-making information may no longer be applicable, affecting the rescue effect. Summary of the Invention

[0003] In view of the above-mentioned disadvantages of the prior art, the purpose of the present disclosure is to provide a method, device, medium, and product for generating fire alarm decision-making to solve the problems in the related art.

[0004] The first aspect of the present disclosure provides a method for generating a fire alarm decision-making, which includes:

[0005] Obtain the first alarm information of a fire alarm event; wherein, the first alarm information includes one or more of an alarm address, personnel situation information, and alarm type;

[0006] Generate first decision-making information based on the first alarm information through a fire decision-making generation model; the first decision-making information includes one or more of an alarm route, alarm force, and alarm equipment;

[0007] Based on the first decision-making information, firefighters arrive at the alarm address according to the alarm route and obtain the on-site information of the fire scene of the fire alarm event in real time;

[0008] Updating the first decision information according to the on-site information based on the fire decision-making generation model to obtain the second decision information updated in real time for firefighters to conduct fire rescue according to the second decision information.

[0009] In an embodiment of the first aspect, before generating the first decision information according to the first police situation information through a preset fire decision-making generation model, it further includes:

[0010] Processing the first police situation information through a preset feature representation model to obtain a corresponding police situation information feature vector;

[0011] Inputting the police situation information feature vector into the fire decision-making generation model.

[0012] In an embodiment of the first aspect, generating the first decision information according to the first police situation information through a preset fire decision-making generation model includes:

[0013] Based on a preset fire analysis model, obtaining the surrounding road conditions information of the police situation address according to the police situation address; the surrounding road conditions information is used to calculate the dispatch route; obtaining the dispatch equipment and fire protection plan according to the police situation type; and obtaining the dispatch police force according to the personnel situation information;

[0014] Based on the preset fire decision-making generation model, generating the first decision information according to the dispatch route, dispatch equipment, fire decision, and dispatch police force.

[0015] In an embodiment of the first aspect, the fire analysis model includes a trained deep neural network model, and the training data of the deep neural network model is obtained based on a preset fire professional knowledge base; wherein, the fire professional knowledge base includes historical case information corresponding to different police situation types, and the corresponding fire protection plan is obtained according to the historical information.

[0016] In an embodiment of the first aspect, the first police situation information further includes one or more of the following:

[0017] 1) The information of the building at the incident site corresponding to the police situation address;

[0018] 2) When the police situation type is a fire, the available water source information obtained according to the police situation address;

[0019] 3) The information on the storage of fire protection equipment obtained according to the building information.

[0020] In an embodiment of the first aspect, the on-site information includes one or more of wind direction information, wind force information, and fire intensity information.

[0021] In an embodiment of the first aspect, the fire decision-making generation model updates the first decision-making information according to the on-site information and generates second decision-making information, including:

[0022] Obtain the on-site information in real time;

[0023] Based on the fire decision-making generation model, obtain first sub-decision-making information according to the on-site information at a preset time point;

[0024] Update the first decision-making information according to the first sub-decision-making information to obtain the second decision-making information.

[0025] A second aspect of the present disclosure provides a computer device, which includes:

[0026] A processor and a memory;

[0027] The memory stores program instructions;

[0028] The processor is used to run the program instructions to execute the generation method described in any one of the above embodiments.

[0029] A third aspect of the present disclosure provides a computer-readable storage medium, which stores program instructions, and the program instructions are run to execute the generation method described in any one of the above embodiments.

[0030] A fourth aspect of the present disclosure provides a computer program product, which includes: program instructions for executing the generation method described in any one of the above embodiments.

[0031] Advantages of the present disclosure: Through the fire decision-making generation model, first decision-making information is generated according to the first alarm information, so that firefighters can obtain a detailed action guide before leaving the station, improving the accuracy and timeliness of decision-making. After the firefighters arrive at the scene, by obtaining the on-site information of the fire scene in real time and updating the first decision-making information based on the fire decision-making generation model, real-time updated second decision-making information is generated to ensure the timeliness and accuracy of the decision-making information and better cope with the dynamic changes at the scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart showing a method for generating a fire alarm decision in an embodiment of the present disclosure.

[0033] Figure 2 A flowchart showing a process of processing the first alarm information for input to the fire decision-making model in an embodiment of a generation method of the present disclosure.

[0034] Figure 3A schematic flowchart showing the process of generating first decision information based on the first police situation information in a generation method according to an embodiment of the present disclosure.

[0035] Figure 4 A schematic flowchart showing the process of updating the first decision information based on the on-site information to generate second decision information in a generation method according to an embodiment of the present disclosure.

[0036] Figure 5 A schematic structural diagram of a computer device according to an embodiment of the present disclosure. Detailed implementation manners

[0037] The following uses specific examples to illustrate the embodiments of the present disclosure. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the information disclosed in the present disclosure. The present disclosure can also be implemented or applied through different specific implementation manners. Various details in the present disclosure can also be modified or changed according to different viewpoints and application scenarios without departing from the spirit of the present disclosure. It should be noted that, without conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0038] The following takes the accompanying drawings as a reference and details the embodiments of the present disclosure so that those skilled in the technical field to which the present disclosure belongs can easily implement it. The present disclosure can be embodied in many different forms and is not limited to the embodiments described herein.

[0039] In the description of the present disclosure, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., mean that the specific features, structures, materials, or characteristics represented in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. Moreover, the specific features, structures, materials, or characteristics represented can be combined in any one or a group of embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples represented in the present disclosure and the features of the different embodiments or examples.

[0040] In addition, the terms "first" and "second" are only used for the purpose of indication and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "a group" is two or more, unless otherwise specifically defined.

[0041] To clearly illustrate the present disclosure, devices irrelevant to the description are omitted, and the same or similar constituent elements throughout the specification are given the same reference numerals.

[0042] Throughout the specification, when it is said that a device is "connected" to another device, this includes not only the case of "direct connection", but also the case of "indirect connection" in which other elements are placed in between. Additionally, when it is said that a certain device "includes" a certain constituent element, unless there is a particularly contrary record, it does not exclude other constituent elements, but means that other constituent elements may also be included.

[0043] Although in some examples the terms first, second, etc. are used herein to denote various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first interface and the second interface, etc. are indicated. Furthermore, as used herein, the singular forms "a", "an" and "the" are intended to also include the plural forms unless the context clearly dictates otherwise. It should be further understood that the terms "comprising", "including" indicate the presence of the stated features, steps, operations, elements, modules, items, kinds, and / or groups, but do not preclude the presence, occurrence or addition of one or more other features, steps, operations, elements, modules, items, kinds, and / or groups. The term "or" and "and / or" used herein are to be construed inclusively, or to mean any one or any combination. Thus, "A, B or C" or "A, B and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C". An exception to this definition will occur only when the combination of elements, functions, steps or operations are mutually exclusive in some manner.

[0044] The technical terms used herein are only for referring to specific embodiments and are not intended to limit the present disclosure. The singular forms used herein also include the plural forms as long as the statements do not clearly indicate the contrary meaning. The meaning of "including" used in the specification is to embody specific characteristics, regions, integers, steps, operations, elements and / or components, and does not exclude the existence or addition of other characteristics, regions, integers, steps, operations, elements and / or components.

[0045] Although not defined differently, including the technical terms and scientific terms used herein, all terms have the same meaning as generally understood by those skilled in the technical field to which the present disclosure pertains. Terms defined in commonly used dictionaries are additionally interpreted to have a meaning consistent with the relevant technical literature and the currently presented message, and should not be over-interpreted as ideal or overly formulaic meanings as long as they are not defined.

[0046] In related technologies, the dispatch of firefighters often mainly relies on the limited information provided by the alarm personnel, resulting in the problem that firefighters may face insufficient information during the dispatch process, affecting the accuracy of decision-making. For example, in the fire disposal of residential areas, the fire scene documents usually only contain the address information and do not provide the actual structural information of the residential area, such as the number of building floors, the surrounding environment, etc. This makes it impossible for firefighters to quickly judge the building type of the residential area, thus affecting the formulation of the disposal plan. Another example is that it is impossible to judge whether the residential area belongs to a multi-story building, and it is impossible to obtain the surrounding road traffic information and available water source information in a timely manner. This leads to the problem that firefighters may face information lag during the dispatch process, affecting the rescue effect. On the other hand, when firefighters arrive at the scene, they often lack effective means to obtain and update the on-site information in real time, resulting in the initial decision-making information may no longer be applicable, affecting the rescue effect.

[0047] Therefore, to solve the above problems, the present disclosure provides a method for generating a fire alarm decision. Through a fire decision-making generation model, first decision-making information is generated based on the first alarm information, so that firefighters can obtain a detailed action guide before leaving the station, improving the accuracy and timeliness of decision-making. After firefighters arrive at the scene, by obtaining the on-site information of the fire scene in real time, the fire decision-making generation model can also be used to update the first decision-making information to generate the second decision-making information that is updated in real time, so as to ensure the timeliness and accuracy of the decision-making information and better respond to the dynamic changes at the scene.

[0048] Figure 1 The flowchart showing a method for generating a fire alarm decision in an embodiment of the present disclosure is shown.

[0049] In Figure 1 the example, the method for generating a fire alarm decision includes:

[0050] Step S1, obtaining the first alarm information of a fire alarm event.

[0051] Among them, the first alarm information includes one or more of the alarm address, personnel situation information, and alarm type. When rescue is needed in case of a fire, on-site personnel send a distress signal to firefighters and describe the on-site situation to form the first alarm information corresponding to the fire alarm event. The first alarm information can be the alarm information directly described by the on-site distress personnel or the alarm information obtained after being sorted out by the alarm receiver. Here, no excessive restrictions are imposed on the format of the first alarm information.

[0052] Specifically, when a fire alarm event occurs, the alarm reporter can report the alarm situation to the fire command center in various ways such as by phone, text message, mobile application, etc. The fire command center can record the accurate alarm address through automatic speech recognition technology or by the alarm operator. Optionally, when the alarm device has a GPS positioning function, the fire command center can directly obtain the geographical location of the alarm reporter to ensure the accuracy of the address information. In addition to the alarm address, the alarm reporter also needs to provide the personnel situation at the scene, including whether there are people trapped, the number of injured people and the degree of injury, etc., to provide certain basic information for subsequent strategy generation. The type of alarm situation provided by the alarm reporter when reporting the alarm, such as fire, gas leakage, explosion, etc. Different types of alarm situations may require different response strategies and rescue means.

[0053] Optionally, the first alarm information further includes one or more of the following: 1) the information of the building at the incident location corresponding to the alarm address; 2) when the type of alarm situation is a fire, the available water source information obtained according to the alarm address; 3) the fire-fighting equipment storage information obtained according to the building information.

[0054] As an example, after obtaining the above first alarm information, through the Geographic Information System (GIS) and Building Information Model (BIM), key data such as the building structure, entrance and exit locations, floor plans, etc. corresponding to the alarm address can be quickly obtained as the building information. Thus, it can help firefighters understand the internal structure of the building in advance, plan the best entry path, reduce the rescue time, and improve the rescue efficiency.

[0055] As an example, when the type of alarm situation is confirmed to be a fire, match the nearby water source locations according to the alarm address, such as fire hydrants, lakes, rivers, etc., and calculate the distance and accessibility from each water source point to the fire scene to ensure the continuity and effectiveness of the fire extinguishing operation.

[0056] As an example, based on the above alarm address and / or building information, the location of the nearest fire station to the alarm address and the details of its equipment configuration can also be obtained, including but not limited to fire extinguishers, water hoses, demolition tools, etc. At the same time, for high-rise buildings or special-purpose places (such as chemical plants, hospitals, etc.), the locations of the safety facilities equipped inside, such as emergency evacuation channels, smoke-proof stairwells, automatic sprinkler systems, etc., will also be specially marked to facilitate firefighters to quickly locate and utilize these resources for effective disposal.

[0057] Further, in some embodiments, the first alarm information may further include the floor where the fire breaks out, the type of the burning substance, and the spread of the smoke and fire, so as to evaluate the development trend of the fire and formulate corresponding fire extinguishing strategies through the subsequent fire fighting decision-making generation model. It should be noted that the first alarm information may be any one or more of the above, or may not be the content exemplified above. The first alarm information changes according to the actual situation. The first alarm information represents the alarm situation at the scene, and all kinds of alarm situations should be included in the protection scope of the present disclosure.

[0058] Step S2, generate first decision information according to the first alarm information through a fire fighting decision-making generation model; the first decision information includes one or more of the alarm route, the alarm force, and the alarm equipment.

[0059] Among them, the alarm receiver inputs the first alarm information into a trained fire fighting decision-making generation model, and the fire fighting decision-making generation model integrates and generates the first decision information according to the first alarm information. The first decision information includes the detailed on-site situation and relevant information that can enable the fire fighters to quickly go to the scene. Thus, the problem that the fire fighters in the related art delay the rescue time and affect the rescue effect due to the lack of a detailed rescue plan is solved.

[0060] Specifically, the obtained first alarm information (including the basic alarm information provided by the alarm reporter and the supplementary information further obtained according to the basic alarm information) is used as an input and passed into the fire fighting decision-making generation model.

[0061] Figure 2 Show a schematic flowchart of processing the first alarm information for input into the fire fighting decision model in a generation method according to an embodiment of the present disclosure.

[0062] Optionally, before generating the first decision information according to the first alarm information through the preset fire fighting decision-making generation model, Figure 2 In an example, the generation method further includes:

[0063] Step S21, process the first alarm information through a preset feature representation model to obtain a corresponding alarm information feature vector;

[0064] Step S22, input the alarm information feature vector into the fire fighting decision-making generation model.

[0065] Specifically, in some embodiments, a feature representation model is preset. The model needs to be pre-trained, and the training data includes a large number of historical cases to ensure that the model can accurately generate the feature vectors corresponding to the case texts associated with the historical cases.

[0066] In some embodiments, the feature representation model can be One-Hot. The One-Hot representation method can effectively convert text information into a binary vector, indicating which words appear in the text, thereby preserving the semantic information of the text.

[0067] In some embodiments, the feature representation model can also be TF-IDF. TF-IDF can convert text information into a numerical feature vector, which is suitable for processing police situation information in text form.

[0068] In yet another embodiment, the feature representation model can also be word2vector. Both of the above two models have a drawback that if the text is very long, the extracted feature words / phrases will be numerous, and the vector representing a sentence will be even longer. Word2vector is based on a large amount of text corpus and maps each word into a vector of a fixed dimension through training similar to a neural network model.

[0069] Figure 3 The flowchart shows a method for generating first decision information based on the first police situation information in an embodiment of the present disclosure.

[0070] Optionally, generating the first decision information based on the first police situation information through a preset fire decision-making generation model includes:

[0071] Step S121: Based on a preset fire analysis model, obtain the surrounding road conditions information of the police situation address according to the police situation address; the surrounding road conditions information is used to calculate the dispatch route; obtain the dispatch equipment and fire protection plan according to the police situation type; and obtain the dispatched police force according to the personnel situation information.

[0072] Step S122: Based on the preset fire decision-making generation model, generate the first decision information according to the dispatch route, dispatch equipment, fire decision, and dispatched police force.

[0073] Specifically, the fire analysis model may include multiple small models, including a fire analysis sub-model, a route planning sub-module, an equipment selection sub-module, and a police force allocation sub-module.

[0074] Through the fire analysis model, access the data of the traffic management department to obtain the surrounding road conditions information in real time according to the police situation address. This includes real-time traffic data, road closure conditions, traffic congestion conditions, etc.

[0075] According to the obtained surrounding road conditions information, use the route planning sub-module to calculate the optimal dispatch route. This includes the specific driving path, estimated arrival time, and precautions.

[0076] Based on the type of police situation, use the equipment selection sub-module to determine the required emergency response equipment. For example, for a high-rise building fire, the system will recommend bringing equipment such as a fire truck with a ladder and a high-pressure water gun; for a chemical substance leak, professional chemical accident handling equipment is required.

[0077] Based on the type of police situation and building information, generate corresponding fire-fighting plans, including fire-extinguishing strategies, evacuation plans, and rescue measures.

[0078] Based on the personnel situation information, use the police force sub-allocation module to determine the required emergency response police force. For example, for a fire with people trapped, it is recommended to dispatch more firefighters and special rescue teams; for a fire without people trapped, the emergency response police force can be appropriately reduced.

[0079] Integrate the calculated emergency response route, emergency response equipment, fire-fighting plan, and emergency response police force to generate the first decision-making information.

[0080] Optionally, the fire-fighting analysis model includes a trained deep neural network model, and the training data of the deep neural network model is obtained based on a preset fire-fighting professional knowledge base; wherein, the fire-fighting professional knowledge base includes historical case information corresponding to different types of police situations, and the corresponding fire-fighting plan is obtained according to the historical information.

[0081] Specifically, in some embodiments, first, construct a fire-fighting professional knowledge base, which contains a large amount of historical case information covering various different types of police situations. Each case information includes but is not limited to the following aspects: Type of police situation: fire, explosion, chemical leak, rescue, etc. Case description: detailed situation of the accident, including time, location, environmental conditions, etc. Casualty situation: number of injured people, degree of casualties, etc. Property damage: types and values of damaged property. Fire-fighting response measures: number of fire trucks and personnel dispatched, specific measures taken, etc. Final treatment result: final treatment effect, including whether the fire was successfully extinguished, whether the rescue was successful, etc. Extract the feature vectors of the above content through the feature representation model, and perform deep feature extraction through a deep neural network model. The input of the deep neural network model is the preprocessed case features, and the output is the fire-fighting plan corresponding to each case feature. The model structure can include multiple hidden layers to improve the model's expressive ability. Finally, integrate through the fire-fighting decision-making generation model to form the first decision-making information.

[0082] In Figure 1 In the example, in step S13, based on the first decision-making information, the firefighters arrive at the police situation address according to the emergency response route and obtain the on-site information of the fire-fighting scene of the fire alarm event in real time.

[0083] Specifically, in some embodiments, the first decision information generated according to the fire fighting decision-making model includes the best fire truck dispatch route, the estimated arrival time, and the required fire fighting resources. After receiving this information, the fire fighters immediately initiate the dispatch procedure and travel to the incident address along the route provided by the navigation system. During the dispatch, the navigation system updates the traffic conditions in real time to ensure that the fire truck can choose the optimal path to quickly reach the scene. After arriving at the incident address, the fire fighters quickly confirm the location and conduct a preliminary assessment. In some embodiments, drones can also be used for aerial reconnaissance to obtain an aerial view and a thermal imaging map of the scene. Devices such as temperature sensors and smoke sensors installed at the scene transmit data to the command center in real time, and the fire fighters report the on-site situation, including the development of the fire and casualties, to the command center in real time through handheld terminals or walkie-talkies.

[0084] In Figure 1 the example, step S14 updates the first decision information according to the on-site information based on the fire fighting decision-making model to obtain the second decision information updated in real time for the fire fighters to conduct fire fighting and rescue operations according to the second decision information.

[0085] When the rescue personnel arrive at the scene, the first decision information can be updated in real time according to the on-site information, which solves the situation in the related art that the decision information may no longer be suitable for the rescue scene due to untimely information, thus affecting the rescue effect.

[0086] Specifically, in some embodiments, when the fire fighters arrive at the scene and collect on-site information including the development of the fire, casualties, environmental conditions, etc. in real time, this information is transmitted to the command center in real time through handheld terminals or sensor devices. Optionally, the on-site information includes one or more of wind direction information, wind force information, and fire information. After receiving this on-site information, the fire fighting decision-making model analyzes and processes it, and dynamically adjusts and optimizes the first decision information. The generated second decision information includes updated fire truck dispatch plans, resource allocation, rescue measures, etc., ensuring that the fire fighters can take the most effective actions according to the latest situation. Specifically, for example, if the fire at the scene is more serious than expected, the second decision information may increase the dispatch of fire trucks and personnel, or adjust the priorities of fire fighting and rescue. In this way, the fire fighters can conduct fire fighting and rescue operations more accurately and efficiently according to the second decision information updated in real time, ensuring the safety of personnel and reducing property losses.

[0087] Figure 4 A flowchart showing the process of updating the first decision information according to the on-site information to generate the second decision information in a generation method according to an embodiment of the present disclosure.

[0088] Optionally, the fire decision-making generation model updates the first decision information according to the on-site information and generates second decision information, including:

[0089] Step S141, obtaining the on-site information in real time;

[0090] Step S142, based on the fire decision-making generation model, obtaining first sub-decision information according to the on-site information at a preset time point;

[0091] Step S143, updating the first decision information according to the first sub-decision information to obtain the second decision information.

[0092] Specifically, in some embodiments, the information processing system of the command center automatically triggers information analysis at a preset time point (for example, every 5 minutes). The on-site information obtained in real time (including wind direction, wind force, and fire information) is input into the pre-trained fire decision-making generation model. The fire decision-making generation model generates first sub-decision information based on this information, combined with historical cases and expert knowledge. For example, the model may recommend adjusting the driving route of the fire truck, increasing the use of fire extinguishing agents, or changing the angle and direction of the fire extinguishing equipment. The staff of the command center updates the original first decision information according to the first sub-decision information. For example, if the first sub-decision information recommends increasing the use of fire extinguishing agents, the command center will adjust the resource allocation to ensure that there is sufficient fire extinguishing agent supply at the scene. The updated second decision information includes a new fire dispatch plan, resource allocation, rescue measures, etc., to ensure that the firefighters can take the most effective actions according to the latest situation. For example, if the fire suddenly intensifies, the second decision information may dispatch more fire forces or change the rescue priority. The updated second decision information is conveyed to the firefighters on the scene through the communication system to guide them to perform rescue tasks according to the new instructions. At the same time, the command center continuously monitors the rescue progress and further adjusts the decision according to the subsequent feedback information.

[0093] Optionally, in some embodiments, when there is an inconsistency between the information reported by on-site personnel and the information provided by the unmanned aerial vehicle (UAV), for example, if the UAV image shows that there is no open fire in a certain area, but the on-site personnel report that there is smoke in that area, the system may call other UAVs or ground monitoring devices to further check that area. In some cases, the system may not be able to fully automate the resolution of information conflicts, and in this case, the staff of the command center needs to intervene and decide which information to use as the decision-making basis according to their experience and judgment. Further, in order to prevent the data transmitted on-site from being updated too quickly within a certain period of time, resulting in the second decision information being updated too quickly, therefore, the update time can be set according to the on-site situation, and the first decision information can be updated according to the latest on-site transmission situation to obtain the latest second decision information.

[0094] As Figure 5 shown, a schematic structural diagram of a computer device in an embodiment of the present disclosure is shown.

[0095] The computer device 100 can be exemplified as a processing terminal in a cloud platform, such as a server, a desktop computer, a laptop computer, a tablet computer, a smart phone, or other terminals.

[0096] The computer device 100 includes a bus 101, a processor 102, and a memory 103. The processor 102 and the memory 103 can communicate through the bus 101. Program instructions can be stored in the memory 103. The processor 102 implements the steps in the generation method in the previous embodiments by running the program instructions in the memory 103, such as Figure 1 .

[0097] The bus 101 can be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, although only a thick line is used in the figure, it does not mean that there is only one bus or one type of bus.

[0098] In some embodiments, the processor 102 can be implemented as a Central Processing Unit (CPU), a Microcontroller Unit (MCU), a System On Chip, or a Field Programmable Gate Array (FPGA), etc. The memory 103 can include a volatile memory for temporarily storing data when running a program, such as a Random Access Memory (RAM).

[0099] The memory 103 can also include a non-volatile memory for data storage, such as a Read-Only Memory (ROM), a flash memory, a Hard Disk Drive (HDD), or a Solid-State Disk (SSD).

[0100] In some embodiments, the computer device 100 may further include a communicator 104. The communicator 104 is used for external communication. In a specific example, the communicator 104 may include one or a set of wired and / or wireless communication circuit modules. For example, the communicator 104 may include one or more of a wired network card, a USB module, a serial interface module, etc. The wireless communication protocols followed by the wireless communication module include, for example, Near Field Communication (NFC) technology, Infrared (IR) technology, Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Bluetooth (BT), Global Navigation Satellite System (GNSS), etc.

[0101] In the embodiments of the present disclosure, a computer-readable storage medium may also be provided, storing program instructions that, when run, implement the generation method in any of the previous embodiments.

[0102] That is, the method steps in the above embodiments are implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-transitory machine-readable medium and downloaded through a network and will be stored in a local recording medium, so that the method represented herein can be stored on such a recording medium and processed by such software using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA).

[0103] In the embodiments of the present disclosure, a computer program product may also be provided, including program instructions for executing the generation method described in any one of the above.

Claims

1. A method for generating a fire emergency decision, characterized in that, Comprising: Obtaining first alarm information of a fire alarm event; wherein, the first alarm information includes one or more of: alarm address, personnel situation information, and alarm type; Generating first decision information according to the first alarm information through a fire decision-making generation model; the first decision information includes one or more of: fire truck route, fire truck deployment, and fire equipment; Based on the first decision information, fire personnel arrive at the alarm address according to the fire truck route and obtain on-site information of the fire scene of the fire alarm event in real time; Updating the first decision information based on the on-site information according to the fire decision-making generation model to obtain a second decision information updated in real time for fire personnel to carry out fire rescue according to the second decision information.

2. The generation method according to claim 1, characterized in that Before generating the first decision information according to the first alarm information through a preset fire decision-making generation model, it further includes: Processing the first alarm information through a preset feature representation model to obtain a corresponding alarm information feature vector; Inputting the alarm information feature vector into the fire decision-making generation model.

3. The generation method according to claim 1, wherein Generating the first decision information according to the first alarm information through a preset fire decision-making generation model includes: Based on a preset fire analysis model, obtaining the surrounding road conditions information of the alarm address according to the alarm address; the surrounding road conditions information is used to calculate the fire truck route; obtaining the fire equipment and fire fighting plan according to the alarm type; and obtaining the fire truck deployment according to the personnel situation information; Based on the preset fire decision-making generation model, generating the first decision information according to the fire truck route, fire equipment, fire decision, and fire truck deployment.

4. The generation method according to claim 3, wherein The fire analysis model includes a trained deep neural network model, and the training data of the deep neural network model is obtained based on a preset fire professional knowledge base; wherein, the fire professional knowledge base includes historical case information corresponding to different alarm types, and the corresponding fire fighting plan is obtained according to the historical information.

5. The generation method according to claim 1, characterized in that The first alarm information further includes one or more of the following: 1) Information of the building at the incident location corresponding to the alarm address; 2) Available water source information obtained according to the alarm address when the alarm type is a fire; 3) Fire equipment storage information obtained according to the building information.

6. The generation method according to claim 1, wherein The on-site information includes one or more of wind direction information, wind force information, and fire intensity information.

7. The generation method according to claim 1, wherein Updating the first decision information based on the on-site information according to the fire decision-making generation model and generating the second decision information includes: Obtaining the on-site information in real time; Based on the fire decision-making generation model, obtaining first sub-decision information according to the on-site information at a preset time point; Updating the first decision information according to the first sub-decision information to obtain the second decision information.

8. A computer device, characterized in that, Comprising: A processor and a memory; The memory stores program instructions; The processor is used to run the program instructions to execute the generation method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, Stores program instructions, and the program instructions are run to execute the generation method according to any one of claims 1-7.

10. A computer program product, characterized in that, Comprising: Program instructions for executing the generation method according to any one of claims 1-7.

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