Automatic fire extinguishing system without pipe network

Abstract

The invention relates to the technical field of fire extinguishing, and discloses a pipe-network-free automatic fire extinguishing system, which comprises a fire extinguishing node monitoring module, a monitoring result mapping module, a fire extinguishing parameter arrangement module, a suppression scheme simulation module, an efficiency pre-estimated value preferential module and an optimal scheme compiling module, digital mirror image mapping is carried out on the actual space and the attribute parameters of the target environment, and a virtual mirror image environment is constructed; monitoring a non-pipe network fire extinguishing node in real time, and mapping a monitoring result to a virtual mirror image environment to obtain an initialized fire source state; according to the initialized fire source state, arranging the relation between the multi-dimensional starting parameters of the non-pipe-network fire extinguishing node and spatial deployment to obtain a collaborative suppression scheme; performing full-period quantitative simulation on the collaborative suppression scheme to obtain a multi-dimensional efficiency estimated value; according to a preset decision rule, selecting an optimal multi-dimensional efficiency estimated value to obtain an optimal collaborative suppression scheme; compiling the optimal collaborative suppression scheme in a standardized manner to obtain an optimal control instruction; the pipe-network-free automatic fire extinguishing efficiency can be improved.

Description

Technical Field

[0001] This invention relates to the field of fire extinguishing technology, and in particular to an automatic fire extinguishing system without a pipe network. Background Technology

[0002] Pipeless fire suppression systems, as an independent fire protection method, integrate detection and extinguishing functions at their extinguishing nodes and have been widely used in specific protected locations such as data centers, cultural relic warehouses, and power facilities. With advancements in the Internet of Things (IoT) and sensor technology, these systems are gradually developing towards intelligence, capable of collecting parameters such as ambient temperature and smoke concentration in real time and triggering extinguishing actions at individual nodes. Furthermore, existing technologies are beginning to explore the use of digital twin concepts, constructing virtual replicas of the protected environment through 3D modeling to intuitively display equipment status and fire location. Simultaneously, research has addressed the coordination between multiple extinguishing nodes, involving preliminary orchestration of activation sequences based on fixed rules or simple logic to improve extinguishing coverage efficiency.

[0003] Existing methods still face significant limitations in achieving highly adaptive and globally optimized collaborative fire suppression. Current environmental digitization techniques mostly focus on the geometric reproduction of spatial structures, failing to deeply and accurately integrate key physicochemical properties such as combustible material characteristics and environmental dynamic parameters with spatial models. This leads to biases in fire development prediction and fire suppression effect assessment based on these models, making it difficult to support precise fire suppression strategy formulation. At the level of fire suppression strategy generation and optimization, existing multi-node collaborative activation mechanisms mostly rely on static, pre-set response logic, lacking a closed-loop optimization method that can dynamically combine, simulate, and quantify the effectiveness of multiple dimensions such as activation sequence, dosage, and spray angle of fire suppression nodes based on a holographic virtual environment. Therefore, it is impossible to generate and select the globally optimal solution in real time for complex and ever-changing real fire situations. Thus, how to improve the efficiency of piped automatic fire suppression has become an urgent problem to be solved. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a pipeless automatic fire extinguishing system, characterized in that the system includes a fire extinguishing node monitoring module, a monitoring result mapping module, a fire extinguishing parameter arrangement module, a suppression scheme simulation module, an effectiveness prediction and optimization module, and an optimal scheme compilation module, wherein:

[0005] The fire extinguishing node monitoring module is used to digitally mirror the actual space and attribute parameters of the target environment to construct a virtual mirror environment of the target environment, specifically for:

[0006] A high-precision three-dimensional scan is performed on the actual spatial layout of the target environment to obtain the three-dimensional spatial data of the target environment;

[0007] Multispectral identification is performed on objects in the target environment to obtain object material data of the target environment;

[0008] The three-dimensional spatial data and the object material data are fused together to construct a digital spatial framework of the target environment;

[0009] The digital space framework is integrated with environmental attributes to obtain a virtual mirror environment of the target environment;

[0010] The monitoring result mapping module is used to monitor the fire extinguishing nodes without pipe network in the target environment in real time, and synchronously map the monitoring results to the virtual mirror environment to obtain the initial fire source status in the virtual mirror environment;

[0011] The fire extinguishing parameter arrangement module is used to dynamically respond to and arrange the multi-dimensional activation parameters and spatial deployment relationship of the fire extinguishing node without pipeline network according to the initial fire source state, so as to obtain the collaborative suppression scheme of the virtual mirror environment.

[0012] The suppression scheme simulation module is used to perform full-cycle quantitative simulation of the synergistic suppression scheme to obtain a multi-dimensional performance estimate of the synergistic suppression scheme.

[0013] The efficiency prediction evaluation module is used to comprehensively evaluate the multi-dimensional efficiency prediction based on preset decision rules to obtain the optimal collaborative suppression scheme for the target environment.

[0014] The optimal scheme compilation module is used to standardize and compile the optimal cooperative suppression scheme to obtain the optimal control instructions for the target environment.

[0015] In a preferred embodiment, when the monitoring result mapping module performs real-time monitoring of the pipeless fire suppression node in the target environment and synchronously maps the monitoring results to the virtual mirror environment to obtain the initial fire source status in the virtual mirror environment, it is specifically used for:

[0016] Multi-dimensional status synchronization data is collected on the pipeless fire suppression nodes deployed in the target environment to obtain real-time operating data of the target environment;

[0017] Based on the real-time operating data, the abnormal state characteristics of the fire extinguishing node without pipe network are identified to obtain the potential fire source characteristic information of the target environment.

[0018] Based on the spatial topology of the virtual mirror environment, the potential fire source feature information is spatially correlated and located to obtain the fire source state mapping relationship of the virtual mirror environment;

[0019] Based on the fire source state mapping relationship, the virtual mirror environment is configured to obtain the initial fire source state in the virtual mirror environment.

[0020] In a preferred embodiment, when the fire extinguishing parameter orchestration module performs dynamic response orchestration of the multi-dimensional activation parameters and spatial deployment relationship of the pipeless fire extinguishing node based on the initialized fire source state to obtain the collaborative suppression scheme of the virtual mirror environment, it is specifically used for:

[0021] The initial fire source state is analyzed to obtain the key fire source features of the initial fire source state;

[0022] Based on the key fire source characteristics, the activation sequence, spray dosage and action angle of the pipeless fire extinguishing node are optimized in a coordinated manner to obtain a multi-dimensional action description of the pipeless fire extinguishing node.

[0023] Based on the key fire source characteristics and the spatial location of the pipeless fire suppression node, the overlap of the suppression range and the degree of influence of the blind zone of the pipeless fire suppression node are evaluated to obtain the spatial coupling relationship of the pipeless fire suppression node.

[0024] Based on the multidimensional action representation and the spatial coupling relationship, the action logic and parameter configuration of the pipeless fire extinguishing node are integrated and arranged to obtain the collaborative suppression scheme of the virtual mirror environment.

[0025] In a preferred embodiment, when the fire extinguishing parameter orchestration module performs coordinated optimization of the activation sequence, spray dosage, and action angle of the pipeless fire extinguishing node based on the key fire source characteristics to obtain a multi-dimensional action description of the pipeless fire extinguishing node, it is specifically used for:

[0026] By performing key mining on the key fire source features, the core attribute identifiers of the fire source feature vector are obtained;

[0027] The fire source type and intensity information in the core attribute identifier are mapped to a preset fire extinguishing medium database to obtain the target fire extinguishing medium for the pipeless fire extinguishing node.

[0028] Based on the fire spread characteristics in the core attribute identifier and the spatial topology relationship of the pipeless fire extinguishing node, the collaborative start-up and shutdown logic of the pipeless fire extinguishing node is globally optimized, and the optimization result is compiled into the timing scheduling scheme of the pipeless fire extinguishing node.

[0029] Based on the target fire extinguishing medium type and the timing scheduling scheme, the final action configuration of the pipeless fire extinguishing node is calibrated to obtain a multi-dimensional action description of the pipeless fire extinguishing node.

[0030] In a preferred embodiment, when the suppression scheme simulation module performs a full-cycle quantitative simulation of the synergistic suppression scheme and obtains the multidimensional performance estimate of the synergistic suppression scheme, it is specifically used for:

[0031] The response of the cooperative suppression scheme is simulated in the virtual mirror environment to obtain the simulation results of the suppression process in the virtual mirror environment;

[0032] The simulation results of the suppression process are monitored in real time to obtain the initial simulation sequence of the virtual mirror environment;

[0033] The initial simulation sequence is normalized to obtain a standardized simulation dataset of the virtual mirror environment;

[0034] Based on the standardized simulation dataset, the fire suppression effect of the fire suppression node without pipe network is comprehensively evaluated and a multi-dimensional performance estimate of the collaborative suppression scheme is obtained.

[0035] In a preferred embodiment, when the suppression scheme simulation module performs a comprehensive effect quantification evaluation of the fire suppression effect of the pipeless fire suppression node based on the standardized simulation dataset to obtain a multi-dimensional performance prediction value of the collaborative suppression scheme, it is specifically used for:

[0036] The evaluation dimensions of the standardized simulation dataset are extracted to obtain the preliminary evaluation dimensions of the cooperative inhibition scheme;

[0037] The key performance indicators of the synergistic inhibition scheme are obtained by confirming the key dimensions of the preliminary assessment.

[0038] The correlation and influence of the key performance indicators are analyzed to obtain the weight allocation relationship of the synergistic inhibition scheme;

[0039] Based on the weight allocation relationship, the suppression data in the standardized simulation dataset is subjected to performance conversion to obtain independent scores for the key performance indicators.

[0040] The combined effectiveness of the weight allocation relationship and the corresponding independent scores is summarized to obtain the multidimensional effectiveness estimate of the collaborative inhibition scheme.

[0041] In a preferred embodiment, when the suppression scheme simulation module performs performance conversion on the suppression data in the standardized simulation dataset according to the weight allocation relationship to obtain independent scores for the key performance indicators, the calculation formula for the independent scores is as follows:

[0042] ;

[0043] In the formula, For the first Independent scores for the key performance indicators mentioned above. The total number of time steps in the initial simulation sequence. The time step index of the initial simulation sequence. In order to time step The set of the pipeless fire suppression nodes that are activated at that time. In order to time step No. The fire suppression node without pipe network generated by the first Suppression data related to the aforementioned key performance indicators For the first The fire suppression node without pipe network described above is for the first The influence weights of the aforementioned key performance indicators In order to target the The aforementioned pipeless fire suppression node is in the first The preset benchmark performance thresholds for the aforementioned key performance indicators It is a natural exponential function.

[0044] In a preferred embodiment, when the performance prediction optimization module performs a comprehensive optimization of the multi-dimensional performance predictions according to preset decision rules to obtain the optimal collaborative suppression scheme for the target environment, it is specifically used for:

[0045] The multidimensional performance prediction value is analyzed by feature dimension analysis to obtain the multidimensional performance feature data of the synergistic inhibition scheme;

[0046] Based on preset decision rules, the multi-dimensional performance feature data are integrated and evaluated to obtain the comprehensive performance score of the collaborative inhibition scheme;

[0047] A global ranking analysis is performed on the comprehensive effectiveness score to obtain the optimal scheme identifier of the synergistic inhibition scheme;

[0048] Based on the optimal scheme identifier, the cooperative suppression scheme is matched and searched to obtain the optimal cooperative suppression scheme for the target environment.

[0049] In a preferred embodiment, when the optimal scheme compilation module performs standardized compilation of the optimal cooperative suppression scheme to obtain the optimal control instructions for the target environment, it is specifically used for:

[0050] The optimal synergistic inhibition scheme is subjected to element identification to obtain the core inhibition elements of the optimal synergistic inhibition scheme;

[0051] Based on the core suppression elements, the element representation format of the optimal collaborative suppression scheme is standardized to obtain the standardized element framework of the optimal collaborative suppression scheme.

[0052] According to the preset control command protocol specification, the standardized element framework is subjected to protocol matching verification to obtain the protocol adaptation elements of the optimal cooperative suppression scheme.

[0053] Based on protocol adaptation elements, the logic of the optimal cooperative suppression scheme is encapsulated into instructions to obtain the optimal control instructions for the target environment.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. This invention combines high-precision 3D scanning with multispectral recognition to not only reproduce spatial geometric structures but also accurately acquire object material data. It integrates dynamic environmental attributes such as temperature and humidity into a digital spatial framework, thereby constructing a holographic virtual mirror environment containing physical and chemical properties. This environment provides a high-fidelity model foundation for accurately predicting fire development and assessing firefighting effectiveness, making the firefighting strategies formulated accordingly more targeted. It achieves multi-attribute fusion in digital twins, improving the accuracy of firefighting strategies.

[0056] 2. Based on a holographic virtual environment, this invention dynamically responds to and combines multi-dimensional parameters such as the activation sequence, spray dosage, and action angle of fire suppression nodes without pipe networks. Through full-cycle quantitative simulation and deduction, it comprehensively evaluates and selects the best suppression effect of each scheme from multiple dimensions. This closed-loop optimization process can generate and execute the globally optimal collaborative suppression scheme for real-time fire conditions, overcoming the limitations of static preset logic, realizing a dynamically optimizable collaborative strategy, and improving the overall efficiency of fire suppression operations. Attached Figure Description

[0057] Figure 1 This is a system architecture diagram of a pipeless automatic fire extinguishing system provided in an embodiment of the present invention;

[0058] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0061] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0062] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0063] In practice, the server-side equipment deployed in a pipeless automatic fire suppression system may consist of one or more devices. This pipeless automatic fire suppression system can be implemented as: a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node to provide a pipeless automatic fire suppression system to various user terminals. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various user terminals. Or, it can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide a pipeless automatic fire suppression system to various user terminals.

[0064] In terms of implementation, the pipeless automatic fire suppression system and the user terminal are mutually compatible. That is, if the pipeless automatic fire suppression system is implemented as an application installed on a cloud service platform, the user terminal is implemented as a client that establishes a communication connection with the application; or if the pipeless automatic fire suppression system is implemented as a website, the user terminal is implemented as a webpage; or if the pipeless automatic fire suppression system is implemented as a cloud service platform, the user terminal is implemented as a mini-program in an instant messaging application.

[0065] like Figure 1 The diagram shown is a system architecture diagram of an automatic fire extinguishing system without pipe network provided in an embodiment of the present invention.

[0066] The pipeless automatic fire suppression system 100 described in this invention can be installed on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed on the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the pipeless automatic fire suppression system 100 may include a fire suppression node monitoring module 101, a monitoring result mapping module 102, a fire suppression parameter arrangement module 103, a suppression scheme simulation module 104, an effectiveness prediction and optimization module 105, and an optimal scheme compilation module 106. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0067] In this embodiment of the invention, in a pipeless automatic fire suppression system, each of the above-mentioned modules can be implemented independently and can be invoked by other modules. Invocation here can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the pipeless automatic fire suppression system provided by this embodiment of the invention, the applicable scope of a pipeless automatic fire suppression system architecture can be adjusted by adding modules and directly invoking them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the pipeless automatic fire suppression system. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0068] The following describes, with reference to specific embodiments, the various components and specific workflow of a pipeless automatic fire suppression system:

[0069] The fire extinguishing node monitoring module 101 is used to perform digital mirror mapping of the actual space and attribute parameters of the target environment in order to construct a virtual mirror environment of the target environment.

[0070] In this embodiment of the invention, when the fire extinguishing node monitoring module performs digital mirror mapping of the actual space and attribute parameters of the target environment to construct a virtual mirror environment of the target environment, it is specifically used for:

[0071] A high-precision three-dimensional scan is performed on the actual spatial layout of the target environment to obtain the three-dimensional spatial data of the target environment;

[0072] Multispectral identification is performed on objects in the target environment to obtain object material data of the target environment;

[0073] The three-dimensional spatial data and the object material data are fused together to construct a digital spatial framework of the target environment;

[0074] The digital space framework is integrated with environmental attributes to obtain a virtual mirror environment of the target environment.

[0075] The target environment must be free of obstacles that obstruct the scanning line of sight and remain stable. Specialized scanning equipment is used to comprehensively collect the overall structure of the target environment from multiple angles, including the outlines of walls, floors, and ceilings, as well as the location and shape of fixed facilities. During the collection process, it is ensured that every area is covered without any omissions, and finally, three-dimensional spatial data is obtained. This data is a complete set of data that accurately reflects the actual size of the target environment, the relative positions of each part, and the overall shape. Information such as the length, width, and height of rooms, the location of beams and columns, and the distribution of doors and windows are all included.

[0076] The objects in the target environment are uncovered and stationary. All objects in the target environment are illuminated using a device that can emit various types of light. Different materials reflect and absorb different types of light differently. By capturing these different reactions, the specific material of each object is distinguished one by one, and finally the material data of the object is obtained. This data records the material category of each object in the target environment. Information such as whether the tables and chairs are made of wood, metal or plastic, and whether the walls are painted or tiled is clearly recorded.

[0077] Complete and accurate 3D spatial data and object material data have been acquired, and the recording range of both corresponds completely to the target environment. The environmental structure information reflected by the 3D spatial data is combined with the material information recorded by the object material data. The corresponding object material is marked at each position in the 3D spatial data, so that the structure of the environment and the material of the objects form a unified whole. Finally, a digital spatial framework is obtained. This framework is a basic data set that combines the spatial structure of the target environment and the material information of objects. It can clearly present the spatial form of the environment and the material distribution of various objects in it.

[0078] The digital spatial framework has complete data and has collected environmental attribute information such as temperature, humidity, and ventilation of the target environment. The collected environmental attribute information is added to the digital spatial framework according to its actual location, so that the digital spatial framework not only contains structural and material information, but also reflects the real-time status of the environment, and finally obtains a virtual mirror environment. This environment is a comprehensive digital replica of the target environment, which not only restores the physical space and material of the environment, but also includes various environmental attributes, and can accurately simulate the actual situation of the target environment.

[0079] The beneficial effects are as follows: accurate and complete 3D spatial data provides a solid spatial foundation for the construction of virtual mirror environments, avoids the problem of discrepancies between virtual and actual environments caused by spatial data deviations, accurately acquires object material data, provides key basis for fire extinguishing systems to formulate targeted fire extinguishing strategies, integrates spatial structure and material information in the digital spatial framework, simplifies subsequent data processing, ensures smooth integration of environmental attributes, and fully replicates the key characteristics of the target environment in the virtual mirror environment, helping to predict fire risks and spread paths, and improving the response efficiency and fire extinguishing effect of fire extinguishing systems.

[0080] The monitoring result mapping module 102 is used to monitor the fire extinguishing nodes without pipe network in the target environment in real time, and synchronously map the monitoring results to the virtual mirror environment to obtain the initial fire source status in the virtual mirror environment;

[0081] In this embodiment of the invention, when the monitoring result mapping module performs real-time monitoring of the pipeless fire suppression node in the target environment and synchronously maps the monitoring results to the virtual mirror environment to obtain the initial fire source status in the virtual mirror environment, it is specifically used for:

[0082] Multi-dimensional status synchronization data is collected on the pipeless fire suppression nodes deployed in the target environment to obtain real-time operating data of the target environment;

[0083] Based on the real-time operating data, the abnormal state characteristics of the fire extinguishing node without pipe network are identified to obtain the potential fire source characteristic information of the target environment.

[0084] Based on the spatial topology of the virtual mirror environment, the potential fire source feature information is spatially correlated and located to obtain the fire source state mapping relationship of the virtual mirror environment;

[0085] Based on the fire source state mapping relationship, the virtual mirror environment is configured to obtain the initial fire source state in the virtual mirror environment.

[0086] The target environment refers to the specific space requiring fire monitoring and fire suppression protection, such as enclosed or semi-enclosed areas like computer rooms and factories. Piped fire suppression nodes are independent devices deployed within this space, possessing fire detection and suppression capabilities, and include components such as fire detectors and fire extinguishing devices. Multi-dimensional synchronous status acquisition refers to the simultaneous collection of multiple key operational indicators from all piped fire suppression nodes. These indicators include ambient temperature, smoke concentration, the node's own power supply status, and extinguishing agent storage levels. During the acquisition process, it is ensured that relevant data from all nodes are acquired at the same time, avoiding information discrepancies caused by asynchronous data collection. Real-time operational data refers to the raw information obtained through the above acquisition methods that fully reflects the current environmental conditions of the target environment and the operational status of the piped fire suppression nodes, including monitoring data and operational parameters of each node.

[0087] Real-time operational data serves as the foundation. Abnormal state characteristics refer to the operational status of fire suppression nodes without piped networks or the characteristics of their monitored environmental parameters deviating from preset normal standards. Examples include ambient temperature exceeding safety thresholds, detection of smoke particles, node power outages, and extinguishing agent storage levels below normal. Feature identification involves comparing the collected real-time operational data with preset normal state standards one by one, filtering out abnormal data that does not meet the standards, and identifying the specific feature types corresponding to these abnormal data, distinguishing between abnormal environmental parameters and abnormal node operation itself. Potential ignition source characteristic information refers to the set of relevant information that may cause a fire, determined through feature identification. This includes the specific values ​​of abnormal environmental parameters, the location of the abnormality, the duration of the abnormal state, and the type of abnormality, directly pointing to potential fire hazards in the target environment.

[0088] A virtual mirror environment is a digital simulation built at a 1:1 scale according to the actual spatial layout, building structure, and deployment locations of fire suppression nodes without pipes in the target environment. It accurately reproduces the physical spatial characteristics of the target environment. Spatial topology refers to the relative positional relationships, distance distribution, and connection methods between various spatial areas and fire suppression nodes within the virtual mirror environment, clearly presenting the associated layout of different areas and fire suppression nodes. Spatial correlation and positioning refers to accurately matching the physical location information contained in the potential fire source's characteristic information with the spatial topology of the virtual mirror environment to determine the corresponding coordinates of the potential fire source in the virtual mirror environment, and associating it with the distribution of fire suppression nodes without pipes and the layout of spatial passages in the vicinity. Fire source status mapping refers to the corresponding association information between the specific characteristics of potential fire sources in the target environment and their corresponding locations and the status of surrounding fire suppression nodes in the virtual mirror environment, achieving a complete mapping of fire hazard information from the real environment to the virtual mirror environment.

[0089] The fire source status mapping relationship serves as the core basis. Status configuration refers to digitally marking the coordinates of potential fire sources in the virtual mirror environment according to this mapping relationship. At the same time, it records the abnormality type, severity, duration, and other characteristic information of the fire source, as well as the distribution and operation status of surrounding fire extinguishing nodes without pipe networks. Initializing the fire source status refers to the initial simulated state formed by the virtual mirror environment after the above status configuration, which can accurately replicate the actual situation of potential fire sources in the target environment. This state is completely consistent with the fire hazards in the real environment, providing a digital foundation for subsequent fire extinguishing plan simulation and fire extinguishing strategy optimization.

[0090] The beneficial effects are as follows: multi-dimensional status synchronous collection ensures comprehensive and timely real-time operational data, avoids the omission of fire hazards, provides reliable raw data support for accurate identification of potential fire sources, abnormal status feature identification quickly filters key abnormal information, eliminates invalid interference, improves the efficiency and accuracy of potential fire source identification, spatial correlation positioning enables the accurate mapping of potential fire sources to the virtual environment, visualizes the distribution of fire sources and surrounding fire extinguishing resources, reduces positioning errors, and the initial fire source status formed by virtual mirror environment status configuration realistically restores fire source hazards, providing a precise digital foundation for fire extinguishing plan simulation and strategy optimization, and ensuring scientific and effective fire extinguishing decisions.

[0091] The fire extinguishing parameter arrangement module 103 is used to dynamically respond to and arrange the multi-dimensional activation parameters and spatial deployment relationship of the fire extinguishing node without pipeline network according to the initial fire source state, so as to obtain the collaborative suppression scheme of the virtual mirror environment.

[0092] In this embodiment of the invention, when the fire extinguishing parameter orchestration module performs dynamic response orchestration of the multi-dimensional activation parameters and spatial deployment relationship of the pipelineless fire extinguishing node based on the initialized fire source state to obtain the collaborative suppression scheme of the virtual mirror environment, it is specifically used for:

[0093] The initial fire source state is analyzed to obtain the key fire source features of the initial fire source state;

[0094] Based on the key fire source characteristics, the activation sequence, spray dosage and action angle of the pipeless fire extinguishing node are optimized in a coordinated manner to obtain a multi-dimensional action description of the pipeless fire extinguishing node.

[0095] Based on the key fire source characteristics and the spatial location of the pipeless fire suppression node, the overlap of the suppression range and the degree of influence of the blind zone of the pipeless fire suppression node are evaluated to obtain the spatial coupling relationship of the pipeless fire suppression node.

[0096] Based on the multidimensional action representation and the spatial coupling relationship, the action logic and parameter configuration of the pipeless fire extinguishing node are integrated and arranged to obtain the collaborative suppression scheme of the virtual mirror environment.

[0097] When the fire extinguishing parameter orchestration module performs coordinated optimization of the activation sequence, spray dosage, and action angle of the pipeless fire extinguishing node based on the key fire source characteristics to obtain a multi-dimensional action description of the pipeless fire extinguishing node, it is specifically used for:

[0098] By performing key mining on the key fire source features, the core attribute identifiers of the fire source feature vector are obtained;

[0099] The fire source type and intensity information in the core attribute identifier are mapped to a preset fire extinguishing medium database to obtain the target fire extinguishing medium for the pipeless fire extinguishing node.

[0100] Based on the fire spread characteristics in the core attribute identifier and the spatial topology relationship of the pipeless fire extinguishing node, the collaborative start-up and shutdown logic of the pipeless fire extinguishing node is globally optimized, and the optimization result is compiled into the timing scheduling scheme of the pipeless fire extinguishing node.

[0101] Based on the target fire extinguishing medium type and the timing scheduling scheme, the final action configuration of the pipeless fire extinguishing node is calibrated to obtain a multi-dimensional action description of the pipeless fire extinguishing node.

[0102] Initializing the fire source status refers to the basic information about the fire source collected in the early stages of a fire through smoke detectors, heat detectors, and flame detectors in a pipeless fire suppression system. This includes the specific spatial location of the fire source, the concentration of smoke produced by combustion, the temperature range of the flame, and the form of the burning material. Feature analysis involves checking and sorting through this basic information to extract key information that directly determines the fire suppression method. For example, the initial intensity of the fire can be judged based on the smoke concentration and temperature range, and the type of fire source—whether it is solid combustion, liquid combustion, gas combustion, or combustion of electrical equipment—can be determined based on the form and characteristics of the burning material. The key fire source features are the set of information that, after extraction and sorting, can provide the core basis for subsequent fire suppression decisions.

[0103] Activation sequence refers to the order in which multiple fire suppression nodes without piped networks are activated. Spray dosage is the total amount of extinguishing agent released by each fire suppression node. The angle of action is the angle of the nozzle of the fire suppression node toward the fire source. During collaborative optimization, based on the fire spread rate in the key fire source characteristics, if the fire spreads rapidly in a certain direction, the fire suppression nodes in that direction are activated first. Based on the fire source type and fire intensity, such as solid fires requiring a covering spray, the required amount of extinguishing agent for each node is determined. Then, combined with the fire source location and the installation location of the fire suppression nodes, the nozzle angle of each node is adjusted to ensure that the sprayed extinguishing agent can accurately cover the fire source area. The multi-dimensional action description is a complete action instruction that integrates the activation sequence, spray volume, and spray angle of each fire suppression node. Each node has corresponding explicit action requirements.

[0104] The fire source range in the key fire source characteristics determines the total coverage area required for fire suppression. The spatial location of fire suppression nodes without pipe network refers to the installation coordinates and distribution of each node within the protected area. During the assessment, first, determine the spatial area that each fire suppression node can cover according to the preset spray angle and dosage. Then, compare the overlapping area of ​​this area with the fire source range and the coverage areas of other nodes to determine whether the size of the overlapping area will cause waste of extinguishing media. At the same time, check whether there are any uncovered fire source-related areas (blind spots) after combining the coverage areas of all nodes, and the degree of impact of blind spots on the fire suppression effect. Spatial coupling relationship refers to the interrelationship of the coverage range of each fire suppression node, including the specific location and range of overlapping areas, the location and size of blind spots, and the correlation logic of how the coverage range between nodes can be adjusted to compensate for blind spots and reduce ineffective overlap.

[0105] The virtual mirror environment is a digital simulation of the actual protected space, fire source location, and fire situation of fire suppression nodes deployed without piped networks. During integration and orchestration, the multi-dimensional action descriptions of each fire suppression node are correlated with spatial coupling relationships, and the action logic is adjusted. For example, if the coverage areas of two nodes overlap too much, the spray dosage of one node is appropriately reduced to avoid waste; if there are blind spots, the action angle or activation sequence of adjacent nodes is adjusted to cover the blind spots; the parameter configuration is based on the adjusted action logic to determine the final activation time, spray volume, and angle of each node. The collaborative suppression scheme is a complete fire suppression solution that has been verified in the virtual mirror environment to enable all fire suppression nodes to work collaboratively, efficiently cover the fire source area, and avoid waste and blind spots. It includes the final action logic and parameter settings of each node and can directly guide actual fire suppression operations.

[0106] Key fire source characteristics contain information from multiple dimensions. Key mining involves filtering out the core information that plays a decisive role in the selection of extinguishing media and the formulation of activation logic. For example, it can identify the specific type of combustion from the fire source type, determine the specific level from the fire intensity, and extract the main spread direction and spread speed from the fire spread characteristics. Core attribute identification is a set of labels after clearly classifying this decisive information, with each label corresponding to a key decision-making basis.

[0107] The pre-configured fire extinguishing medium database is pre-organized and stored, containing the correspondence between various fire source types, fire intensities, and suitable fire extinguishing media. For example, it specifies that solid fires are suitable for ABC dry powder extinguishing agents, electrical equipment fires are suitable for carbon dioxide extinguishing agents, and metal fires are suitable for dedicated Class D dry powder extinguishing agents. The mapping process compares the fire source type and intensity information in the core attribute identifier with the corresponding relationships in the database to find a perfectly matching fire extinguishing medium. This matching fire extinguishing medium is the target fire extinguishing medium for fire-fighting nodes without piped fire suppression systems, ensuring effective extinguishing of the current fire source.

[0108] Fire spread characteristics refer to the main direction, speed, and potential extent of fire spread. Spatial topology refers to the relative positions of each fire suppression node without a fire network, as well as the distance between each node and the fire source center. During global optimization, the direction and speed of fire spread are comprehensively considered. If the fire spreads evenly from the fire source center outwards, all nodes are activated simultaneously. If the fire spreads rapidly in a specific direction, nodes in that direction are activated in order of distance from the fire source center, ensuring rapid fire suppression. The optimization result is the optimal activation / deactivation sequence determined after comprehensive consideration. This sequence is organized into explicit time-based instructions, and the timing scheduling scheme is this list of instructions containing the activation and deactivation times of all nodes.

[0109] The type of the target extinguishing medium determines its diffusion speed and coverage pattern after spraying. The timing scheduling scheme clarifies the activation time of each node. During coordinated action calibration, the spray dosage of each node is adjusted according to the diffusion speed of the target extinguishing medium to ensure that the extinguishing medium can fully diffuse and cover the target area within the activation time. At the same time, according to the activation sequence in the timing scheduling scheme, the action angle of each node is coordinated to avoid the spray airflow of the first node affecting the medium diffusion of the later nodes. Finally, the precise configuration of the activation time, spray dosage, and action angle of each node is determined. The multi-dimensional action description is this complete action instruction that integrates all the precise configurations, ensuring that the actions of each node cooperate with each other to achieve the best fire extinguishing effect.

[0110] The beneficial effects include: extracting key characteristics of the fire source, providing clear basis for firefighting decisions, laying the foundation for precise firefighting, optimizing the activation sequence, spray dosage, and angle of action of nodes, avoiding improper actions, improving the firefighting effectiveness of individual nodes, reducing waste of fire extinguishing media, avoiding firefighting blind spots, ensuring the comprehensiveness and economy of firefighting coverage, forming a node collaborative working system, maximizing overall firefighting capacity, improving firefighting efficiency, reducing fire losses, focusing on core information for firefighting decisions, simplifying processes, improving decision-making efficiency and accuracy, ensuring that fire extinguishing media are compatible with the fire source, avoiding ineffective firefighting or secondary risks, improving firefighting reliability, determining the optimal node activation sequence, quickly suppressing the fire, preventing the fire from spreading, gaining firefighting time, coordinating cooperation between nodes, giving full play to the role of fire extinguishing media, and optimizing the overall firefighting effect.

[0111] The suppression scheme simulation module 104 is used to perform full-cycle quantitative simulation of the synergistic suppression scheme to obtain a multi-dimensional performance prediction of the synergistic suppression scheme.

[0112] In this embodiment of the invention, when the suppression scheme simulation module performs a full-cycle quantization simulation of the synergistic suppression scheme and obtains the multidimensional performance prediction of the synergistic suppression scheme, it is specifically used for:

[0113] The response of the cooperative suppression scheme is simulated in the virtual mirror environment to obtain the simulation results of the suppression process in the virtual mirror environment;

[0114] The simulation results of the suppression process are monitored in real time to obtain the initial simulation sequence of the virtual mirror environment;

[0115] The initial simulation sequence is normalized to obtain a standardized simulation dataset of the virtual mirror environment;

[0116] Based on the standardized simulation dataset, the fire suppression effect of the fire suppression node without pipe network is comprehensively evaluated and a multi-dimensional performance estimate of the collaborative suppression scheme is obtained.

[0117] When the suppression scheme simulation module performs a comprehensive performance quantification evaluation of the fire suppression effect of the pipeless fire suppression node based on the standardized simulation dataset, and obtains a multi-dimensional performance prediction value of the collaborative suppression scheme, it is specifically used for:

[0118] The evaluation dimensions of the standardized simulation dataset are extracted to obtain the preliminary evaluation dimensions of the cooperative inhibition scheme;

[0119] The key performance indicators of the synergistic inhibition scheme are obtained by confirming the key dimensions of the preliminary assessment.

[0120] The correlation and influence of the key performance indicators are analyzed to obtain the weight allocation relationship of the synergistic inhibition scheme;

[0121] Based on the weight allocation relationship, the suppression data in the standardized simulation dataset is subjected to performance conversion to obtain independent scores for the key performance indicators.

[0122] The combined effectiveness of the weight allocation relationship and the corresponding independent scores is summarized to obtain the multidimensional effectiveness estimate of the collaborative inhibition scheme.

[0123] When the suppression scheme simulation module performs performance conversion on the suppression data in the standardized simulation dataset according to the weight allocation relationship to obtain independent scores for the key performance indicators, the calculation formula for the independent scores is as follows:

[0124] ;

[0125] In the formula, For the first Independent scores for the key performance indicators mentioned above. The total number of time steps in the initial simulation sequence. The time step index of the initial simulation sequence. In order to time step The set of the pipeless fire suppression nodes that are activated at that time. In order to time step No. The fire suppression node without pipe network generated by the first Suppression data related to the aforementioned key performance indicators For the first The fire suppression node without pipe network described above is for the first The influence weights of the aforementioned key performance indicators In order to target the The aforementioned pipeless fire suppression node is in the first The preset benchmark performance thresholds for the aforementioned key performance indicators It is a natural exponential function.

[0126] The virtual mirror environment is a virtual space that replicates a real firefighting scenario. It includes all real elements such as the building structure where the fire occurs, environmental conditions, and the installation location and performance parameters of fire suppression nodes without piped networks. The collaborative suppression scheme is a pre-set firefighting plan in which multiple fire suppression nodes work together. The collaborative suppression scheme is simulated in the virtual mirror environment, meaning that it runs completely in the virtual mirror environment according to the real fire development pattern and the action logic of the fire suppression nodes. The simulation results show a complete record of all dynamic changes and data during the simulation, covering all relevant information such as the fire spread trajectory, the activation time of the fire suppression nodes, the spray range and dosage of fire extinguishing agents, and changes in environmental parameters.

[0127] Based on the simulation results of the suppression process, real-time monitoring is carried out to continuously track the dynamic data and event changes at each time node in the simulation results, including the increase or decrease of fire intensity, the working status of fire extinguishing nodes, and the effect of chemical agents, without missing any key changes. The initial simulation sequence is an ordered data set formed by organizing the continuous dynamic data monitored in real time in chronological order. Each data entry corresponds to the fire extinguishing scenario status and related parameters at a specific time point.

[0128] The initial simulation sequence is used as the processing object for data standardization. The recording format, unit of measurement and data precision of all data in the initial simulation sequence are standardized. Duplicate records, invalid data and abnormal data that exceed the reasonable range are removed to ensure the consistency and validity of the data. The standardized simulation dataset is a standardized data set after format standardization and data purification. All data meet the requirements for subsequent comprehensive effect quantitative evaluation.

[0129] The evaluation dimensions are extracted based on the standardized simulation dataset. Various specific aspects that can reflect the fire suppression effect are extracted from the standardized simulation dataset, including the activation response speed of the fire suppression node, the duration of fire control, the coverage of the fire suppression range, the consumption of fire extinguishing agents, and the environmental recovery after fire suppression. The preliminary evaluation dimensions are the set of specific directions extracted that can comprehensively cover the evaluation of fire suppression effect.

[0130] Key confirmations were conducted based on the preliminary assessment dimensions. The impact of each preliminary assessment dimension on the success of the firefighting mission and the quality of the suppression effect was analyzed one by one. Dimensions that play a decisive role in the overall firefighting effectiveness were selected, while dimensions with minor or non-core impact were excluded. The key performance indicators are the core evaluation dimensions determined after screening, which can accurately reflect the core performance level of the coordinated suppression plan.

[0131] The analysis focuses on key performance indicators (KPIs) to analyze their relationships. It examines the interactions between each KPI to determine whether they are mutually reinforcing, mutually restrictive, or mutually unaffected. It also assesses the strength of these relationships. The weighting is determined based on the importance of each KPI and the degree of influence between them, reflecting the proportion of importance each KPI occupies in the overall evaluation.

[0132] Performance conversion is performed based on weighted distribution relationships and standardized simulation datasets. The original data corresponding to each key performance indicator is extracted from the standardized simulation dataset, and the original data is converted into quantifiable scores according to a unified conversion standard. This ensures that the performance of each key performance indicator can be intuitively reflected through specific scores. The independent score is the exclusive score obtained for each key performance indicator after data conversion, reflecting the single performance level of the corresponding indicator.

[0133] The fusion performance is summarized based on the weighting relationship and the independent scores of each key performance indicator. The independent score of each key performance indicator is calculated with the corresponding weight ratio to obtain the weighted score of each indicator. Then, the weighted scores of all key performance indicators are added together. The multidimensional performance estimate is the final comprehensive score, which can comprehensively reflect the overall expected effect of the synergistic inhibition scheme in the core performance dimensions.

[0134] In the calculation formula for the independent scoring of the key performance indicators, For the first The independent scores of each key performance indicator are used to quantify the performance level of a single core evaluation dimension, providing a single basis for subsequent comprehensive performance summarization and matching the needs of performance evaluation of synergistic suppression schemes. The total number of time steps in the initial simulation sequence refers to the initial simulation sequence obtained through real-time monitoring of the suppression process simulation results. The total number of time steps is the total number of time nodes covered by the sequence throughout the entire cycle, derived from the time dimension division of the suppression process simulation results, and used to define the time range for score calculation. This is the time step index of the initial simulation sequence, corresponding to each specific time node in the initial simulation sequence. It serves as an identifier for extracting data at different times and is related to the total number of time steps. Used in conjunction with the time dimension setting derived from the initial simulation sequence, For time step This set represents the collection of activated pipeless fire suppression nodes. These nodes are the fire suppression execution units deployed in the target environment. The "activated state" refers to the node's operational status. This set is derived from the node activation status data recorded at each time step during the simulation results of the suppression process, used to pinpoint the specific node participating in fire suppression at each moment. For time step Time The fire extinguishing node without pipe network generated the first Suppression data related to key performance indicators. This suppression data refers to specific data affecting fire suppression effectiveness during node operation, derived from a standardized simulation dataset after data normalization. , , This establishes a correspondence between data and evaluation objects, execution units, and time points, enabling precise matching. For the first The fire suppression node without pipe network is for the first The influence weights of each key performance indicator are derived from the weight allocation relationship obtained after analyzing the correlation between key performance indicators. This is used to distinguish the differences in the performance contribution of different nodes to the same indicator and to highlight the role of core nodes. In order to target the The fire suppression node without pipe network was at the first The preset benchmark performance thresholds for each key performance indicator are established in advance based on the node's design performance parameters and indicator evaluation standards. These serve as reference benchmarks for measuring whether a node meets the standards for that indicator and provide a basis for calculating performance deviations. It is a natural exponential function used to perform nonlinear transformation on performance-related calculation results, reducing the excessive interference of extreme data on the score and making the score more consistent with the actual performance of the node.

[0135] The core logic of the formula is to accurately quantify the performance of individual key performance indicators by integrating performance data across the entire lifecycle and multiple nodes: First, for each time step... The algorithm identifies all pipeless fire suppression nodes activated at that moment, calculates the deviation rate between the actual suppression data of each node and the baseline effectiveness threshold at that time step, and then combines the node's influence weight on the indicator to obtain the influence of each node on the first... The weighted efficiency contribution of each indicator is calculated. Then, the weighted efficiency contributions of all activated nodes at the same time step are summed to obtain the comprehensive efficiency contribution value at that moment. This is then optimized nonlinearly using the natural exponential function to avoid interference from extreme biases. Next, the optimized comprehensive efficiency contribution values ​​for all time steps throughout the entire cycle are accumulated to obtain the total efficiency contribution. Finally, the total efficiency contribution is divided by the total number of time steps to obtain the average efficiency level throughout the entire cycle, which is the [i]th [indicator / indicator]. The independent scoring of each key performance indicator enables precise quantification of the synergistic effect of a single performance indicator throughout the entire cycle and across multiple nodes, taking into account both the continuity of the time dimension and the synergy of the node dimension.

[0136] The beneficial effects include: testing without real-world scenarios, avoiding resource waste and safety risks; replicating complex scenarios, ensuring the simulation results are realistic and comprehensive; capturing key data and dynamic changes; fully reflecting the implementation process of the solution; providing a complete and continuous data foundation; eliminating problems such as inconsistent data formats and invalid data interference; ensuring the dataset is standardized and effective; providing reliable support for quantitative evaluation; covering all aspects of fire extinguishing effectiveness evaluation; avoiding omissions of key factors; laying a comprehensive foundation for the selection of core indicators; focusing on core evaluation dimensions; eliminating secondary interference; highly targeted indicators; improving the accuracy of evaluation; clarifying the correlation and influence between indicators; scientifically and reasonably allocating weights; providing a reliable basis for comprehensive evaluation; transforming raw data into intuitive quantitative scores; clearly measuring individual effectiveness; providing a concise numerical foundation; integrating multiple indicators to form a comprehensive evaluation; intuitively reflecting the overall expected effectiveness of the solution; and assisting in solution optimization and selection.

[0137] The efficiency prediction value selection module 105 is used to comprehensively select the multi-dimensional efficiency prediction value according to the preset decision rules to obtain the optimal collaborative suppression scheme of the target environment.

[0138] In this embodiment of the invention, when the performance prediction optimization module performs comprehensive optimization on the multi-dimensional performance prediction according to preset decision rules to obtain the optimal collaborative suppression scheme for the target environment, it is specifically used for:

[0139] The multidimensional performance prediction value is analyzed by feature dimension analysis to obtain the multidimensional performance feature data of the synergistic inhibition scheme;

[0140] Based on preset decision rules, the multi-dimensional performance feature data are integrated and evaluated to obtain the comprehensive performance score of the collaborative inhibition scheme;

[0141] A global ranking analysis is performed on the comprehensive effectiveness score to obtain the optimal scheme identifier of the synergistic inhibition scheme;

[0142] Based on the optimal scheme identifier, the cooperative suppression scheme is matched and searched to obtain the optimal cooperative suppression scheme for the target environment.

[0143] Multidimensional performance prediction is a set of values ​​obtained in advance across multiple preset performance characteristic dimensions for different synergistic suppression schemes in a target environment. These performance characteristic dimensions are determined in conjunction with the application scenario of the pipeless automatic fire suppression system, including fire extinguishing speed, coverage uniformity, residue status, environmental performance, and compatibility with protected objects. When analyzing the feature dimensions of the multidimensional performance prediction, the specific direction of each preset performance characteristic dimension is first clarified. Then, the multidimensional performance prediction of each synergistic suppression scheme is mapped one by one to these dimensions. The specific values ​​corresponding to each dimension are extracted one by one from the multidimensional performance prediction. The set of specific values ​​for each scheme across all preset performance characteristic dimensions is the multidimensional performance characteristic data of the synergistic suppression scheme.

[0144] The preset decision-making rules are formulated based on the applicable scenarios of the pipeless automatic fire suppression system, clarifying the importance of different performance characteristic dimensions in the comprehensive evaluation. For example, in protected areas where people are present, environmental performance and compatibility with the protected objects are more important than other dimensions. In unattended small computer rooms, fire suppression speed and coverage uniformity are more important. Based on these preset decision-making rules, when integrating and evaluating the multi-dimensional performance characteristic data of the collaborative suppression scheme, all specific values ​​of all dimensions in the multi-dimensional performance characteristic data of the scheme are included in the evaluation scope. According to the importance of each dimension, all values ​​are comprehensively summarized and evaluated. The final score that reflects the overall fire suppression effectiveness of the collaborative suppression scheme is the comprehensive performance score of the collaborative suppression scheme.

[0145] Collect the comprehensive effectiveness scores of all participating synergistic inhibition schemes, arrange these scores in descending order of score, and clarify the order of each comprehensive effectiveness score among all scores. The optimal scheme identifier is a unique and exclusive mark corresponding to each synergistic inhibition scheme, which may be the name or number of the scheme. By sorting the comprehensive effectiveness scores, determine the synergistic inhibition scheme corresponding to the highest comprehensive effectiveness score, and extract the exclusive mark of the scheme. This mark is the optimal scheme identifier of the synergistic inhibition scheme.

[0146] All synergistic suppression schemes have been pre-stored with their respective unique tags, forming a complete scheme-tag correspondence library. When matching and searching for synergistic suppression schemes based on the optimal scheme identifier, the optimal scheme identifier is used as the search criterion. The search is conducted in the pre-established scheme-tag correspondence library to find the synergistic suppression scheme that completely corresponds to the optimal scheme identifier. This scheme includes specific details such as the type of extinguishing agent, release method, and activation timing. It is the optimal synergistic suppression scheme for the target environment.

[0147] The beneficial effects are that it transforms abstract estimated values ​​into specific dimensional data, providing a precise basis for comprehensive evaluation, avoiding evaluation bias caused by data ambiguity, unifying the comprehensive effectiveness scoring standard, comprehensively considering the effectiveness of each aspect of the solution, avoiding the one-sidedness of single-dimensional evaluation, quickly screening the candidate solution with the best comprehensive effectiveness, simplifying the screening process, improving the efficiency of selecting the best solution, accurately locating the best synergistic suppression solution, ensuring that the solution is highly adapted to the needs of the target environment, and improving the applicability of the system and the reliability of fire suppression.

[0148] The optimal scheme compilation module 106 is used to standardize and compile the optimal cooperative suppression scheme to obtain the optimal control instructions for the target environment.

[0149] In this embodiment of the invention, when the optimal scheme compilation module performs standardized compilation of the optimal cooperative suppression scheme to obtain the optimal control instructions for the target environment, it is specifically used for:

[0150] The optimal synergistic inhibition scheme is subjected to element identification to obtain the core inhibition elements of the optimal synergistic inhibition scheme;

[0151] Based on the core suppression elements, the element representation format of the optimal collaborative suppression scheme is standardized to obtain the standardized element framework of the optimal collaborative suppression scheme.

[0152] According to the preset control command protocol specification, the standardized element framework is subjected to protocol matching verification to obtain the protocol adaptation elements of the optimal cooperative suppression scheme.

[0153] Based on protocol adaptation elements, the logic of the optimal cooperative suppression scheme is encapsulated into instructions to obtain the optimal control instructions for the target environment.

[0154] The prerequisite is that the fire risk situation of the target environment has been clearly defined, including spatial characteristics, types of combustibles, and locations of potential ignition sources. An optimal synergistic suppression plan has also been developed for this target environment. The optimal synergistic suppression plan refers to a comprehensive plan that can effectively suppress fires in the target environment. The optimal synergistic suppression plan is subject to element identification. All relevant contents involving fire extinguishing execution in the plan are comprehensively reviewed, and the impact of each content on the fire extinguishing effect is analyzed one by one. Key contents that directly determine whether the fire extinguishing can be successful, the efficiency of fire extinguishing, and the safety of system operation are selected. The core suppression elements are the set of key contents obtained after screening, which specifically include the type of fire extinguishing medium, the total amount released, the release start time, the duration, and the key areas covered, which are core information that directly affect fire suppression.

[0155] The prerequisites are that the element identification has been completed and the core suppression elements have been obtained, and the unified expression standards have been clarified. Based on the core suppression elements, the names, description order, and information presentation forms of the core suppression elements are uniformly adjusted according to the unified expression standards to ensure that the expression of each core suppression element is consistent, clear, and unambiguous, and that there are no cases where different expression forms express the same meaning. The standardized element framework is a structured information set formed after standardization, which arranges the core suppression elements in a fixed logical order, and the expression format of each element is unified, which facilitates the identification and processing of subsequent stages.

[0156] The prerequisites are that a standardized element framework has been obtained, and a control command protocol specification adapted to the fire extinguishing execution equipment has been pre-set in the system. The control command protocol specification is a unified standard that specifies the information expression format, data transmission requirements, etc. that the fire extinguishing execution equipment can recognize. According to the pre-set control command protocol specification, each element information in the standardized element framework is compared with the corresponding requirements in the protocol specification one by one. It is checked whether the expression format and information dimension of the element conform to the protocol specification. Any non-conforming parts are adjusted and corrected according to the protocol specification requirements to ensure that all element information can be accurately recognized by the fire extinguishing execution equipment. The protocol adaptation element is a set of element information that has been verified and adjusted to fully comply with the requirements of the control command protocol specification and can be directly adapted to the control module of the fire extinguishing execution equipment.

[0157] The prerequisite is that the protocol adaptation elements have been obtained and the execution logic relationships between the various protocol adaptation elements have been clarified. The execution logic relationships refer to the logical connections such as the sequence of various suppression actions and the way they cooperate during the fire extinguishing process. Based on the protocol adaptation elements, the execution logic corresponding to each protocol adaptation element is first sorted out. According to the sequence of execution logic, the protocol adaptation elements are transformed into instruction forms that the fire extinguishing execution equipment control module can directly read and execute. At the same time, all instructions are integrated and encapsulated according to logical relationships to form a complete instruction set. The optimal control instruction is the complete instruction set obtained after encapsulation, which can accurately control the fire extinguishing execution equipment to execute various fire extinguishing actions in an orderly manner according to the requirements of the optimal collaborative suppression scheme, so as to effectively suppress the fire in the target environment.

[0158] The beneficial effects include: accurately screening core suppression elements, eliminating redundant information, reducing subsequent processing load, ensuring the targeting of fire suppression control, avoiding command deviations, standardizing the expression format of core suppression elements, eliminating information ambiguity, providing a unified processing basis for subsequent stages, improving information processing efficiency, preventing information distortion, ensuring that element information conforms to control command protocol specifications, ensuring accurate equipment identification, avoiding equipment unresponsiveness, ensuring smooth and accurate transmission of control commands, converting adaptable elements into executable commands, clarifying execution logic, ensuring orderly and coherent fire suppression actions, and achieving precise suppression of fires in the target environment.

[0159] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0160] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A pipeless automatic fire extinguishing system, characterized in that, The system includes a fire extinguishing node monitoring module, a monitoring result mapping module, a fire extinguishing parameter arrangement module, a suppression scheme simulation module, an effectiveness prediction and optimization module, and an optimal scheme compilation module, wherein: The fire extinguishing node monitoring module is used to digitally mirror the actual space and attribute parameters of the target environment to construct a virtual mirror environment of the target environment, specifically for: A high-precision three-dimensional scan is performed on the actual spatial layout of the target environment to obtain the three-dimensional spatial data of the target environment; Multispectral identification is performed on objects in the target environment to obtain object material data of the target environment; The three-dimensional spatial data and the object material data are fused together to construct a digital spatial framework of the target environment; The digital space framework is integrated with environmental attributes to obtain a virtual mirror environment of the target environment; The monitoring result mapping module is used to monitor the fire extinguishing nodes without pipe network in the target environment in real time, and synchronously map the monitoring results to the virtual mirror environment to obtain the initial fire source status in the virtual mirror environment; The fire extinguishing parameter arrangement module is used to dynamically respond to and arrange the multi-dimensional activation parameters and spatial deployment relationship of the fire extinguishing node without pipeline network according to the initial fire source state, so as to obtain the collaborative suppression scheme of the virtual mirror environment. The suppression scheme simulation module is used to perform full-cycle quantitative simulation of the synergistic suppression scheme to obtain a multi-dimensional performance estimate of the synergistic suppression scheme. The efficiency prediction evaluation module is used to comprehensively evaluate the multi-dimensional efficiency prediction based on preset decision rules to obtain the optimal collaborative suppression scheme for the target environment. The optimal scheme compilation module is used to standardize and compile the optimal cooperative suppression scheme to obtain the optimal control instructions for the target environment.

2. The pipeless automatic fire extinguishing system as described in claim 1, characterized in that, When the monitoring result mapping module performs real-time monitoring of the pipeless fire suppression node in the target environment and synchronously maps the monitoring results to the virtual mirror environment to obtain the initial fire source status in the virtual mirror environment, it is specifically used for: Multi-dimensional status synchronization data is collected on the pipeless fire suppression nodes deployed in the target environment to obtain real-time operating data of the target environment; Based on the real-time operating data, the abnormal state characteristics of the fire extinguishing node without pipe network are identified to obtain the potential fire source characteristic information of the target environment. Based on the spatial topology of the virtual mirror environment, the potential fire source feature information is spatially correlated and located to obtain the fire source state mapping relationship of the virtual mirror environment; Based on the fire source state mapping relationship, the virtual mirror environment is configured to obtain the initial fire source state in the virtual mirror environment.

3. The pipeless automatic fire extinguishing system as described in claim 1, characterized in that, When the fire extinguishing parameter orchestration module performs dynamic response orchestration of the multi-dimensional activation parameters and spatial deployment relationship of the pipelineless fire extinguishing node based on the initialized fire source state to obtain the collaborative suppression scheme of the virtual mirror environment, it is specifically used for: The initial fire source state is analyzed to obtain the key fire source features of the initial fire source state; Based on the key fire source characteristics, the activation sequence, spray dosage and action angle of the pipeless fire extinguishing node are optimized in a coordinated manner to obtain a multi-dimensional action description of the pipeless fire extinguishing node. Based on the key fire source characteristics and the spatial location of the pipeless fire suppression node, the overlap of the suppression range and the degree of influence of the blind zone of the pipeless fire suppression node are evaluated to obtain the spatial coupling relationship of the pipeless fire suppression node. Based on the multidimensional action representation and the spatial coupling relationship, the action logic and parameter configuration of the pipeless fire extinguishing node are integrated and arranged to obtain the collaborative suppression scheme of the virtual mirror environment.

4. The pipeless automatic fire extinguishing system as described in claim 3, characterized in that, When the fire extinguishing parameter orchestration module performs coordinated optimization of the activation sequence, spray dosage, and action angle of the pipeless fire extinguishing node based on the key fire source characteristics to obtain a multi-dimensional action description of the pipeless fire extinguishing node, it is specifically used for: By performing key mining on the key fire source features, the core attribute identifiers of the fire source feature vector are obtained; The fire source type and intensity information in the core attribute identifier are mapped to a preset fire extinguishing medium database to obtain the target fire extinguishing medium for the pipeless fire extinguishing node. Based on the fire spread characteristics in the core attribute identifier and the spatial topology relationship of the pipeless fire extinguishing node, the collaborative start-up and shutdown logic of the pipeless fire extinguishing node is globally optimized, and the optimization result is compiled into the timing scheduling scheme of the pipeless fire extinguishing node. Based on the target fire extinguishing medium type and the timing scheduling scheme, the final action configuration of the pipeless fire extinguishing node is calibrated to obtain a multi-dimensional action description of the pipeless fire extinguishing node.

5. The pipeless automatic fire extinguishing system as described in claim 1, characterized in that, When the suppression scheme simulation module performs a full-cycle quantitative simulation of the collaborative suppression scheme and obtains the multidimensional performance estimate of the collaborative suppression scheme, it is specifically used for: The response of the cooperative suppression scheme is simulated in the virtual mirror environment to obtain the simulation results of the suppression process in the virtual mirror environment; The simulation results of the suppression process are monitored in real time to obtain the initial simulation sequence of the virtual mirror environment; The initial simulation sequence is normalized to obtain a standardized simulation dataset of the virtual mirror environment; Based on the standardized simulation dataset, the fire suppression effect of the fire suppression node without pipe network is comprehensively evaluated and a multi-dimensional performance estimate of the collaborative suppression scheme is obtained.

6. The pipeless automatic fire extinguishing system as described in claim 5, characterized in that, When the suppression scheme simulation module performs a comprehensive performance quantification evaluation of the fire suppression effect of the pipeless fire suppression node based on the standardized simulation dataset, and obtains a multi-dimensional performance prediction value of the collaborative suppression scheme, it is specifically used for: The evaluation dimensions of the standardized simulation dataset are extracted to obtain the preliminary evaluation dimensions of the cooperative inhibition scheme; The key performance indicators of the synergistic inhibition scheme are obtained by confirming the key dimensions of the preliminary assessment. The correlation and influence of the key performance indicators are analyzed to obtain the weight allocation relationship of the synergistic inhibition scheme; Based on the weight allocation relationship, the suppression data in the standardized simulation dataset is subjected to performance conversion to obtain independent scores for the key performance indicators. The combined effectiveness of the weight allocation relationship and the corresponding independent scores is summarized to obtain the multidimensional effectiveness estimate of the collaborative inhibition scheme.

7. The pipeless automatic fire extinguishing system as described in claim 6, characterized in that, When the suppression scheme simulation module performs performance conversion on the suppression data in the standardized simulation dataset according to the weight allocation relationship to obtain independent scores for the key performance indicators, the calculation formula for the independent scores is as follows: ； In the formula, For the first Independent scores for the key performance indicators mentioned above. The total number of time steps in the initial simulation sequence. The time step index of the initial simulation sequence. In order to time step The set of the pipeless fire suppression nodes that are activated at that time. In order to time step No. The fire suppression node without pipe network generated by the first Suppression data related to the aforementioned key performance indicators For the first The fire suppression node without pipe network described above is for the first The influence weights of the aforementioned key performance indicators In order to target the The aforementioned pipeless fire suppression node is in the first The preset benchmark performance thresholds for the aforementioned key performance indicators It is a natural exponential function.

8. The pipeless automatic fire extinguishing system as described in claim 1, characterized in that, When the performance prediction optimization module performs a comprehensive optimization of the multi-dimensional performance predictions according to preset decision rules to obtain the optimal collaborative suppression scheme for the target environment, it is specifically used for: The multidimensional performance prediction value is analyzed by feature dimension analysis to obtain the multidimensional performance feature data of the synergistic inhibition scheme; Based on preset decision rules, the multi-dimensional performance feature data are integrated and evaluated to obtain the comprehensive performance score of the collaborative inhibition scheme; A global ranking analysis is performed on the comprehensive effectiveness score to obtain the optimal scheme identifier of the synergistic inhibition scheme; Based on the optimal scheme identifier, the cooperative suppression scheme is matched and searched to obtain the optimal cooperative suppression scheme for the target environment.

9. The pipeless automatic fire extinguishing system as described in claim 1, characterized in that, When the optimal scheme compilation module performs standardized compilation of the optimal cooperative suppression scheme to obtain the optimal control instructions for the target environment, it is specifically used for: The optimal synergistic inhibition scheme is subjected to element identification to obtain the core inhibition elements of the optimal synergistic inhibition scheme; Based on the core suppression elements, the element representation format of the optimal collaborative suppression scheme is standardized to obtain the standardized element framework of the optimal collaborative suppression scheme. According to the preset control command protocol specification, the standardized element framework is subjected to protocol matching verification to obtain the protocol adaptation elements of the optimal cooperative suppression scheme. Based on protocol adaptation elements, the logic of the optimal cooperative suppression scheme is encapsulated into instruction encoding to obtain the optimal control instruction for the target environment.

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