Wireless communication method and device in fire extinguishing scene, equipment and medium

Abstract

The invention relates to the technical field of wireless communication in a fire extinguishing scene. The wireless communication method and device in the fire extinguishing scene, the equipment and the medium are provided, and the method comprises the following steps: carrying out hierarchical deployment processing on an algorithm library component set, a parameter set configuration table and a verification rule table, and constructing a physical layer radio frequency configuration scheme, a network layer routing protocol stack and an application layer encryption module set; receiving a commander configuration instruction through a preset security instruction interface, performing hot update processing on the algorithm library component set, the parameter set configuration table and the verification rule table, and generating an updated algorithm library component set, an updated parameter set configuration table and an updated verification rule table; and carrying out weight migration processing based on the fire type migration characteristics monitored in real time, the updated algorithm library component set, the updated parameter set configuration table and the updated verification rule table, and outputting a dominant strategy activation instruction, so as to achieve the technical effects of improving the communication environment adaptability, enhancing the protocol stack dynamic balance capability and improving the security authentication anti-interference performance.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology in firefighting scenarios, and in particular to wireless communication methods, devices, equipment and media in firefighting scenarios. Background Technology

[0002] With the rapid development of intelligent fire protection systems, wireless communication technology plays a crucial role in fire command and dispatch, environmental monitoring, and rescue coordination. An efficient and reliable communication system is the core foundation for ensuring the safety of firefighting operations, and its performance directly affects personnel safety and the effectiveness of disaster control.

[0003] Traditional technologies use fixed-frequency communication to deal with complex electromagnetic interference in fire scenes, but cannot dynamically avoid specific interference frequency bands, resulting in insufficient environmental adaptability. They rely on pre-built static routing protocol stacks to adapt to the communication needs of different fire scenarios, but it is difficult to dynamically balance latency and fault tolerance requirements, and the protocol stack lacks flexibility. In addition, voiceprint authentication and device fingerprint authentication with fixed thresholds ensure communication security, but the identification effect is affected in the high noise and strong electromagnetic interference environment of fire scenes, and the anti-interference capability of the security mechanism is weak. Summary of the Invention

[0004] Therefore, it is necessary to provide wireless communication methods, devices, equipment and media for firefighting scenarios to address the above-mentioned technical problems, so as to improve the adaptability of communication environment, enhance the dynamic balancing capability of protocol stack, and improve the anti-interference performance of security authentication.

[0005] Firstly, this application provides a wireless communication method for firefighting scenarios, the method comprising:

[0006] The fire type identification result is obtained by multi-dimensional feature fusion processing of the environmental sensor dataset collected by the preset environmental sensors, the pre-stored building structure database, and the manual annotation instructions input by the commander.

[0007] Based on the fire type identification result, the pre-configured strategy matching rule library is called to perform customized encryption strategy matching processing, and an algorithm library component set, parameter set configuration table and verification rule table are generated.

[0008] The algorithm library component set, parameter set configuration table and verification rule table are deployed in layers to build a physical layer radio frequency configuration scheme, a network layer routing protocol stack and an application layer encryption module set.

[0009] The system receives the commander's configuration instructions through the preset security instruction interface, performs hot update processing on the algorithm library component set, parameter set configuration table and verification rule table, and generates the updated algorithm library component set, updated parameter set configuration table and updated verification rule table.

[0010] Weight migration processing is performed based on real-time monitored fire type migration characteristics, updated algorithm library component set, updated parameter set configuration table, and updated verification rule table, and the dominant strategy activation command is output.

[0011] In one embodiment, based on the fire type identification result, a pre-configured policy matching rule base is invoked to perform customized encryption policy matching processing, generating an algorithm library component set, a parameter set configuration table, and a verification rule table, including:

[0012] Based on the fire type identification results, scene type matching processing is performed to generate scene classification labels;

[0013] The pre-configured strategy matching rule base is called to optimize the parameter set, and the scenario classification identifier is processed by spreading parameter mapping to generate an anti-interference frequency hopping spreading parameter set.

[0014] The algorithm selection rule set in the strategy matching rule base is called to perform encryption algorithm mapping on the scene classification identifier, generating an intrinsically secure encryption algorithm library.

[0015] The authentication rule set in the policy matching rule base is invoked to perform authentication mechanism mapping processing on the scene classification identifier, and a voiceprint-device fingerprint two-factor authentication mechanism is generated.

[0016] The verification rule set in the policy matching rule base is invoked to perform redundant protocol mapping on the scene classification identifier and generate an adaptive redundant verification protocol.

[0017] The anti-interference frequency hopping spread spectrum parameter set, intrinsically safe encryption algorithm library, voiceprint-device fingerprint two-factor authentication mechanism and adaptive redundancy verification protocol are integrated into the strategy components to generate the algorithm library component set, parameter set configuration table and verification rule table.

[0018] In one embodiment, the authentication rule set in the policy matching rule base is invoked to perform authentication mechanism mapping processing on the scene classification identifier, generating a voiceprint-device fingerprint two-factor authentication mechanism, including:

[0019] Based on scene classification and identification, environmental attenuation coefficient analysis is performed to generate signal attenuation feature vectors.

[0020] The voiceprint template library in the authentication rule set is called to perform anti-distortion algorithm selection processing on the signal attenuation feature vector to generate a robust voiceprint feature extractor.

[0021] The device fingerprint rule group in the authentication rule set is invoked to perform low signal-to-noise ratio optimization on the signal attenuation feature vector, thereby generating an anti-interference device fingerprint extractor.

[0022] The authentication tolerance is calculated by using a dynamic threshold configuration algorithm in the authentication rule set to generate adaptive authentication threshold parameters by processing the signal attenuation feature vector.

[0023] A robust voiceprint feature extractor, an anti-interference device fingerprint extractor, and adaptive authentication threshold parameters are subjected to two-factor fusion processing to generate a voiceprint-device fingerprint two-factor authentication mechanism.

[0024] In one embodiment, a parameter optimization rule set from a pre-configured policy matching rule base is invoked to perform spread spectrum parameter mapping processing on the scene classification identifier, generating an anti-interference frequency hopping spread spectrum parameter set, including:

[0025] Interference feature vectors are extracted and processed based on scene classification labels to generate electromagnetic interference feature vectors.

[0026] The frequency avoidance rule group in the parameter optimization rule set is invoked to perform interference frequency band avoidance processing on the electromagnetic interference feature vector and generate a safe frequency point set.

[0027] By using a sequence optimization algorithm based on a set of parameter optimization rules, a frequency hopping sequence is generated from the set of safe frequency points to produce an optimized frequency hopping sequence.

[0028] Based on the scene classification identifier, the dwell time configuration table in the call parameter optimization rule set is used to perform adaptive dwell time calculation and generate dynamic dwell time parameters.

[0029] The optimized frequency hopping sequence and dynamic dwell time parameters are encapsulated to generate an anti-interference frequency hopping spread spectrum parameter set.

[0030] In one embodiment, the algorithm library component set, parameter set configuration table, and verification rule table are deployed in a layered manner to construct a physical layer radio frequency configuration scheme, a network layer routing protocol stack, and an application layer encryption module set, including:

[0031] Physical layer parameter extraction processing is performed on the parameter set configuration table to generate RF modulation parameter set and power control parameter set;

[0032] Anti-interference radio frequency configuration processing is performed based on radio frequency modulation parameter set and power control parameter set to construct physical layer radio frequency configuration scheme;

[0033] The algorithm library component set is subjected to protocol core screening to generate latency-sensitive core component set and fault-tolerant enhanced core component set;

[0034] Based on the fire scenario type identifier, dynamic protocol stack assembly is performed on the latency-sensitive core component set and the fault-tolerant enhanced core component set to construct a network layer routing protocol stack.

[0035] The verification rule table is subjected to encryption policy binding processing to generate a dynamic encryption rule chain;

[0036] Modular service encapsulation is performed based on dynamic encryption rule chains to build an application-layer encryption module set.

[0037] In one embodiment, dynamic protocol stack assembly is performed on the latency-sensitive core component set and the fault-tolerant enhanced core component set based on the fire scene type identifier to construct a network layer routing protocol stack, including:

[0038] The fire scene type identifier is parsed and processed to generate a latency sensitivity coefficient and fault tolerance requirement level.

[0039] Based on the latency sensitivity coefficient, the latency-sensitive core component set is prioritized to generate an ordered latency-sensitive component sequence.

[0040] Based on the fault tolerance requirement level, the redundancy of the fault-tolerant enhanced core component set is optimized to generate a survivability enhanced component set.

[0041] Dynamically fuse ordered delay-sensitive component sequences and robust component sets to generate a dual-mode protocol core framework;

[0042] The dual-mode protocol core framework is processed by a pre-defined protocol stack compiler to generate protocol entities and build a network layer routing protocol stack.

[0043] In one embodiment, the network layer routing protocol stack is obtained using the following formula, including:

[0044]

[0045] in, This represents the network layer routing protocol stack. This indicates the protocol stack compilation operator. Represents the dynamic fusion operator. This indicates a priority sorting operator. This represents the redundancy optimization operator. This represents a set of latency-sensitive core components. This represents a fault-tolerant enhanced core component set. Represents the time delay sensitivity coefficient. Indicates the fault tolerance requirement level. This represents the fusion ratio function.

[0046] Secondly, this application also provides a wireless communication device for firefighting scenarios, the device comprising:

[0047] The multi-dimensional fusion identification module is used to perform multi-dimensional feature fusion processing on the environmental sensor dataset collected by the preset environmental sensors, the pre-stored building structure database, and the manual annotation instructions input by the commander to obtain the fire type identification result.

[0048] The strategy matching generation module is used to perform customized encryption strategy matching processing by calling the pre-configured strategy matching rule library based on the fire type identification result, and to generate an algorithm library component set, parameter set configuration table and verification rule table.

[0049] The layered deployment module is used to perform layered deployment processing of the algorithm library component set, parameter set configuration table and verification rule table, and to build the physical layer radio frequency configuration scheme, network layer routing protocol stack and application layer encryption module set.

[0050] The strategy hot update module is used to receive the commander's configuration instructions through the preset security instruction interface, and perform hot update processing on the algorithm library component set, parameter set configuration table and verification rule table to generate the updated algorithm library component set, updated parameter set configuration table and updated verification rule table.

[0051] The weight migration activation module is used to perform weight migration processing based on the real-time monitored fire type migration characteristics, the updated algorithm library component set, the updated parameter set configuration table, and the updated verification rule table, and output the dominant strategy activation command.

[0052] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods in the first aspect of this application.

[0053] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods in the first aspect of this application.

[0054] The wireless communication method, device, equipment, and medium for fire extinguishing scenarios provided in this application include: integrating a dataset collected by preset environmental sensors, a pre-stored building structure database, and commander's manual annotation instructions to perform multi-dimensional feature fusion to obtain a fire type identification result; based on the identification, calling a pre-configured strategy matching rule base to generate an adapted algorithm library component set, parameter set configuration table, and verification rule table; and then constructing a communication architecture covering the physical layer, network layer, and application layer through layered deployment.

[0055] Relying on the preset safety command interface to receive the commander's configuration command to complete the hot update of the strategy, and combining the real-time monitoring of fire type migration characteristics to perform weight migration processing and output the dominant strategy activation command. The above series of operations not only improve the adaptability of the communication environment by dynamically adapting to the complex electromagnetic environment of the fire scene, but also enhance the dynamic balance between latency and fault tolerance by using the customized assembly of the protocol stack and dynamic parameter optimization. At the same time, the deployment of customized encryption strategies and authentication mechanisms adapted to the scenario effectively improves the anti-interference performance of security authentication in high noise and strong electromagnetic interference scenarios. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart of a wireless communication method in a fire extinguishing scenario according to an embodiment of the present invention;

[0058] Figure 2 The flowchart illustrates how, in one embodiment of the present invention, a parameter optimization rule set in a pre-configured strategy matching rule base is invoked to perform spread spectrum parameter mapping processing on the scene classification identifier, thereby generating an anti-interference frequency hopping spread spectrum parameter set.

[0059] Figure 3 This is a structural diagram of a wireless communication device in a fire extinguishing scenario according to one embodiment of the present invention. Detailed Implementation

[0060] To make the above-mentioned objects, features, and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0061] First, the application scenarios of the embodiments of this application are described. The embodiments of this application provide wireless communication methods, devices, equipment, and media applicable to firefighting scenarios such as high-rise building fires, underground space fires, industrial plant fires, and forest and grassland fires.

[0062] In illustrative purposes, the wireless communication methods, devices, equipment, and media provided in the fire extinguishing scenarios of this application can also be applied to other emergency rescue scenarios with complex environmental interference and requiring multi-subject collaborative communication, such as chemical accident rescue, earthquake disaster site rescue, tunnel accident handling, etc. This is only an example and does not limit the specific application scenarios.

[0063] like Figure 1 As shown, this application provides a wireless communication method in a firefighting scenario, the method comprising:

[0064] S101: Perform multi-dimensional feature fusion processing on the environmental sensor dataset collected by the preset environmental sensors, the pre-stored building structure database, and the manual annotation instructions input by the commander to obtain the fire type identification result.

[0065] For example, the environmental sensor dataset collected by the preset environmental sensors is subjected to feature filtering and standardization to extract environmental feature items related to the fire development status. The pre-stored building structure database is subjected to structural feature parsing to extract building structure feature items that affect the fire spread path and the degree of environmental interference. Then, the manual annotation instructions input by the commander are subjected to semantic parsing and feature transformation to extract manual annotation feature items related to the on-site disaster situation assessment.

[0066] The extracted environmental features, building structure features, and manually labeled features are calibrated and their types are standardized to ensure compatibility. Then, a feature weighted fusion mechanism is used to perform multi-dimensional feature fusion on the calibrated environmental features, building structure features, and manually labeled features, integrating the disaster-related information carried by various features. The fire scene category is determined by the fused comprehensive feature information to obtain the fire type identification result.

[0067] S102: Based on the fire type identification result, call the pre-configured strategy matching rule library to perform customized encryption strategy matching processing, and generate algorithm library component set, parameter set configuration table and verification rule table.

[0068] For example, based on the fire type identification results, the core features of wireless communication-related scenarios are extracted, a pre-configured policy matching rule library is called, and the adapted customized encryption policy rule entries are retrieved through the built-in scenario-policy mapping index. The rule entries are parsed layer by layer to separate algorithm selection rules, parameter configuration rules and verification mechanism rules. Algorithm component types are selected according to scenario communication requirements, parameter items and value ranges are adjusted, and verification methods are optimized. The selected algorithm component types are classified and integrated to generate an algorithm library component set. The adjusted parameter items and value ranges are standardized and organized to generate a parameter set configuration table. The optimized verification methods are systematically sorted to generate a verification rule table.

[0069] S103: Perform layered deployment processing of the algorithm library component set, parameter set configuration table and verification rule table to build physical layer radio frequency configuration scheme, network layer routing protocol stack and application layer encryption module set.

[0070] For example, physical layer adaptation parameters are extracted from the parameter set configuration table, and parameters related to radio frequency modulation and power control are screened and adjusted for compatibility verification. Combined with the fire scene transmission environment, a physical layer radio frequency configuration scheme is constructed. Core network layer protocol components are screened from the algorithm library component set, distinguishing between latency adaptation and fault tolerance components. Protocol logic is logically associated and assembled hierarchically according to the communication requirements of the fire scene, clarifying data interaction rules and invocation mechanisms, and constructing a network layer routing protocol stack. Application layer encryption-related verification rules are extracted from the verification rule table, and encryption algorithm adaptation and data verification process rules are sorted out. These rules are then modularly decomposed, encapsulated, and integrated into encryption function modules, constructing an application layer encryption module set.

[0071] S104: Receives the commander's configuration command through the preset security command interface, performs hot update processing on the algorithm library component set, parameter set configuration table and verification rule table, and generates the updated algorithm library component set, updated parameter set configuration table and updated verification rule table.

[0072] For example, the system receives the commander's configuration command through a preset security command interface, verifies the identity and format of the commander's configuration command, and after confirming its validity, parses the algorithm library component update requirements, parameter configuration modification requirements, and verification rule adjustment requirements. For the algorithm library component update requirements, the system performs structural matching and compatibility adaptation with the algorithm library component set, replaces or adds compliant algorithm components, and performs parameter correlation verification and value compliance verification with the parameter set configuration table for the parameter configuration modification requirements, and updates the corresponding parameter items.

[0073] To address the need for adjustments to the verification rules and to verify the logical consistency of the verification rule table, the relevant verification process was optimized. While ensuring communication continuity, hot updates were executed. After the update validity was verified, the updated algorithm library component set, the updated parameter set configuration table, and the updated verification rule table were generated.

[0074] S105: Based on the real-time fire type migration characteristics, the updated algorithm library component set, the updated parameter set configuration table, and the updated verification rule table, perform weight migration processing and output the dominant strategy activation command.

[0075] For example, the real-time monitoring of fire type migration characteristics is analyzed, core migration information related to fire scene changes is extracted, and the core migration information is matched with the updated algorithm library component set, the updated parameter set configuration table, and the updated verification rule table to clarify the core content of the three updated sets that are adapted to the fire type migration status.

[0076] Based on the adaptation results, the initial weight allocation ratio of various core contents is set. The initial weight allocation ratio is adjusted in combination with the trend characteristics of fire type migration. The weight ratio of core contents that are highly adapted to the current migration status is strengthened. The communication requirement adaptability of the adjusted weight allocation results is verified. Weight configurations that do not meet the core requirements of fire scene communication are eliminated. The optimal adaptation strategy is selected based on the verified weight allocation results, and the activation command of the dominant strategy is generated.

[0077] The wireless communication method in a fire extinguishing scenario provided in one embodiment of this application includes: integrating a dataset collected by a preset environmental sensor, a pre-stored building structure database, and manual annotation instructions from the commander to perform multi-dimensional feature fusion to obtain a fire type identification result; based on the identification, calling a pre-configured strategy matching rule base to generate an adapted algorithm library component set, parameter set configuration table, and verification rule table; and then constructing a communication architecture covering the physical layer, network layer, and application layer through layered deployment.

[0078] Relying on the preset safety command interface to receive the commander's configuration command to complete the hot update of the strategy, and combining the real-time monitoring of fire type migration characteristics to perform weight migration processing and output the dominant strategy activation command. The above series of operations not only improve the adaptability of the communication environment by dynamically adapting to the complex electromagnetic environment of the fire scene, but also enhance the dynamic balance between latency and fault tolerance by using the customized assembly of the protocol stack and dynamic parameter optimization. At the same time, the deployment of customized encryption strategies and authentication mechanisms adapted to the scenario effectively improves the anti-interference performance of security authentication in high noise and strong electromagnetic interference scenarios.

[0079] In one embodiment, based on the fire type identification result, a pre-configured policy matching rule base is invoked to perform customized encryption policy matching processing, generating an algorithm library component set, a parameter set configuration table, and a verification rule table, including:

[0080] (1) Based on the fire type identification results, perform scene type matching processing to generate scene classification identification.

[0081] For example, the core attribute features of a fire scene are deeply analyzed based on the fire type identification results. These core attribute features include fire scale level, environmental interference type, building structural characteristics, and data transmission requirements. The core attribute features are compared with the preset scene classification standards one by one in multiple dimensions. The non-compliant classification items are eliminated by scene feature fit verification index. The attribute calibration process is performed according to the degree of fit between the core attributes of the scene and the classification standards to eliminate feature matching deviation and generate scene classification labels.

[0082] The scenario classification identifiers include scenario environment characteristic identifiers, communication requirement level identifiers, interference intensity level identifiers, and data transmission priority identifiers.

[0083] (2) Call the parameter optimization rule set in the pre-configured strategy matching rule base, perform spread spectrum parameter mapping processing on the scene classification identifier, and generate an anti-interference frequency hopping spread spectrum parameter set.

[0084] For example, the parameter optimization rule set in the pre-configured strategy matching rule base is invoked to extract feature information related to electromagnetic interference intensity, transmission link stability, and signal attenuation from the scene classification identifier. The extracted feature information is then matched with the spread spectrum parameter adaptation conditions in the parameter optimization rule set according to the rule priority. Spread spectrum parameter types that meet the scene's anti-interference requirements and transmission efficiency requirements are selected. The anti-interference performance of the selected spread spectrum parameter types is then optimized and adjusted in combination with the scene's dynamic interference characteristics to refine the adaptation range of parameter values. The spread spectrum parameter mapping process is then completed to generate an anti-interference frequency hopping spread spectrum parameter set.

[0085] The anti-interference frequency hopping spread spectrum parameter set includes safe frequency point configuration parameters, frequency hopping sequence generation parameters, dynamic dwell time parameters, and spread spectrum gain adjustment parameters.

[0086] (3) Call the algorithm selection rule set in the strategy matching rule base, perform encryption algorithm mapping processing on the scene classification identifier, and generate an intrinsically secure encryption algorithm library.

[0087] For example, the algorithm selection rule set in the policy matching rule base is invoked to parse the security protection level, data sensitivity level and minimum transmission efficiency requirements corresponding to the scenario classification identifier. Based on the encryption algorithm adaptation criteria in the algorithm selection rule set, encryption algorithm types that meet the intrinsic security requirements are selected according to security priority. The selected encryption algorithms are then subjected to scenario communication protocol adaptability verification and resource utilization assessment. Algorithm types with insufficient adaptability or excessive resource consumption are eliminated. The verified encryption algorithms are grouped according to functional modules, the calling relationship between modules is clarified, and they are classified and integrated to form an intrinsically secure encryption algorithm library.

[0088] The intrinsically secure encryption algorithm library includes subsets of symmetric encryption algorithms, subsets of asymmetric encryption algorithms, subsets of hash algorithms, and subsets of key management algorithms.

[0089] (4) Call the authentication rule set in the policy matching rule base, perform authentication mechanism mapping processing on the scene classification identifier, and generate a voiceprint-device fingerprint two-factor authentication mechanism.

[0090] For example, the authentication rule set in the policy matching rule base is invoked to extract features such as the type and intensity of environmental interference, the magnitude and trend of signal transmission attenuation, and the minimum threshold of authentication reliability from the scene classification identifier. Based on the two-factor authentication adaptation logic in the authentication rule set, the voiceprint feature extraction standard and device fingerprint collection specification are determined in combination with the scene noise characteristics. Anti-distortion processing technology is used for the voiceprint extraction algorithm, and low signal-to-noise ratio optimization technology is used for the device fingerprint extraction algorithm to optimize anti-interference. The verification order, data interaction method and mutual exclusion / complementary logic of two-factor authentication are clarified, a collaborative verification process is formulated, and a voiceprint-device fingerprint two-factor authentication mechanism is generated.

[0091] The voiceprint-device fingerprint dual-factor authentication mechanism includes a robust voiceprint extraction module, an anti-interference device fingerprint extraction module, a dual-factor collaborative verification module, and an adaptive threshold adjustment module.

[0092] (5) Call the verification rule set in the policy matching rule base, perform redundant protocol mapping processing on the scene classification identifier, and generate an adaptive redundant verification protocol.

[0093] For example, the verification rule set in the policy matching rule base is invoked to analyze in detail the maximum tolerance range of data transmission error, the link stability fluctuation range and the data integrity guarantee level corresponding to the scene classification identifier. Based on the redundancy protocol adaptation rules in the verification rule set, the redundant data generation ratio, the verification code calculation method and the redundant transmission triggering conditions are determined in combination with the scene link quality characteristics. The monitoring indicators of scene changes and the gradient standard of redundancy adjustment are associated, and the redundancy dynamic adjustment logic based on the real-time status of the scene is formulated. The adjustment trigger threshold and adjustment range are clarified, and an adaptive redundancy verification protocol is generated.

[0094] The adaptive redundancy check protocol includes redundancy data generation specifications, check code calculation standards, redundancy transmission triggering mechanisms, and redundancy dynamic adjustment rules.

[0095] (6) The anti-interference frequency hopping spread spectrum parameter set, intrinsically safe encryption algorithm library, voiceprint-device fingerprint two-factor authentication mechanism and adaptive redundancy verification protocol are integrated into the strategy components to generate the algorithm library component set, parameter set configuration table and verification rule table.

[0096] For example, a full-dimensional functional compatibility verification is performed on the anti-interference frequency hopping spread spectrum parameter set, the intrinsically secure encryption algorithm library, the voiceprint-device fingerprint two-factor authentication mechanism, and the adaptive redundancy check protocol. The focus is on verifying the interface compatibility of functional modules, the adaptability of parameter configurations, and the synergy of verification mechanisms. The standardized interface protocols and data interaction logic between each strategy component are clarified. Each component is functionally decomposed and classified according to the deployment requirements of the physical layer, network layer, and application layer. The dependencies between components are sorted out and conflict detection and resolution are performed. The algorithm units in the intrinsically secure encryption algorithm library and the core units of the voiceprint-device fingerprint two-factor authentication mechanism are classified and integrated into an algorithm library component set. The various parameter items in the anti-interference frequency hopping spread spectrum parameter set are standardized and organized into a parameter set configuration table according to the configuration dimension. The verification logic in the adaptive redundancy check protocol and the verification standards of the voiceprint-device fingerprint two-factor authentication mechanism are sorted into a verification rule table. The algorithm library component set, parameter set configuration table, and verification rule table are generated.

[0097] The algorithm library component set includes encryption algorithm units and authentication mechanism core units. The parameter set configuration table includes spread spectrum parameter items, adaptation scenario descriptions and parameter adjustment ranges. The verification rule table includes redundant verification processes, authentication verification standards and exception handling rules.

[0098] In one embodiment, the authentication rule set in the policy matching rule base is invoked to perform authentication mechanism mapping processing on the scene classification identifier, generating a voiceprint-device fingerprint two-factor authentication mechanism, including:

[0099] (1) Based on the scene classification identifier, the environmental attenuation coefficient is analyzed and processed to generate the signal attenuation feature vector.

[0100] For example, based on scene classification and identification, core information directly related to signal transmission, such as environmental interference type, propagation path characteristics, medium attenuation degree, and interference duration, is extracted. Strictly referring to the attenuation coefficient analysis standard in the authentication rule set, the core information is standardized and quantified according to the preset grading rules, the feature dimensions and data format are unified, and invalid interference data unrelated to the attenuation law is eliminated according to the signal attenuation correlation criterion. Through feature correlation integration, a feature set that can reflect the dynamic law of signal attenuation is formed, and the environmental attenuation coefficient analysis processing is completed to generate a signal attenuation feature vector.

[0101] The signal attenuation feature vector includes frequency attenuation amplitude features, time-domain attenuation trend features, spatial attenuation distribution features, interference superposition attenuation features, and attenuation fluctuation period features.

[0102] (2) Call the voiceprint template library in the authentication rule set, perform anti-distortion algorithm selection processing on the signal attenuation feature vector, and generate a robust voiceprint feature extractor.

[0103] For example, the voiceprint template library in the authentication rule set is called to parse the specific distortion types and corresponding distortion degrees, such as frequency distortion, time-domain distortion, and amplitude distortion, contained in the signal attenuation feature vector. The various distortion features are matched with the pre-set anti-distortion algorithm adaptation conditions in the voiceprint template library according to the adaptation priority. The optimal anti-distortion algorithm type for the distortion characteristics of the current attenuation scenario is selected. According to the distortion degree and signal attenuation law, the feature extraction window size, adaptive filtering parameter value range, and noise reduction threshold standard of the algorithm are finely adjusted to complete the anti-distortion algorithm selection process and generate a robust voiceprint feature extractor.

[0104] The robust voiceprint feature extractor includes distortion type recognition logic, adaptive filtering strategy, anti-interference feature enhancement method, and voiceprint template matching rules.

[0105] (3) Call the device fingerprint rule group in the authentication rule set, perform low signal-to-noise ratio optimization processing on the signal attenuation feature vector, and generate an anti-interference device fingerprint extractor.

[0106] For example, the device fingerprint rule group in the authentication rule set is invoked to accurately extract signal-to-noise ratio (SNR) related features such as the ratio of signal strength to noise intensity, noise spectrum distribution, and signal distortion amplitude from the signal attenuation feature vector. The system analyzes the specific interference dimensions of low SNR environment on device fingerprint feature recognition and extraction accuracy. Based on the optimization strategies in the device fingerprint rule group that are adapted to different SNR scenarios, targeted fingerprint feature enhancement algorithms and noise interference suppression algorithms are selected. The feature sampling frequency, fingerprint feature screening threshold, and interference filtering intensity of the algorithm are dynamically adjusted in combination with the SNR level and attenuation features to complete the low SNR optimization processing and generate an anti-interference device fingerprint extractor.

[0107] The anti-interference device fingerprint extractor includes signal-to-noise ratio level evaluation logic, fingerprint feature enhancement method, interference signal filtering strategy, and device fingerprint validity verification standard.

[0108] (4) The signal attenuation feature vector is processed by the dynamic threshold configuration algorithm in the authentication rule set to calculate the authentication tolerance and generate adaptive authentication threshold parameters.

[0109] For example, by using a dynamic threshold configuration algorithm in the authentication rule set, the influence weight of each attenuation feature in the signal attenuation feature vector on authentication accuracy and pass rate is determined by the feature correlation analysis method. The core evaluation dimensions of authentication tolerance are clarified, including the false positive rate control range, the false negative rate control standard, and the authentication response efficiency requirements. The tolerance dynamic adjustment factor is set by combining the dynamic change characteristics of scene attenuation and the amplitude of interference fluctuation. By comprehensively allocating weights and adjusting factors through the algorithm, the minimum acceptable range of authentication error under different attenuation scenarios is derived, the authentication tolerance calculation is completed, and adaptive authentication threshold parameters are generated.

[0110] The adaptive authentication threshold parameters include the basic threshold for voiceprint authentication, the basic threshold for device fingerprint authentication, the linkage threshold for two-factor collaborative authentication, the dynamic adjustment coefficient based on attenuation fluctuations, and the threshold activation conditions.

[0111] (5) Perform two-factor fusion processing on the robust voiceprint feature extractor, the anti-interference device fingerprint extractor and the adaptive authentication threshold parameter to generate a voiceprint-device fingerprint two-factor authentication mechanism.

[0112] For example, the feature output format, data accuracy requirements, and extraction response time of the robust voiceprint feature extractor are analyzed; the fingerprint feature dimensions, feature stability indicators, and extraction trigger conditions of the anti-interference device fingerprint extractor are analyzed; and the applicable scenarios and adjustment of the adaptive authentication threshold parameters are analyzed. Based on the need to balance the security and efficiency of fire scene authentication, the collaborative logic of two-factor authentication is determined, including the authentication execution order, feature complementarity rules, and result judgment priority. The specific methods of feature fusion, the process standards for cross-validation, and the contingency plans for handling abnormal situations are formulated. The three types of components are functionally adapted and integrated according to a unified interface protocol. The data interaction path, information transmission format, and call timing requirements between each component are clarified. The two-factor fusion processing is completed to generate a voiceprint-device fingerprint two-factor authentication mechanism.

[0113] The voiceprint-device fingerprint dual-factor authentication mechanism includes feature fusion methods, cross-validation processes, authentication result judgment criteria, threshold dynamic adjustment logic, and anomaly handling contingency plans.

[0114] like Figure 2 As shown, the pre-configured strategy matching rule base is used to call the parameter optimization rule set, and the scene classification identifier is processed by spreading parameter mapping to generate an anti-interference frequency hopping spreading parameter set, including:

[0115] S201: Extract interference feature vectors based on scene classification identifiers to generate electromagnetic interference feature vectors.

[0116] For example, based on scene classification and identification, core information related to wireless communication, such as electromagnetic interference sources, interference intensity levels, interference frequency band ranges, and interference duration characteristics, is extracted. Referring to the interference feature analysis standard in the parameter optimization rule set, the dimensions of various core information are regularized and standardized, invalid information unrelated to the spread spectrum parameter configuration is eliminated, and a feature set that can reflect the electromagnetic interference state is formed through feature correlation integration. The interference feature vector extraction process is completed, and an electromagnetic interference feature vector is generated.

[0117] Among them, the electromagnetic interference feature vector includes interference frequency band distribution characteristics, interference intensity fluctuation characteristics, interference type identification characteristics, interference duration characteristics, and interference superposition effect characteristics.

[0118] S202: Call the frequency avoidance rule group in the parameter optimization rule set to perform interference frequency band avoidance processing on the electromagnetic interference feature vector and generate a safe frequency point set.

[0119] For example, the frequency avoidance rule group in the parameter optimization rule set is invoked to comprehensively analyze the interference frequency band range, interference intensity peak and interference coverage density in the electromagnetic interference feature vector. The analysis results are matched with the preset frequency band avoidance strategies in the frequency avoidance rule group according to the interference level priority. Available frequency points that are not covered by interference and whose interference intensity is lower than the safety threshold are selected. The signal transmission quality of available frequency points is evaluated and compatibility is checked. Frequency points with insufficient transmission stability are eliminated and integrated to form a set of frequency points that meet the requirements of spread spectrum communication. The interference frequency band avoidance processing is completed and a safe frequency point set is generated.

[0120] The set of secure frequency points includes core communication frequency points, backup switching frequency points, frequency point priority ranking, and frequency point interference risk rating.

[0121] S203: By using the sequence optimization algorithm in the parameter optimization rule set, frequency hopping sequence generation processing is performed on the safe frequency point set to generate an optimized frequency hopping sequence.

[0122] For example, by using a sequence optimization algorithm in the parameter optimization rule set, the signal attenuation characteristics, interference risk fluctuation trends, and frequency switching response speed of each frequency point in the safe frequency point set are analyzed. Based on the frequency hopping sequence generation criteria built into the algorithm, the core constraints for sequence construction are determined, including frequency switching interval, sequence randomness requirements, and anti-interference redundancy standards. The sequence generation parameters are adjusted in combination with the real-time requirements of scenario communication to construct a frequency hopping sequence that can avoid continuous interference and ensure transmission continuity. This completes the frequency hopping sequence generation process and generates an optimized frequency hopping sequence.

[0123] The optimized frequency hopping sequence includes the primary frequency hopping sequence, the backup frequency hopping sequence, the sequence switching trigger condition, and the sequence synchronization calibration mechanism.

[0124] S204: Based on the scene classification identifier, call the parameter optimization rule set of the dwell time configuration table, perform adaptive dwell time calculation processing, and generate dynamic dwell time parameters.

[0125] For example, based on the scenario classification identifier, core communication features such as communication data volume level, transmission latency requirements, and link stability requirements are extracted. The dwell time configuration table in the parameter optimization rule set is called, and the communication features are matched with the preset dwell time adaptation range in the configuration table. Combined with the signal quality score and interference dynamic change characteristics of each frequency point in the safe frequency point set, the optimal dwell time under different frequency points is calculated, the dwell time dynamic adjustment threshold is set, the adaptive dwell time calculation process is completed, and dynamic dwell time parameters are generated.

[0126] The dynamic dwell time parameters include the reference dwell time for each frequency point, the dwell time adjustment range, the adjustment trigger signal quality threshold, and the minimum dwell time limit.

[0127] S205: Perform parameter encapsulation processing on the optimized frequency hopping sequence and dynamic dwell time parameters to generate an anti-interference frequency hopping spread spectrum parameter set.

[0128] For example, the structure and format of the frequency hopping sequence, the frequency calling logic and the switching rules are sorted out and optimized, the relationship between the dynamic dwell time parameter and each frequency point is clarified, the triggering conditions are adjusted, and the two types of parameters are formatted and logically associated according to the parameter encapsulation standard in the parameter optimization rule set. The parameter usage instructions, adaptation scenario labels and anomaly handling rules are supplemented to complete the parameter encapsulation process and generate an anti-interference frequency hopping spread spectrum parameter set.

[0129] The anti-interference frequency hopping spread spectrum parameter set includes optimized frequency hopping sequence data, dynamic dwell time configuration, parameter association mapping table, usage constraints, and anomaly adaptation scheme.

[0130] In one embodiment, the algorithm library component set, parameter set configuration table, and verification rule table are deployed in a layered manner to construct a physical layer radio frequency configuration scheme, a network layer routing protocol stack, and an application layer encryption module set, including:

[0131] (1) Extract physical layer parameters from the parameter set configuration table to generate RF modulation parameter set and power control parameter set.

[0132] For example, the parameter set configuration table is structurally parsed to locate the parameter categories related to physical layer communication. Based on the requirements of radio frequency transmission functions, relevant parameters such as modulation mode, encoding format, and carrier frequency are selected and classified into the category of radio frequency modulation parameters. Relevant parameters such as transmit power, power control threshold, and power adjustment step size are extracted and classified into the category of power control parameters. The validity of the two types of parameters is verified and the format is standardized. Invalid or conflicting parameters are removed to complete the physical layer parameter extraction process and generate the radio frequency modulation parameter group and the power control parameter group.

[0133] The radio frequency modulation parameter group includes modulation mode configuration, coding format parameters, carrier frequency parameters, and modulation efficiency optimization parameters. The power control parameter group includes reference transmit power parameters, power adjustment threshold parameters, dynamic power adjustment step size, and power protection limit parameters.

[0134] (2) Based on the RF modulation parameter group and power control parameter group, anti-interference RF configuration processing is carried out to construct the physical layer RF configuration scheme.

[0135] For example, based on the RF modulation parameter group and power control parameter group, and combined with the characteristics of the electromagnetic interference environment in the fire scene and the signal transmission distance requirements, the two types of parameters are adapted and adjusted for compatibility to ensure the coordinated matching of modulation method and power configuration. An anti-interference RF parameter combination scheme is formulated, the parameter effective timing and adjustment trigger conditions are clarified, the anti-interference capability and transmission stability of RF signal are optimized, the anti-interference RF configuration processing is completed, and the physical layer RF configuration scheme is constructed.

[0136] The physical layer radio frequency configuration scheme includes radio frequency parameter combination strategies, anti-interference optimization configuration, parameter dynamic adjustment mechanism, and transmission quality assurance standards.

[0137] (3) Perform protocol core screening on the algorithm library component set to generate a latency-sensitive core component set and a fault-tolerant enhanced core component set.

[0138] For example, the algorithm library component set is decomposed into functional dimensions, and the core functions, performance indicators and applicable scenarios of each component are analyzed. Based on the network layer protocol operation requirements, components directly related to data transmission latency control are selected and classified into the latency-sensitive core component category, and components related to data transmission fault tolerance and recovery are selected and classified into the fault-tolerant enhanced core component category. Functional integrity verification and performance adaptability evaluation are performed on the two types of components, and components with redundant functions or insufficient adaptability are eliminated. The core protocol screening process is completed, and the latency-sensitive core component set and the fault-tolerant enhanced core component set are generated.

[0139] The latency-sensitive core component set includes a data forwarding component, a fast routing decision component, and a link priority scheduling component, while the fault-tolerant enhanced core component set includes a data retransmission component, a link backup switching component, and an error verification and repair component.

[0140] (4) Based on the fire scene type identifier, perform dynamic protocol stack assembly processing on the delay-sensitive core component set and the fault-tolerant enhanced core component set to construct the network layer routing protocol stack.

[0141] For example, based on the communication latency requirements, data reliability requirements, and dynamic characteristics of network topology corresponding to the fire scenario type identifier resolution, the deployment priority and functional integration ratio of latency-sensitive core component sets and fault-tolerant enhanced core component sets are determined. Interface adaptation standards and data interaction logic between components are formulated, the protocol stack hierarchy is sorted out, the two types of components are assembled in an orderly manner according to the hierarchy, functional conflicts and dependencies between components are handled, dynamic protocol stack assembly is completed, and a network layer routing protocol stack is constructed.

[0142] The network layer routing protocol stack includes a hierarchical component deployment structure, component interaction interface specifications, dynamic routing decision logic, and fault tolerance and recovery mechanisms.

[0143] (5) Bind the encryption policy to the verification rule table to generate a dynamic encryption rule chain.

[0144] For example, the various verification rules in the verification rule table are functionally categorized, the security protection objectives corresponding to each type of rule are clarified, the rules are associated and bound with the appropriate encryption algorithms and key management strategies, the bound rules are arranged in an orderly manner according to the data transmission process and security verification priority, the rule triggering conditions and execution order logic are set, the encryption strategy binding process is completed, and a dynamic encryption rule chain is generated.

[0145] The dynamic encryption rule chain includes the rule-encryption policy binding relationship, rule execution order, trigger condition configuration, and security level adaptation standard.

[0146] (6) Based on the dynamic encryption rule chain, modular service encapsulation is carried out to build an application layer encryption module set.

[0147] For example, based on a dynamic encryption rule chain, the verification and encryption logic in the rule chain is split into functional modules, and closely related logical units are encapsulated into independent service units. The input and output formats, calling interfaces and collaborative interaction methods of each service unit are defined. Functional compatibility testing and security performance verification are performed on the encapsulated service units to ensure the independent operation and collaborative work capabilities of the service units. Modular service encapsulation processing is completed, and an application layer encryption module set is constructed.

[0148] The application layer encryption module set includes an encryption service unit, a verification service unit, a key management service unit, and a security collaboration service unit.

[0149] In one embodiment, dynamic protocol stack assembly is performed on the latency-sensitive core component set and the fault-tolerant enhanced core component set based on the fire scene type identifier to construct a network layer routing protocol stack, including:

[0150] (1) Perform scene characteristic analysis on the fire scene type identifier to generate the time delay sensitivity coefficient and fault tolerance requirement level.

[0151] For example, deep scene characteristic analysis is performed on the fire scene type identifier to extract the core characteristic information that affects communication transmission in the fire scene, including the urgency of data transmission, the priority of service type, the dynamic characteristics of environmental interference, and the stability of network topology. The core characteristic information is quantitatively evaluated and classified according to the preset characteristic analysis standard. A quantitative index reflecting the scene's requirements for transmission speed is derived through a latency sensitivity assessment model. The level of data reliability assurance of the scene is determined through a fault tolerance requirement assessment system. The scene characteristic analysis is completed, and the latency sensitivity coefficient and fault tolerance requirement level are generated.

[0152] Among them, the latency sensitivity coefficient includes the service latency weight, the transmission response priority coefficient, and the latency tolerance threshold parameter, while the fault tolerance requirement level includes the data integrity guarantee level, the link failure recovery level, and the redundancy backup requirement level.

[0153] (2) Prioritize the set of time-delay-sensitive core components based on the time-delay sensitivity coefficient to generate an ordered sequence of time-delay-sensitive components.

[0154] For example, based on the latency sensitivity coefficient, the impact weight of each component in the latency-sensitive core component set on data transmission latency, response speed performance indicators, and functional irreplaceability are analyzed. According to the priority rules in the latency sensitivity coefficient and the component performance evaluation results, component sorting criteria are formulated, including latency optimization priority, functional dependency order, resource consumption adaptability, etc. The latency-sensitive core component set is arranged in an orderly manner according to the criteria. Functional conflict verification and timing rationality verification are performed on the sorting results. Component configurations with contradictory sorting logic are eliminated, the priority sorting process is completed, and an ordered latency-sensitive component sequence is generated.

[0155] The ordered latency-sensitive component sequence includes the component priority ranking result, the component activation sequence arrangement, the component resource allocation ratio, and the component interaction dependency relationship.

[0156] (3) Based on the fault tolerance requirement level, the redundancy of the fault-tolerant enhanced core component set is optimized to generate the survivability enhanced component set.

[0157] For example, based on the fault tolerance requirement level, the fault recovery capability, data retransmission efficiency, and link backup support function of each component in the fault-tolerant enhanced core component set are analyzed. Combined with the redundancy configuration standards corresponding to the fault tolerance requirement level, the baseline for the number of redundant deployments of components, backup triggering conditions, and switching mechanisms are determined. The redundancy of the core components is adapted and adjusted to enhance the component backup capability in high fault tolerance requirement scenarios and simplify the excessive redundancy configuration in low fault tolerance requirement scenarios. The optimized component set is subjected to tamper resistance testing and resource consumption assessment to ensure that the redundancy configuration matches the fault tolerance requirements and that there is no resource waste. The redundancy optimization process is completed, and a tamper resistance enhanced component set is generated.

[0158] The resilience enhancement component set includes core functional components, redundant backup components, failover triggering components, and data recovery auxiliary components.

[0159] (4) Dynamically fuse the ordered delay-sensitive component sequence and the resilience enhancement component set to generate the dual-mode protocol core framework.

[0160] For example, a full-dimensional compatibility analysis is performed on the ordered latency-sensitive component sequence and the resilience-enhancing component set to clarify the functional complementarity, interface adaptation requirements, and resource occupation conflict points of the two types of component sets. Based on the latency and fault tolerance balance requirements of the fire scenario, dynamic fusion rules are formulated, including component-level deployment schemes, data interaction flow paths, and resource scheduling priority strategies. According to the fusion rules, the components in the ordered latency-sensitive component sequence are embedded into the corresponding layers of the protocol stack according to priority. The components in the resilience-enhancing component set are deployed in association with the core components according to the redundancy configuration requirements. Functional overlap and logical conflicts between components are handled to complete the dynamic fusion process and generate the dual-mode protocol core framework.

[0161] The core framework of the dual-mode protocol includes a latency optimization module architecture, a fault-tolerant redundancy module architecture, a component interaction interface specification, a dynamic resource scheduling mechanism, and dual-mode collaborative working logic.

[0162] (5) The dual-mode protocol core framework is processed by the preset protocol stack compiler to generate protocol entities and build the network layer routing protocol stack.

[0163] For example, by using a pre-defined protocol stack compiler, the core framework of the dual-mode protocol is parsed and its structure is verified. The component logic, interaction rules, and scheduling mechanism in the framework are converted into executable code that conforms to the protocol stack standard. The generated code is standardized in format and integrated with functional modules in accordance with the network layer communication protocol specification. The underlying support logic required for the operation of the protocol entity is embedded, including data encapsulation / decapsulation rules, link management logic, exception handling process, etc. The generated protocol entity is subjected to functional integrity testing and compatibility verification to ensure that the protocol entity can adapt to the communication requirements of fire scenario. The protocol entity generation process is completed, and the network layer routing protocol stack is constructed.

[0164] The network layer routing protocol stack includes a protocol hierarchy, component execution logic, data transmission protocol specifications, fault tolerance and recovery execution procedures, and latency optimization and control mechanisms. A pre-configured protocol stack compiler is a specialized compilation tool that is pre-configured and capable of converting the component logic and interaction rules of the core protocol framework into executable protocol entities that conform to network layer communication standards and are adapted to the communication needs of fire scenarios.

[0165] In one embodiment, the network layer routing protocol stack is obtained using the following formula, including:

[0166]

[0167] in, This represents the network layer routing protocol stack. This indicates the protocol stack compilation operator. Represents the dynamic fusion operator. This indicates a priority sorting operator. This represents the redundancy optimization operator. This represents a set of latency-sensitive core components. This represents a fault-tolerant enhanced core component set. Represents the time delay sensitivity coefficient. Indicates the fault tolerance requirement level. This represents the fusion ratio function.

[0168] For example, a priority sorting operator, combined with a latency sensitivity coefficient, is used to process the latency-sensitive core component set, resulting in an ordered sequence of latency-sensitive components. Simultaneously, a redundancy optimization operator, combined with a fault tolerance requirement level, is used to process the fault-tolerant enhanced core component set, resulting in a robust enhanced component set. A dynamic fusion operator, combined with a fusion ratio function generated based on the latency sensitivity coefficient and fault tolerance requirement level, merges the two processed component sets into a dual-mode protocol core framework. A protocol stack compilation operator then compiles this dual-mode protocol core framework to obtain the network layer routing protocol stack.

[0169] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0170] In one embodiment, such as Figure 3 As shown, this application also provides a wireless communication device 300 for firefighting scenarios, the device 300 comprising:

[0171] The multi-dimensional fusion identification module 301 is used to perform multi-dimensional feature fusion processing on the environmental sensor dataset collected by the preset environmental sensors, the pre-stored building structure database, and the manual annotation instructions input by the commander to obtain the fire type identification result.

[0172] The strategy matching generation module 302 is used to perform customized encryption strategy matching processing by calling the pre-configured strategy matching rule library based on the fire type identification result, and to generate an algorithm library component set, a parameter set configuration table and a verification rule table.

[0173] The layered deployment module 303 is used to perform layered deployment processing on the algorithm library component set, parameter set configuration table and verification rule table, and to build the physical layer radio frequency configuration scheme, network layer routing protocol stack and application layer encryption module set.

[0174] The strategy hot update module 304 is used to receive the commander's configuration command through the preset security command interface, perform hot update processing on the algorithm library component set, parameter set configuration table and verification rule table, and generate the updated algorithm library component set, updated parameter set configuration table and updated verification rule table.

[0175] The weight migration activation module 305 is used to perform weight migration processing based on the real-time monitored fire type migration characteristics, the updated algorithm library component set, the updated parameter set configuration table, and the updated verification rule table, and output the dominant strategy activation command.

[0176] Specifically, the multi-dimensional fusion identification module 301 performs feature filtering and standardization on the environmental sensor dataset collected by pre-set environmental sensors, extracting environmental features related to the fire development status. It analyzes the structural features of the pre-stored building structure database, extracting building structure features that influence the fire spread path and the degree of environmental interference. It performs semantic analysis and feature transformation on the manual annotation instructions input by the commander, extracting manual annotation features related to on-site disaster assessment. It performs feature dimension calibration and type unification processing on the extracted environmental features, building structure features, and manual annotation features to ensure compatibility for fusion. A feature weighted fusion mechanism is used to perform multi-dimensional feature fusion processing on the calibrated environmental features, building structure features, and manual annotation features, integrating the disaster-related information carried by various features. The fire scene category is determined using the fused comprehensive feature information to obtain the fire type identification result.

[0177] The strategy matching generation module 302 analyzes the core features of the scene corresponding to the fire type identification result, calls the parameter optimization rule set in the pre-configured strategy matching rule library, performs spread spectrum parameter mapping processing on the core features of the scene, and generates an anti-interference frequency hopping spread spectrum parameter set. It calls the algorithm selection rule set in the strategy matching rule library, performs encryption algorithm mapping processing on the core features of the scene, and generates an intrinsically safe encryption algorithm library. It calls the authentication rule set in the strategy matching rule library, performs authentication mechanism mapping processing on the core features of the scene, and generates a voiceprint-device fingerprint two-factor authentication mechanism. It calls the verification rule set in the strategy matching rule library, performs redundancy protocol mapping processing on the core features of the scene, and generates an adaptive redundancy verification protocol. Finally, it integrates the anti-interference frequency hopping spread spectrum parameter set, the intrinsically safe encryption algorithm library, the voiceprint-device fingerprint two-factor authentication mechanism, and the adaptive redundancy verification protocol into strategy components, generating an algorithm library component set, a parameter set configuration table, and a verification rule table.

[0178] The layered deployment module 303 extracts physical layer parameters from the parameter set configuration table to generate RF modulation parameter sets and power control parameter sets. Based on these sets, it performs anti-interference RF configuration processing to construct the physical layer RF configuration scheme. It then performs protocol core filtering on the algorithm library component set to generate latency-sensitive and fault-tolerant enhanced core component sets. Based on the fire scenario type identifier, it performs dynamic protocol stack assembly on these sets to construct the network layer routing protocol stack. Finally, it performs encryption policy binding on the verification rule table to generate a dynamic encryption rule chain. Based on this chain, it performs modular service encapsulation to construct the application layer encryption module set.

[0179] The strategy hot update module 304 receives the commander's configuration command through a preset security command interface. It verifies the commander's identity and format, and after confirming the command's validity, parses the algorithm library component update requirements, parameter configuration modification requirements, and verification rule adjustment requirements contained within. For the algorithm library component update requirements, it performs structural matching and compatibility adaptation with the algorithm library component set, replacing or adding compliant algorithm components. For the parameter configuration modification requirements, it performs parameter correlation verification and value compliance checks with the parameter set configuration table, updating the corresponding parameter items. For the verification rule adjustment requirements, it performs logical consistency verification with the verification rule table, optimizing related verification processes. While ensuring communication continuity, the hot update is executed. After verifying the update's validity, it generates the updated algorithm library component set, the updated parameter set configuration table, and the updated verification rule table.

[0180] The weighted migration activation module 305 analyzes the real-time monitored fire type migration characteristics and extracts core migration information related to changes in the fire scene. It then performs correlation matching with the updated algorithm library component set, the updated parameter set configuration table, and the updated verification rule table to identify the core content adapted to the fire type migration state in the three updated sets. Based on the adaptation results, it sets the initial weight allocation ratio for each type of core content, and adjusts the initial weight allocation ratio according to the trend characteristics of fire type migration, strengthening the weight proportion of core content highly adapted to the current migration state. The adjusted weight allocation results are verified for communication requirement adaptability, and weight configurations that do not meet the core requirements of fire scene communication are eliminated. Based on the verified final weight allocation results, the optimal adaptation strategy is selected, and the dominant strategy activation command is output.

[0181] The strategy matching generation module 302 is also used for:

[0182] Based on the fire type identification results, scene type matching processing is performed to generate scene classification labels;

[0183] The pre-configured strategy matching rule base is called to optimize the parameter set, and the scenario classification identifier is processed by spreading parameter mapping to generate an anti-interference frequency hopping spreading parameter set.

[0184] The algorithm selection rule set in the strategy matching rule base is called to perform encryption algorithm mapping on the scene classification identifier, generating an intrinsically secure encryption algorithm library.

[0185] The authentication rule set in the policy matching rule base is invoked to perform authentication mechanism mapping processing on the scene classification identifier, and a voiceprint-device fingerprint two-factor authentication mechanism is generated.

[0186] The verification rule set in the policy matching rule base is invoked to perform redundant protocol mapping on the scene classification identifier and generate an adaptive redundant verification protocol.

[0187] The anti-interference frequency hopping spread spectrum parameter set, intrinsically safe encryption algorithm library, voiceprint-device fingerprint two-factor authentication mechanism and adaptive redundancy verification protocol are integrated into the strategy components to generate the algorithm library component set, parameter set configuration table and verification rule table.

[0188] The strategy matching generation module 302 is also used for:

[0189] Based on scene classification and identification, environmental attenuation coefficient analysis is performed to generate signal attenuation feature vectors.

[0190] The voiceprint template library in the authentication rule set is called to perform anti-distortion algorithm selection processing on the signal attenuation feature vector to generate a robust voiceprint feature extractor.

[0191] The device fingerprint rule group in the authentication rule set is invoked to perform low signal-to-noise ratio optimization on the signal attenuation feature vector, thereby generating an anti-interference device fingerprint extractor.

[0192] The authentication tolerance is calculated by using a dynamic threshold configuration algorithm in the authentication rule set to generate adaptive authentication threshold parameters by processing the signal attenuation feature vector.

[0193] A robust voiceprint feature extractor, an anti-interference device fingerprint extractor, and adaptive authentication threshold parameters are subjected to two-factor fusion processing to generate a voiceprint-device fingerprint two-factor authentication mechanism.

[0194] The strategy matching generation module 302 is also used for:

[0195] Interference feature vectors are extracted and processed based on scene classification labels to generate electromagnetic interference feature vectors.

[0196] The frequency avoidance rule group in the parameter optimization rule set is invoked to perform interference frequency band avoidance processing on the electromagnetic interference feature vector and generate a safe frequency point set.

[0197] By using a sequence optimization algorithm based on a set of parameter optimization rules, a frequency hopping sequence is generated from the set of safe frequency points to produce an optimized frequency hopping sequence.

[0198] Based on the scene classification identifier, the dwell time configuration table in the call parameter optimization rule set is used to perform adaptive dwell time calculation and generate dynamic dwell time parameters.

[0199] The optimized frequency hopping sequence and dynamic dwell time parameters are encapsulated to generate an anti-interference frequency hopping spread spectrum parameter set.

[0200] Layered deployment build module 303 is also used for:

[0201] Physical layer parameter extraction processing is performed on the parameter set configuration table to generate RF modulation parameter set and power control parameter set;

[0202] Anti-interference radio frequency configuration processing is performed based on radio frequency modulation parameter set and power control parameter set to construct physical layer radio frequency configuration scheme;

[0203] The algorithm library component set is subjected to protocol core screening to generate latency-sensitive core component set and fault-tolerant enhanced core component set;

[0204] Based on the fire scenario type identifier, dynamic protocol stack assembly is performed on the latency-sensitive core component set and the fault-tolerant enhanced core component set to construct a network layer routing protocol stack.

[0205] The verification rule table is subjected to encryption policy binding processing to generate a dynamic encryption rule chain;

[0206] Modular service encapsulation is performed based on dynamic encryption rule chains to build an application-layer encryption module set.

[0207] Layered deployment build module 303 is also used for:

[0208] The fire scene type identifier is parsed and processed to generate a latency sensitivity coefficient and fault tolerance requirement level.

[0209] Based on the latency sensitivity coefficient, the latency-sensitive core component set is prioritized to generate an ordered latency-sensitive component sequence.

[0210] Based on the fault tolerance requirement level, the redundancy of the fault-tolerant enhanced core component set is optimized to generate a survivability enhanced component set.

[0211] Dynamically fuse ordered delay-sensitive component sequences and robust component sets to generate a dual-mode protocol core framework;

[0212] The dual-mode protocol core framework is processed by a pre-defined protocol stack compiler to generate protocol entities and build a network layer routing protocol stack.

[0213] In one embodiment, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0214] In one embodiment, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0215] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0216] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method of wireless communication in a fire suppression scenario, characterized in that, The method comprises: Multi-dimensional feature fusion processing is performed on the preset environment sensor data set collected by the environment sensor, the pre-stored building structure database and the artificial marking instruction input by the commander to obtain a fire type identification result; Based on the fire type identification result, a pre-configured strategy matching rule library is called for customized encryption strategy matching processing to generate an algorithm library component set, a parameter set configuration table and a verification rule table; The algorithm library component set, the parameter set configuration table and the verification rule table are subjected to hierarchical deployment processing to construct a physical layer radio frequency configuration scheme, a network layer routing protocol stack and an application layer encryption module set; A commander configuration instruction is received through a preset security instruction interface, and the algorithm library component set, the parameter set configuration table and the verification rule table are subjected to hot update processing to generate an updated algorithm library component set, an updated parameter set configuration table and an updated verification rule table; Based on the real-time monitored fire type migration feature, the updated algorithm library component set, the updated parameter set configuration table and the updated verification rule table, weight migration processing is performed to output a dominant strategy activation instruction.

2. The method of claim 1, wherein, The method comprises: Based on the fire type identification result, scene type matching processing is performed to generate a scene classification identification; A parameter optimization rule set in the pre-configured strategy matching rule library is called to perform spread spectrum parameter mapping processing on the scene classification identification to generate an anti-interference frequency hopping spread spectrum parameter set; An algorithm selection rule set in the strategy matching rule library is called to perform encryption algorithm mapping processing on the scene classification identification to generate an intrinsically safe encryption algorithm library; An authentication rule set in the strategy matching rule library is called to perform authentication mechanism mapping processing on the scene classification identification to generate a voiceprint-device fingerprint two-factor authentication mechanism; A verification rule set in the strategy matching rule library is called to perform redundant protocol mapping processing on the scene classification identification to generate an adaptive redundancy verification protocol; The anti-interference frequency hopping spread spectrum parameter set, the intrinsically safe encryption algorithm library, the voiceprint-device fingerprint two-factor authentication mechanism and the adaptive redundancy verification protocol are subjected to strategy component integration processing to generate the algorithm library component set, the parameter set configuration table and the verification rule table.

3. The method of claim 2, wherein, The method comprises: Based on the scene classification identification, environment attenuation coefficient analysis processing is performed to generate a signal attenuation feature vector; A voiceprint template library in the authentication rule set is called to perform anti-distortion algorithm selection processing on the signal attenuation feature vector to generate a robust voiceprint feature extractor; A device fingerprint rule group in the authentication rule set is called to perform low signal-to-noise ratio optimization processing on the signal attenuation feature vector to generate an anti-interference device fingerprint extractor; The dynamic threshold configuration algorithm in the authentication rule set is used for authentication tolerance calculation processing on the signal attenuation feature vector, to generate an adaptive authentication threshold parameter; The robust voiceprint feature extractor, the anti-interference device fingerprint extractor, and the adaptive authentication threshold parameter are subjected to double-factor fusion processing, to generate a voiceprint-device fingerprint double-factor authentication mechanism.

4. The method of claim 2, wherein, The parameter optimization rule set in the preconfigured strategy matching rule library is called to perform spread spectrum parameter mapping processing on the scene classification identifier, to generate an anti-interference frequency hopping spread spectrum parameter set, including: Based on the scene classification identifier, interference feature vector extraction processing is performed, to generate an electromagnetic interference feature vector; The frequency point avoidance rule group in the parameter optimization rule set is called to perform interference frequency band avoidance processing on the electromagnetic interference feature vector, to generate a safe frequency point set; The sequence optimization algorithm in the parameter optimization rule set is used for frequency hopping sequence generation processing on the safe frequency point set, to generate an optimized frequency hopping sequence; Based on the scene classification identifier, the parameter optimization rule set is called to perform adaptive dwell time calculation processing on the parameter optimization rule set, to generate a dynamic dwell time parameter; The optimized frequency hopping sequence and the dynamic dwell time parameter are subjected to parameter packaging processing, to generate the anti-interference frequency hopping spread spectrum parameter set.

5. The method of claim 1, wherein, The algorithm library component set, the parameter set configuration table, and the verification rule table are subjected to hierarchical deployment processing, to construct a physical layer radio frequency configuration scheme, a network layer routing protocol stack, and an application layer encryption module set, including: Physical layer parameter extraction processing is performed on the parameter set configuration table, to generate a radio frequency modulation parameter group and a power control parameter group; Based on the radio frequency modulation parameter group and the power control parameter group, anti-interference radio frequency configuration processing is performed, to construct the physical layer radio frequency configuration scheme; Protocol core screening processing is performed on the algorithm library component set, to generate a time delay sensitive core component set and a fault tolerance enhanced core component set; Based on the fire scene type identifier, dynamic protocol stack assembly processing is performed on the time delay sensitive core component set and the fault tolerance enhanced core component set, to construct the network layer routing protocol stack; Encryption strategy binding processing is performed on the verification rule table, to generate a dynamic encryption rule chain; Based on the dynamic encryption rule chain, modular service packaging processing is performed, to construct the application layer encryption module set.

6. The method of wireless communication in a fire-extinguishing scene according to claim 5, characterized in that, Based on the fire scene type identifier, dynamic protocol stack assembly processing is performed on the time delay sensitive core component set and the fault tolerance enhanced core component set, to construct the network layer routing protocol stack, including: Scene characteristic analysis processing is performed on the fire scene type identifier, to generate a time delay sensitive coefficient and a fault tolerance demand level; Based on the time delay sensitive coefficient, priority sorting processing is performed on the time delay sensitive core component set, to generate an ordered time delay sensitive component sequence; Based on the fault tolerance demand level, redundancy optimization processing is performed on the fault tolerance enhanced core component set, to generate an invulnerability enhanced component set; Dynamic fusion processing is performed on the ordered time delay sensitive component sequence and the invulnerability enhanced component set, to generate a dual-mode protocol core framework; The dual-mode protocol core framework is processed by a preset protocol stack compiler to generate protocol entities and construct the network layer routing protocol stack.

7. The method of wireless communication in a fire-extinguishing scene according to claim 6, characterized in that, The network layer routing protocol stack is obtained using the following formula, including: wherein, represents a network layer routing protocol stack, represents a protocol stack compiling operator, represents a dynamic fusion operator, represents a priority ordering operator, represents a redundancy optimization operator, represents a latency sensitive core component set, represents a fault tolerance enhanced core component set, represents a latency sensitivity coefficient, represents a fault tolerance requirement level, represents a fusion proportionality function.

8. Wireless communication device in a fire extinguishing scenario, characterized in that The device includes: The multi-dimensional fusion identification module is used to perform multi-dimensional feature fusion processing on the environmental sensor dataset collected by the preset environmental sensors, the pre-stored building structure database, and the manual annotation instructions input by the commander to obtain the fire type identification result. The strategy matching generation module is used to call the pre-configured strategy matching rule library to perform customized encryption strategy matching processing based on the fire type identification result, and generate an algorithm library component set, a parameter set configuration table and a verification rule table. A layered deployment module is used to perform layered deployment processing on the algorithm library component set, the parameter set configuration table and the verification rule table, and to build a physical layer radio frequency configuration scheme, a network layer routing protocol stack and an application layer encryption module set. The strategy hot update module is used to receive the commander's configuration command through a preset security command interface, and perform hot update processing on the algorithm library component set, the parameter set configuration table and the verification rule table to generate the updated algorithm library component set, the updated parameter set configuration table and the updated verification rule table. The weight migration activation module is used to perform weight migration processing based on the real-time monitored fire type migration characteristics, the updated algorithm library component set, the updated parameter set configuration table, and the updated verification rule table, and output the dominant strategy activation command. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. When the processor executes the computer program, it implements the steps of the wireless communication method in the fire extinguishing scenario as described in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the wireless communication method in the fire extinguishing scenario as described in any one of claims 1 to 7.

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