A Fire Alarm Linkage Method and System Applied to Smart Locks

Abstract

The present application relates to the technical field of fire alarm, and discloses a fire alarm linkage method and system applied to an intelligent lock. The method includes: performing communication pairing and data exchange between the intelligent lock and the fire alarm system to obtain local sensing data and fire situation monitoring data; performing multi-source fusion and spatio-temporal correlation analysis to obtain a comprehensive fire situation assessment result; constructing an adaptive decision-making model and a differential response strategy; performing task decomposition and collaborative execution to obtain an intelligent lock linkage control instruction set and obtain an execution effect evaluation index; performing real-time monitoring and edge computing analysis on the execution effect evaluation index and environmental parameter change data to obtain a target feedback data stream; performing knowledge graph reasoning on the target feedback data stream to obtain decision model update parameters, and updating the adaptive decision-making model based on the decision model update parameters to obtain a target decision-making model. The present application realizes the intelligent linkage between the intelligent lock and the fire alarm system.

Description

Technical Field

[0001] The present application relates to the technical field of fire alarm, and particularly to a fire alarm linkage method and system applied to an intelligent lock. Background Art

[0002] With the rapid development of smart home technology, intelligent locks, as important home security devices, have been widely used. However, traditional intelligent lock systems still have many deficiencies in fire alarm and emergency response. Existing fire alarm systems are usually independent of intelligent locks and lack an effective linkage mechanism, resulting in the inability to fully utilize the functional advantages of intelligent locks in case of a fire.

[0003] In addition, current fire alarm systems generally have problems such as high false alarm rates and slow response speeds, making it difficult to cope with complex and changeable fire situations. Especially in large buildings or multi-story residences, due to the lack of accurate fire situation assessment and intelligent decision-making support, the evacuation efficiency is often low, increasing the risk of casualties. At the same time, existing intelligent lock systems often lack flexible response strategies and adaptive capabilities in the face of emergencies such as fires. This not only limits the role of intelligent locks in fire safety but also fails to meet the personalized security needs in different scenarios. Summary of the Invention

[0004] The present application provides a fire alarm linkage method and system applied to an intelligent lock, which is used to realize the intelligent linkage between the intelligent lock and the fire alarm system.

[0005] In a first aspect, the present application provides a fire alarm linkage method applied to an intelligent lock, and the fire alarm linkage method applied to an intelligent lock includes:

[0006] Conduct communication pairing and data exchange between the intelligent lock and the fire alarm system to obtain an equipment association topology map, and acquire the local sensing data of the intelligent lock and the fire situation monitoring data of the fire alarm system;

[0007] Based on the equipment association topology map, conduct multi-source fusion and spatio-temporal correlation analysis on the local sensing data and the fire situation monitoring data to obtain a comprehensive fire situation assessment result;

[0008] Conduct deep reinforcement learning and context awareness processing on the comprehensive fire situation assessment result to construct an adaptive decision-making model and a differentiated response strategy;

[0009] Decompose and collaboratively execute the adaptive decision-making model and the differentiated response strategy to obtain an intelligent lock linkage control instruction set, and acquire the execution effect evaluation index of the intelligent lock linkage control instruction set;

[0010] Perform real-time monitoring and edge computing analysis on the execution effect evaluation indicators and environmental parameter change data to obtain a target feedback data stream;

[0011] Perform knowledge graph reasoning on the target feedback data stream to obtain decision model update parameters, and update the adaptive decision model based on the decision model update parameters to obtain a target decision model.

[0012] In a second aspect, the present application provides a fire alarm linkage system applied to an intelligent lock. The fire alarm linkage system applied to the intelligent lock includes:

[0013] An acquisition module, configured to perform communication pairing and data exchange on the intelligent lock and the fire alarm system to obtain a device association topology diagram, and acquire the local sensing data of the intelligent lock and the fire situation monitoring data of the fire alarm system;

[0014] A fusion module, configured to perform multi-source fusion and spatio-temporal correlation analysis on the local sensing data and the fire situation monitoring data based on the device association topology diagram to obtain a comprehensive fire situation assessment result;

[0015] A construction module, configured to perform deep reinforcement learning and situation awareness processing on the comprehensive fire situation assessment result to construct an adaptive decision model and a differential response strategy;

[0016] An execution module, configured to perform task decomposition and collaborative execution on the adaptive decision model and the differential response strategy to obtain an intelligent lock linkage control instruction set, and acquire the execution effect evaluation indicators of the intelligent lock linkage control instruction set;

[0017] An analysis module, configured to perform real-time monitoring and edge computing analysis on the execution effect evaluation indicators and environmental parameter change data to obtain a target feedback data stream;

[0018] An update module, configured to perform knowledge graph reasoning on the target feedback data stream to obtain decision model update parameters, and update the adaptive decision model based on the decision model update parameters to obtain a target decision model.

[0019] In a third aspect of the present application, there is provided a fire alarm linkage device applied to an intelligent lock, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory so that the fire alarm linkage device applied to the intelligent lock executes the above-mentioned fire alarm linkage method applied to the intelligent lock.

[0020] The fourth aspect of the present application provides a computer-readable storage medium storing instructions that, when run on a computer, cause the computer to execute the above-mentioned fire alarm linkage method applied to an intelligent lock.

[0021] In the technical solution provided by the present application, through communication pairing and data exchange between the intelligent lock and the fire alarm system, a device association topology map is constructed, realizing seamless integration between systems, and improving the efficiency and reliability of data transmission. By adopting multi-source fusion and spatio-temporal correlation analysis technologies, comprehensive processing is performed on local sensing data and fire situation monitoring data, significantly enhancing the accuracy and comprehensiveness of fire situation assessment. By introducing deep reinforcement learning and context-aware processing technologies, an adaptive decision-making model and a differential response strategy are constructed, enhancing the system's ability to handle complex fire situations. Through a task decomposition and collaborative execution mechanism, precise generation and execution of intelligent lock linkage control instructions are achieved, improving the flexibility and efficiency of system response. By using edge computing technology to monitor and analyze the execution effect and environmental parameters in real time, it is ensured that the system can quickly adapt to environmental changes and improve the timeliness of decision-making. By introducing knowledge graph reasoning technology, in-depth analysis of feedback data and model updating are carried out, realizing continuous optimization and self-improvement of the system, and enhancing the stability and reliability of long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 It is a schematic diagram of an embodiment of the fire alarm linkage method applied to an intelligent lock in an embodiment of the present application;

[0024] Figure 2 It is a schematic diagram of an embodiment of the fire alarm linkage system applied to an intelligent lock in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The embodiments of the present application provide a fire alarm linkage method and system applied to an intelligent lock. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the fire alarm linkage method applied to an intelligent lock in the embodiments of the present application includes:

[0027] Step S101: Perform communication pairing and data exchange between the intelligent lock and the fire alarm system to obtain a device association topology diagram, and obtain the local sensing data of the intelligent lock and the fire situation monitoring data of the fire alarm system;

[0028] It can be understood that the execution subject of the present application can be a fire alarm linkage system applied to an intelligent lock, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application will be described by taking the server as the execution subject as an example.

[0029] Specifically, by matching the protocols of the communication interfaces of the smart lock and the fire alarm system, seamless communication between the two is ensured, and a communication protocol parameter set is generated during this process to guide the subsequent establishment of a secure channel. This secure channel ensures the security of data transmission through encryption technology, forming an encrypted data transmission link. For the established encrypted data transmission link, bandwidth testing and latency analysis are carried out to generate a link quality assessment report. This report describes the performance of the link during data transmission, and based on the content of the report, the data transmission strategy is adaptively adjusted to optimize the data exchange scheme and improve the efficiency and reliability of data transmission. The physical location information of the smart lock and the fire alarm system is spatially mapped to obtain an initial device distribution map, and combined with the optimized data exchange scheme, the communication paths between devices are dynamically planned to construct a multi-level network topology. Redundancy analysis and reliability assessment are carried out on the multi-level network topology to generate network robustness indicators. These indicators are used to measure the stability and reliability of the network under different failure conditions. Based on the network robustness indicators, weight distribution is carried out on the communication nodes to form a device association topology map to ensure that the roles and status of each node in the network are reasonably allocated. At the same time, the data collection frequencies of the built-in sensors of the smart lock and the monitoring devices of the fire alarm system are synchronized to form a unified data sampling strategy to ensure the consistency and coordination of data collection. According to the unified data sampling strategy and the device association topology map, the real-time data stream is optimized for routing and load balancing to construct an efficient data collection network. This network improves data transmission efficiency, reduces network congestion, and ensures the real-time and accuracy of data by optimizing the data routing path and balancing the load of each node. With the support of the efficient data collection network, the local sensing data of the smart lock and the fire monitoring data of the fire alarm system are continuously obtained.

[0030] Step S102: Based on the device association topology map, perform multi-source fusion and spatio-temporal correlation analysis on the local sensing data and the fire monitoring data to obtain a comprehensive fire situation assessment result;

[0031] Specifically, perform a structural analysis on the device - associated topology diagram to determine the data transmission paths and latency estimates between devices. Based on the data transmission paths and latency estimates, perform time - synchronization processing on the local sensing data and fire - monitoring data to obtain a calibrated data set, ensuring that the timestamps of each data source are consistent. Perform multi - scale feature extraction on the calibrated data set, and through different - level feature representation methods, obtain multi - level feature representations of the data. These feature representations can not only reveal the local details of the data but also reflect the global trends. According to the multi - level feature representations, perform anomaly detection and denoising processing on the calibrated data set to remove noise and outliers, obtaining optimized feature data. Perform dimensionality reduction processing on the optimized feature data to reduce the data dimension and obtain a dimensionality - reduced feature vector. During the dimensionality reduction process, maintain the main information of the data while reducing the computational complexity. According to the dimensionality - reduced feature vector, perform a non - linear mapping on the calibrated data set to obtain a fused feature space. In the fused feature space, perform temporal - pattern matching to identify the spatio - temporal correlation information of the data. These information reveal the distribution and change laws of the data in time and space. According to the spatio - temporal correlation information, perform context reasoning to obtain a scene semantic description. Perform multi - factor risk assessment on the scene semantic description, comprehensively consider multiple factors, and calculate the fire risk score. According to the fire risk score and historical data, perform probability reasoning to obtain a comprehensive fire situation assessment result. This assessment result not only reflects the severity of the current fire but also can predict the future development trend of the fire, providing a scientific basis for the linkage between the intelligent lock and the fire alarm system.

[0032] Step S103: Perform deep reinforcement learning and context - awareness processing on the comprehensive fire situation assessment result to construct an adaptive decision - making model and a differential response strategy;

[0033] Specifically, feature extraction is performed on the comprehensive fire situation assessment results to obtain fire feature vectors, which contain various key parameters and indicators of the fire situation. Based on the fire feature vectors, a basic decision-making framework is created to preliminarily determine the basic strategies and plans for dealing with the fire. Dynamic parameter adjustment is performed on the basic decision-making framework to obtain an initial decision-making model. By adjusting each parameter, the decision-making model can more flexibly adapt to different fire situations. According to the initial decision-making model, the current environmental information is extracted to obtain a set of situation features, which reflect the specific conditions of the current fire and the surrounding environment. Importance ranking is performed on the set of situation features to identify key situation factors. Key situation factors are the main factors affecting the fire response decision-making. According to these factors, the initial decision-making model is fine-tuned to obtain an adaptive decision-making model. The adaptive decision-making model can be adjusted according to the actual situation to ensure the effectiveness and flexibility of the response strategy. After obtaining the adaptive decision-making model, multiple groups of fire scenarios are input into the model to generate a set of alternative response strategies. The set of alternative response strategies includes various possible response measures and plans. Conflict detection and optimization are performed on the set of alternative response strategies to identify and eliminate conflicts between strategies, obtaining a subset of executable strategies. Effect prediction and ranking are performed on the subset of executable strategies to generate a list of strategy priorities. Effect prediction evaluates the effectiveness and reliability by simulating the execution results of different strategies. According to the evaluation results, the strategies are ranked to determine the priorities. The optimal strategy combination is selected from the list of strategy priorities to obtain a differentiated response strategy. The differentiated response strategy can provide the most appropriate response measures for different fires and environments to ensure the efficiency and safety of fire disposal.

[0034] Step S104: Decompose and collaboratively execute the adaptive decision-making model and the differentiated response strategy to obtain a set of intelligent lock linkage control instructions, and obtain the execution effect evaluation indicators of the set of intelligent lock linkage control instructions;

[0035] Specifically, the adaptive decision-making model is analyzed for its strategies to obtain a high-level task objective set, which reflects the core tasks and objectives of fire response. Based on the high-level task objective set, the differentiated response strategies are matched and screened to select an executable strategy subset that can meet these task objectives. The executable strategy subset is hierarchically decomposed to obtain a multi-level subtask sequence. These subtask sequences decompose complex high-level tasks into several executable specific operation steps, and a task dependency graph is constructed according to the dependency relationships between these steps to determine the priority order of task execution. According to the priority order of task execution, the required resources are reasonably allocated and scheduling optimized to generate a preliminary execution plan to ensure that each task can be efficiently executed under resource-constrained conditions. Based on the preliminary execution plan, primitive control primitives for the smart lock are generated to form an original instruction sequence. These original instruction sequences contain specific control instructions for guiding the operation of the smart lock in case of fire. After generating the original instruction sequence, a semantic consistency check is performed to ensure that these instructions do not cause ambiguity or conflict during execution, and a final smart lock linkage control instruction set is obtained. Execution effect evaluation indicators are designed according to the smart lock linkage control instruction set, including aspects such as the accuracy of task completion, response speed, and resource utilization. The evaluation index system is quantified to form a computable scoring criterion, so that each index can be quantified into specific numerical values for real-time evaluation. According to the computable scoring criterion, the execution results are evaluated in real time to obtain the execution effect evaluation indicators of the smart lock linkage control instruction set. Through the evaluation, the performance of the smart lock in fire linkage is monitored in real time to ensure that each instruction is effectively executed, and corresponding adjustments and optimizations are made according to the evaluation results.

[0036] Step S105: Monitor and perform edge computing analysis on the execution effect evaluation indicators and environmental parameter change data in real time to obtain a target feedback data stream;

[0037] Specifically, data standardization processing is performed on the execution effect evaluation indicators to obtain a normalized evaluation indicator set, eliminating the dimensional differences between different indicators and making each indicator comparable. A multi-dimensional evaluation space is constructed based on the normalized evaluation indicator set to form an indicator vector matrix, ensuring comprehensive analysis of each evaluation indicator in the multi-dimensional space. Temporal segmentation is performed on the environmental parameter change data, dividing the continuous environmental data into multiple time window data blocks, and extracting environmental change characteristics according to the time window data blocks to obtain a dynamic environmental feature set. The dynamic environmental feature set reflects the change trends and characteristics of environmental parameters in different time periods. Correlation analysis is performed on the dynamic environmental feature set and the indicator vector matrix to obtain an association weight table. The association weight table is used to quantify the association degree between each environmental feature and the evaluation indicator. Based on these association weights, the data is sorted by priority to form a weighted data stream. Sliding window analysis is performed on the weighted data stream to obtain real-time data slices. Through the sliding window method, the change trend of the data is continuously monitored to capture short-term fluctuations in the data. Based on the real-time data slices, an anomaly detection algorithm is executed to identify anomalies in the data, obtaining anomaly event markers. Cluster analysis is performed on the anomaly event markers to obtain the event type classification results. Through cluster analysis, similar anomaly events are classified to identify different types of anomaly phenomena. According to the event type classification results and the weighted data stream, data packet conversion is performed on the data to form a target feedback data stream. The target feedback data stream synthesizes the results of real-time monitoring and analysis, providing real-time feedback on the system operation status.

[0038] Step S106: Perform knowledge graph reasoning on the target feedback data stream to obtain decision model update parameters, and update the adaptive decision model based on the decision model update parameters to obtain a target decision model.

[0039] Specifically, semantic parsing is performed on the target feedback data stream to obtain a structured data set. Through semantic parsing, unstructured data is converted into structured data with clear meanings. Key entities and relationships are extracted from the structured data set to form a set of knowledge triples, which reflects the basic elements and their mutual relationships in the data. Ontology mapping is performed on the set of knowledge triples to obtain a domain knowledge graph. Through ontology mapping, the set of knowledge triples is matched with predefined domain knowledge to ensure the accuracy and consistency of the knowledge graph. Based on the domain knowledge graph, an inference rule base is constructed to form a graph inference engine. The inference rule base contains various inference rules for performing inference operations in the knowledge graph. Path query analysis is performed on the graph inference engine to obtain a causal relationship chain. Through path query, the causal relationships between entities in the knowledge graph are revealed to form a causal relationship chain. Based on the causal relationship chain, decision impact assessment is performed to obtain a decision sensitivity matrix, which is used to quantify the impact degree of each decision factor on the final decision result. Feature importance ranking is performed on the decision sensitivity matrix to identify the features with the greatest impact on the decision, and update parameters for the decision model are obtained. The update parameters will be used to adjust the adaptive decision model to improve the accuracy and adaptability of the model. According to the update parameters of the decision model, parameter adjustment is performed on the adaptive decision model to obtain an updated model structure. Verification tests are performed on the updated model structure. Through a series of tests, the performance of the model is evaluated, and a model performance evaluation report is generated. The model performance evaluation report includes the performance of the model under various test conditions. According to the model performance evaluation report, model integration is performed to integrate the updated model structure with the existing model to form the final target decision model.

[0040] In the embodiment of the present application, by performing communication pairing and data exchange between the intelligent lock and the fire alarm system, a device association topology graph is constructed, realizing seamless integration between systems and improving the efficiency and reliability of data transmission. By adopting multi-source fusion and spatio-temporal correlation analysis technologies, comprehensive processing is performed on local sensing data and fire monitoring data, significantly improving the accuracy and comprehensiveness of fire situation assessment. By introducing deep reinforcement learning and context-aware processing technologies, an adaptive decision model and a differential response strategy are constructed, enhancing the system's ability to cope with complex fire situations. Through a task decomposition and collaborative execution mechanism, precise generation and execution of intelligent lock linkage control instructions are realized, improving the flexibility and efficiency of system response. By using edge computing technology to monitor and analyze the execution effect and environmental parameters in real time, it is ensured that the system can quickly adapt to environmental changes and improve the timeliness of decision-making. By introducing knowledge graph inference technology, in-depth analysis of feedback data and model update are performed, realizing continuous optimization and self-improvement of the system and enhancing the stability and reliability of long-term operation.

[0041] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0042] (1) Perform protocol matching on the communication interfaces of the intelligent lock and the fire alarm system to obtain a communication protocol parameter set, and establish a secure channel between the intelligent lock and the fire alarm system according to the communication protocol parameter set to obtain an encrypted data transmission link;

[0043] (2) Conduct bandwidth testing and latency analysis on the encrypted data transmission link to obtain a link quality assessment report, and adaptively adjust the data transmission strategy according to the link quality assessment report to obtain an optimized data exchange scheme;

[0044] (3) Perform spatial mapping on the physical location information of the intelligent lock and the fire alarm system to obtain an initial device distribution map, and dynamically plan the communication paths between devices according to the initial device distribution map and the optimized data exchange scheme to obtain a multi-level network topology structure;

[0045] (4) Conduct redundancy analysis and reliability assessment on the multi-level network topology structure to obtain network robustness indicators, and allocate weights to communication nodes according to the network robustness indicators to obtain a device association topology map;

[0046] (5) Synchronize the data acquisition frequencies of the built-in sensors of the intelligent lock and the monitoring devices of the fire alarm system to obtain a unified data sampling strategy;

[0047] (6) Optimize the routing and load balance of the real-time data stream according to the unified data sampling strategy and the device association topology map to obtain an efficient data acquisition network, and continuously obtain the local sensing data of the intelligent lock and the fire monitoring data of the fire alarm system.

[0048] Specifically, perform protocol matching on the communication interfaces of the intelligent lock and the fire alarm system to obtain a communication protocol parameter set. Identify and compare the communication protocols used by the intelligent lock and the fire alarm system, and form a unified communication protocol parameter set by matching parameters such as protocol version, data format, and encryption method. For example, assume that the intelligent lock uses the Zigbee protocol and the fire alarm system uses the Wi-Fi protocol. The communication between the two can be achieved through a protocol conversion gateway. Establish a secure channel between the intelligent lock and the fire alarm system according to the communication protocol parameter set to ensure the confidentiality and integrity of data transmission. The encrypted data transmission link is implemented through the SSL / TLS protocol, which exchanges keys through a handshake process and uses a symmetric encryption algorithm such as AES (Advanced Encryption Standard) for encryption in subsequent data transmissions. The formula is as follows:

[0049] C = E k (P);

[0050] Among them, C represents the encrypted ciphertext, E represents the encryption function, k represents the symmetric key, and P represents the original data. After establishing an encrypted data transmission link, bandwidth testing and latency analysis are performed on the link to evaluate its performance. By sending and receiving a series of test packets, the transmission time and throughput of the packets are measured. Assuming the size of the sent test packet is S and the transmission time is T, the bandwidth B can be expressed as:

[0051]

[0052] The results of bandwidth testing and latency analysis form a link quality assessment report. Based on this report, the data transmission strategy can be adaptively adjusted. For example, if the link bandwidth is insufficient or the latency is too high, the packet size or transmission frequency can be adjusted. The optimized data exchange scheme ensures the stability and efficiency of data transmission. The physical location information of the smart lock and the fire alarm system is spatially mapped to obtain an initial device distribution map. By combining the physical locations and communication ranges of each device, the connection relationships between the devices are initially drawn. Assuming that the smart lock and the fire alarm system are located in multiple rooms respectively, a two-dimensional coordinate system can be used to represent the positions of each device. For example, the smart lock is located at coordinates (x1, y1), and the fire alarm system is located at coordinates (x2, y2). Based on the initial device distribution map and the optimized data exchange scheme, the communication paths between the devices are dynamically planned to form a multi-level network topology structure. Through the Dijkstra algorithm or the A* algorithm, the optimal path between the devices is found to ensure low latency and high reliability of data transmission. This network topology structure can be divided into different levels, such as the core layer, the aggregation layer, and the access layer. The devices in each layer are classified and connected according to their functions and positions. Redundancy analysis and reliability evaluation are performed on the multi-level network topology structure to obtain network robustness indicators. Redundancy analysis ensures that there are multiple backup paths in the network. When a certain path fails, the system can quickly switch to the backup path to maintain normal communication. Assuming the reliability of each node in the network is R i , the total reliability R can be expressed as:

[0053]

[0054] Among them, n is the number of network nodes, and the overall reliability of the network can be obtained through calculation. According to the network robustness index, weight distribution is carried out on communication nodes to form a device association topology graph. The weight distribution considers factors such as node reliability, transmission delay, and bandwidth to ensure that critical nodes have a higher priority, thereby optimizing the performance and stability of the entire network. Synchronize the data acquisition frequencies of the built-in sensors of the intelligent lock and the monitoring devices of the fire alarm system to obtain a unified data sampling strategy. By unifying the sampling frequency, ensure that the data of each device can be collected and transmitted synchronously, avoiding analysis errors caused by asynchronous data. Assume that the sampling frequency of the intelligent lock is f1 and the sampling frequency of the fire alarm system is f2. The unified sampling frequency f can be achieved by adjusting the sampling periods of the two to make it satisfy a certain common frequency f. According to the unified data sampling strategy and the device association topology graph, optimize the routing and load balancing of the real-time data stream to form an efficient data acquisition network. Routing optimization reduces transmission delay by selecting the optimal path to transmit data; load balancing distributes network resources to avoid overloading of a certain node. The implementation of load balancing can be achieved through hash algorithms or polling algorithms to evenly distribute the data stream to each node. In the optimized data acquisition network, continuously obtain the local sensing data of the intelligent lock and the fire monitoring data of the fire alarm system to form an efficient and reliable data collection and transmission system. Assume that the sensing data of the intelligent lock is D1 and the monitoring data of the fire alarm system is D2. Through data fusion algorithms such as weighted average method or Kalman filter, fuse the data of the two to obtain a comprehensive data set D

[0055] D = w1·D1 + w2·D2;

[0056] Among them, w1 and w2 are the data weights of the intelligent lock and the fire alarm system respectively.

[0057] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0058] (1) Conduct a structural analysis of the device association topology graph to obtain the data transmission paths and delay estimates between devices;

[0059] (2) According to the data transmission paths and delay estimates, perform time synchronization processing on the local sensing data and the fire monitoring data to obtain a calibrated data set;

[0060] (3) Perform multi-scale feature extraction on the calibrated data set to obtain a multi-level feature representation, and according to the multi-level feature representation, perform anomaly detection and denoising processing on the calibrated data set to obtain optimized feature data;

[0061] (4)Dimensionality reduction processing is performed on the optimized feature data to obtain a dimensionality-reduced feature vector, and based on the dimensionality-reduced feature vector, a non-linear mapping is performed on the calibrated data set to obtain a fused feature space;

[0062] (5)Temporal pattern matching is performed on the fused feature space to obtain spatio-temporal correlation information, and context reasoning is performed based on the spatio-temporal correlation information to obtain a scene semantic description;

[0063] (6)Multi-factor risk assessment is performed on the scene semantic description to obtain a fire risk score, and probability reasoning is performed based on the fire risk score and historical data to obtain a comprehensive fire situation assessment result.

[0064] Specifically, structural analysis is performed on the device association topology graph to determine the data transmission path and delay estimation between devices. Suppose there is a network containing multiple smart locks and fire alarm system nodes, and the physical locations and connection states of each node form a topology graph. Through the shortest path algorithm in graph theory, such as the Dijkstra algorithm, the shortest transmission path between each pair of devices is found, and its transmission delay is calculated. The transmission delay can be expressed as the sum of the node transmission delays on each path. According to the data transmission path and delay estimation, time synchronization processing is performed on the local sensing data and fire monitoring data to obtain a calibrated data set, ensuring that data from different devices can be compared and analyzed under the same time reference. Suppose the data timestamp of the smart lock is t lock , and the data timestamp of the fire alarm system is t alarm . Through a time synchronization algorithm, such as the Network Time Protocol, the data timestamps of different devices are calibrated to a unified time reference t sync, so that the calibrated data set has consistent timestamps. Perform multi-scale feature extraction on the calibrated data set to obtain multi-level feature representations. The methods of multi-scale feature extraction can include wavelet transform, Fourier transform, etc. Through these methods, the original data is analyzed at different scales to extract features at different levels. For example, in wavelet transform, the signal is decomposed into a combination of different frequency components, which respectively represent the details and overall trends of the data. By analyzing these features, a multi-level feature representation of the data is obtained. According to the multi-level feature representation, perform anomaly detection and denoising processing on the calibrated data set to obtain optimized feature data. The purpose of anomaly detection is to identify and remove outliers in the data, which may be caused by sensor failures or data transmission errors. Denoising processing is to eliminate noise in the data and improve clarity and accuracy. Use statistical methods such as mean and standard deviation to identify outliers, or use machine learning algorithms such as the Isolation Forest algorithm for anomaly detection. Perform dimensionality reduction processing on the optimized feature data to obtain a dimensionality-reduced feature vector. The purpose of dimensionality reduction processing is to reduce the dimension of the feature space, thereby reducing the computational complexity while maintaining the main information of the data. Commonly used dimensionality reduction algorithms include Principal Component Analysis and t-SNE. Assume that the optimized feature data is X, and after PCA processing, the dimensionality-reduced feature vector is Y:

[0065] Y = W T X;

[0066] where W is the feature vector matrix. By selecting the first k principal components, the dimension of the features can be effectively reduced. According to the dimensionality-reduced feature vector, perform a non-linear mapping on the calibrated data set to obtain a fused feature space. The non-linear mapping can be achieved through kernel methods or neural networks, which map the data in the high-dimensional space to the low-dimensional space while maintaining the structural relationship of the data. For example, use kernel PCA in kernel methods to map the feature vector to a non-linear space to obtain a fused feature space. In the fused feature space, perform time series pattern matching to identify spatio-temporal correlation information in the data. The methods of time series pattern matching can include Dynamic Time Warping and Hidden Markov Models. Through these methods, the patterns and rules in the data are identified. Assume that the time series data is X(t). Through DTW, similar patterns in the data can be found and their distance D can be calculated:

[0067]

[0068] Based on spatio-temporal correlation information, perform context reasoning to obtain a description of the scene semantics. The method of context reasoning can be implemented through a rule engine or a Bayesian network, which converts the correlation information in the data into a scene description. For example, through a rule engine, according to predefined rules, a specific sensor data pattern is recognized as a fire alarm event, thereby generating a corresponding scene semantic description. Conduct a multi-factor risk assessment on the scene semantic description to obtain a fire risk score. The method of multi-factor risk assessment can include fuzzy logic and multi-criteria decision analysis, and calculate the fire risk score by comprehensively considering the influence of different factors. Perform probability reasoning based on the fire risk score and historical data to obtain a comprehensive fire situation assessment result. The method of probability reasoning can be implemented through Bayesian reasoning or a Markov decision process, which compares the current risk score with historical data to predict the future development trend of the fire situation. Assuming that the probability distribution of historical data is P and the current fire risk score is R, the comprehensive fire situation assessment result can be expressed as a conditional probability:

[0069] P(R|historical data) = P(R ∩ historical data) / P(historical data);

[0070] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0071] (1) Extract features from the comprehensive fire situation assessment result to obtain a fire feature vector, and create a basic decision framework based on the fire feature vector;

[0072] (2) Dynamically adjust the parameters of the basic decision framework to obtain an initial decision model, and extract the current environmental information according to the initial decision model to obtain a set of situation features;

[0073] (3) Sort the importance of the set of situation features to obtain key situation factors, and fine-tune the initial decision model according to the key situation factors to obtain an adaptive decision model;

[0074] (4) Input multiple groups of fire scenarios into the adaptive decision model to obtain a set of alternative response strategies, and perform conflict detection and optimization on the set of alternative response strategies to obtain a subset of executable strategies;

[0075] (5) Estimate and sort the effects of the subset of executable strategies to obtain a list of strategy priorities, and select the optimal strategy combination from the list of strategy priorities to obtain a differentiated response strategy.

[0076] Specifically, feature extraction is performed on the comprehensive fire situation assessment result to obtain a fire situation feature vector. Through feature extraction, the complex fire situation assessment result is transformed into a set of key parameters, which can accurately reflect various important information of the fire. Assuming that the fire situation assessment result includes multiple indicators such as temperature, smoke concentration, and flame detection, these indicators are extracted into a feature vector through algorithms such as principal component analysis. The feature vector can be expressed as:

[0077] F = [f1, f2, …, f n ;

[0078] where F represents the fire situation feature vector, and f i represents the specific value of each feature. According to the fire situation feature vector, a basic decision framework is created. The basic decision framework includes the basic strategies and measures for dealing with different fires. By mapping the feature vector to specific decision rules, the strategy for dealing with the fire is initially determined. For example, if the temperature feature value f1 exceeds a certain threshold and the smoke concentration feature value f2 also exceeds the standard at the same time, the basic decision framework may recommend immediately triggering a fire alarm and opening the intelligent lock. The dynamic parameters of the basic decision framework are adjusted to adapt to different fire scenarios to obtain an initial decision model. The dynamic parameter adjustment can be achieved through machine learning algorithms. For example, through reinforcement learning algorithms, according to historical fire data and current environmental feedback, the decision parameters are continuously optimized. Assuming that the basic decision parameter is θ, after the adjustment of reinforcement learning, the optimized initial decision parameter θ′ is obtained:

[0079]

[0080] where α is the learning rate and J(θ) is the decision benefit function. According to the initial decision model, the current environmental information is extracted to form a situation feature set. The situation feature set includes all the key information in the current environment, such as temperature, humidity, and personnel location. Through the sensor network and data acquisition system, this environmental information is obtained in real time and integrated into the situation feature set. The importance of the situation feature set is sorted to identify the key situation factors. Through feature selection algorithms, such as information gain or mutual information, by calculating the contribution degree of each feature to the decision result, they are sorted. After the key situation factors are sorted, it is determined which factors are the most important in the current fire environment, so as to fine-tune the initial decision model. Assuming that the key situation factors are C = [c1, c2, …, c m , the initial decision model is adjusted according to these factors to obtain an adaptive decision model:

[0081]

[0082] where β is the adjustment coefficient, w iis the weight of each situation factor. Multiple groups of fire scenarios are input into the adaptive decision-making model to generate a set of alternative response strategies. These fire scenarios can be generated through simulation experiments or historical data and include different types and severities of fires. The adaptive decision-making model generates a series of possible response strategies based on these scenarios, such as evacuation route selection, alarm method adjustment, etc. Conflict detection and optimization are performed on the set of alternative response strategies to ensure that there are no conflicts between the strategies. For example, if one strategy requires closing all door locks while another requires opening the door locks, the system needs to perform conflict detection and select the optimal solution. The optimized result forms an executable strategy subset, and strategy selection and combination are performed through optimization algorithms such as genetic algorithms. Effect prediction and ranking are performed on the executable strategy subset to generate a strategy priority list. Effect prediction evaluates its impact on fire control and personnel safety by simulating the process of strategy execution. The prediction results can be obtained through simulation models or historical execution data. Assume the strategy set is S = {s1, s2, …, s k}, predict the effect E(s i ) of each strategy, and sort according to the effect value to form a priority list:

[0083] Priority(s i ) = E(s i )

[0084] Select the optimal strategy combination from the strategy priority list to form a differentiated response strategy. The differentiated response strategy takes into account different fire scenarios and environmental changes and provides customized response measures.

[0085] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0086] (1) Perform strategy parsing on the adaptive decision-making model to obtain a high-level task objective set, and perform matching and screening on the differentiated response strategy according to the high-level task objective set to obtain an executable strategy subset;

[0087] (2) Perform hierarchical decomposition on the executable strategy subset to obtain a multi-level subtask sequence, and construct a task dependency graph according to the multi-level subtask sequence to obtain a task execution priority ranking;

[0088] (3) Perform resource allocation and scheduling optimization on the task execution priority ranking to obtain a preliminary execution plan, and generate smart lock control primitives according to the preliminary execution plan to obtain an original instruction sequence;

[0089] (4) Perform semantic consistency check on the original instruction sequence to obtain a smart lock linkage control instruction set, and design an execution effect evaluation index according to the smart lock linkage control instruction set to obtain an evaluation index system;

[0090] (5) Quantify the evaluation index system to obtain a computable scoring standard, and evaluate the execution results in real time according to the computable scoring standard to obtain the evaluation index of the execution effect of the intelligent lock linkage control instruction set.

[0091] Specifically, analyze the strategies of the adaptive decision-making model to obtain a high-level task objective set. By analyzing the decision rules and response strategies in the model, identify the main task objectives that the system needs to achieve in different fire scenarios. These task objectives may include personnel evacuation, fire alarm, activation of the fire extinguishing system, etc. Suppose in a certain fire scenario, the main task objective set is T = {t1, t2, …, t n}, where t i represents a specific task objective. For example, t1 represents "activate the fire protection system" and t2 represents "open the emergency exit". Match and screen the differential response strategies according to the high-level task objective set to obtain an executable strategy subset. By comparing the task objectives with the predefined response strategies, screen out the strategies that conform to the current fire scenario. Suppose the differential response strategy set is S = {s1, s2, …, s m}, and through the strategy matching algorithm, screen out the executable strategy subset that conforms to the task objective set T Perform hierarchical decomposition on the executable strategy subset to obtain a multi-level subtask sequence. Hierarchical decomposition decomposes each strategy into specific operation steps or subtasks. For example, activating the fire protection system can be decomposed into "detect fire signal", "activate the pumping station", "open the sprinkler valve", etc. Through decomposition, a more refined task sequence U = {u1, u2, …, u k} is obtained, and each subtask u i corresponds to a specific execution step. Construct a task dependency graph according to the multi-level subtask sequence to obtain the task execution priority ranking. The task dependency graph represents the dependency relationship between tasks through nodes and edges to ensure that tasks are executed in the correct order. Suppose the nodes in the task dependency graph represent subtasks and the edges represent dependency relationships. The topological sorting algorithm can be used to perform priority sorting on the tasks to obtain the sorted task sequence V = {v1, v2, …, v k}, where v i represents the subtask after priority sorting. Perform resource allocation and scheduling optimization on the task execution priority ranking to obtain a preliminary execution plan. Resource allocation and scheduling optimization consider the resource limitations of the system, such as network bandwidth, processing power, energy consumption, etc., and reasonably allocate resources to ensure the efficient execution of tasks. Suppose each task v i requires resource r i , and the total resources of the system are R. The optimization goal is to maximize the resource utilization rate, and the formula is as follows:

[0092]

[0093] Generate the smart lock control primitives according to the preliminary execution plan to obtain the original instruction sequence. The smart lock control primitives are basic control commands, and complex operations can be achieved by combining commands. Assume that the tasks determined by the preliminary execution plan are \(V = \{v_1, v_2, \ldots, v k \}\), and the corresponding control primitives are \(C = \{c_1, c_2, \ldots, c k \}\), then the original instruction sequence can be expressed as:

[0094] C = \{c_1, c_2, \ldots, c k \};

[0095] Conduct semantic consistency checks on the original instruction sequence to ensure that each instruction does not cause conflicts or misunderstandings during execution, and obtain the smart lock linkage control instruction set. Semantic consistency checks verify the logical relationships and execution orders of the instructions to ensure their accuracy and consistency. Design execution effect evaluation indicators based on the smart lock linkage control instruction set to form an evaluation index system. The evaluation index system is used to measure the execution effect of the instruction set, including response time, task completion rate, resource utilization rate, etc. Assume that the evaluation indicators are \(E = \{e_1, e_2, \ldots, e m \}\), where \(e i \) represents specific evaluation indicators. For example, \(e_1\) represents the response time, and \(e_2\) represents the task completion rate. Quantify the evaluation index system to obtain a computable scoring standard. The scoring standard quantifies each evaluation indicator so that it can be compared and analyzed. Assume that the weight of each evaluation indicator \(e i \) is \(w i \), then the overall score \(S\) can be expressed as:

[0096]

[0097] According to the computable scoring standard, conduct real-time evaluation on the execution results to obtain the execution effect evaluation indicators of the smart lock linkage control instruction set. By monitoring the actual execution effect of the system, feedback the evaluation results to the scoring system to update each score in real time, so as to obtain dynamic evaluation indicators.

[0098] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0099] (1) Conduct data standardization processing on the execution effect evaluation indicators to obtain a normalized evaluation index set, and construct a multi-dimensional evaluation space based on the normalized evaluation index set to obtain an index vector matrix;

[0100] (2) Perform temporal segmentation on the environmental parameter change data to obtain multiple time window data blocks, and extract environmental change characteristics based on the time window data blocks to obtain a dynamic environmental feature set;

[0101] (3) Perform a correlation analysis on the dynamic environment feature set and the index vector matrix to obtain an association weight table, and prioritize the data according to the association weight table to obtain a weighted data stream;

[0102] (4) Perform a sliding window analysis on the weighted data stream to obtain real-time data slices, and execute an anomaly detection algorithm based on the real-time data slices to obtain anomaly event markers;

[0103] (5) Perform a clustering analysis on the anomaly event markers to obtain an event type classification result, and perform a data packet conversion based on the event type classification result and the weighted data stream to obtain a target feedback data stream.

[0104] Specifically,

[0105] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0106] (1) Perform semantic parsing on the target feedback data stream to obtain a structured data set, and extract key entities and relationships based on the structured data set to obtain a knowledge triple set;

[0107] (2) Perform ontology mapping on the knowledge triple set to obtain a domain knowledge graph, and construct an inference rule base based on the domain knowledge graph to obtain a graph inference engine;

[0108] (3) Perform path query analysis on the graph inference engine to obtain a causal relationship chain, and perform a decision impact evaluation based on the causal relationship chain to obtain a decision sensitivity matrix;

[0109] (4) Perform a feature importance ranking on the decision sensitivity matrix to obtain decision model update parameters;

[0110] (5) Adjust the parameters of the adaptive decision model according to the decision model update parameters to obtain an updated model structure;

[0111] (6) Perform a verification test on the updated model structure to obtain a model performance evaluation report, and perform model integration based on the model performance evaluation report to obtain a target decision model.

[0112] Specifically, perform data standardization processing on the execution effect evaluation indicators, convert data with different dimensions into dimensionless data, and make them comparable and analyzable on the same scale to obtain a normalized evaluation index set. Construct a multi-dimensional evaluation space based on the normalized evaluation index set. In the multi-dimensional evaluation space, each normalized index represents a dimension, and these dimensions together constitute the evaluation space. Suppose there are m evaluation indicators, and each indicator corresponds to a dimension, then the point P in the multi-dimensional evaluation space can be represented as an m-dimensional vector:

[0113] P = (z1, z2, …, zm )

[0114] In this way, multiple evaluation indicators are integrated into a unified framework to construct an index vector matrix M, where each row represents a sample and each column represents an evaluation indicator. The time series segmentation is performed on the environmental parameter change data, and the continuous environmental data is divided into multiple time window data blocks. Assuming the environmental parameter data is E(t), through time series segmentation, it is divided into multiple time windows W k , and each time window contains environmental data for a period of time:

[0115] W k ={E(t1), E(t2), …, E(t n )};

[0116] Within each time window, the environmental change features are extracted to form a dynamic environmental feature set F. Assuming a certain feature is temperature T, humidity H, and wind speed V, the dynamic environmental feature set can be expressed as:

[0117] F k ={T k , H k , V k};

[0118] Perform a correlation analysis on the dynamic environmental feature set and the index vector matrix to determine the influence degree of each environmental feature on the evaluation indicator. By calculating the correlation coefficient r between the feature and the indicator, an association weight table is obtained. The calculation formula of the correlation coefficient is as follows:

[0119]

[0120] where r ij represents the correlation coefficient between the environmental feature F i and the evaluation indicator M j , and are the means of the feature and the indicator respectively. According to the correlation coefficient, an association weight table R is constructed, and the data is sorted by priority according to the weight to obtain a weighted data stream. Perform a sliding window analysis on the weighted data stream to obtain real-time data slices. The sliding window analysis is performed by moving a fixed-size window on the data stream and calculating relevant statistics within each window. Assuming the sliding window size is w, the data slice at time t is:

[0121] S(t)={D(t - w + 1), D(t - w + 2), …, D(t)};

[0122] Execute anomaly detection algorithms based on real-time data slices to identify anomaly events in the data. Anomaly detection algorithms can adopt statistical methods, such as z-score detection, or machine learning methods, such as Isolation Forest. Assume the detected outliers are A = {a1, a2, …, a k}, and these outliers will be marked as anomaly events. Conduct clustering analysis on the anomaly event markings to obtain the event type classification results. Clustering analysis can use the k-means algorithm or the DBSCAN algorithm to identify different types of anomaly events by clustering similar anomaly events together. Assume the clustering result is C = {c1, c2, …, c m}, where each c i represents a type of anomaly event. Perform data packet conversion based on the event type classification results and the weighted data stream to obtain the target feedback data stream. Data packet conversion combines the classification results with the weighted data stream to form a new data packet format that can adapt to subsequent processing flows. The target feedback data stream T can be expressed as:

[0123] T = {C, S};

[0124] The fire alarm linkage method applied to the smart lock in the embodiment of the present application is described above. Next, the fire alarm linkage system applied to the smart lock in the embodiment of the present application will be described. Please refer to Figure 2 In an embodiment, the fire alarm linkage system applied to the smart lock in the embodiment of the present application includes:

[0125] An acquisition module 201, configured to perform communication pairing and data exchange between the smart lock and the fire alarm system to obtain a device association topology diagram, and acquire local sensing data of the smart lock and fire monitoring data of the fire alarm system;

[0126] A fusion module 202, configured to perform multi-source fusion and spatio-temporal correlation analysis on the local sensing data and the fire monitoring data based on the device association topology diagram to obtain a comprehensive fire situation assessment result;

[0127] A construction module 203, configured to perform deep reinforcement learning and context awareness processing on the comprehensive fire situation assessment result to construct an adaptive decision-making model and a differentiated response strategy;

[0128] An execution module 204, configured to perform task decomposition and collaborative execution on the adaptive decision-making model and the differentiated response strategy to obtain a smart lock linkage control instruction set, and acquire the execution effect evaluation index of the smart lock linkage control instruction set;

[0129] An analysis module 205, configured to perform real-time monitoring and edge computing analysis on the execution effect evaluation index and the environmental parameter change data to obtain the target feedback data stream;

[0130] An update module 206 is configured to perform knowledge graph reasoning on the target feedback data stream to obtain decision model update parameters, and update the adaptive decision model based on the decision model update parameters to obtain a target decision model.

[0131] Through the collaborative cooperation of the above-mentioned various components, by performing communication pairing and data exchange on the intelligent lock and the fire alarm system, an equipment association topology map is constructed, realizing seamless integration between systems, and improving the efficiency and reliability of data transmission. By adopting multi-source fusion and spatio-temporal correlation analysis technologies, comprehensive processing is performed on local sensing data and fire situation monitoring data, significantly improving the accuracy and comprehensiveness of fire situation assessment. By introducing deep reinforcement learning and context-aware processing technologies, an adaptive decision model and a differential response strategy are constructed, enhancing the system's ability to cope with complex fire situations. Through the task decomposition and collaborative execution mechanism, precise generation and execution of intelligent lock linkage control instructions are achieved, improving the flexibility and efficiency of system response. By using edge computing technology to monitor and analyze the execution effect and environmental parameters in real time, it is ensured that the system can quickly adapt to environmental changes and improve the timeliness of decision-making. By introducing knowledge graph reasoning technology, in-depth analysis and model update of feedback data are performed, realizing continuous optimization and self-improvement of the system, and enhancing the stability and reliability of long-term operation.

[0132] This application also provides a fire alarm linkage device applied to an intelligent lock. The fire alarm linkage device applied to the intelligent lock includes a memory and a processor. Computer-readable instructions are stored in the memory. When the computer-readable instructions are executed by the processor, the processor executes the steps of the fire alarm linkage method applied to the intelligent lock in the above-mentioned various embodiments.

[0133] This application also provides a computer-readable storage medium. The computer-readable storage medium can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer executes the steps of the fire alarm linkage method applied to the intelligent lock.

[0134] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0135] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0136] As described above, the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of this application.

Claims

1. A fire alarm linkage method applied to a smart lock, characterized in that: The method comprises: Perform communication pairing and data exchange between the smart lock and the fire alarm system to obtain a device association topology diagram, and acquire local sensor data of the smart lock and fire monitoring data of the fire alarm system; Based on the equipment association topology diagram, multi-source fusion and spatiotemporal association analysis are performed on the local sensor data and the fire monitoring data to obtain a comprehensive fire situation assessment result; Perform deep reinforcement learning and situational awareness processing on the comprehensive fire situation assessment results to construct an adaptive decision model and a differentiated response strategy; specifically, the method includes: extracting features from the comprehensive fire situation assessment results to obtain a fire feature vector, and creating a basic decision framework based on the fire feature vector; dynamically adjusting parameters of the basic decision framework to obtain an initial decision model, and extracting current environmental information based on the initial decision model to obtain a situational feature set; sorting the situational feature set by importance to obtain key situational factors, and fine-tuning the initial decision model based on the key situational factors to obtain an adaptive decision model; inputting multiple groups of fire scenarios into the adaptive decision model to obtain a set of alternative response strategies, and performing conflict detection and optimization based on the alternative response strategy set to obtain an executable strategy subset; estimating and sorting the effects of the executable strategy subset to obtain a strategy priority list, and selecting the optimal strategy combination from the strategy priority list to obtain a differentiated response strategy; Decomposing and co-executing the adaptive decision model and the differentiated response strategy, obtaining a smart lock linkage control instruction set, and obtaining an execution effect evaluation index of the smart lock linkage control instruction set; Perform real-time monitoring and edge computing analysis on the execution effect evaluation index and environmental parameter change data to obtain a target feedback data stream; Knowledge graph reasoning is performed on the target feedback data stream to obtain decision model update parameters, and the adaptive decision model is updated based on the decision model update parameters to obtain a target decision model.

2. The fire alarm linkage method for smart locks according to claim 1 is characterized in that: The communication pairing and data exchange between the smart lock and the fire alarm system to obtain a device association topology diagram, and to obtain the local sensor data of the smart lock and the fire monitoring data of the fire alarm system include: Performing protocol matching on the communication interface of the smart lock and the fire alarm system to obtain a communication protocol parameter set, and establishing a secure channel between the smart lock and the fire alarm system according to the communication protocol parameter set to obtain an encrypted data transmission link; Performing bandwidth testing and delay analysis on the encrypted data transmission link to obtain a link quality evaluation report, and adaptively adjusting the data transmission strategy according to the link quality evaluation report to obtain an optimized data exchange solution; Performing spatial mapping on the physical location information of the smart lock and the fire alarm system to obtain an initial device distribution map, and dynamically planning the communication paths between devices according to the initial device distribution map and the optimized data exchange scheme to obtain a multi-level network topology structure; Performing redundancy analysis and reliability evaluation on the multi-level network topology structure to obtain a network robustness index, and assigning weights to communication nodes according to the network robustness index to obtain a device association topology diagram; Synchronize the data acquisition frequency of the built-in sensor of the smart lock and the monitoring equipment of the fire alarm system to obtain a unified data sampling strategy; According to the unified data sampling strategy and the device association topology diagram, the real-time data stream is routed and load balanced to obtain an efficient data acquisition network, and the local sensor data of the smart lock and the fire monitoring data of the fire alarm system are continuously obtained.

3. The fire alarm linkage method applied to smart locks according to claim 2 is characterized in that: The multi-source fusion and spatiotemporal correlation analysis of the local sensor data and the fire monitoring data based on the device association topology diagram is performed to obtain a comprehensive fire situation assessment result, including: Performing structural analysis on the device association topology diagram to obtain data transmission paths and delay estimates between devices; According to the data transmission path and delay estimation, time synchronization processing is performed on the local sensor data and the fire monitoring data to obtain a calibrated data set; Performing multi-scale feature extraction on the calibrated data set to obtain a multi-level feature representation, and performing anomaly detection and denoising on the calibrated data set based on the multi-level feature representation to obtain optimized feature data; Performing dimensionality reduction processing on the optimized feature data to obtain a reduced-dimensionality feature vector, and performing nonlinear mapping on the calibrated data set according to the reduced-dimensionality feature vector to obtain a fused feature space; Performing temporal pattern matching on the fused feature space to obtain spatiotemporal correlation information, and performing contextual reasoning based on the spatiotemporal correlation information to obtain a scene semantic description; A multi-factor risk assessment is performed on the scene semantic description to obtain a fire risk score, and probabilistic reasoning is performed based on the fire risk score and historical data to obtain a comprehensive fire situation assessment result.

4. The fire alarm linkage method applied to smart locks according to claim 1, characterized in that: The task decomposition and collaborative execution of the adaptive decision model and the differentiated response strategy to obtain a smart lock linkage control instruction set, and obtaining an execution effect evaluation index of the smart lock linkage control instruction set, include: Performing strategy analysis on the adaptive decision model to obtain a high-level task goal set, and matching and screening the differentiated response strategy according to the high-level task goal set to obtain an executable strategy subset; Decomposing the executable strategy subset hierarchically to obtain a multi-level subtask sequence, and constructing a task dependency graph based on the multi-level subtask sequence to obtain a task execution priority ranking; Perform resource allocation and scheduling optimization on the task execution priority sorting to obtain a preliminary execution plan, and generate smart lock control primitives according to the preliminary execution plan to obtain an original instruction sequence; Performing a semantic consistency check on the original instruction sequence to obtain a smart lock linkage control instruction set, and designing an execution effect evaluation index based on the smart lock linkage control instruction set to obtain an evaluation index system; The evaluation index system is quantified to obtain a computable scoring standard, and the execution result is evaluated in real time according to the computable scoring standard to obtain the execution effect evaluation index of the smart lock linkage control instruction set.

5. The fire alarm linkage method applied to smart locks according to claim 1, characterized in that: The real-time monitoring and edge computing analysis of the execution effect evaluation index and environmental parameter change data to obtain the target feedback data stream includes: Performing data standardization processing on the execution effect evaluation index to obtain a normalized evaluation index set, and constructing a multidimensional evaluation space according to the normalized evaluation index set to obtain an index vector matrix; Performing time series segmentation on the environmental parameter change data to obtain a plurality of time window data blocks, and extracting environmental change features according to the time window data blocks to obtain a dynamic environmental feature set; Performing correlation analysis on the dynamic environment feature set and the indicator vector matrix to obtain an associated weight table, and prioritizing the data according to the associated weight table to obtain a weighted data stream; Performing sliding window analysis on the weighted data stream to obtain real-time data slices, and executing an anomaly detection algorithm based on the real-time data slices to obtain an abnormal event marker; Cluster analysis is performed on the abnormal event markers to obtain event type classification results, and data packet conversion is performed based on the event type classification results and the weighted data stream to obtain a target feedback data stream.

6. The fire alarm linkage method for smart locks according to claim 1, characterized in that: The performing knowledge graph reasoning on the target feedback data stream to obtain decision model update parameters, and performing model update on the adaptive decision model based on the decision model update parameters to obtain a target decision model, includes: Performing semantic parsing on the target feedback data stream to obtain a structured data set, and extracting key entities and relationships based on the structured data set to obtain a knowledge triple set; Performing ontology mapping on the knowledge triple set to obtain a domain knowledge graph, and constructing an inference rule library based on the domain knowledge graph to obtain a graph inference engine; Performing path query analysis on the graph reasoning engine to obtain a causal relationship chain, and performing decision impact assessment based on the causal relationship chain to obtain a decision sensitivity matrix; Sorting the decision sensitivity matrix by feature importance to obtain decision model update parameters; Adjusting the parameters of the adaptive decision model according to the decision model update parameters to obtain an updated model structure; The updated model structure is verified and tested to obtain a model performance evaluation report, and model integration is performed based on the model performance evaluation report to obtain a target decision model.

7. A fire alarm linkage system applied to a smart lock, characterized in that: The system is used to execute the fire alarm linkage method applied to a smart lock according to any one of claims 1 to 6, and comprises: An acquisition module is used to perform communication pairing and data exchange between the smart lock and the fire alarm system, obtain a device association topology diagram, and obtain local sensor data of the smart lock and fire monitoring data of the fire alarm system; A fusion module, used for performing multi-source fusion and spatiotemporal correlation analysis on the local sensor data and the fire monitoring data based on the device association topology diagram to obtain a comprehensive fire situation assessment result; A construction module is used to perform deep reinforcement learning and situational awareness processing on the comprehensive fire situation assessment results, and to construct an adaptive decision model and a differentiated response strategy; An execution module, used to perform task decomposition and collaborative execution on the adaptive decision model and the differentiated response strategy, obtain a smart lock linkage control instruction set, and obtain an execution effect evaluation index of the smart lock linkage control instruction set; An analysis module is used to perform real-time monitoring and edge computing analysis on the execution effect evaluation index and environmental parameter change data to obtain a target feedback data stream; An updating module is used to perform knowledge graph reasoning on the target feedback data stream to obtain decision model update parameters, and to update the adaptive decision model based on the decision model update parameters to obtain a target decision model.

8. A fire alarm linkage device applied to a smart lock, characterized in that: The fire alarm linkage device applied to the smart lock comprises: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory so that the fire alarm linkage device applied to the smart lock executes the fire alarm linkage method applied to the smart lock as described in any one of claims 1-6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, a fire alarm linkage method applied to a smart lock is implemented as described in any one of claims 1-6.

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