Fire alarm false alarm suppression method and system based on terminal area deployment and space-time correlation

Abstract

The present application relates to the field of fire-fighting Internet of Things and intelligent fire-fighting technology, and discloses a terminal area deployment and space-time correlation fire-fighting false alarm suppression method and system, which comprises the following steps: modeling the regional deployment of wireless smoke sensing terminals in buildings, and correlating the regions, positions and adjacent relationships of the terminals; receiving and processing alarm, device state and time data uploaded by the terminals; when any terminal triggers a suspected fire alarm, extracting the data of the correlated terminals in the preset time window in the region and adjacent regions where the terminal is located; then performing alarm continuity, collaborative alarm, alarm timing and device state validity analysis to generate an event determination result; classifying the event according to the result and outputting the corresponding alarm processing result; the present application can reduce fire-fighting false alarms caused by single-point abnormalities, device abnormalities or environmental disturbances, improve the accuracy of real fire identification, and does not need to change the structure of the front-end wireless smoke sensing terminal, and is suitable for the rapid deployment and stock transformation of intelligent fire-fighting projects.

Description

Technical Field

[0001] This invention relates to the fields of fire protection IoT and smart fire protection technology, specifically a method and system for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation. Background Technology

[0002] Existing intelligent fire protection systems typically monitor abnormal smoke by deploying wireless smoke detectors within the building space and upload alarm information to a backend platform for unified management and alarm output. In practical applications, most wireless smoke detectors operate independently, making them susceptible to factors such as steam, dust, fumes, construction disturbances, equipment aging, low battery power, and communication failures, leading to false alarms, frequent alarms, or repeated alarms. This is particularly problematic in older residential areas, rental properties, street-front shops, and small businesses, where the brands and models of front-end wireless smoke detectors are inconsistent, and installation locations and environments vary significantly, making it difficult to accurately distinguish between a real fire and environmental disturbances when a single terminal triggers an alarm. Existing technologies employ two approaches: one focuses on improving the structure of the smoke detector itself, while the other only performs simple threshold judgments for individual alarm events. Neither approach considers the deployment relationships of terminals within the building space, the linkage status of adjacent terminals, or the temporal continuity of alarm events. Therefore, their ability to suppress false alarms is limited, and they struggle to balance alarm accuracy and timely response.

[0003] Therefore, it is necessary to provide a fire false alarm suppression scheme based on terminal area deployment and spatiotemporal correlation, so as to reduce the false alarm rate and improve the accuracy of real fire identification without changing the structure of the front-end smoke detection terminal. Summary of the Invention

[0004] The purpose of this invention is to solve the problem that single-point alarms in the prior art are easily affected by environmental disturbances and equipment malfunctions, resulting in a high false alarm rate. Therefore, this invention proposes a fire alarm false alarm suppression method and system based on terminal area deployment and spatiotemporal correlation.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation includes the following steps: S1. Model the regional deployment of multiple wireless smoke detectors deployed in the building space, establish terminal identifiers for each wireless smoke detector, and bind each wireless smoke detector to its corresponding area identifier, location identifier, and adjacent terminal relationship. S2. Receive data uploaded by each of the wireless smoke detection terminals, the data including at least alarm status data, device status data and reporting time data, and perform field normalization and time alignment processing on the data; S3. When any wireless smoke detector triggers a suspected fire alarm condition, the wireless smoke detector that triggered the suspected fire alarm condition is identified as the target terminal, and relevant data of the target terminal's area and adjacent terminals within a preset time window are extracted. S4. Based on the relevant data of the target terminal and the associated terminal within the preset time window, perform spatiotemporal correlation analysis on the suspected fire alarm event. The spatiotemporal correlation analysis includes at least target terminal alarm continuity analysis, associated terminal collaborative alarm analysis, and equipment status effectiveness analysis, and generate event judgment results based on the analysis results. S5. Classify suspected fire alarm events according to the event determination results and output alarm processing results corresponding to the classification results to suppress false fire alarms caused by single-point anomalies.

[0006] A fire false alarm suppression system based on terminal area deployment and spatiotemporal correlation includes: The front-end deployment management module is used to divide multiple wireless smoke detectors in the building space into areas and establish the association between each wireless smoke detector and area identifiers, location identifiers, and relationships between adjacent terminals. The data access and processing module is used to receive alarm status data, device status data and reporting time data uploaded by each wireless smoke detection terminal, and to perform field normalization and time alignment processing on the received data. The event triggering module is used to identify the wireless smoke detector as the target terminal when any wireless smoke detector triggers a suspected fire alarm condition, and to extract relevant data of the target terminal's area and adjacent related terminals within a preset time window. The spatiotemporal correlation analysis module is used to perform spatiotemporal correlation analysis on suspected fire alarm events based on the relevant data of the target terminal and the associated terminal within the preset time window. The spatiotemporal correlation analysis includes at least target terminal alarm continuity analysis, associated terminal collaborative alarm analysis, and equipment status effectiveness analysis, and generates event judgment results based on the analysis results. The alarm output module is used to classify suspected fire alarm events according to the event judgment results and output alarm processing results corresponding to the classification results.

[0007] Compared with the prior art, the technical solution of this application has the following beneficial technical effects: 1. This invention no longer relies solely on the independent alarm results of a single wireless smoke detector terminal, but instead combines the regional deployment relationship of terminals and the status of adjacent terminals for comprehensive judgment, thereby reducing the false alarm rate caused by single-point anomalies.

[0008] 2. This invention incorporates device status data such as online status, battery status, communication status, and self-test status into the analysis process, which can reduce the interference of device malfunctions or aging terminals on the alarm results.

[0009] 3. This invention improves the accuracy of identifying real fires and reduces duplicate alarms and invalid dispatches by performing spatiotemporal correlation analysis within a preset time window.

[0010] 4. This invention does not require changes to the hardware structure of the wireless smoke detection terminal itself. It mainly relies on the front-end deployment relationship and the back-end analysis logic for implementation, making it suitable for the rapid deployment and retrofitting of existing smart fire protection systems. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the overall system architecture of the present invention; Figure 2 This is a schematic diagram of the regional deployment of the wireless smoke detector terminal of the present invention; Figure 3 This is a schematic diagram of the method flow of the present invention; Figure 4 This is a schematic diagram of the spatiotemporal correlation analysis of the backend of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] A method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation includes the following steps: S1. Model the regional deployment of multiple wireless smoke detectors deployed in the building space, establish terminal identifiers for each wireless smoke detector, and bind each wireless smoke detector to its corresponding area identifier, location identifier, and adjacent terminal relationship. S2. Receive data uploaded by each of the wireless smoke detection terminals, the data including at least alarm status data, device status data and reporting time data, and perform field normalization and time alignment processing on the data; S3. When any wireless smoke detector triggers a suspected fire alarm condition, the wireless smoke detector that triggered the suspected fire alarm condition is identified as the target terminal, and relevant data of the target terminal's area and adjacent terminals within a preset time window are extracted. S4. Based on the relevant data of the target terminal and the associated terminal within the preset time window, perform spatiotemporal correlation analysis on the suspected fire alarm event. The spatiotemporal correlation analysis includes at least target terminal alarm continuity analysis, associated terminal collaborative alarm analysis, and equipment status effectiveness analysis, and generate event judgment results based on the analysis results. S5. Classify suspected fire alarm events according to the event determination results and output alarm processing results corresponding to the classification results to suppress false fire alarms caused by single-point anomalies.

[0014] The process of regionalizing the deployment model of multiple wireless smoke-detecting terminals within the building space includes: The building space is divided into zones according to floors, rooms, corridors, stairwells, or functional areas; Based on the installation location of the wireless smoke detectors, each wireless smoke detector is mapped to its corresponding area. The adjacent terminal relationships are established based on the installation location relationships within the same area and the cross-regional adjacency relationships.

[0015] The device status data includes one or more of the following: online status, battery status, communication status, self-test status, and fault codes.

[0016] The field normalization process includes mapping the alarm status field, device status field, and time field uploaded by different models of wireless smoke detectors to a unified data structure; the time alignment process includes unifying the time base of the data reported by different wireless smoke detectors to the time base of the backend platform.

[0017] The suspected fire alarm conditions include at least one of the following: The wireless smoke detector terminal reports the alarm status. The wireless smoke detector terminal continuously reports abnormal status a preset number of times within a preset time period; The wireless smoke detector repeatedly triggers the alarm a preset number of times within a preset time period.

[0018] The preset time window includes a first time period before the target terminal triggers a suspected fire alarm condition and a second time period thereafter.

[0019] The spatiotemporal correlation analysis also includes alarm timing analysis of multiple terminals in the same area, used to determine whether multiple terminals trigger alarms sequentially at preset time intervals within the preset time window.

[0020] The target terminal alarm continuity analysis includes: determining whether the target terminal continuously maintains an alarm state within the preset time window, or whether it repeatedly triggers an alarm a preset number of times within the preset time window.

[0021] The associated terminal collaborative alarm analysis includes: determining whether at least one associated terminal and the target terminal jointly trigger an alarm within the preset time window, and determining whether multiple alarm terminals are located in the same area or adjacent areas.

[0022] The device status validity analysis includes: correcting the reliability of the data reported by each terminal based on the online status, battery status, communication status, or self-test status of the target terminal and associated terminals.

[0023] The process of generating event determination results based on the analysis results includes: When the target terminal continuously alarms and at least one associated terminal triggers an alarm within the preset time window, the event level is increased. When only a single target terminal alarms and the target terminal has an abnormal device status, the event level is reduced; When the same terminal triggers an alarm repeatedly within the deduplication time while the associated terminal does not trigger an alarm, the duplicate alarm events are merged.

[0024] The classification results include at least one or more of the following: fire alarm events, verification alarm events, equipment malfunction events, and low-confidence disturbance events.

[0025] The alarm processing result corresponding to the output and classification result includes at least one of the following: When the classification result is a fire alarm event, a high-priority alarm is output; When the classification result is a review alarm event, a review prompt is output and a delayed review process is triggered; When the classification result is an equipment malfunction event, output a maintenance prompt or inspection prompt. When the classification result is a low-confidence perturbation event, record the event information or output a low-priority prompt.

[0026] The outlier filtering process for the data includes at least one of the following: Remove data records that are missing key fields; Remove data records with abnormal timestamps; Remove data records with invalid device status fields; Remove duplicate records that exceed the preset duplicate reporting threshold.

[0027] A fire false alarm suppression system based on terminal area deployment and spatiotemporal correlation includes: The front-end deployment management module is used to divide multiple wireless smoke detectors in the building space into areas and establish the association between each wireless smoke detector and area identifiers, location identifiers, and relationships between adjacent terminals. The data access and processing module is used to receive alarm status data, device status data and reporting time data uploaded by each wireless smoke detection terminal, and to perform field normalization and time alignment processing on the received data. The event triggering module is used to identify the wireless smoke detector as the target terminal when any wireless smoke detector triggers a suspected fire alarm condition, and to extract relevant data of the target terminal's area and adjacent related terminals within a preset time window. The spatiotemporal correlation analysis module is used to perform spatiotemporal correlation analysis on suspected fire alarm events based on the relevant data of the target terminal and the associated terminal within the preset time window. The spatiotemporal correlation analysis includes at least target terminal alarm continuity analysis, associated terminal collaborative alarm analysis, and equipment status effectiveness analysis, and generates event judgment results based on the analysis results. The alarm output module is used to classify suspected fire alarm events according to the event judgment results and output alarm processing results corresponding to the classification results.

[0028] The spatiotemporal correlation analysis module includes: The continuity analysis unit is used to analyze the persistence or repetitiveness of alarms from target terminals. The collaborative analysis unit is used to analyze the alarm coordination of associated terminals within a preset time window; The status correction unit is used to correct the event determination result based on the device status of the target terminal and associated terminals.

[0029] The alarm output module is configured as follows: When the event is determined to be a fire alarm, a high-priority alarm will be output. When the event determination result is a review alarm event, output a review prompt; When the event is determined to be an equipment malfunction event, a maintenance prompt or inspection prompt will be output.

[0030] Example 1: System Overall Structure like Figure 1 As shown, this embodiment provides a fire false alarm suppression system based on terminal area deployment and spatiotemporal correlation, including a front-end deployment management module, a data access processing module, an event triggering module, a spatiotemporal correlation analysis module, and an alarm output module.

[0031] The front-end deployment management module is used to divide multiple wireless smoke detectors in the building space into areas and establish the association between each wireless smoke detector and area identifiers, location identifiers, and relationships with adjacent terminals.

[0032] The data access and processing module is used to receive alarm status data, device status data and reporting time data uploaded by each wireless smoke detection terminal, and to perform field normalization, time alignment and outlier filtering on the received data.

[0033] The event triggering module is used to identify any wireless smoke detector as the target terminal when a suspected fire alarm condition is triggered, and to extract relevant data of the target terminal’s area and adjacent terminals within a preset time window.

[0034] The spatiotemporal correlation analysis module is used to perform spatiotemporal correlation analysis on suspected fire alarm events based on relevant data of the target terminal and associated terminals within a preset time window, and generate event judgment results.

[0035] The alarm output module is used to classify suspected fire alarm events based on the event judgment results and output the alarm processing results corresponding to the classification results.

[0036] In this embodiment, the wireless smoke detection terminal is preferably a wireless photoelectric smoke fire alarm, but it is not limited to this and can also be other smoke detection terminals with smoke detection and wireless communication capabilities.

[0037] Example 2: Regional Deployment Modeling like Figure 2 As shown, multiple wireless smoke detectors are deployed in the building space. The building space can be divided into areas such as floors, rooms, corridors, stairwells, shops, kitchens, storage areas, and electrical distribution rooms.

[0038] In this embodiment, each wireless smoke detector terminal corresponds to at least the following information: terminal identifier, area identifier, location identifier, and a set of neighboring terminal identifiers.

[0039] The relationship between adjacent terminals can be established in the following ways: Method 1: Establish adjacent relationships within the same area based on a preset distance threshold or installation order; Method 2: Establish adjacent relationships across regions, such as establishing a connection between the end of a corridor and the end of an adjacent room, or establishing a connection between the end of a stairwell and the end of a floor corridor.

[0040] Through the above deployment and modeling, the backend platform can not only identify the alarm status of a terminal itself, but also identify the terminal's positional and adjacency relationships in the building space, providing a foundation for subsequent spatiotemporal correlation analysis.

[0041] Example 3: Data Access and Suspected Fire Alarm Triggering like Figure 3 As shown, the backend platform receives data uploaded by each wireless smoke detection terminal. This data includes at least alarm status data, device status data, and reporting time data.

[0042] Preferably, the device status data includes one or more of the following: online status, battery status, communication status, self-test status, and fault codes.

[0043] The data access processing module processes the received data as follows: A1. Field normalization processing enables data fields reported by different terminal models to be uniformly mapped to a unified data structure; A2. Time alignment processing enables data from different terminals to be compared under a unified time base; A3. Outlier filtering is used to remove data records that are obviously missing, have incorrect time, have invalid status, or are reported repeatedly.

[0044] When any wireless smoke detector alarms, or when it reports an abnormal status a preset number of times within a preset time, the event triggering module identifies the wireless smoke detector as the target terminal and triggers the subsequent spatiotemporal correlation analysis process.

[0045] Example 4: Spatiotemporal Correlation Analysis like Figure 4 As shown, after the target terminal triggers a suspected fire alarm condition, the spatiotemporal correlation analysis module extracts relevant data of the target terminal's region and adjacent related terminals within a preset time window.

[0046] Preferably, the preset time window includes a first time period before the target terminal triggers and a second time period afterward. In one embodiment, the first time period can be 30 seconds and the second time period can be 90 seconds; in another embodiment, the first and second time periods can also be configured according to the actual scenario.

[0047] Spatiotemporal correlation analysis should include at least the following: (1) Alarm continuity analysis Analyze whether the target terminal continuously alarms within a preset time window, or whether it repeatedly triggers multiple times in a short period of time.

[0048] (2) Collaborative alarm analysis Analyze whether one or more associated terminals in the target terminal’s area or adjacent areas have alarms within a preset time window to determine whether there are coordinated changes.

[0049] (3) Alarm timing analysis Analyze the sequence and time intervals of alarm triggering from multiple terminals to determine whether the alarms exhibit spatial propagation or regional diffusion characteristics.

[0050] (4) Equipment status effectiveness analysis Analyze the online status, battery status, communication status, self-test status, or fault codes of the target terminal and associated terminals to determine whether the alarm data is affected by equipment malfunction.

[0051] In one implementation, the backend platform generates an event determination result based on the above analysis results. The event determination result can be generated using a rule-based calculation method, for example: When the target terminal alarms continuously and at least one associated terminal alarms within a preset time window, the event level is increased. When only the target terminal triggers a single alarm, and the target terminal has abnormal battery, communication, or self-test conditions, the event level is reduced. When multiple terminals in the same area trigger alarms consecutively within a short period of time, the event level should be increased. When the same terminal triggers multiple times within the deduplication time and there is a lack of coordinated alarms from associated terminals, the duplicate events are merged and the event level is reduced.

[0052] This invention does not limit the event determination result to be calculated using a specific mathematical formula; it can also be achieved by using preset determination rules, hierarchical strategies, or a combination of rules and models.

[0053] Example 5: Event Classification and Output The alarm output module categorizes suspected fire alarm events based on the event determination results. Preferably, the classification results include at least one or more of the following: fire alarm events, verification alarm events, equipment malfunction events, and low-confidence disturbance events. Different classification results correspond to different processing strategies. For example: When an event is determined to be a fire alarm, a high-priority alarm is directly output and pushed to the duty platform, management terminal or mobile terminal. When an event is determined to be a review alarm event, a review prompt is output and a delayed review process is triggered. When an event is determined to be a device malfunction event, a maintenance prompt or inspection prompt will be output. When an event is determined to be a low-confidence disturbance event, only the event information is recorded or a low-priority warning is given.

[0054] Through the above methods, the present invention can suppress false alarms based on terminal deployment relationships and backend analysis processes without changing the internal structure of the front-end wireless smoke detection terminal.

[0055] Example 6: Application in a residential building scenario In a residential building scenario, multiple wireless smoke detectors are deployed in the corridors of each floor. Each detector is bound to a floor number, a location number, and the number of adjacent detectors. The backend platform receives alarm status, battery status, online status, and reporting time reported by each detector.

[0056] When an alarm is triggered on a target terminal on a certain floor, the backend platform extracts data from adjacent terminals within a preset time window. If only the target terminal triggers an alarm briefly, and adjacent terminals do not trigger alarms but the target terminal's battery status is abnormal, the event is classified as a verification alarm event or a device malfunction event. If the target terminal and at least one adjacent terminal trigger alarms consecutively within the preset time window, the event is classified as a fire alarm event and pushed directly to the system.

[0057] Example 7: Application in the scenario of street-front shops In the street-front shop scenario, wireless smoke-detecting terminals are deployed in the sales area, kitchen, and storage area, and a zone relationship is established based on the area attributes.

[0058] When a single terminal in the kitchen area alarms but terminals in adjacent areas do not alarm synchronously, the backend platform performs a delayed review based on preset scenario rules; when multiple alarms occur consecutively in the kitchen and storage areas, the event level is increased and a high-priority alarm is triggered.

[0059] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0060] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation, characterized in that, Includes the following steps: S1. Model the regional deployment of multiple wireless smoke detectors deployed in the building space, establish terminal identifiers for each wireless smoke detector, and bind each wireless smoke detector to its corresponding area identifier, location identifier, and adjacent terminal relationship. S2. Receive data uploaded by each of the wireless smoke detection terminals, the data including at least alarm status data, device status data and reporting time data, and perform field normalization and time alignment processing on the data; S3. When any wireless smoke detector triggers a suspected fire alarm condition, the wireless smoke detector that triggered the suspected fire alarm condition is identified as the target terminal, and relevant data of the target terminal's area and adjacent terminals within a preset time window are extracted. S4. Based on the relevant data of the target terminal and the associated terminal within the preset time window, perform spatiotemporal correlation analysis on the suspected fire alarm event. The spatiotemporal correlation analysis includes at least target terminal alarm continuity analysis, associated terminal collaborative alarm analysis, and equipment status effectiveness analysis, and generate event judgment results based on the analysis results. S5. Classify suspected fire alarm events according to the event determination results and output alarm processing results corresponding to the classification results to suppress false fire alarms caused by single-point anomalies.

2. The method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation according to claim 1, characterized in that, The regional deployment modeling of multiple wireless smoke-detecting terminals deployed within the building space, as described in S1, includes: The building space is divided into zones according to floors, rooms, corridors, stairwells, or functional areas; Based on the installation location of the wireless smoke detectors, each wireless smoke detector is mapped to its corresponding area. The adjacent terminal relationships are established based on the installation location relationships within the same area and the cross-regional adjacency relationships.

3. The method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation according to claim 1, characterized in that, The suspected fire alarm conditions described in S3 include at least one of the following: The wireless smoke detector terminal reports the alarm status. The wireless smoke detector terminal continuously reports abnormal status a preset number of times within a preset time period; The wireless smoke detector repeatedly triggers the alarm a preset number of times within a preset time period.

4. The method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation according to claim 1, characterized in that, The spatiotemporal correlation analysis described in S4 also includes alarm timing analysis of multiple terminals in the same area, used to determine whether multiple terminals trigger alarms sequentially at preset time intervals within the preset time window.

5. The method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation according to claim 1, characterized in that, The target terminal alarm continuity analysis described in S4 includes: determining whether the target terminal continuously maintains an alarm state within the preset time window, or whether it repeatedly triggers an alarm a preset number of times within the preset time window.

6. The method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation according to claim 1, characterized in that, The associated terminal collaborative alarm analysis described in S4 includes: determining whether at least one associated terminal and the target terminal jointly trigger an alarm within the preset time window, and determining whether multiple alarm terminals are located in the same area or adjacent areas.

7. The method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation according to claim 1, characterized in that, S4 describes generating event determination results based on the analysis results, including: When the target terminal continuously alarms and at least one associated terminal triggers an alarm within the preset time window, the event level is increased. When only a single target terminal alarms and the target terminal has an abnormal device status, the event level is reduced; When the same terminal triggers an alarm repeatedly within the deduplication time while the associated terminal does not trigger an alarm, the duplicate alarm events are merged.

8. The method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation according to claim 1, characterized in that, The classification results described in S5 include at least one or more of the following: fire alarm events, verification alarm events, equipment malfunction events, and low-confidence disturbance events.

9. A method for suppressing false fire alarms based on terminal area deployment and spatiotemporal correlation according to claim 8, characterized in that, The alarm processing result corresponding to the output and classification result includes at least one of the following: When the classification result is a fire alarm event, a high-priority alarm is output; When the classification result is a review alarm event, a review prompt is output and a delayed review process is triggered; When the classification result is an equipment malfunction event, output a maintenance prompt or inspection prompt. When the classification result is a low-confidence perturbation event, record the event information or output a low-priority prompt.

10. A fire false alarm suppression system based on terminal area deployment and spatiotemporal correlation according to claim 1, characterized in that, include: The front-end deployment management module is used to divide multiple wireless smoke detectors in the building space into areas and establish the association between each wireless smoke detector and area identifiers, location identifiers, and relationships between adjacent terminals. The data access and processing module is used to receive alarm status data, device status data and reporting time data uploaded by each wireless smoke detection terminal, and to perform field normalization and time alignment processing on the received data. The event triggering module is used to identify the wireless smoke detector as the target terminal when any wireless smoke detector triggers a suspected fire alarm condition, and to extract relevant data of the target terminal's area and adjacent related terminals within a preset time window. The spatiotemporal correlation analysis module is used to perform spatiotemporal correlation analysis on suspected fire alarm events based on the relevant data of the target terminal and the associated terminal within the preset time window. The spatiotemporal correlation analysis includes at least target terminal alarm continuity analysis, associated terminal collaborative alarm analysis, and equipment status effectiveness analysis, and generates event judgment results based on the analysis results. The alarm output module is used to classify suspected fire alarm events according to the event judgment results and output alarm processing results corresponding to the classification results.

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