Alarm control method and device, electronic equipment and storage medium

Abstract

The invention provides an alarm control method and device, electronic equipment and a storage medium, and the method comprises the steps: responding to a condition that a first alarm detects that a first concentration value of smoke is greater than a first preset concentration value at a first moment; acquiring a second concentration value of the smoke detected by the second alarm at the first moment and a second moment when the concentration value of the smoke detected by the second alarm is greater than a second preset concentration value; determining a first score according to the first moment and the first concentration value, and determining a second score according to the second concentration value and the second moment; in response to the fact that the first score is larger than the second score, alarming is carried out based on first alarm information, and the first alarm information comprises position information of the first alarm; and in response to the fact that the first score is smaller than the second score, giving an alarm based on second alarm information which comes from a second alarm and comprises position information of the second alarm. And the combined alarm capability among a plurality of alarms can be improved.

Description

Technical Field

[0001] This application relates to the field of general control technology, and in particular to an alarm control method, device, electronic equipment and storage medium. Background Technology

[0002] In scenarios involving multiple fire alarms, some fire alarms, to enhance their intelligence, simultaneously broadcast the location of the fire, aiding users familiar with the terrain in evacuation. However, currently, if multiple fire alarms detect a fire independently based on their own sensors (such as smoke and carbon monoxide sensors), they will each broadcast an independent voice announcement. If the fire spreads rapidly, multiple alarms may trigger adjacent alarms, leading to mixed location information in the voice announcements from different alarms.

[0003] Traditional methods involve broadcasting alarm information to other alarms after a target alarm is triggered, thus enabling simultaneous broadcasting of the target alarm's message by other alarms. However, this approach only considers the location of the fire and not its subsequent development. For example, a fire may start at location one, but as it develops, the fire may become more severe at location two due to less combustible material at location one compared to more at location two. Yet, the current alarm broadcast is only for location one. It's clear that current alarm coordination only broadcasts to the first alarm triggered, failing to address the complexities of a fire's progression. Therefore, improving the coordinated alarm capability among multiple alarms is a pressing technical challenge in this field. Summary of the Invention

[0004] This application provides an alarm control method, device, electronic device, and storage medium, which can improve the joint alarm capability between alarms by controlling the alarms.

[0005] In a first aspect, this application provides an alarm control method, which is applied to a first alarm among a plurality of alarms, wherein the plurality of alarms are connected by communication, and each of the plurality of alarms is equipped with a sensor, the sensor being a fire detection-related sensor, the method comprising: In response to the first alarm detecting that the first concentration value of smoke is greater than the first preset concentration value at a first moment, a second concentration value and a second moment are obtained. The second concentration value is the concentration value of smoke detected by the second alarm at the first moment, and the second moment is the moment when the concentration value of smoke detected by the second alarm is greater than the second preset concentration value. The second alarm is an alarm that is different from the first alarm among multiple alarms. A first score is determined based on a first time point and a first concentration value, and a second score is determined based on a second concentration value and a second time point; In response to the first score being greater than the second score, an alarm is triggered based on the first alarm information, which includes the location information of the first alarm device. In response to the first score being less than the second score, an alarm is triggered based on the second alarm information, which comes from the second alarm device and includes the location information of the second alarm device.

[0006] As can be seen, in this application, when the first alarm detects a fire, it not only determines whether to alarm itself, but also actively acquires the status data of other alarms. The response time when the trigger concentration value in the status data exceeds the threshold reflects the order of alarms, and the concentration value reflects the actual environmental state at the current location. Based on this, a score is calculated for each alarm to determine which alarm should trigger the alarm. This mechanism avoids the problem of address information conflicts caused by multiple alarms broadcasting simultaneously, improving the accuracy of alarm information determination. Furthermore, by determining and comparing the score of each alarm based on the order of alarms and the actual environmental state, the alarm corresponding to the more severe area in the fire development stage can be comprehensively identified. For that alarm, an alarm is generated based on its own information, thus improving the alarm's ability to comprehensively process information from other alarms and improving the accuracy of alarm information generation. Moreover, compared to other alarms that simply broadcast the alarm information of the first alarm to sound, the alarm in this application comprehensively analyzes the fire severity relative to other alarms when triggering an alarm, and determines whether to generate its own alarm or receive alarm information broadcast by other alarms based on the severity comparison, improving the joint alarm capability between alarms.

[0007] In a feasible example, the first score is determined based on the first time point and the first concentration value, including: The first score is determined based on the first moment, the first concentration value, and the first confidence level. The first confidence level is used to characterize the device stability of the first alarm.

[0008] In this application, the confidence level can reflect the stability of the device, thereby determining the reliability of the device. Combining the confidence level with the calculation of the first score helps to improve the accuracy of the first score.

[0009] In a feasible example, the method also includes: Acquire the device usage time, historical false alarm rate, and multiple third concentration values ​​of the first alarm device within the first time period before the first moment; A first index is determined based on multiple third concentration values, and the first index is used to characterize the changes in concentration values. The second index is determined based on equipment usage time and historical false alarm rate. Equipment usage time and historical false alarm rate are inversely proportional to the second index. The first confidence level is determined based on the first index and the second index.

[0010] In this application, the first index reflects the dynamic trend of fire occurrence (such as whether the concentration continues to rise) and is used to determine whether the current alarm has the characteristics of a real fire. The second index, combined with the aging degree of the equipment (usage time) and reliability history (false alarm rate), inversely affects the confidence level, thereby suppressing the alarm weight of old or false alarm-prone equipment. The first confidence level is calculated by combining these two indices, enabling the system to dynamically assess the credibility of the current alarm and avoid the propagation of false alarms due to equipment performance degradation or environmental interference. This mechanism enhances the intelligent decision-making capability of the alarm system, ensuring that in multi-alarm linkage scenarios, the high-credibility equipment takes the lead in alarming, thereby outputting accurate location information, helping users clarify the escape direction, and significantly improving overall escape efficiency.

[0011] In a feasible example, the first index is determined based on multiple third concentration values, including: Determine the concentration range between multiple third concentration values, and divide the concentration range into multiple concentration sub-ranges; Determine the number of third concentration values ​​included in each of the multiple concentration sub-intervals; The first fractal dimension is obtained by calculating the fractal dimension based on the number of multiple third concentration values, the number of third concentration values ​​included in each concentration sub-interval, and the maximum and minimum concentration values ​​of multiple third concentration values. The third index is obtained by calculating the chaos index based on the first fractal dimension. The first index is determined based on the third index.

[0012] In this application, the changes in fire smoke / gas concentration exhibit typical nonlinear, abrupt, and chaotic characteristics (such as the step increase in concentration when smoldering turns into open flame). This application quantifies the complexity of the concentration sequence using fractal dimensions and combines this with the chaotic index to identify "irregular fluctuation patterns unique to real fires," which can effectively distinguish fire signals from stable interference sources such as cooking fumes and steam.

[0013] In a feasible example, the first score is determined based on the first time point, the first concentration value, and the first confidence level, including: Determine the first difference between the first moment and the third moment. The third moment is the time when the concentration value of the first alarm is greater than the third preset concentration value before the first moment, and the third preset concentration value is less than the first preset concentration value. Fire simulation is performed in the chaotic system model based on the first difference, the first concentration value, and the first confidence level, and the first score is determined.

[0014] In this application, by introducing a third moment as a historical state reference point and calculating the time difference, combined with the current concentration value and equipment reliability parameters, multi-dimensional information is input into a chaotic system model for nonlinear mapping, thereby more realistically reflecting the severity and urgency of the fire. This scoring mechanism not only considers current detection data but also integrates historical change rates and equipment reliability, resulting in higher predictability and discriminative power in the scoring results. Compared to scoring methods based solely on thresholds or simple weighting, the scoring results obtained in this application implicitly reveal the physical laws governing the fire's development stages, avoiding the "slow temperature rise misjudged as a fire" or "delayed response to sudden fires" caused by linear weighting, and more accurately identifying fire sources that truly require priority alarm.

[0015] In a feasible example, a fire simulation is performed on a chaotic system model based on a first difference, a first concentration value, and a first confidence level, to determine a first score, including: The first concentration value is mapped to the initial parameter corresponding to the first variable in the chaotic system model. The first variable is used for the reaction space dimension. The first difference is mapped to the initial parameters corresponding to the second variable in the chaotic system model, and the second variable is used to react to the time dimension. Map the first confidence level to the control parameters in the chaotic system model; Based on the initial parameters corresponding to the first variable, the initial parameters corresponding to the second variable, and the control parameters, perform multiple chaotic iterations to obtain the result parameters corresponding to the first variable; The result parameters corresponding to the first variable are normalized to determine the first score.

[0016] In this application, by mapping the first concentration value to the initial parameter corresponding to the first variable in the chaotic system model, mapping the first difference to the initial parameter corresponding to the second variable, and mapping the first confidence level to the control parameter, multiple chaotic iterations are performed to obtain the result parameter corresponding to the first variable, and the result parameter is normalized to determine the first score. This achieves the technical effect of simulating the fire development process through nonlinear dynamics, making the score simultaneously reflect the "fire spatial spread trend" and "time urgency", generating result parameters with high discrimination, and converting them into a unified scoring scale to support objective comparison and priority ranking among multiple alarms.

[0017] In a feasible example, after receiving the second alarm information from the second alarm, the method further includes: In response to the fact that no second alarm information is received within the second time period, a first message is sent to the second alarm device, the first message being used to determine whether the second alarm device has triggered an alarm. Receive second information from the second alarm; In response to determining, based on the second information, that the second alarm has not triggered, a first event is recorded, and an alarm is triggered based on the first alarm information. The first event is used to characterize the possibility of a false alarm. In response to determining that the second alarm has been triggered based on the second information, a third information is sent to the second alarm, the third information being used to determine the alarm information; Receive the third alarm information from the second alarm and trigger an alarm based on the third alarm information.

[0018] In this application, a secondary confirmation mechanism is introduced. After the main alarm receives the alarm information from the secondary alarm, if no valid feedback is received within a preset time window, it proactively initiates a status check and sends a first message to confirm whether the secondary alarm has actually triggered. By receiving the second message and judging its status, the system can distinguish between false alarms and real alarms. If it is determined to be a false alarm, the first event is recorded for subsequent system optimization or troubleshooting, and the alarm continues to be executed based on its own alarm information to avoid unnecessary panic or misleading due to false alarms. If it is confirmed to be a real alarm, a third message is sent to obtain more complete third alarm information, ensuring the accuracy and completeness of the alarm content. This process enhances the robustness and reliability of the alarm system, prevents the spread of false alarms caused by communication delays, equipment failures, or false triggering, and ensures that users receive verified real fire location information. Combined with the pre-scoring mechanism, this scheme achieves dual protection of "priority + authenticity," significantly improving the credibility of alarm information and the accuracy of users' escape decisions, ultimately achieving the technical effect of improving escape efficiency.

[0019] In a feasible example, the method also includes: Obtain the audio database, which includes a prompt tone library, an event type library, a spatial location library, and a command action library. The prompt tone library includes general alarm audio, the event type library includes audio describing the hazard source, the spatial location library includes audio containing the location information of the alarm, and the command action library includes audio instructions used to guide user actions. The first audio is matched from the prompt sound library based on the first score. The higher the first score, the more urgent the fire situation is to be reflected by the target prompt sound. Match the second and third audio frequencies from the event type library and the command action library based on the sensor type; The first alarm information is determined based on the first, second, third, and fourth audio frequencies, with the fourth audio frequency originating from a spatial location database.

[0020] In this application, a structured audio database is constructed to decompose alarm information into multiple combinable audio modules (prompt tone, event type, spatial location, and instruction action), achieving dynamic splicing and semantic clarity of alarm content. The first score serves as an urgency indicator, used to select audio corresponding to the level of urgency from the prompt tone library, allowing users to intuitively perceive the severity of the fire. The sensor type is used to match specific hazard descriptions and escape instructions, enhancing the professionalism and relevance of the information. The spatial location library provides precise geographical location voice broadcasts, ensuring users clearly identify the fire's location. This solution transforms the previously chaotic multi-source alarm information into structured, hierarchical voice output, avoiding the information redundancy and confusion caused by traditional independent alarms. Simultaneously, standardized audio modules enable rapid response and a unified broadcast style. Upon hearing the alarm, users can not only identify the fire's location but also understand the current risk level and the appropriate actions. Even in unfamiliar environments, they can determine safe routes based on non-alarm areas, significantly improving escape efficiency and decision-making accuracy.

[0021] Secondly, this application provides an alarm control device, which is applied to a first alarm among a plurality of alarms, wherein the plurality of alarms are connected in communication, and each of the plurality of alarms is equipped with a sensor, the sensor being a fire detection-related sensor, and the device includes: The communication unit is used to respond to the first alarm detecting that the first concentration value of smoke is greater than the first preset concentration value at a first moment, to obtain a second concentration value and a second moment, wherein the second concentration value is the concentration value of smoke detected by the second alarm at the first moment, and the second moment is the moment when the concentration value of smoke detected by the second alarm is greater than the second preset concentration value, and the second alarm is an alarm that is different from the first alarm among a plurality of alarms. The processing unit is configured to determine a first score based on a first time point and a first concentration value, and to determine a second score based on a second concentration value and a second time point; The processing unit is used to issue an alarm based on the first alarm information in response to the first score being greater than the second score; The processing unit is also configured to, in response to the first score being less than the second score, issue an alarm based on second alarm information, the second alarm information being from a second alarm device and including the location information of the second alarm device.

[0022] Thirdly, this application provides an electronic device including a processor, a memory, and a communication interface. The processor, memory, and communication interface are interconnected and perform communication with each other. The memory stores executable program code, the communication interface is used for wireless communication, and the processor is used to retrieve the executable program code stored in the memory and execute some or all of the steps described in any of the methods in the first aspect.

[0023] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements some or all of the steps described in the first aspect of this application.

[0024] Fifthly, this application provides a computer program product, including a computer program that, when processed and executed, implements some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A schematic diagram of the structure of a control system provided in an embodiment of this application; Figure 2 A flowchart illustrating an alarm control method provided in an embodiment of this application; Figure 3 A flowchart illustrating another alarm control method provided in an embodiment of this application; Figure 4 A flowchart illustrating another alarm control method provided in this application embodiment; Figure 5 A functional unit block diagram of an alarm control device provided in an embodiment of this application; Figure 6 A functional unit block diagram of another alarm control device provided in the embodiments of this application; Figure 7 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0028] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps is not limited to the steps listed, but may optionally include steps not listed, or may optionally include other steps inherent to these processes, methods, products, or apparatuses.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] Currently, fire prevention is a crucial part of building maintenance, and corresponding fire alarms are becoming increasingly intelligent. When a fire alarm sounds, it also carries its location information, allowing users to know the exact location of the fire and facilitating escape. To achieve early detection and warning of fires in every corner of the building, the number of fire alarms in buildings has also increased. For example, multiple fire alarms are installed on the same floor of a building. However, currently, these multiple fire alarms broadcast information independently, leading to mixed information and hindering users' ability to determine their escape location, thus reducing escape efficiency.

[0031] Based on this, this application provides an alarm control method for controlling the alarm of a fire alarm, which allows users to clearly know the corresponding fire location after hearing the alarm information, and even in unfamiliar environments, they can escape in the direction where no alarm has occurred. It can also avoid the difficulty for users to grasp the correct escape direction caused by a single alarm or separate alarms, thus improving the user's escape efficiency.

[0032] Please see Figure 1 , Figure 1 A schematic diagram of the structure of a control system provided in an embodiment of this application is shown below. Figure 1 As shown, the control system 100 includes a first alarm 101 and a second alarm 102.

[0033] The first alarm 101 and the second alarm 102 communicate with each other to exchange data, which can be wired or wireless communication. Each of the first alarm 101 and the second alarm 102 is equipped with a sensor, which can be a sensing element used to detect fire-related physical or chemical parameters (such as smoke concentration, temperature, etc.). It is understood that the sensor can collect fire-related parameters in the environment in real time, providing triggering criteria and data input for the alarm. For example, the sensor can be a photoelectric smoke sensor, an ionization smoke sensor, a thermistor temperature sensor, etc., depending on the detection principle. It is understood that there can be multiple second alarms 102.

[0034] In this application, in response to the first alarm 101 detecting a first concentration value of smoke greater than a first preset concentration value at a first moment, the first alarm 101 acquires a second concentration value and a second moment of the second alarm 102. The second concentration value is the concentration value of smoke detected by the second alarm 102 at the first moment, and the second moment is the moment when the second alarm 102 triggers a smoke concentration value greater than the second preset concentration value corresponding to the second alarm 102. The second alarm 102 is an alarm different from the first alarm 101 among a plurality of alarms. A first score is determined based on the first moment and the first concentration value, and a second score is determined based on the second concentration value and the second moment. In response to the first score being greater than the second score, an alarm is triggered based on first alarm information, which includes the location information of the first alarm 101. In response to the first score being less than the second score, an alarm is triggered based on second alarm information, which comes from the second alarm 102 and includes the location information of the second alarm 102.

[0035] When the first alarm detects a fire, it not only determines whether to sound an alarm itself but also actively acquires status data from other alarms. The response time when the trigger concentration value exceeds the threshold in the status data reflects the order of alarms, and the concentration value reflects the actual environmental conditions at the current location. Based on this, a score is calculated for each alarm to determine which alarm should trigger the alarm. This mechanism avoids the problem of address information conflicts caused by multiple alarms broadcasting simultaneously, ensuring that users hear the alarm information with the highest credibility and the most timely response. Simultaneously, the alarm information includes specific location information, allowing users to clearly identify the location of the fire even in unfamiliar environments, thus enabling them to choose a safe escape route. Compared to traditional independent alarms or fully linked alarm methods, this solution significantly improves the accuracy and understandability of alarm information generated by the alarms, while also enhancing the alarm's collaborative alarm capabilities.

[0036] Based on this, the present application provides an alarm control method, and the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0037] Example 1: The main process of the alarm control method is described below.

[0038] Please see Figure 2 , Figure 2 This is a flowchart illustrating an alarm control method provided in an embodiment of this application. The method is applied to the aforementioned first alarm, such as... Figure 2 As shown, the method includes the following steps.

[0039] Step S201: In response to the first alarm detecting that the first concentration value of smoke is greater than the first preset concentration value at the first moment, a second concentration value and a second moment are obtained, wherein the second concentration value is the concentration value of smoke detected by the second alarm at the first moment.

[0040] Wherein, the second concentration value is the concentration of smoke detected by the second alarm at the first moment, the second moment is the moment when the concentration of smoke detected by the second alarm is greater than the second preset concentration value, and the second alarm is an alarm that is different from the first alarm among multiple alarms.

[0041] The first alarm device can be the selected alarm unit among multiple alarm devices as the current processing unit. Its sensors trigger an evaluation process after detecting smoke concentration exceeding a preset threshold. Understandably, the first alarm device, as the initiator of the current alarm decision, is responsible for acquiring status data from other alarm devices, calculating scores, and deciding whether to initiate the alarm or receive alarm information from other alarm devices.

[0042] The second alarm can be another alarm device, different from the first alarm, among multiple alarms. It is used to acquire and compare status data after the first alarm is triggered. Understandably, the second alarm provides auxiliary alarm status data for the first alarm to score and compare. If its score is higher, it will take the lead in triggering the alarm, thus avoiding duplicate alarms from multiple points.

[0043] In this embodiment, the first concentration value is sampled by the internal sensor of the first alarm at a specific moment, and transmitted to the processing unit after analog-to-digital conversion. The first preset concentration value can be a concentration threshold set according to the actual situation of the first alarm to trigger an alarm, used to determine whether to enter the alarm process. In this embodiment, the first preset concentration value is pre-stored in the control program of the first alarm and can be adjusted through the configuration interface or remote settings. The second concentration value can be the fire-related parameter value detected by the sensor in the second alarm at the first moment. In this embodiment, the second concentration value is synchronously collected by the second alarm at the moment the first alarm is triggered and transmitted back to the first alarm through the communication link.

[0044] Understandably, the first moment serves as a time benchmark, used to synchronously acquire the status data of other alarms at the same point in time, ensuring fairness in the evaluation. In this embodiment, the first moment is recorded by the internal clock system of the first alarm, accurate to the millisecond level or higher. The second moment can be the specific point in time when the sensor in the second alarm detects that its own concentration value is greater than a second preset concentration value. Understandably, the second moment reflects the time when the second alarm independently triggers the alarm, used to evaluate the timeliness of the response, and included in the scoring calculation. In this embodiment, the second moment is recorded by the internal clock system of the second alarm and sent to the first alarm via communication. If the second alarm does not trigger a concentration value greater than the second preset concentration value corresponding to the second alarm at the first moment, then the second moment is the first moment plus a preset duration, such as 20 seconds.

[0045] In response to a first concentration value detected by the sensor in the first alarm being greater than a first preset concentration value, the system acquires a second concentration value and a second time from the second alarm. This can be achieved by triggering a communication protocol to request status data from other alarms when the sensor value detected by the first alarm exceeds a preset threshold. Furthermore, this operation can be performed by broadcasting a query command via a wired bus (such as RS485), with all alarms responding and returning their own status data; or by sending a data request point-to-point via wireless communication (such as Wi-Fi or Zigbee), with only the target second alarm responding. This allows for status synchronization among multiple alarms, providing necessary input data for scoring comparisons.

[0046] Step S202: Determine a first score based on a first time point and a first concentration value, and determine a second score based on a second concentration value and a second time point.

[0047] The first score can be a numerical value representing the alarm priority of the first alarm device, calculated by combining the first concentration value and the first time point. It is understood that the first score is used to compare with the second score to determine whether the first alarm device should trigger the alarm. In this embodiment, the first score is obtained by combining the two input parameters using a weighted function or a rule engine; the weights can be configured according to the scenario.

[0048] Determining the first score based on the first moment and the first concentration value can be achieved by substituting these two parameters into a preset scoring function or rule set, outputting a value representing the priority of the first alarm. Furthermore, this operation can be implemented using a linear weighted formula or a fuzzy logic inference system, thereby quantifying the alarm reliability and urgency of the first alarm and supporting objective comparison.

[0049] The second score, determined based on the second concentration value and the second time point, can also be achieved by substituting the two parameters into a preset scoring function or rule set, outputting a value representing the priority of the second alarm. Furthermore, this operation can be implemented using the same weighting formula or dynamic weight adjustment mechanism as the first score, thereby quantifying the alarm reliability and urgency of the second alarm and supporting objective comparison. In other words, the calculation method for the second score should be similar to that of the first score.

[0050] The first and second concentration values ​​reflect the actual concentration at the location of the two alarms. Based on the concentration values, the actual location of the fire can be preliminarily determined; for example, the higher the concentration value, the closer the fire is likely to be. The first and second moments reflect the order in which the two alarms are triggered; the earlier the alarm is triggered, the closer the fire is likely to be.

[0051] Therefore, comparing the first score, determined by the first moment and the first concentration value, with the second score, determined by the second concentration value and the second moment, can fully reflect the alarm priority between the first and second alarms. The higher the score, the closer the alarm is to the fire location, and the higher the urgency of the danger. In this case, the alarm with the higher score should take the lead in issuing the alarm. Other alarms that also trigger the alarm should provide auxiliary alarms.

[0052] Optionally, a first score is determined based on a first moment and a first concentration value, including: determining the first score based on the first moment, the first concentration value, and a first confidence level, wherein the first confidence level is used to characterize the device stability of the first alarm.

[0053] Optionally, a second score is determined based on a second concentration value and a second time point, including: determining the second score based on the second time point, the second concentration value, and a second confidence level, wherein the second confidence level is used to characterize the device stability of the second alarm.

[0054] The first confidence level can be a quantitative indicator characterizing the stability and data reliability of the first alarm device. In this embodiment, the first confidence level is dynamically updated based on historical operating data, self-test results, or external calibration feedback, and may include factors such as failure rate and signal drift. The second confidence level can be a quantitative indicator characterizing the stability and data reliability of the second alarm device. In this embodiment, the second confidence level is generated based on the second alarm device's own operating status monitoring data and transmitted to the first alarm device via communication.

[0055] Specifically, the first and second confidence levels can reflect the stability and reliability of the two alarm devices. The higher the confidence level, the higher the accuracy of the alarm triggering and the higher the accuracy of the concentration value. For example, a kitchen smoke alarm may frequently produce false alarms because cooking produces fumes, so its confidence level is lower.

[0056] The first score is determined based on the first moment, the first concentration value, and the first confidence level. This can be achieved by substituting these three parameters into a preset scoring function or rule set, outputting a value representing the priority of the first alarm. Furthermore, this operation can be implemented using a linear weighted formula or a fuzzy logic inference system, thereby quantifying the alarm reliability and urgency of the first alarm and supporting objective comparison. The determination of the second score follows the same principle. It can be seen that introducing a confidence level calculation method can further improve the accuracy of determining the first and second scores.

[0057] Step S203: In response to the first score being greater than the second score, an alarm is triggered based on the first alarm information.

[0058] The first alarm message can be alarm content generated and broadcast by the first alarm device, containing the location information of the first alarm device. It is understood that the first alarm message conveys the specific location of the fire to the user and guides the escape route. For example, the first alarm message can include voice broadcasts, LED display information, wireless push notifications, etc., depending on the transmission medium.

[0059] Location information can be data identifying the physical location of the alarm, which can be used to pinpoint the area where a fire has occurred. Understandably, location information allows users to clearly identify the location of the fire and thus choose a safe escape route away from the fire area. In this embodiment, location information is obtained and stored in the alarm through methods such as preset coordinate system encoding, GPS positioning, Bluetooth beacon positioning, or fixed building identification numbers.

[0060] Optionally, the first alarm information can also be sent to the second alarm.

[0061] Step S204: In response to the first score being less than the second score, an alarm is triggered based on the second alarm information.

[0062] The second alarm message can be alarm content generated and broadcast by the second alarm device, containing the location information of the second alarm device. It is understood that when the second score is higher, the first alarm device receives and plays the second alarm message to ensure that the user hears the highest priority alarm. For example, the second alarm message can be broadcast via different media, including voice announcements, LED displays, wireless push notifications, etc.

[0063] In response to a first alarm score being lower than a second alarm score, an alarm is triggered based on the second alarm information. This can be achieved by the first alarm stopping its local alarm function and instead receiving and playing the alarm content from the second alarm. Furthermore, this operation can monitor the broadcast messages of the second alarm via a communication interface, immediately switching the playback source upon receipt; or it can proactively request the second alarm information from the second alarm, initiating playback only after confirmation. This ensures that the user always receives the most reliable alarm information, preventing erroneous escape attempts due to partial false alarms.

[0064] For example, in a fire alarm scenario in a large shopping mall, the alarm control method in this embodiment can be as follows: Multiple alarms are deployed on different floors of the mall. When the alarm on the east side of the first floor (the first alarm) detects that the smoke concentration exceeds the standard, it immediately obtains the current concentration value, trigger time, and equipment stability data of the alarm on the west side of the second floor (the second alarm). The system calculates a first score and a second score. If the first score is higher, the alarm on the east side of the first floor plays the voice message "Fire is located on the east side of the first floor, please evacuate to the west," and simultaneously sends this information to the alarm on the west side of the second floor. If the second score is higher, the alarm on the east side of the first floor stops broadcasting and instead receives and plays the message "Fire is located on the west side of the second floor, please evacuate to the north" from the alarm on the west side of the second floor. Users can hear a clear, unique, and safe-direction alarm prompt regardless of their location, effectively avoiding confusion and misjudgment. Furthermore, the alarm only sounds when the trigger concentration value exceeds the threshold, so even users who cannot understand the alarm information can escape to locations where no alarms have been triggered, improving the user's escape efficiency.

[0065] In this application, when the first alarm detects a fire, it not only determines whether to alarm itself but also actively acquires status data from other alarms. The response time when the trigger concentration value exceeds the threshold in the status data reflects the order of alarms, the concentration value reflects the actual environmental state at the current location, and the confidence level reflects the stability of the equipment, thereby determining the reliability of the equipment. Based on this, a score is calculated for each alarm to determine which alarm should trigger the alarm. This mechanism avoids the problem of address information conflicts caused by multiple alarms broadcasting simultaneously, improving the accuracy of alarm information determination. Furthermore, by determining and comparing the scores of each alarm based on the order of alarms, the actual environmental state, and the confidence level, the alarm corresponding to the most severe area in the fire's development stage can be comprehensively identified. For this alarm, an alarm is generated based on its own information and broadcast to other alarms. This improves the alarm's ability to comprehensively process information from other alarms, thereby improving the accuracy of alarm information generation. Furthermore, compared to other alarm systems that simply broadcast alarm information to the first alarm to sound, this alarm system comprehensively analyzes the fire severity relative to other alarm systems when it is triggered. Based on the comparison of severity, it determines whether to generate its own alarm or receive alarm information broadcast by other alarm systems, thereby improving the joint alarm capability between alarm systems.

[0066] Example 2: The alarm control method will be described in detail below based on the determination details of confidence level.

[0067] Please see Figure 3 , Figure 3 This is a flowchart illustrating another alarm control method provided in an embodiment of this application. This method is applied to the aforementioned first alarm, such as... Figure 3 As shown, the method includes the following steps.

[0068] Step S301: In response to the first alarm detecting that the first concentration value of smoke is greater than the first preset concentration value at a first moment, a second concentration value and a second moment are obtained.

[0069] Step S302: Obtain the device usage time, historical false alarm rate, and multiple third concentration values ​​of the first alarm device within the first time period before the first moment.

[0070] The first alarm can be described in context by storing the usage time and number of false alarms through the built-in electrically erasable programmable read-only memory (EEPROM), updating and reading it periodically, or synchronizing the historical false alarm rate through the cloud database, and obtaining the third concentration value for the most recent period of time by combining it with the local cache.

[0071] The device usage time can be the cumulative operating time of the first alarm since its installation and activation. It is understood that the device usage time, as an input parameter for assessing the degree of device aging, negatively influences the second index, thereby reducing the alarm weight of older devices. In one specific embodiment, the device usage time, as described in the context, can be recorded by the alarm's internal timing module and can be read via firmware or queried by a remote management system. Furthermore, the device usage time can be used in conjunction with the historical false alarm rate and the third concentration value in the calculation of the second index, indirectly affecting the generation of the first confidence level.

[0072] The historical false alarm rate can be a statistical value of the frequency of false alarm events occurring in the first alarm device during past operating cycles. It is understood that the historical false alarm rate reflects the reliability level of the equipment and is used to inversely influence the second index, suppressing the alarm priority of devices prone to false alarms. In this embodiment, the historical false alarm rate can be calculated by combining historical alarm records from local logs or a cloud management platform with actual fire verification results, as explained in the context. Furthermore, the historical false alarm rate can be an input parameter that, together with the equipment usage time, constitutes the second index, used to dynamically adjust the equipment reliability.

[0073] The third concentration value can be multiple fire-related concentration values ​​continuously collected by the sensors of the first alarm during a first time period prior to the first moment. It is understood that the third concentration value is used to analyze concentration change trends, support the calculation of the first index, and determine whether the current alarm has true fire characteristics. In this embodiment, the third concentration value can be described in context as being sampled in real time by sensors and stored in a local cache or uploaded to a cloud server. Furthermore, the third concentration value can, together with the first time period, define a data window used to analyze concentration change trends, ensuring that the first index reflects recent dynamics rather than long-term averages.

[0074] The first time period can be a continuous time interval from a fixed period before the first moment to the first moment itself. It is understood that the first time period limits the data window used to analyze concentration change trends, ensuring that the first index reflects recent dynamics rather than long-term averages. In an exemplary embodiment, the first time period can be preset or configured by the system, such as being set to 30 seconds, 1 minute, etc., and stored in the control program, depending on the context.

[0075] Obtaining the device usage time, historical false alarm rate, and multiple third concentration values ​​within a first time period before the first moment of alarm can be achieved by reading the device runtime, historical alarm records, and recent sensor sampling data from the first alarm's local storage or communication interface. Furthermore, this operation can be achieved by periodically updating and retrieving the device usage time and false alarm count stored in the built-in EEPROM, or by synchronizing historical false alarm rates through a cloud database and combining this with local caching to obtain recent third concentration values. This provides multi-dimensional input data for the first confidence score calculation, enhancing the technical effectiveness of the comprehensive assessment.

[0076] Step S303: Determine the first index based on multiple third concentration values.

[0077] The first index is used to characterize the change in concentration values. Specifically, the first index can be a quantitative indicator representing the trend of concentration change, calculated based on multiple third concentration values. It is understood that the first index is used to assess the dynamic characteristics of the current fire, such as the rate of increase or fluctuation of concentration, thereby improving the ability to determine the authenticity of alarms. In this embodiment, the first index can be derived by processing the sequence of third concentration values ​​through methods such as difference operations, slope fitting, or standard deviation calculation, as described in the context. Furthermore, the first index can be used in conjunction with the third concentration values ​​to characterize the change in concentration values, supporting subsequent confidence level calculations. For example, the first index can use the concentration difference between adjacent sampling points and calculate the average slope, or use a moving average method to smooth the data and calculate the standard deviation, reflecting the degree of fluctuation as the first index.

[0078] Determining a first index based on multiple third concentration values ​​can be achieved by mathematically processing the sequence of third concentration values ​​to extract concentration change trend characteristics. Furthermore, this operation can be accomplished by calculating the concentration difference between adjacent sampling points and obtaining the average slope as the first index, or by smoothing the data using a moving average method and then calculating the standard deviation to reflect the degree of fluctuation as the first index. This allows for the quantification of dynamic concentration change characteristics and the differentiation between real fires and transient disturbances.

[0079] Furthermore, in one embodiment, determining the first index based on multiple third concentration values ​​includes: determining a concentration range between the multiple third concentration values ​​and dividing the concentration range into multiple concentration sub-ranges; determining the number of third concentration values ​​included in each concentration sub-range; calculating the fractal dimension based on the number of the multiple third concentration values, the number of third concentration values ​​included in each concentration sub-range, the maximum concentration value, and the minimum concentration value of the multiple third concentration values ​​to obtain a first fractal dimension; calculating the chaos index based on the first fractal dimension to obtain a third index; and determining the first index based on the third index.

[0080] The concentration interval can be a concentration range defined by the maximum and minimum values ​​among multiple third concentration values. It can be used to provide a basic boundary for dividing concentration sub-intervals, ensuring that all concentration values ​​are covered without omission. For example, the concentration interval is determined by iterating through multiple third concentration values ​​to find their maximum and minimum values; the difference between these two values ​​constitutes the span of the concentration interval. In a specific embodiment, the concentration interval and concentration sub-intervals work together to discretize the concentration value distribution, facilitating subsequent fractal dimension calculations.

[0081] Concentration sub-intervals can be achieved by dividing a concentration interval into several continuous sub-ranges at equal intervals. This is used to statistically analyze the concentration value distribution density and can be used to discretize the concentration value distribution, facilitating subsequent fractal dimension calculations. Determining a concentration interval between multiple third concentration values ​​and dividing this interval into multiple concentration sub-intervals can be achieved by first identifying the maximum and minimum values ​​of the multiple third concentration values ​​to determine the concentration interval, and then dividing it into several sub-intervals. Furthermore, this operation can be performed using an equal-width partitioning method, dividing the concentration interval into N equal sub-intervals, where N is a preset integer.

[0082] The number of third concentration values ​​included in each concentration sub-interval can be the actual number of third concentration values ​​falling within that sub-interval. This number reflects the distribution density of concentration values ​​across different sub-intervals and is a key input parameter for calculating the fractal dimension. In this embodiment, the number of third concentration values ​​included in each concentration sub-interval is obtained by traversing and counting each sub-interval, recording the number of concentration values ​​falling within that sub-interval. Furthermore, this operation can be performed by traversing all third concentration values ​​one by one, determining their respective sub-intervals, and accumulating the corresponding counts; or by using a bucket sort algorithm to pre-allocate array indices corresponding to the sub-intervals and directly counting, thereby obtaining the distribution density of concentration values ​​within each sub-interval and supporting fractal dimension calculation.

[0083] The maximum concentration value among multiple third concentration values ​​can be the largest value appearing in the sequence of multiple third concentration values. It can be used to determine the upper bound of the concentration interval and is an important boundary parameter for constructing the concentration distribution structure. Similarly, the minimum concentration value among multiple third concentration values ​​can be the smallest value appearing in the sequence of multiple third concentration values. It can be used to determine the lower bound of the concentration interval and is also an important boundary parameter for constructing the concentration distribution structure.

[0084] The first fractal dimension can be a quantitative index characterizing the complexity of concentration changes, calculated based on the distribution density of concentration values ​​across concentration sub-intervals. It can be used to reflect the self-similarity and spatial filling characteristics of the concentration change process, and to assess the nonlinear characteristics of fire evolution. In this embodiment, the first fractal dimension is obtained by calculating the fractal dimension based on the number of multiple third concentration values, the number of third concentration values ​​included in each concentration sub-interval, and the maximum and minimum concentration values ​​of the multiple third concentration values. Alternatively, it can be solved by applying a fractal dimension calculation model based on the data distribution after dividing the concentration sub-intervals. Furthermore, this operation can be performed by using box counting to cover the concentration distribution at different scales, counting the required number of boxes, and fitting the slope of a double log-log curve; or by using the information dimension method to calculate the information entropy of the probability distribution of each sub-interval and combining it with the scale change rate to obtain the dimension. This allows for the quantification of the complexity and self-similarity of concentration changes, reflecting the nonlinear dynamic characteristics of the fire development process. For example, the formula for calculating the first fractal dimension can be: D = 2 - (log(number of third concentration values) - log(number of third concentration values ​​included in each concentration sub-interval)) / (log(maximum concentration value) - log(minimum concentration value)), where D is the first fractal dimension.

[0085] The third index can be a quantitative indicator representing the degree of chaos in concentration changes, calculated based on the first fractal dimension. It can be used to quantify the unpredictability of concentration changes and the inherent instability of the system, distinguishing real fires from random fluctuations. The third index is obtained by calculating the chaos index based on the first fractal dimension, which can be achieved by using the first fractal dimension as input and calculating the inherent instability of the system through a chaos metric model. Furthermore, this operation can be performed by calculating the Lyapunov exponent based on the first fractal dimension to reflect the divergence rate of adjacent trajectories; or by combining the first fractal dimension with the reconstructed phase space of the time series to calculate the Kolmogorov entropy, thereby assessing the unpredictability of concentration changes and effectively distinguishing real fires from environmental noise or sensor drift. For example, the formula for calculating the third index can be: C = (D - 1) / (2 - D), where C is the third index and D is the first fractal dimension.

[0086] The first index can be a comprehensive quantitative indicator characterizing the concentration change trend, determined based on the third index. It can be used to determine whether the current alarm possesses true fire characteristics, affecting the final calculation result of the first confidence level. In this embodiment, the first index is converted from the third index into a standardized first index through a mapping function or threshold rule. For example, the first index = 100 × (1 + C).

[0087] In this embodiment, the changes in fire smoke / gas concentration exhibit typical nonlinear, abrupt, and chaotic characteristics (such as the step increase in concentration when smoldering turns into open flame). This application quantifies the complexity of the concentration sequence using fractal dimensions and combines this with the chaos index to identify "irregular fluctuation patterns unique to real fires," effectively distinguishing fire signals from stable interference sources such as cooking fumes and steam.

[0088] Step S304: Determine the second index based on device usage time and historical false alarm rate.

[0089] Among them, the equipment usage time and historical false alarm rate are inversely proportional to the second index.

[0090] The second index can be a quantitative indicator of equipment reliability calculated by combining equipment usage time and historical false alarm rate. It is understood that the second index reflects equipment aging and false alarm tendency, and together with the first index, determines the first confidence level, achieving dynamic weighting of equipment performance. In this embodiment, the second index can be described in context as using an inverse proportional function or normalization processing to convert equipment usage time and historical false alarm rate into negative contributions to the confidence level. For example, the second index can be in formula form, where equipment usage time and historical false alarm rate are weighted by an adjustment coefficient and the reciprocal is taken, or it can use a piecewise function, significantly decreasing when equipment usage time exceeds a threshold or the false alarm rate is higher than a threshold.

[0091] The second index is determined based on equipment usage time and historical false alarm rate. This can be achieved by mapping equipment usage time and historical false alarm rate to a negative evaluation index of equipment reliability. Further, this operation can be implemented using the formula: Second Index = 1 divided by (e multiplied by equipment usage time plus g multiplied by historical false alarm rate), where e and g are adjustment coefficients. Alternatively, a piecewise function can be used, where the second index significantly decreases when equipment usage time exceeds a threshold or the false alarm rate exceeds a threshold. This achieves the technical effect of dynamically adjusting equipment reliability and suppressing the alarm weight of aging or unstable equipment. Optionally, this operation can also be implemented using the formula: Second Index = 100 - (Historical False Alarm Rate × c) - (Equipment Usage Time × d), where c and d are also adjustment coefficients.

[0092] Step S305: Determine the first confidence level based on the first index and the second index.

[0093] The first confidence level can be generated through a weighted combination of a first index and a second index, or a nonlinear fusion function, depending on the context. The weights can be configured to adapt to different scenarios. Furthermore, the first confidence level can be calculated collaboratively with the first and second indices for subsequent alarm priority determination. For example, the first confidence level can use a weighted average model, where the first confidence level equals w1 multiplied by the first index plus w2 multiplied by the second index, where the sum of w1 and w2 equals 1; or a product model, where the first confidence level equals the first index multiplied by the second index, emphasizing the satisfaction of dual conditions.

[0094] The first confidence level is determined based on the first and second indices. This can be achieved by combining the two indices according to a preset rule to output a confidence value representing the overall stability of the equipment. Furthermore, this operation can be implemented using a weighted average or product model, thereby achieving the technical effect of comprehensively considering dynamic trends and equipment reliability to generate more accurate confidence assessment results.

[0095] In this embodiment, the first index reflects the dynamic trend of a fire (such as whether the concentration continues to rise) and is used to determine whether the current alarm has the characteristics of a real fire. The second index, combined with the aging degree of the equipment (usage time) and reliability history (false alarm rate), inversely influences the confidence level, thereby suppressing the alarm weight of old or false alarm-prone equipment. The first confidence level is calculated by combining these two indices, enabling the system to dynamically assess the reliability of the current alarm and avoid the propagation of false alarms due to equipment performance degradation or environmental interference. This mechanism enhances the intelligent decision-making capability of the alarm system, ensuring that in multi-alarm linkage scenarios, high-reliability equipment takes the lead in alarming, thereby outputting accurate location information, helping users clarify the escape direction, and significantly improving overall escape efficiency.

[0096] Step S306: Determine a first score based on a first time point, a first concentration value, and a first confidence level; and determine a second score based on a second concentration value, a second time point, and a second confidence level.

[0097] Understandably, the method for determining the second confidence level is the same as that for determining the first confidence level, and the subject that generates the second confidence level is the second alarm device, which will not be elaborated here.

[0098] Step S307: In response to the first score being greater than the second score, an alarm is triggered based on the first alarm information.

[0099] Step S308: In response to the first score being less than the second score, an alarm is triggered based on the second alarm information.

[0100] Example 3: The alarm control method will be described in detail below based on the determination details of the first score.

[0101] Please see Figure 4 , Figure 4 This is a flowchart illustrating another alarm control method provided in an embodiment of this application. The method is applied to the aforementioned first alarm, such as... Figure 4 As shown, the method includes the following steps.

[0102] Step S401: In response to the first alarm detecting that the first concentration value of smoke is greater than the first preset concentration value at a first moment, a second concentration value and a second moment are obtained.

[0103] Step S402: Determine the first difference between the first time and the third time. The third time is the moment when the concentration value of the first alarm is greater than the third preset concentration value before the first time.

[0104] Step S403: Based on the first difference, the first concentration value, and the first confidence level, input the data into the chaotic system model to perform fire simulation and determine the first score.

[0105] The third moment can be the point in time before the first moment when the concentration value of the first alarm first exceeds a third preset concentration value. This can be used to provide an early warning reference point during the fire's development and to calculate the rate of change before and after the fire occurs. For example, the third moment can be obtained by tracing back the historical data records of the first alarm to pinpoint the point in time when the concentration first exceeds the third preset concentration value.

[0106] The first difference can be the time interval between the first moment and the third moment. It can be used to characterize the speed at which a fire develops from an early warning to the current triggering stage, and to assess the fire spread trend. The smaller the value, the faster the fire develops.

[0107] The third preset concentration value can be a set concentration threshold for the first alarm to enter the warning state. Being lower than the first preset concentration value, it can be used as a criterion for determining early signs of fire and for identifying the location at the third moment. In an exemplary embodiment, the third preset concentration value can be pre-stored in the first alarm control program and can be adjusted through a configuration interface or remote settings. For example, the third preset concentration value can be a safety threshold lower than the first preset concentration value, used to distinguish between early warning and formal alarm states.

[0108] A chaotic system model can be a mathematical model used to simulate the behavior of nonlinear dynamic systems. It can characterize the complex evolutionary characteristics of fire spread and can be used to transform multidimensional input parameters (first difference, first concentration value, first confidence level) into predictive scores, improving the intelligence of the scoring mechanism. In this embodiment, the chaotic system model can be constructed based on the Lorentz equation, logistic mapping, or other chaotic dynamic equations, and output simulation results through numerical integration or iterative calculation. Furthermore, the chaotic system model can receive the first difference, first concentration value, and first confidence level as input parameters and output a first score; it can also work with the first score to complete priority judgment. For example, the chaotic system model can adopt a two-dimensional discrete chaotic model based on Henon mapping, where parameters a and b control the nonlinear intensity and stability, respectively, and are dynamically adjusted with the first confidence level.

[0109] The first score can be a numerical value representing the alarm priority of the first alarm device, derived from a combination of the first difference, the first concentration value, the first confidence level, and the chaotic system simulation results. It can be compared with the second score to determine whether the first alarm device should trigger the alarm. In this embodiment, the first score can be generated by nonlinearly mapping the input parameters using a chaotic system model, and its value depends on changes in the input parameters. Furthermore, the first score can be a normalized value, limited to a range of 0 to 100, facilitating cross-device comparison.

[0110] In this embodiment, by introducing a third moment as a historical state reference point and calculating the time difference, combined with the current concentration value and equipment reliability parameters, multi-dimensional information is input into a chaotic system model for nonlinear mapping, thereby more realistically reflecting the severity and urgency of the fire. This scoring mechanism not only considers the current detection data but also integrates historical change rates and equipment reliability, making the scoring results more predictive and discriminative. Compared to scoring methods based solely on thresholds or simple weighting, the scoring results obtained based on this application implicitly contain the physical laws of the fire development stages, avoiding the "slow temperature rise misjudged as a fire" or "delayed response to sudden fires" caused by linear weighting, and can more accurately identify the fire sources that truly require priority alarm.

[0111] Optionally, in one embodiment, a fire simulation is performed in a chaotic system model based on a first difference, a first concentration value, and a first confidence level to determine a first score. This includes: mapping the first difference to an initial parameter corresponding to a first variable in the chaotic system model, where the first variable is used for the reaction space dimension; mapping the first concentration value to an initial parameter corresponding to a second variable in the chaotic system model, where the second variable is used for the reaction time dimension; mapping the first confidence level to a control parameter in the chaotic system model; performing multiple chaotic iterations based on the initial parameter corresponding to the first variable, the initial parameter corresponding to the second variable, and the control parameter to obtain a result parameter corresponding to the first variable; and normalizing the result parameter corresponding to the first variable to determine the first score.

[0112] The first difference can be the time interval between the first and third moments, reflecting the speed at which a fire develops from early warning to the current triggering stage. It can be used as an initial parameter input for the time dimension variable in a chaotic system model to characterize the rate of fire evolution over time.

[0113] The first concentration value can be the fire-related parameter value detected by the sensor in the first alarm at the first moment, reflecting the current fire intensity. It can be used as the initial parameter input for the spatial dimension variable in a chaotic system model to characterize the spatial spread trend of the fire.

[0114] The first confidence level can be a quantitative indicator characterizing the stability and data reliability of the first alarm device. It can be used as a control parameter input in a chaotic system model to adjust the system's stability and sensitivity, ensuring that high-confidence devices have a higher alarm weight. In one specific embodiment, the first confidence level is dynamically updated based on historical operating data, self-test results, or external calibration feedback, and may include factors such as failure rate and signal drift. Furthermore, the first confidence level can work in conjunction with control parameters to adjust the system's behavioral characteristics. For example, the first confidence level may include, but is not limited to, one or more of the following: control parameters that adjust the system's convergence speed, control parameters that adjust the system's sensitivity, and control parameters that balance the system's periodicity and randomness.

[0115] The first variable can be a variable in a chaotic system model used to reflect the spatial dimension, and its initial parameters are mapped from the first concentration value. It can be used to characterize the spatial spread trend of a fire, and its trajectory reflects the spatial propagation characteristics.

[0116] The second variable can be a variable used in the chaotic system model to reflect the time dimension, and its initial parameters are mapped from the first difference. It can be used to characterize the rate of fire evolution over time, and its trajectory reflects the characteristics of time evolution.

[0117] Control parameters can be parameters used to adjust the overall behavioral characteristics of a chaotic system model, and their values ​​are mapped from the first confidence level. They can be used to control the degree of chaos and stability of the system, giving greater weight to the scores corresponding to high-confidence devices. In this embodiment, the control parameters may include, but are not limited to, one or more of the following: control parameters for adjusting the system's convergence speed, control parameters for adjusting the system's sensitivity, and control parameters for adjusting the balance between the system's periodicity and randomness.

[0118] Initial parameters can be the initial values ​​set for each variable in a chaotic system model before the iteration begins. They can be used to determine the initial state of the chaotic system, affecting the trajectory of subsequent iterations and the final result.

[0119] The resulting parameter can be the final output value of the state variable corresponding to the first variable after multiple chaotic iterations. It can be used as a comprehensive quantitative indicator to reflect the development trend of a fire and to generate the first score. In this embodiment, the resulting parameter is obtained through iterative calculation, and is usually the average value of the last iteration or the stable interval.

[0120] Mapping the first difference to the initial parameter corresponding to the first variable in the chaotic system model can be achieved by converting the first difference into a value suitable for the input range of the chaotic system model using linear or nonlinear functions. Furthermore, mapping the first difference to the initial parameter corresponding to the first variable in the chaotic system model can be achieved by using piecewise linear mapping to scale the time difference proportionally to the [0, 1] interval, or by using an exponential function mapping to highlight the rapid development of fire events in a short period. This allows for the accurate conversion of the fire development rate into a spatial dimension variable, enhancing the model's sensitivity to spatial diffusion trends.

[0121] Mapping the first concentration value to the initial parameter corresponding to the second variable in the chaotic system model can be achieved by converting the concentration value into an acceptable initial value for the model through normalization or standardization functions. Furthermore, mapping the first concentration value to the initial parameter corresponding to the second variable in the chaotic system model can be achieved by using a min-max normalization method to map the concentration value to the [0, 1] interval, or by using a logarithmic transformation to compress the influence of high concentration values, avoiding model saturation. This allows for a reasonable transformation of the current fire intensity into a time-dimensional variable, improving the model's responsiveness to temporal evolution.

[0122] Optionally, the initial parameter x0 of the first variable is equal to the first concentration value / 10.0. For example, if the first concentration value is 2.5 ppm, the initial parameter of the first variable is 0.25. The initial parameter y0 of the second variable is equal to the first difference value / 100.0. For example, if the first difference value is 5 seconds, the initial parameter of the second variable is 0.05.

[0123] Mapping the first confidence level to control parameters in a chaotic system model can be achieved by mapping the confidence level value to key parameters controlling the behavior of the chaotic system, such as the core parameters a and b in the Henon mapping. Optionally, a = 1.4 + (first confidence level / 200.0) (the higher the first confidence level, the larger a; the higher the equipment reliability, the greater the chaos intensity, and the more sensitive it is to reflect fire mutations); b = 0.3 + (first confidence level / 200.0) (the higher the first confidence level, the larger b; the stronger the chaotic stability, suppressing false chaotic responses caused by false alarms).

[0124] By performing multiple chaotic iterations based on the initial parameters corresponding to the first variable, the initial parameters corresponding to the second variable, and the control parameters, the resulting parameters corresponding to the first variable are obtained. This can be achieved by initializing the chaotic system with mapped parameters and performing iterative calculations for a fixed number of steps or until a stable state is reached. Furthermore, obtaining the resulting parameters corresponding to the first variable by performing multiple chaotic iterations based on the initial parameters corresponding to the first variable, the initial parameters corresponding to the second variable, and the control parameters can be achieved through numerical integration using the Euler method or the Runge-Kutta method, updating the state variables at each step, or by using pre-compiled chaotic model library functions that automatically execute iterations and return results after parameters are passed in. This allows for the simulation of the fire development process through nonlinear dynamics, capturing its inherent complexity and uncertainty, and generating discriminative result parameters.

[0125] For example, the formulas for iterating over the first variable x include:

[0126] The formulas for iterating over the second variable y include:

[0127] Where x_next is the value of the first variable x after iteration, y_next is the value of the second variable y after iteration, and a and b are the aforementioned control parameters.

[0128] Normalizing the result parameters corresponding to the first variable to determine the first score can be achieved by mapping the result parameters to a preset scoring range to make them comparable. Furthermore, normalizing the result parameters corresponding to the first variable to determine the first score can be achieved by using the Z-score standardization method to convert the result parameters into a distribution with a mean of 0 and a standard deviation of 1, or by using the sigmoid function to compress the result parameters to the [0, 1] interval and then linearly expanding them to the target scoring range. This transforms the chaotic simulation results into a unified scoring scale, supporting objective comparison and priority ranking among multiple alarms.

[0129] As can be seen from the aforementioned iterative formula, the chaotic system model adopted in this application is constructed based on the Henon map, a discrete mathematical model capable of simulating chaotic behavior in nonlinear dynamic systems. In fire scenarios, the development process of a fire exhibits typical nonlinearity, initial value sensitivity, and multivariate coupling characteristics, making it difficult for traditional linear weighted methods to accurately capture its abrupt changes and critical states.

[0130] Therefore, this application maps concentration values ​​to spatial dimension variables x, time differences to time dimension variables y, and equipment confidence levels to control parameters a and b. This allows the system to simulate the dynamic feedback between fire intensity and spread rate during the iterative process. Parameter a controls the nonlinearity of the system, while parameter b controls the coupling degree between state variables. Both parameters adjust with confidence levels, making the model for high-reliability equipment more sensitive and discriminative.

[0131] During the iteration process, the evolution of system states x and y simulates the fire's spread path in the environment. In the aforementioned iterative formula for x, the constant term "1" represents the theoretical maximum carrying capacity of the environment; the nonlinear term " "This simulates the self-reinforcing and saturation effects of fire intensity. The negative sign indicates a self-inhibiting effect (e.g., oxygen consumption, fuel reduction), the squared term reflects nonlinear saturation (e.g., the greater the fire intensity, the greater the resistance to growth), and the parameter 'a' controls the saturation rate; the memory term '+y' represents the cumulative contribution of the historical development process. Regarding the iterative formula for 'y' mentioned above,..." "and" The coupling of "" simulates the feedback and delay effect of the time dimension on spatial trends; in other words, it "memorizes" the current spatial state into the time dimension, with parameter b controlling the strength of memory retention. The iterative process reflects the development of the fire, and the final states of x and y reflect the "chaotic intensity" of the fire, which is a comprehensive measure of the uncertainty, suddenness, and spread trend of the fire's development. This index, after normalization, is transformed into a first score, which can more realistically reflect the urgency and severity of the fire. This not only improves the accuracy of the alarm system in determining alarm information but also enhances the alarm system's joint alarm capability.

[0132] In this embodiment, by mapping the first concentration value to the initial parameter corresponding to the first variable in the chaotic system model, mapping the first difference to the initial parameter corresponding to the second variable, and mapping the first confidence level to the control parameter, multiple chaotic iterations are performed to obtain the result parameter corresponding to the first variable. The result parameter is then normalized to determine the first score. This achieves the technical effect of simulating the fire development process through nonlinear dynamics, making the score simultaneously reflect the "fire spatial spread trend" and "time urgency", generating result parameters with high discrimination, and converting them into a unified scoring scale to support objective comparison and priority ranking among multiple alarms.

[0133] Step S404: Determine the second score based on the second concentration value, the second time point, and the second confidence level.

[0134] The method for determining the second score is the same as that for the first score. Specifically, it may include: determining a second difference between the second and fourth moments, where the fourth moment is the time before the second moment when the concentration value of the second alarm is greater than a fifth preset concentration value, and the fifth preset concentration value is less than the second preset concentration value; inputting the second difference, the second concentration value, and the second confidence level into a chaotic system model for fire simulation to determine the second score. It is understandable that if the concentration value of the second alarm at the first moment is less than the fifth preset concentration value, then the aforementioned fourth moment can be replaced by the third moment.

[0135] Step S405: In response to the first score being greater than the second score, an alarm is triggered based on the first alarm information.

[0136] Step S406: In response to the first score being less than the second score, an alarm is triggered based on the second alarm information.

[0137] Optionally, after receiving the second alarm information from the second alarm device, the method further includes: in response to not receiving the second alarm information within a second time period, sending first information to the second alarm device, the first information being used to determine whether the second alarm device has triggered an alarm; receiving the second information from the second alarm device; in response to determining, based on the second information, that the second alarm device has not triggered an alarm, recording a first event and triggering an alarm based on the first alarm information, the first event being used to characterize the possibility of a false alarm; in response to determining, based on the second information, that the second alarm device has triggered an alarm, sending third information to the second alarm device, the third information being used to determine the alarm information; receiving the third alarm information from the second alarm device and triggering an alarm based on the third alarm information.

[0138] The second time period can be a preset time window used to determine whether the second alarm responds to the request of the first alarm within a reasonable time. In this embodiment, the second time period can serve as a communication latency tolerance threshold to avoid misjudging equipment failure or failure to trigger an alarm due to brief network fluctuations or processing delays. Furthermore, the second time period can be pre-configured in the first alarm control program and can be dynamically adjusted according to the network environment and system response requirements. For example, the second time period can include, but is not limited to, one or more of 5 seconds, 10 seconds, or 30 seconds to adapt to different communication protocols and deployment scenarios.

[0139] The first information can be a query command sent from the first alarm to the second alarm to confirm whether the second alarm has been triggered. In this embodiment, the first information can be used to initiate a secondary confirmation process to verify the actual status of the second alarm and prevent false alarms from spreading. Furthermore, the first information can work in conjunction with the second information to complete a status verification process; and work in conjunction with the second time period to determine the response timeliness. In a specific embodiment, the first information can be a data frame with a specific command code, sent via TCP / IP or Modbus protocols to ensure that the command is correctly identified and processed.

[0140] The second information can be the response data of the second alarm device to the first information, including its current alarm status (triggered or not triggered). In this embodiment, the second information can provide feedback on the actual status of the second alarm device, supporting the first alarm device in making subsequent decisions. Furthermore, the second information can be generated and returned by the second alarm device based on its own status after receiving the first information. For example, the second information can be a status response packet in JSON format or binary status bytes, transmitted via TCP / IP or Modbus protocols.

[0141] If the calculated first score is lower than the second score, and the first alarm triggers but the second alarm does not, it's possible that the first alarm is a false alarm. In this case, recording this situation as a first event can prevent the spread of false alarms, maintain the accuracy of the alarm system, and avoid misleading users. This can be achieved by writing the first event to a local log file with a timestamp and alarm number, or by reporting the first event to a cloud platform to trigger a remote alarm.

[0142] In response to the determination that the second alarm has been triggered based on the second information, the third information is sent to the second alarm. This can be achieved by sending a request command to obtain complete alarm content after confirming that the second alarm has been triggered. Furthermore, this operation can be performed by sending a request frame with a specific command code to request detailed alarm data; or by calling the alarm information interface of the second alarm through a communication interface (API). This ensures the integrity of the alarm information and avoids user misunderstanding due to missing information.

[0143] The third alarm information can be a complete alarm message, including location information, sent by the second alarm device to the first alarm device after confirming that the alarm has been triggered. In this embodiment, the third alarm information can provide verified and authentic alarm information for the first alarm device to play to guide the user's escape. Furthermore, the third alarm information can be one or more of the following, including but not limited to structured text alarm information, speech-synthesized data packets, and timestamped location coordinate data, to adapt to different alarm output methods.

[0144] In this embodiment, by introducing a secondary confirmation mechanism, after the main alarm receives the alarm information from the secondary alarm, if no valid feedback is received within a preset time window, it proactively initiates a status check, sending a first message to confirm whether the secondary alarm has actually triggered an alarm. By receiving the second message and judging its status, the system can distinguish between false alarms and real alarms. If it is determined to be a false alarm, the first event is recorded for subsequent system optimization or troubleshooting, and the alarm continues to be executed based on its own alarm information, avoiding unnecessary panic or misleading due to false alarms. If it is confirmed to be a real alarm, a third message is further sent to obtain more complete third alarm information, ensuring the accuracy and completeness of the alarm content. This process enhances the robustness and reliability of the alarm system, preventing the spread of false alarms caused by communication delays, equipment failures, or false triggering, thereby ensuring that the user receives verified real fire location information. Combined with the pre-scoring mechanism, this scheme achieves dual protection of "priority + authenticity," significantly improving the credibility of alarm information and the accuracy of user escape decisions, ultimately achieving the technical effect of improving escape efficiency.

[0145] Furthermore, the method for determining alarm information is explained in detail below: In one embodiment, the method further includes: acquiring an audio database, the audio database including a prompt tone library, an event type library, a spatial location library, and a command action library, the prompt tone library including general alarm audio, the event type library including descriptive audio of hazard sources, the spatial location library including audio of alarm location information, and the command action library including command audio for guiding user actions; matching a first audio from the prompt tone library according to a first score, the larger the first score, the more urgent the fire situation reflected by the target prompt tone; matching a second audio and a third audio from the event type library and the command action library according to the sensor type; determining a first alarm information based on the first audio, the second audio, the third audio, and a fourth audio, the fourth audio being derived from the spatial location library.

[0146] The audio database can be a structured collection storing various pre-recorded audio modules, used for dynamically combining and generating alarm voice information. It is understood that the audio database can be organized and stored according to library type by importing pre-recorded and categorized audio files through system initialization or a backend management platform. In this embodiment, the audio database serves as a container for prompt tone libraries, event type libraries, spatial location libraries, and command action libraries, providing a unified access interface for each sub-library and supporting on-demand access to audio from multiple modules. For example, the audio database may include one or more of the following: an audio database categorized based on fire scenarios, an audio database customized based on building types, or an audio database supporting multiple languages. Obtaining the audio database can be achieved by reading pre-installed audio library files from local storage media. Furthermore, obtaining the audio database can be achieved by downloading the latest version of the audio database from a central server via a network protocol, thereby providing complete resource support for subsequent audio module matching and ensuring the availability and consistency of alarm voice content.

[0147] The alert sound library can be an audio collection containing general alarm alert sounds corresponding to different urgency levels, used to reflect the urgency of the fire situation. It is understood that the alert sound library can be extracted from an audio database and contains multiple graded audio segments (such as low, medium, high, and extremely high urgency levels). In this embodiment, the alert sound library is linked to a first score, with the score determining which level of alert sound to play. For example, the alert sound library may include one or more of standard alarm sounds, looping buzzers, and gradually increasing warning sounds. The first audio can be an alert sound reflecting the urgency of the fire, selected based on the first score, used to intuitively convey the current urgency level of the fire and assist the user in judging the risk level. It is understood that the first audio can be dynamically selected by mapping the score value to specific audio entries in the alert sound library. In this embodiment, the first audio may include, but is not limited to, one or more of low urgency alert sounds, medium urgency alert sounds, and high urgency alert sounds. Matching the first audio from the alert sound library according to the first score can be achieved by mapping the first score value to the graded system of the alert sound library and selecting the alert sound corresponding to the urgency level.

[0148] The event type library can be a collection storing hazard description audio for different sensor types. It is used to output a fire cause description based on the sensor type that triggered the alarm, improving the professionalism and understandability of the information. The event type library retrieves matching hazard description audio from the audio database based on the sensor type index. In this embodiment, the event type library is associated with the sensor type; when a smoke sensor is detected, it automatically matches audio related to "smoke." For example, the event type library may include one or more of the following: a smoke-related event audio library, a carbon monoxide-related event audio library, and a high-temperature-related event audio library. The second audio can be a hazard description audio selected based on the sensor type, used to explain the specific cause of the fire, enhancing the user's accurate understanding of the risk. The second audio can be queried from the event type library based on the sensor type identifier, returning the corresponding audio content.

[0149] The command action library can be a collection of command-lined voice content stored to guide users in escaping or responding to emergencies. It provides targeted action guidance based on sensor type and fire scenario, improving user response efficiency. Understandably, the command action library selects matching command audio from an audio database based on sensor type and preset logical rules. In this embodiment, the command action library collaborates with an event type library to form complete emergency response voice content. For example, the command action library may include one or more of evacuation command voice libraries, evacuation action voice libraries, and help call voice libraries. The third audio can be user action guidance audio selected based on sensor type matching, providing clear escape or response operation guidance and reducing user decision-making delays. Understandably, the third audio can combine sensor type and preset strategies to select the most suitable command voice from the command action library.

[0150] The spatial location database can be a collection of voice expressions containing the location information of the alarm, used to accurately announce the location of the fire. It is understood that the spatial location database retrieves corresponding voice segments from an audio database based on the registered geographical location or building coordinates of the alarm. In this embodiment, the spatial location database is bound to the location information field in the first alarm information, providing voice-based expression support. For example, the spatial location database may include one or more of the following: a floor number voice database, a room number voice database, a relative orientation description voice database, etc. The fourth audio can be a voice expression of the alarm's location information from the spatial location database, used to accurately inform the user of the specific location of the fire, supporting the user's location of the hazard. It is understood that the fourth audio can retrieve corresponding voice segments from the spatial location database based on the location code registered by the alarm. In this embodiment, the fourth audio may include, but is not limited to, one or more of the following: absolute coordinate location voice, relative direction location voice, landmark reference location voice, etc.

[0151] Determining the first alarm information based on the first, second, third, and fourth audio signals can be achieved by combining the four audio modules in a preset order or logically to generate a complete alarm voice message. Furthermore, determining the first alarm information based on the first, second, third, and fourth audio signals can be achieved by concatenating the audio streams in the order of "event description → location information → command action" and then superimposing a "prompt tone." This achieves the technical effect of forming a structured and semantically clear alarm voice message, improving user comprehension efficiency and response accuracy.

[0152] In this embodiment, by constructing a structured audio database, alarm information is decomposed into multiple combinable audio modules (prompt tone, event type, spatial location, and instruction action), achieving dynamic splicing and semantic clarity of alarm content. Specifically, the first score serves as an urgency indicator, used to select audio corresponding to the level of urgency from the prompt tone library, allowing users to intuitively perceive the severity of the fire; the sensor type is used to match specific hazard descriptions and escape instructions, enhancing the professionalism and relevance of the information; and the spatial location library provides precise geographical location voice broadcasts, ensuring users clearly identify the fire's location. This solution transforms the originally mixed multi-source alarm information into structured, hierarchical voice output, avoiding the information redundancy and confusion caused by traditional independent alarms. Simultaneously, standardized audio modules enable rapid response and a unified broadcast style. Upon hearing the alarm, users can not only identify the fire's location but also understand the current risk level and the actions to be taken. Even in unfamiliar environments, they can determine safe routes based on non-alarm areas, significantly improving escape efficiency and decision-making accuracy.

[0153] For embodiments consistent with those shown above, please refer to... Figure 5 , Figure 5 This is a functional unit block diagram of an alarm control device provided in an embodiment of this application. The alarm control device is the first alarm or a part of the first alarm, such as... Figure 5 As shown, the alarm control device 50 includes: The communication unit 501 is used to respond to the first alarm detecting that the first concentration value of smoke is greater than the first preset concentration value at a first moment, to obtain a second concentration value and a second moment, wherein the second concentration value is the concentration value of smoke detected by the second alarm at the first moment, and the second moment is the moment when the concentration value of smoke detected by the second alarm is greater than the second preset concentration value, and the second alarm is an alarm that is different from the first alarm among a plurality of alarms. Processing unit 502 is configured to determine a first score based on a first time point and a first concentration value, and to determine a second score based on a second concentration value and a second time point; Processing unit 502 is used to issue an alarm based on the first alarm information in response to the first score being greater than the second score; The processing unit 502 is also configured to, in response to the first score being less than the second score, issue an alarm based on second alarm information, the second alarm information being from a second alarm device and including the location information of the second alarm device.

[0154] In one feasible embodiment, in determining the first score based on the first time point and the first concentration value, the processing unit 502 is specifically configured to: The first score is determined based on the first moment, the first concentration value, and the first confidence level. The first confidence level is used to characterize the device stability of the first alarm.

[0155] In one feasible embodiment, the processing unit 502 is further configured to: Acquire the device usage time, historical false alarm rate, and multiple third concentration values ​​of the first alarm device within the first time period before the first moment; A first index is determined based on multiple third concentration values, and the first index is used to characterize the changes in concentration values. The second index is determined based on equipment usage time and historical false alarm rate. Equipment usage time and historical false alarm rate are inversely proportional to the second index. The first confidence level is determined based on the first index and the second index.

[0156] In one feasible embodiment, in determining the first index based on a plurality of third concentration values, the processing unit 502 is specifically configured to: Determine the concentration range between multiple third concentration values, and divide the concentration range into multiple concentration sub-ranges; Determine the number of third concentration values ​​included in each of the multiple concentration sub-intervals; The first fractal dimension is obtained by calculating the fractal dimension based on the number of multiple third concentration values, the number of third concentration values ​​included in each concentration sub-interval, and the maximum and minimum concentration values ​​of multiple third concentration values. The third index is obtained by calculating the chaos index based on the first fractal dimension. The first index is determined based on the third index.

[0157] In one feasible embodiment, in determining the first score based on the first time point, the first concentration value, and the first confidence level, the processing unit 502 is specifically configured to: Determine the first difference between the first moment and the third moment. The third moment is the time when the concentration value of the first alarm is greater than the third preset concentration value before the first moment, and the third preset concentration value is less than the first preset concentration value. Fire simulation is performed in the chaotic system model based on the first difference, the first concentration value, and the first confidence level, and the first score is determined.

[0158] In one feasible embodiment, in determining a first score by performing fire simulation based on a first difference, a first concentration value, and a first confidence level input into a chaotic system model, the processing unit 502 is specifically used for: The first concentration value is mapped to the initial parameter corresponding to the first variable in the chaotic system model. The first variable is used for the reaction space dimension. The first difference is mapped to the initial parameters corresponding to the second variable in the chaotic system model, and the second variable is used to react to the time dimension. Map the first confidence level to the control parameters in the chaotic system model; Based on the initial parameters corresponding to the first variable, the initial parameters corresponding to the second variable, and the control parameters, perform multiple chaotic iterations to obtain the result parameters corresponding to the first variable; The result parameters corresponding to the first variable are normalized to determine the first score.

[0159] In one feasible embodiment, after receiving the second alarm information from the second alarm device, the communication unit 501 is further configured to: In response to the fact that no second alarm information is received within the second time period, a first message is sent to the second alarm device, the first message being used to determine whether the second alarm device has triggered an alarm. Receive second information from the second alarm; The processing unit 502 is further configured to: in response to determining, based on the second information, that the second alarm has not triggered an alarm, record a first event and trigger an alarm based on the first alarm information, wherein the first event is used to characterize the possible false alarm situation; The communication unit 501 is also configured to: in response to determining that the second alarm has triggered an alarm based on the second information, send third information to the second alarm, wherein the third information is used to determine the alarm information; Receive a third alarm message from the second alarm device; The processing unit 502 is also used to: issue an alarm based on the third alarm information.

[0160] In one feasible embodiment, the processing unit 502 is further configured to: Obtain the audio database, which includes a prompt tone library, an event type library, a spatial location library, and a command action library. The prompt tone library includes general alarm audio, the event type library includes audio describing the hazard source, the spatial location library includes audio containing the location information of the alarm, and the command action library includes audio instructions used to guide user actions. The first audio is matched from the prompt sound library based on the first score. The higher the first score, the more urgent the fire situation is to be reflected by the target prompt sound. Match the second and third audio frequencies from the event type library and the command action library based on the sensor type; The first alarm information is determined based on the first, second, third, and fourth audio frequencies, with the fourth audio frequency originating from a spatial location database.

[0161] It is understood that since the method embodiments and the device embodiments are different presentations of the same technical concept, the content of the method embodiment section in this application should be adapted to the device embodiment section in a synchronous manner, and will not be repeated here.

[0162] When using integrated units, such as Figure 6 As shown, Figure 6 This is a block diagram of the functional units of another alarm control device provided in an embodiment of this application. Figure 6 In this document, the alarm control device 50 includes a processing module 612 and a communication module 611. The processing module 612 controls and manages the actions of the alarm control device 50, such as the steps of the processing unit 502, and / or performs other processes according to the techniques described herein. The communication module 611 supports interaction between the alarm control device 50 and other devices, such as the steps of the communication unit 501. Figure 6 As shown, the alarm control device 50 may also include a storage module 613, which is used to store the program code and data of the alarm control device 50.

[0163] The processing module 612 can be a processor or controller, such as a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication module 611 can be a transceiver, RF circuitry, or a communication interface, etc. The storage module 613 can be a memory.

[0164] All relevant content for each scenario involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here. The above-mentioned alarm control device 50 can all perform the above-mentioned... Figures 2 to 4 The alarm control method shown.

[0165] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0166] Figure 7 This is a structural block diagram of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device 700 may include one or more of the following components: processor 701, memory 702 and communication interface 703. The processor 701, memory 702 and communication interface 703 are interconnected and perform communication between them. The memory 702 may store one or more computer programs. The one or more computer programs may be configured to implement the methods described in the above embodiments when executed by one or more processors 701.

[0167] Processor 701 may include one or more processing cores. Processor 701 connects to various parts within the electronic device 700 using various interfaces and lines, and performs various functions and processes data of the electronic device 700 by running or executing instructions, programs, code sets, or instruction sets stored in memory 702, and by calling data stored in memory 702. Optionally, processor 701 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 701 may integrate one or more of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. It is understood that the aforementioned modem may also not be integrated into processor 701, but may be implemented separately through a communication chip.

[0168] The memory 702 may include random access memory (RAM) or read-only memory (ROM). The memory 702 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 702 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described above. The data storage area may also store data created by the electronic device 700 during use.

[0169] It is understood that the electronic device 700 may include more or fewer structural elements than those shown in the above block diagram, such as a power module, physical buttons, WiFi (Wireless Fidelity) module, speaker, Bluetooth module, sensor, etc., without limitation.

[0170] The aforementioned electronic device 700 may be a first alarm or a part of a first alarm.

[0171] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements some or all of the steps of any of the alarm control methods described in the above method embodiments.

[0172] This application also provides a computer program product, including a computer program that, when executed by a processor, implements some or all of the steps of any of the alarm control methods described in the above method embodiments. This computer program product can be a software installation package.

[0173] It should be noted that, for the sake of simplicity, all of the aforementioned alarm control method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0174] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce a good effect.

[0175] Those skilled in the art will understand that all or part of the steps in the various method embodiments of any of the above-described alarm control methods can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk, or an optical disk, etc.

[0176] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of an alarm control method, device, electronic device, and storage medium of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, based on the ideas of an alarm control method, device, electronic device, and storage medium of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

[0177] This application is described with reference to flowchart illustrations and / or block diagrams of methods, hardware products, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0178] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0179] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0180] It is understood that any product that is controlled or configured to perform the processing method described in the flowchart of the method embodiment of the alarm control method of this application, such as the terminal and computer program product of the above flowchart, falls within the scope of the related products described in this application.

[0181] Obviously, those skilled in the art can make various modifications and variations to the alarm control method, apparatus, electronic device, and storage medium provided in this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. An alarm control method, characterized in that, The method is applied to a first alarm among a plurality of alarms, wherein the plurality of alarms are connected by communication. The method includes: In response to the first alarm detecting that the first concentration value of smoke is greater than the first preset concentration value at a first moment, a second concentration value and a second moment are obtained. The second concentration value is the concentration value of smoke detected by the second alarm at the first moment, and the second moment is the moment when the concentration value of smoke detected by the second alarm is greater than the second preset concentration value. The second alarm is an alarm that is different from the first alarm among the plurality of alarms. A first score is determined based on the first time point and the first concentration value, and a second score is determined based on the second concentration value and the second time point; In response to the first score being greater than the second score, an alarm is triggered based on the first alarm information, which includes the location information of the first alarm device. In response to the first score being less than the second score, an alarm is triggered based on second alarm information, which comes from the second alarm device and includes the location information of the second alarm device.

2. The method according to claim 1, characterized in that, The step of determining the first score based on the first time point and the first concentration value includes: A first score is determined based on the first time point, the first concentration value, and the first confidence level, wherein the first confidence level is used to characterize the device stability of the first alarm.

3. The method according to claim 2, characterized in that, The method further includes: The device usage time, historical false alarm rate, and multiple third concentration values ​​of the first alarm device within a first time period before the first moment are obtained. A first index is determined based on the plurality of third concentration values, and the first index is used to characterize the change in concentration values; A second index is determined based on the device usage time and the historical false alarm rate, wherein the device usage time and the historical false alarm rate are inversely proportional to the second index. The first confidence level is determined based on the first index and the second index.

4. The method according to claim 3, characterized in that, The determination of the first index based on the plurality of third concentration values ​​includes: Determine the concentration range between the plurality of third concentration values, and divide the concentration range into a plurality of concentration sub-ranges; Determine the number of the third concentration values ​​included in each of the plurality of concentration sub-intervals; The first fractal dimension is obtained by calculating the fractal dimension based on the number of the plurality of third concentration values, the number of the third concentration values ​​included in each concentration sub-interval, and the maximum and minimum concentration values ​​of the plurality of third concentration values. The third index is obtained by calculating the chaos index based on the first fractal dimension. The first index is determined based on the third index.

5. The method according to claim 2, characterized in that, The step of determining the first score based on the first time point, the first concentration value, and the first confidence level includes: Determine a first difference between the first time point and the third time point, wherein the third time point is the time before the first time point when the concentration value of the first alarm is greater than a third preset concentration value, and the third preset concentration value is less than the first preset concentration value; Based on the first difference, the first concentration value, and the first confidence level, a fire simulation is performed in the chaotic system model to determine the first score.

6. The method according to claim 5, characterized in that, The step of inputting the first difference, the first concentration value, and the first confidence level into the chaotic system model to perform fire simulation and determine the first score includes: The first concentration value is mapped to the initial parameter corresponding to the first variable in the chaotic system model, where the first variable is used to react the spatial dimension. The first difference is mapped to the initial parameter corresponding to the second variable in the chaotic system model, where the second variable is used to react to the time dimension. Map the first confidence level to the control parameters in the chaotic system model; Based on the initial parameters corresponding to the first variable, the initial parameters corresponding to the second variable, and the control parameters, perform multiple chaotic iterations to obtain the result parameters corresponding to the first variable; The result parameters corresponding to the first variable are normalized to determine the first score.

7. The method according to claim 1, characterized in that, After receiving the second alarm information from the second alarm device, the method further includes: In response to the fact that the second alarm information is not received within the second time period, a first message is sent to the second alarm device, the first message being used to determine whether the second alarm device has triggered an alarm. Receive second information from the second alarm; In response to determining, based on the second information, that the second alarm has not triggered an alarm, a first event is recorded, and an alarm is triggered based on the first alarm information. The first event is used to characterize the possibility of a false alarm. In response to determining that the second alarm has triggered an alarm based on the second information, a third information is sent to the second alarm, the third information being used to determine the alarm information; Receive a third alarm message from the second alarm device and trigger an alarm based on the third alarm message.

8. An alarm control device, characterized in that, The device is applied to a first alarm among a plurality of alarms, wherein the plurality of alarms are connected by communication. The device includes: A communication unit is configured to respond to the first alarm detecting a first concentration value of smoke greater than a first preset concentration value at a first moment, and to acquire a second concentration value and a second moment, wherein the second concentration value is the concentration value of smoke detected by the second alarm at the first moment, and the second moment is the moment when the concentration value of smoke detected by the second alarm is greater than the second preset concentration value, and the second alarm is an alarm that is different from the first alarm among the plurality of alarms; The processing unit is configured to determine a first score based on the first time point and the first concentration value, and to determine a second score based on the second concentration value and the second time point; The processing unit is configured to issue an alarm based on the first alarm information in response to the first score being greater than the second score. The processing unit is further configured to, in response to the first score being less than the second score, issue an alarm based on second alarm information, the second alarm information being from the second alarm device and including the location information of the second alarm device.

9. An electronic device, the device comprising a processor, a memory, and executable program code stored in the memory, characterized in that, The processor is configured to retrieve the executable program code stored in the memory to perform the method as described in any one of claims 1-7.

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

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