Fire alarm method and device based on fire risk score

Abstract

The invention discloses a fire alarm method and device based on a fire risk score, and the method comprises the steps: obtaining the first environment data of a detected environment, and the first environment data comprises the smoke concentration; calculating an oscillation coefficient according to the smoke concentration, wherein the oscillation coefficient is used for representing the fluctuation degree of the smoke concentration in the preset time window; determining a first fire risk score of the detected environment according to the first environment data and the oscillation coefficient; and determining whether to execute fire alarm according to the first fire risk score. According to the invention, the accuracy of fire alarm can be improved.

Description

Technical Field

[0001] This invention relates to the field of fire safety alarm devices, and more particularly to a fire alarm method and device based on fire risk scoring. Background Technology

[0002] In the current field of fire alarm devices, the mainstream independent photoelectric smoke detectors operate on the fixed threshold comparison method. These devices require setting a fixed smoke concentration threshold. When the sampled smoke concentration exceeds the preset threshold and the duration exceeds a certain time, the microcontroller determines it as a fire and triggers an alarm. Some high-end products incorporate "temperature compensation," which fine-tunes the smoke threshold when the temperature rises. However, traditional detectors cannot distinguish between transient smoke or moisture in special scenarios (such as cooking or brewing tea) and actual fire smoke, leading to the problem of low accuracy and frequent false alarms in existing independent photoelectric smoke detectors. Summary of the Invention

[0003] To address the aforementioned issues, this application provides a fire alarm method and apparatus based on fire risk scoring. Adopting the solution of this application is beneficial for improving the accuracy of fire alarms.

[0004] In a first aspect, embodiments of this application provide a fire alarm method based on fire risk scoring. The method includes: acquiring first environmental data of the measured environment, the first environmental data including smoke concentration; calculating an oscillation coefficient based on the smoke concentration, the oscillation coefficient being used to characterize the degree of fluctuation of the smoke concentration within a preset time window; determining a first fire risk score of the measured environment based on the first environmental data and the oscillation coefficient; and determining whether to execute a fire alarm based on the first fire risk score.

[0005] As can be seen, in this embodiment of the application, by introducing an oscillation coefficient that characterizes the degree of smoke concentration fluctuation and combining it with basic environmental data to jointly determine the fire risk score, the difference between the real fire scene and environmental interference can be identified more accurately, avoiding the problem of misjudgment that may be caused by relying solely on a single instantaneous concentration value, thereby improving the accuracy and reliability of fire alarms.

[0006] In conjunction with the first aspect, in one possible embodiment, the preset time window includes multiple time nodes, and the smoke concentration includes multiple smoke concentrations corresponding to the multiple time nodes respectively; calculating the oscillation coefficient based on the smoke concentration includes: calculating the average value of the multiple smoke concentrations; calculating the absolute value of the difference between each smoke concentration and the average value in the multiple smoke concentrations respectively and summing them to obtain the oscillation coefficient.

[0007] As can be seen, in this embodiment of the application, by calculating the average value of smoke concentration at multiple time points, and determining the oscillation coefficient based on the sum of the absolute deviations of the concentration value at each point from the average value, the oscillation coefficient can reflect the stability of smoke concentration, which helps to comprehensively judge the fire risk in combination with other environmental data. It can distinguish the concentration fluctuations caused by the temporary interference of cooking fumes, water vapor, etc., from the continuous changes when a real fire occurs, thereby improving the accuracy of fire risk scoring.

[0008] In conjunction with the first aspect, in one possible embodiment, the first environmental data further includes multiple environmental humidity levels corresponding to multiple time points; the method further includes: if the target environmental humidity is greater than a preset humidity threshold, then determining whether the target smoke concentration corresponding to the target environmental humidity is greater than a preset concentration threshold; wherein, the target environmental humidity is one of multiple environmental humidity levels, and the target smoke concentration corresponds to the same time point as the target environmental humidity; if the target smoke concentration is greater than the preset concentration threshold, then determining the corresponding smoke concentration compensation value based on the target environmental humidity; and performing attenuation compensation on the target smoke concentration based on the smoke concentration compensation value.

[0009] As can be seen, in this embodiment of the application, by introducing humidity data to verify and compensate for the smoke concentration, the false alarms of the sensor caused by the high humidity environment are effectively filtered out, and the smoke concentration value is attenuated and corrected, thereby improving the accuracy of the smoke concentration data, providing a more reliable data basis for subsequent fire risk scoring, and further reducing the false alarm rate of the overall system.

[0010] In conjunction with the first aspect, in one possible embodiment, the first environmental data further includes temperature data. Determining a first fire risk score for the measured environment based on the first environmental data and an oscillation coefficient includes: determining a smoke concentration coefficient based on smoke concentration and a first weight; determining smoke growth data based on smoke concentration and a smoke growth coefficient based on the smoke growth data and a second weight; determining a temperature coefficient based on temperature data and a third weight; and determining the first fire risk score for the measured environment based on the smoke concentration coefficient, smoke growth coefficient, temperature coefficient, and oscillation coefficient.

[0011] In conjunction with the first aspect, in one possible embodiment, a first fire risk score of the measured environment is determined based on the smoke concentration coefficient, smoke growth coefficient, temperature coefficient, and oscillation coefficient, including: determining an initial fire risk score of the measured environment based on the smoke concentration coefficient, smoke growth coefficient, and temperature coefficient; and correcting the initial fire risk score based on the oscillation coefficient to obtain the first fire risk score.

[0012] As can be seen, in this embodiment of the application, the alarm device achieves a more refined assessment of fire risk by first fusing core features to obtain an initial score and then using the oscillation coefficient for secondary correction through a cascaded processing method. This incorporates considerations of data volatility and stability, thereby improving the adaptability and accuracy of the score in complex environments.

[0013] In conjunction with the first aspect, in one possible embodiment, the method further includes: correcting a first weight based on an oscillation coefficient, the oscillation coefficient being inversely correlated with the corrected first weight, and the smoke concentration coefficient being determined based on the smoke concentration and the corrected first weight.

[0014] As can be seen from the embodiments of this application, the alarm device realizes the confidence assessment of the core indicator of smoke concentration through the inverse correlation dynamic adjustment mechanism of the oscillation coefficient and the first weight, and gives full play to its role when the environment is stable, thereby improving the robustness and adaptability of the fire risk scoring model in different scenarios, and further optimizing the accuracy and reliability of the fire alarm.

[0015] In conjunction with the first aspect, in one possible embodiment, determining whether to execute a fire alarm based on a first fire risk score includes: executing a fire alarm if the first fire risk score is greater than a first threshold; determining the user reachability status based on first environmental data if the first fire risk score is not greater than the first threshold but greater than a second threshold; wherein the second threshold is less than the first threshold, and the user reachability status indicates whether the user can receive the warning information; and lowering the first threshold if the user reachability status is an unreachable status.

[0016] As can be seen, in this embodiment of the application, by introducing the user reachability status as a decision factor and dynamically adjusting the alarm trigger threshold based on the status, the linkage judgment between risk level and user status is realized. When the user cannot respond, the system's alert level can be automatically increased, ensuring that the alarm can be triggered in a timely or early manner under special circumstances, thereby improving the intelligence level and security capability of the fire alarm system.

[0017] The methods described in the above embodiments demonstrate that by introducing an oscillation coefficient characterizing the degree of smoke concentration fluctuation and combining it with basic environmental data to jointly determine the fire risk score, the accuracy and reliability of fire alarms are improved. Introducing humidity data to verify and compensate for smoke concentration further reduces the overall system's false alarm rate. The cascaded processing method, which first fuses core features to obtain an initial score and then uses the oscillation coefficient for secondary correction, improves the adaptability and accuracy of the score in complex environments. The dynamic adjustment mechanism based on the inverse correlation between the oscillation coefficient and the first weight further optimizes the accuracy and reliability of fire alarms. Introducing user reachability as a decision factor enhances the intelligence level and security capabilities of the fire alarm system.

[0018] Secondly, embodiments of this application provide a fire alarm device based on fire risk scoring, comprising: The acquisition unit is used to acquire first environmental data of the measured environment, including smoke concentration. The calculation unit is used to calculate the oscillation coefficient based on the smoke concentration. The oscillation coefficient is used to characterize the degree of fluctuation of the smoke concentration within a preset time window. The determination unit is used to determine the first fire risk score of the measured environment based on the first environmental data and the oscillation coefficient. The judgment unit is used to determine whether to execute a fire alarm based on the first fire risk score.

[0019] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, a communication interface, and one or more programs, the one or more programs being stored in the memory and configured to be executed by the processor, and one or more instructions being adapted to be loaded by the processor and to execute part or all of the methods of the first aspect and / or the second aspect.

[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform part or all of the methods of the first aspect and / or the second aspect.

[0021] Fifthly, this application provides a computer program product that, when read and executed by a computer, causes the computer to perform part or all of the methods of the first aspect and / or the second aspect.

[0022] It is understood that the beneficial effects of the embodiments of the second to fifth aspects can be referred to the beneficial effects of the method of the first aspect, and will not be repeated here. Attached Figure Description

[0023] 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.

[0024] Figure 1 A schematic diagram illustrating an application scenario of a fire alarm method based on fire risk scoring, provided in an embodiment of this application; Figure 2 A flowchart illustrating a fire alarm method based on fire risk scoring, provided for an embodiment of this application; Figure 3This application provides a schematic diagram of changes in smoke concentration data collected in a fire scene, as an embodiment of the present application. Figure 4 This application provides a schematic diagram illustrating the changes in smoke concentration data collected in a cooking scenario, as part of an embodiment of the present application. Figure 5 A schematic diagram illustrating the logic for determining an reachable state, provided in an embodiment of this application; Figure 6 A flowchart illustrating another fire alarm method based on fire risk scoring provided in this application embodiment; Figure 7 A flowchart illustrating another fire alarm method based on fire risk scoring provided in this application embodiment; Figure 8 A schematic diagram of a fire alarm device based on fire risk scoring provided for an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0025] Explanation of reference numerals: 100: Application scenario; 101: Alarm device; 102: First security device; 103: Second security device; 800: Fire alarm device based on fire risk score; 801: Acquisition unit; 802: Calculation unit; 803: Determination unit; 804: Judgment unit; 900: Electronic device; 901: Memory; 902: Processor; 903: Communication interface; 904: Bus. Detailed Implementation

[0026] 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.

[0027] 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 or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0028] 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.

[0029] The embodiments of this application will now be described with reference to the accompanying drawings.

[0030] Example 1: Please refer to Figure 1 , Figure 1 This is a schematic diagram of an application scenario for a fire alarm method based on fire risk scoring provided in an embodiment of this application. The application scenario 100 includes an alarm device 101, a first security device 102, and a second security device 103.

[0031] The alarm device 101 is used to acquire first environmental data obtained by the first security device 102 and the second security device 103 from the detected environment, and to determine whether there is a fire risk in the detected environment based on the first environmental data, and then to execute a fire alarm operation when there is a large fire risk in the detected environment.

[0032] The first security device 102 and the second security device 103 are security devices deployed in the environment under test. The first security device 102 is a smoke detector, specifically used to detect one or more of smoke-related data such as smoke concentration, smoke type or smoke range. The second security device 103 is specifically a temperature detector used to detect temperature data in the environment under test. Furthermore, in some possible embodiments, more or fewer security devices may be included, or other security devices such as humidity detectors, cameras, and microphones may be deployed that can collect environmental data and send the collected environmental data to alarm device 101.

[0033] In this embodiment, the alarm device 101 calculates an oscillation coefficient based on the acquired smoke concentration. The oscillation coefficient characterizes the degree of fluctuation in smoke concentration within a preset time window. For example, multiple smoke concentration samples can be collected within a time window, and the fluctuation characteristics of smoke concentration can be quantified by analyzing the amplitude or dispersion of these samples. The purpose of introducing the oscillation coefficient is to identify abnormal fluctuation patterns in smoke concentration, such as the difference between frequent fluctuations caused by environmental interference and the continuous increase or violent fluctuations during a real fire, thereby providing richer dimensions for subsequent risk assessment.

[0034] After calculating the oscillation coefficient, the alarm device 101 combines the first environmental data (such as smoke concentration) with the oscillation coefficient to jointly determine the first fire risk score of the measured environment. This means that the fire risk score not only depends on the current absolute value of smoke concentration, but also takes into account the fluctuation characteristics of smoke concentration over time, thus enabling a more comprehensive and dynamic reflection of the actual fire risk status of the measured environment.

[0035] Finally, the alarm device 101 determines whether to activate the fire alarm based on the calculated first fire risk score. For example, one or more score thresholds can be set, and when the first fire risk score meets specific conditions, the corresponding alarm action is triggered.

[0036] As can be seen, in this embodiment of the application, by introducing an oscillation coefficient that characterizes the degree of smoke concentration fluctuation and combining it with basic environmental data to jointly determine the fire risk score, the difference between the real fire scene and environmental interference can be identified more accurately, avoiding the problem of misjudgment that may be caused by relying solely on a single instantaneous concentration value, thereby improving the accuracy and reliability of fire alarms.

[0037] Please see Figure 2 , Figure 2 This application provides a flowchart illustrating a fire alarm method based on fire risk scoring, which can be based on... Figure 1 The application scenario 100 shown is implemented as follows: Figure 2 As shown, it includes steps S201-S204.

[0038] S201: The alarm device acquires the first environmental data of the measured environment, including the smoke concentration.

[0039] S202: The alarm device calculates the oscillation coefficient based on the smoke concentration. The oscillation coefficient is used to characterize the degree of fluctuation of the smoke concentration within a preset time window.

[0040] Specifically, in this embodiment of the application, the higher the fluctuation of the smoke concentration within the preset time window, the higher the oscillation coefficient, and the closer the characteristics of the smoke concentration are to those of cooking fumes and water vapor; conversely, the lower the fluctuation of the smoke concentration within the preset time window, the lower the oscillation coefficient, and the less the characteristics of the smoke concentration are to those of cooking fumes and water vapor.

[0041] S203: The alarm device determines the first fire risk score of the measured environment based on the first environmental data and the vibration coefficient.

[0042] As an example, the initial environmental data may include carbon monoxide concentration. The alarm device determines a carbon monoxide coefficient based on the concentration, for example, by normalizing the raw carbon monoxide concentration. The alarm device then fuses the carbon monoxide coefficient with an oscillation coefficient, for example, by calculating a weighted sum and / or product of the two, to obtain a first fire risk score. Carbon monoxide is a characteristic gas produced by combustion in a fire, and including it in the assessment can supplement information beyond smoke concentration.

[0043] As another example, the initial environmental data may include flame spectral data. The alarm device determines flame characteristic coefficients based on the flame spectral data, for example, by analyzing the energy intensity of specific bands in the spectrum. Subsequently, the alarm device combines the flame characteristic coefficients with an oscillation coefficient, for example, by taking the maximum value of both, or by first determining whether the flame characteristic coefficient exceeds a threshold and then adjusting it in conjunction with the oscillation coefficient, to obtain a first fire risk score. Flame spectral data directly reflects the presence of open flames, facilitating rapid fire identification.

[0044] As another example, the first environmental data may include image data. The alarm device analyzes the image data, extracting visual features such as smoke coverage area and flame area proportion, and determines the image risk coefficient accordingly. Subsequently, the alarm device fuses the image risk coefficient with an oscillation coefficient, for example, using the image risk coefficient as a base value and the oscillation coefficient as a correction factor to attenuate or enhance the base value, to obtain a first fire risk score. Image data can provide spatial distribution information, complementing point sensor data.

[0045] S204: The alarm device determines whether to activate the fire alarm based on the first fire risk score.

[0046] Optionally, the preset time window includes multiple time nodes, and the smoke concentration includes multiple smoke concentrations corresponding to the multiple time nodes respectively; the oscillation coefficient is calculated based on the smoke concentration, including: calculating the average value of multiple smoke concentrations; calculating the absolute value of the difference between each smoke concentration and the average value in the multiple smoke concentrations and summing them to obtain the oscillation coefficient.

[0047] Optionally, the preset time window includes multiple time nodes, and the smoke concentration includes multiple smoke concentrations corresponding to the multiple time nodes respectively; the oscillation coefficient is calculated based on the smoke concentration, including: calculating the average value of multiple smoke concentrations; calculating the absolute value of the difference between each smoke concentration and the average value in the multiple smoke concentrations and summing them to obtain the oscillation coefficient.

[0048] Specifically, in this embodiment, the specific calculation method of the oscillation coefficient is explained. The preset time window includes multiple consecutive time nodes with the same interval, and correspondingly, the acquired smoke concentration data includes the smoke concentration values ​​corresponding to each of these time nodes.

[0049] First, the arithmetic mean of these multiple smoke concentration values ​​is calculated. This average represents the baseline level of smoke concentration within this time window. Then, the absolute value of the difference between the smoke concentration at each time point and this average is calculated. Finally, all the calculated absolute values ​​of the difference are summed, and the sum is used as the oscillation coefficient.

[0050] For example, smoke concentration data can be collected for 10 consecutive time points. By calculating the sum of the absolute deviations of these data from their average value, the overall fluctuation range of smoke concentration within that time window can be quantified. This calculation method can effectively capture the degree of dispersion of smoke concentration around a central value; the greater the fluctuation range, the more drastic the change in smoke concentration and the higher the oscillation coefficient.

[0051] Please see Figure 3 , Figure 3 This application provides a schematic diagram illustrating changes in smoke concentration data collected in a fire scene, as part of an embodiment of the present application. Please refer to [link / reference]. Figure 4 , Figure 4 This application provides a schematic diagram illustrating the changes in smoke concentration data collected in a cooking scenario, as shown in the embodiment of the present application. Figure 3 The smoke concentration data for the fire scene shown fluctuates relatively little, while Figure 4 The smoke concentration data for the cooking scenarios shown fluctuated significantly.

[0052] therefore Figure 4 The corresponding smoke concentration is more similar to the cooking scenario than... Figure 3 The degree of similarity between the corresponding smoke concentration and the cooking scenario, and then based on... Figure 3 The corresponding smoke concentration calculation has a low oscillation coefficient, based on Figure 4 The oscillation coefficient calculated for the corresponding smoke concentration is relatively high.

[0053] As can be seen, in this embodiment of the application, by calculating the average value of smoke concentration at multiple time points, and determining the oscillation coefficient based on the sum of the absolute deviations of the concentration value at each point from the average value, the oscillation coefficient can reflect the stability of smoke concentration, which helps to comprehensively judge the fire risk in combination with other environmental data. It can distinguish the concentration fluctuations caused by the temporary interference of cooking fumes, water vapor, etc., from the continuous changes when a real fire occurs, thereby improving the accuracy of fire risk scoring.

[0054] Optionally, the first environmental data also includes multiple environmental humidity levels corresponding to multiple time points; the method further includes: if the target environmental humidity is greater than a preset humidity threshold, then determining whether the target smoke concentration corresponding to the target environmental humidity is greater than a preset concentration threshold; wherein, the target environmental humidity is one of multiple environmental humidity levels, and the target smoke concentration corresponds to the same time point as the target environmental humidity; if the target smoke concentration is greater than a preset concentration threshold, then determining the corresponding smoke concentration compensation value based on the target environmental humidity; and performing attenuation compensation on the target smoke concentration based on the smoke concentration compensation value.

[0055] Specifically, in this embodiment, considering that high humidity environments such as water vapor may cause false alarms from the smoke sensor, humidity is introduced as an auxiliary judgment factor. The first environmental data includes not only smoke concentrations at multiple time points, but also multiple environmental humidity values ​​corresponding to these time points.

[0056] When the target ambient humidity at a certain time point exceeds a preset humidity threshold (e.g., 90%), the alarm device determines that there may be high humidity interference in the current environment. At this time, the alarm device will further check whether the target smoke concentration at the same time point corresponding to the target ambient humidity also exceeds a preset concentration threshold. If the target smoke concentration also exceeds the standard, the alarm device will determine that the current high smoke concentration is likely a false trigger caused by the high humidity environment.

[0057] To eliminate this interference, the alarm equipment determines a corresponding smoke concentration compensation value based on the specific humidity level of the target environment. For example, the higher the humidity, the larger the compensation value may be. Subsequently, this compensation value is used to attenuate the target smoke concentration, that is, to reduce or correct the smoke concentration reading at that time point, making it closer to the value under actual fire conditions.

[0058] As can be seen, in this embodiment of the application, by introducing humidity data to verify and compensate for the smoke concentration, the false alarms of the sensor caused by the high humidity environment are effectively filtered out, and the smoke concentration value is attenuated and corrected, thereby improving the accuracy of the smoke concentration data, providing a more reliable data basis for subsequent fire risk scoring, and further reducing the false alarm rate of the overall system.

[0059] Optionally, determining whether to execute a fire alarm based on a first fire risk score includes: executing a fire alarm if the first fire risk score is greater than a first threshold; determining the user's reachability status based on first environmental data if the first fire risk score is not greater than the first threshold but greater than a second threshold; wherein the second threshold is less than the first threshold, and the user's reachability status indicates whether the user can receive the warning information; if the user's reachability status is unreachable, the first threshold is lowered.

[0060] Specifically, in this embodiment of the application, the alarm device determines whether to activate a fire alarm based on a preset first threshold and a second threshold.

[0061] The alarm device first compares the calculated initial fire risk score with a preset initial threshold. If the score is greater than the initial threshold, indicating an extremely high fire risk, the system immediately activates the fire alarm without waiting for user confirmation or other conditions.

[0062] If the first fire risk score is not greater than the first threshold, but greater than a lower second threshold, the alarm device enters an intermediate decision-making stage. At this time, the system will determine the current user reachability status based on the acquired first environmental data or other relevant information. User reachability status is a key indicator used to characterize whether the user can receive the warning information issued by the system, such as whether the user is online, whether the mobile phone is accessible, or whether the user is in sleep mode.

[0063] After determining a user's reachability, if the system determines that the user is unreachable—for example, the user may be asleep, their phone may be on silent, or they may be offline—and thus unable to receive and respond to alerts in a timely manner, the system will automatically lower the first threshold originally used to trigger the alert. Lowering the threshold means that the system is more sensitive to subsequent risks, so that it can intervene and trigger alerts earlier when the user cannot actively intervene, thereby compensating for the lack of human response.

[0064] As can be seen, in this embodiment of the application, by introducing the user reachability status as a decision factor and dynamically adjusting the alarm trigger threshold based on the status, the linkage judgment between risk level and user status is realized. When the user cannot respond, the system's alert level can be automatically increased, ensuring that the alarm can be triggered in a timely or early manner under special circumstances, thereby improving the intelligence level and security capability of the fire alarm system.

[0065] Optionally, the first environmental data also includes sound data and carbon dioxide concentration. If the first fire risk score is not greater than the first threshold and is greater than the second threshold, the user's reachability status is determined based on the first environmental data, including: if the current time is daytime, the user's reachability status is determined based on the sound data; if the current time corresponds to nighttime, the carbon dioxide data is used to determine whether the carbon dioxide concentration in the measured environment is greater than a preset concentration; if the carbon dioxide concentration is greater than the preset concentration, the user's reachability status is determined to be an unreachable status.

[0066] Specifically, in this embodiment of the application, the reachability of a user is determined by combining sound data and carbon dioxide concentration with time periods.

[0067] When the alarm device determines that the first fire risk score is between the first threshold and the second threshold, it will enter the user reachability determination process, and this determination process will use different environmental data as the basis depending on the current time.

[0068] For example, please refer to Figure 5 , Figure 5 This is a schematic diagram illustrating the logic for determining reachability in an embodiment of this application. If the current time is during daytime, such as a typical user activity period, the alarm device focuses on determining user reachability through sound data. For example, it can collect sound information from the environment and analyze whether there are sounds of user activity (such as talking, footsteps, etc.). If obvious human voices or activity sounds are detected, the user can be considered reachable; conversely, if the environment is silent for a long time, the user may be determined to be unreachable.

[0069] If the current time corresponds to nighttime, such as during typical sleep hours, the likelihood of the user making a sound is low, and relying solely on sound may be inaccurate. In this case, the system uses carbon dioxide concentration as an auxiliary indicator.

[0070] In a scenario where people sleep with the doors closed at night, the carbon dioxide concentration gradually accumulates due to the relatively enclosed space caused by their breathing. Therefore, if the carbon dioxide concentration in the tested environment exceeds a preset threshold, it can be inferred that someone is likely resting in the space. However, because they are asleep, their ability to receive external warning messages is weak, thus the user is determined to be in an unreachable state.

[0071] As can be seen, in this embodiment of the application, by introducing the time dimension and combining environmental data of different dimensions such as sound data and carbon dioxide concentration, the user's reachability status is comprehensively determined, making the status judgment more in line with the user's living habits and physiological characteristics at different times, and effectively improving the accuracy of user status recognition.

[0072] Optionally, if the user's reachability status is unreachable, the method further includes: acquiring a second environmental parameter after a preset time, and determining a second fire risk score based on the second environmental parameter; if the second fire risk score is not greater than a first threshold, determining the fire risk growth trend of the measured environment based on the first environmental parameter and the second environmental parameter; if the fire risk growth trend is greater than a preset threshold, then executing a fire alarm.

[0073] Specifically, in this embodiment, when the alarm device determines the user's reachability status as unreachable, it does not stop monitoring or ignore the risk. Instead, it initiates a delayed reassessment process. The alarm device waits for a preset time interval (e.g., 60 seconds), during which it continuously or re-collects a second environmental parameter of the measured environment. The second environmental parameter may include data of the same type as the first environmental parameter, such as smoke concentration, temperature, humidity, etc.

[0074] After obtaining the second environmental parameter, the alarm device uses the same or similar scoring logic as before to calculate a second fire risk score. If this second fire risk score still does not exceed the first threshold used to trigger an immediate alarm, the alarm device will not immediately sound an alarm, but will further analyze the trend of risk changes.

[0075] Specifically, the alarm device compares the first environmental parameter (representing the environmental state at the initial moment) with the second environmental parameter (representing the environmental state after a delay) to calculate a quantitative value that characterizes the trend of fire risk growth, such as the rate of increase in smoke concentration and the magnitude of temperature increase.

[0076] If this growth trend exceeds the preset threshold, it indicates that the fire is developing and the user is unable to respond. At this point, even if the current score has not yet reached the first threshold, the system will consider it necessary to take proactive action and directly execute the fire alarm.

[0077] As can be seen, in this embodiment of the application, by introducing a delayed retest and trend analysis mechanism when the user is unreachable, the shortcomings of static threshold judgment are made up for, effectively avoiding fire false alarms caused by user disconnection, and further improving the safety and reliability of the fire alarm system in complex scenarios.

[0078] Example 2: The above-mentioned application embodiment provides a fire alarm method based on fire risk score. Based on this, focusing on determining the first fire risk score, this application embodiment also provides a more detailed fire alarm method based on fire risk score.

[0079] Please see Figure 6 , Figure 6 A flowchart illustrating another fire alarm method based on fire risk scoring provided in this application embodiment is shown below. Figure 1 The application scenario 100 shown is implemented as follows: Figure 6 As shown, steps S601-S607 are included.

[0080] S601: The alarm device acquires the first environmental data of the measured environment, including the smoke concentration.

[0081] S602: The alarm device calculates the oscillation coefficient based on the smoke concentration. The oscillation coefficient is used to characterize the degree of fluctuation of the smoke concentration within a preset time window.

[0082] S603: The alarm device determines the smoke concentration coefficient based on the smoke concentration and the first weight.

[0083] S604: The alarm device determines the smoke growth data based on the smoke concentration and determines the smoke growth coefficient based on the smoke growth data and the second weight.

[0084] S605: The alarm device determines the temperature coefficient based on temperature data and a third weight.

[0085] S606: Alarm devices determine the first fire risk score of the measured environment based on the smoke concentration coefficient, smoke growth coefficient, temperature coefficient, and vibration coefficient.

[0086] Specifically, in this embodiment of the application, after acquiring the first environmental data, including smoke concentration and temperature data, the alarm device also normalizes these raw physical quantities and converts them into dimensionless standard coefficients, which are then used as direct inputs for subsequent scoring formulas.

[0087] In some possible examples, when calculating the smoke concentration coefficient, the alarm device maps the original smoke concentration to a range of 0 to 1.0.

[0088] In addition, a saturation threshold S is set in the example. max (e.g., 0.3 dB / m), smoke concentration coefficient S norm =min(1.0,S raw / S max )×A, where S raw Let A be the smoke concentration, and A be a preset or predetermined first weight. This smoke concentration coefficient reflects the contribution of the current absolute smoke concentration level to the fire risk.

[0089] When calculating the smoke growth coefficient, the alarm device captures the rate of smoke accumulation through physical differential calculations. For example, it can calculate the concentration change Vs over 3 seconds. raw =S raw (t)-S raw (t-3), where S raw (t) represents the smoke concentration at time node (t); S raw (t-3) represents the smoke concentration at time node (t-3).

[0090] In addition, the example also sets a rapid growth threshold Vs. max (For example, 0.1 dB / m represents a concentration surge within 3 seconds). If Vs raw If the smoke growth coefficient Vs > 0, then the smoke growth coefficient Vs norm=min(1.0,Vs raw / Vs max )×B, where B is a preset or determined second weight; if Vs raw If ≤0, then Vs max =0. This coefficient is used to quantify the speed at which a fire spreads.

[0091] When calculating the temperature coefficient, the alarm device extracts the accompanying temperature rise characteristics to distinguish between cold smoke (interference) and hot smoke (real fire).

[0092] In some possible examples, calculate the temperature change Vt over 10 seconds. raw =T raw (t)-T raw (t-10), where T raw (t) represents the temperature data at time node (t); T raw (t-10) represents the temperature data at time node (t-10).

[0093] In addition, a significant temperature rise threshold Vt was set in the example. max (e.g., 5°C means a temperature increase of 5 degrees Celsius in 10 seconds). If Vt raw If the temperature coefficient Vt > 0, then the temperature coefficient Vt norm =min(1.0,Vt raw / Vt max )×C, where C is a preset or determined third weight; if Vt raw If ≤0, then Vt norm =0.

[0094] Optionally, a first fire risk score for the measured environment is determined based on the smoke concentration coefficient, smoke growth coefficient, temperature coefficient, and oscillation coefficient, including: determining an initial fire risk score for the measured environment based on the smoke concentration coefficient, smoke growth coefficient, and temperature coefficient; and correcting the initial fire risk score based on the oscillation coefficient to obtain the first fire risk score.

[0095] Specifically, in this embodiment, after obtaining the smoke concentration coefficient, smoke growth coefficient, and temperature coefficient, the alarm device first fuses these three coefficients, which characterize the core features of a fire, to calculate an initial fire risk score. This initial score mainly reflects the fire risk level assessed based on the three core dimensions of smoke concentration, fire spread rate, and temperature change.

[0096] After obtaining the initial fire risk score, the alarm device further introduces an oscillation coefficient, which is used to correct the initial score, and finally obtains the first fire risk score. The first risk score satisfies the following formula (1).

[0097]

[0098] Where S norm Vs is the smoke concentration coefficient; norm Vt is the smoke growth coefficient. norm For temperature coefficient, This represents the oscillation coefficient. The alarm equipment uses the oscillation coefficient as a correction factor, and together with the weighted calculation results above, determines the final fire risk score of the measured environment.

[0099] Optionally, the oscillation coefficient can be used to further refine the score, for example, by subtracting the oscillation coefficient from the first fire risk score, or by multiplying it by the oscillation coefficient.

[0100] As can be seen, in this embodiment of the application, the alarm device achieves a more refined assessment of fire risk by first fusing core features to obtain an initial score and then using the oscillation coefficient for secondary correction through a cascaded processing method. This incorporates considerations of data volatility and stability, thereby improving the adaptability and accuracy of the score in complex environments.

[0101] S607: The alarm device determines whether to activate the fire alarm based on the first fire risk score.

[0102] For explanations regarding steps S601, S602, and S607, please refer to the relevant content of steps S201-S204, which will not be repeated here.

[0103] Example 3: The above-mentioned application embodiments provide a fire alarm method based on fire risk score, which calculates a first fire risk score based on smoke concentration coefficient, smoke growth coefficient, and temperature coefficient. Based on this, this application embodiment also provides a more detailed fire alarm method based on fire risk score. Please refer to [link to relevant documentation]. Figure 7 , Figure 7 A flowchart illustrating another fire alarm method based on fire risk scoring provided in this application embodiment is shown, which can be based on... Figure 1 The application scenario 100 shown is implemented as follows: Figure 7 As shown, it includes steps S701-S708.

[0104] S701: The alarm device acquires the first environmental data of the measured environment, including the smoke concentration.

[0105] S702: The alarm device calculates the oscillation coefficient based on the smoke concentration. The oscillation coefficient is used to characterize the degree of fluctuation of the smoke concentration within a preset time window.

[0106] S703: The alarm device corrects the first weight based on the oscillation coefficient. The oscillation coefficient is inversely correlated with the corrected first weight. The smoke concentration coefficient is determined based on the smoke concentration and the corrected first weight.

[0107] Specifically, in this embodiment of the application, considering that the fluctuation of smoke concentration can reflect the intensity of environmental disturbance or the instability of fire development, the alarm device uses the previously calculated oscillation coefficient to adaptively correct the first weight (i.e., the basic concentration weight corresponding to the smoke concentration coefficient).

[0108] This correction follows the inverse correlation principle: the higher the oscillation coefficient, the lower the corrected first weight; conversely, the lower the oscillation coefficient, the higher the corrected first weight.

[0109] For example, when the oscillation coefficient is large, it indicates that the smoke concentration fluctuates drastically within the time window, which may be caused by unstable interference sources such as oil fumes and water vapor. In this case, the system should appropriately reduce the contribution ratio of the smoke concentration coefficient in the total score to reduce the impact of interference. When the oscillation coefficient is small, it indicates that the smoke concentration changes steadily and the data is highly reliable. Therefore, the first weight should be maintained or increased so that the smoke concentration coefficient can play a greater role in the scoring.

[0110] After the revision, the alarm equipment uses a new first weight, combined with the current smoke concentration, to redetermine the smoke concentration coefficient, which is then used for subsequent fire risk score calculations.

[0111] As can be seen from the embodiments of this application, the alarm device realizes the confidence assessment of the core indicator of smoke concentration through the inverse correlation dynamic adjustment mechanism of the oscillation coefficient and the first weight, and gives full play to its role when the environment is stable, thereby improving the robustness and adaptability of the fire risk scoring model in different scenarios, and further optimizing the accuracy and reliability of the fire alarm.

[0112] S704: The alarm device determines the smoke concentration coefficient based on the smoke concentration and the first weight.

[0113] S705: The alarm device determines the smoke growth data based on the smoke concentration and determines the smoke growth coefficient based on the smoke growth data and a second weight.

[0114] S706: The alarm device determines the temperature coefficient based on temperature data and a third weight.

[0115] S707: Alarm devices determine the first fire risk score of the measured environment based on the smoke concentration coefficient, smoke growth coefficient, temperature coefficient, and vibration coefficient.

[0116] Specifically, apart from the calculation method of the first fire risk score described in step S606, in this embodiment of the application, the alarm device uses the smoke concentration coefficient, smoke growth coefficient, temperature coefficient and oscillation coefficient as inputs, and calculates the first fire risk score through a preset mathematical operation combination.

[0117] For example, the alarm device can multiply the four coefficients and use the product as the first fire risk score; or take the maximum value of the four coefficients as the first fire risk score; or calculate the sum of squares or square root of the four coefficients, and amplify the contribution of high-risk factors through nonlinear transformation while suppressing the influence of low-risk factors.

[0118] S708: The alarm device determines whether to activate the fire alarm based on the first fire risk score.

[0119] For detailed explanations of steps S701-S706 and S708, please refer to the relevant content of steps S601-S607, which will not be repeated here.

[0120] The methods described in the above embodiments demonstrate that by introducing an oscillation coefficient characterizing the degree of smoke concentration fluctuation and combining it with basic environmental data to jointly determine the fire risk score, the accuracy and reliability of fire alarms are improved. Introducing humidity data to verify and compensate for smoke concentration further reduces the overall system's false alarm rate. The cascaded processing method, which first fuses core features to obtain an initial score and then uses the oscillation coefficient for secondary correction, improves the adaptability and accuracy of the score in complex environments. The dynamic adjustment mechanism based on the inverse correlation between the oscillation coefficient and the first weight further optimizes the accuracy and reliability of fire alarms. Introducing user reachability as a decision factor enhances the intelligence level and security capabilities of the fire alarm system.

[0121] Based on the description of the above configuration method embodiments, this application also provides a fire alarm device 800 based on fire risk scoring, which can operate in... Figure 1 The alarm device 101 shown has a computer program (including program code) for execution. Figure 2 , Figure 6 and Figure 7 The method shown. Please refer to [link / reference]. Figure 8 , Figure 8 A schematic diagram of a fire alarm device based on fire risk scoring is provided for an embodiment of this application. The fire alarm device 800 based on fire risk scoring includes: Acquisition unit 801 is used to acquire first environmental data of the measured environment, the first environmental data including smoke concentration; The calculation unit 802 is used to calculate the oscillation coefficient based on the smoke concentration. The oscillation coefficient is used to characterize the degree of fluctuation of the smoke concentration within a preset time window. Unit 803 is used to determine the first fire risk score of the measured environment based on the first environmental data and the oscillation coefficient. The judgment unit 804 is used to determine whether to execute a fire alarm based on the first fire risk score.

[0122] In one possible embodiment, the preset time window includes multiple time nodes, and the smoke concentration includes multiple smoke concentrations corresponding to the multiple time nodes respectively; in terms of calculating the oscillation coefficient based on the smoke concentration, the calculation unit 802 is further specifically used to: calculate the average value of the multiple smoke concentrations; calculate the absolute value of the difference between each smoke concentration and the average value in the multiple smoke concentrations respectively and sum them to obtain the oscillation coefficient.

[0123] In one possible embodiment, the first environmental data further includes multiple environmental humidity levels corresponding to multiple time points; the judgment unit 804 is further specifically used to: if the target environmental humidity is greater than a preset humidity threshold, determine whether the target smoke concentration corresponding to the target environmental humidity is greater than a preset concentration threshold; wherein, the target environmental humidity is one of multiple environmental humidity levels, and the target smoke concentration corresponds to the same time point as the target environmental humidity; if the target smoke concentration is greater than the preset concentration threshold, determine the corresponding smoke concentration compensation value based on the target environmental humidity; and perform attenuation compensation on the target smoke concentration based on the smoke concentration compensation value.

[0124] In one possible embodiment, the first environmental data further includes temperature data. Determining a first fire risk score for the measured environment based on the first environmental data and the oscillation coefficient includes: determining a smoke concentration coefficient based on smoke concentration and a first weight; determining smoke growth data based on smoke concentration and determining a smoke growth coefficient based on smoke growth data and a second weight; determining a temperature coefficient based on temperature data and a third weight; and determining a first fire risk score for the measured environment based on the smoke concentration coefficient, smoke growth coefficient, temperature coefficient, and oscillation coefficient.

[0125] In one possible embodiment, in determining the first fire risk score of the measured environment based on the smoke concentration coefficient, smoke growth coefficient, temperature coefficient, and oscillation coefficient, the determining unit 803 is further specifically used to: determine the initial fire risk score of the measured environment based on the smoke concentration coefficient, smoke growth coefficient, and temperature coefficient; and correct the initial fire risk score based on the oscillation coefficient to obtain the first fire risk score.

[0126] In one possible embodiment, the determining unit 803 is further specifically used to: correct the first weight according to the oscillation coefficient, the oscillation coefficient being inversely correlated with the corrected first weight, and the smoke concentration coefficient being determined based on the smoke concentration and the corrected first weight.

[0127] In one possible embodiment, in determining whether to execute a fire alarm based on a first fire risk score, the determining unit 803 is further specifically configured to: execute a fire alarm if the first fire risk score is greater than a first threshold; if the first fire risk score is not greater than the first threshold but greater than a second threshold, determine the user reachability status based on first environmental data; wherein the second threshold is less than the first threshold, and the user reachability status indicates whether the user can receive the warning information; if the user reachability status is an unreachable status, lower the first threshold.

[0128] Based on the description of the above method and device embodiments, please refer to... Figure 9 , Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 9 The electronic device 900 shown (specifically, the electronic device 900 may be a computer device, Figure 1 The alarm device 101 shown includes a memory 901, a processor 902, a communication interface 903, and a bus 904. The memory 901, processor 902, and communication interface 903 are interconnected via the bus 904.

[0129] The memory 901 may be a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM).

[0130] The memory 901 can store programs. When the program code stored in the memory 901 is executed by the processor 902, the processor 902 and the communication interface 903 are used to execute the various steps of the fire alarm method based on fire risk scoring according to the embodiments of this application.

[0131] The processor 902 may be a general-purpose central processing unit (CPU), microcontroller, application-specific integrated circuit (ASIC), graphics processing unit (GPU), or one or more integrated circuits, used to execute relevant programs to achieve the functions required by the units in the electronic device 900 of this application embodiment, or to execute the fire alarm method based on fire risk scoring of this application method embodiment.

[0132] The processor 902 can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of the fire alarm method based on fire risk scoring in this application can be completed by the integrated logic circuits in the hardware of the processor 902 or by instructions in software form. The processor 902 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microcontroller or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory 901. The processor 902 reads the information in the memory 901 and, in conjunction with its hardware, performs the functions required by the units included in the electronic device 900 of this application embodiment, or executes the fire alarm method based on fire risk scoring of the method embodiment of this application.

[0133] The communication interface 903 uses transceiver devices, such as, but not limited to, transceivers, to enable communication between the electronic device 900 and other devices or communication networks. For example, data can be acquired through the communication interface 903.

[0134] Bus 904 may include a pathway for transmitting information between various components of electronic device 900 (e.g., memory 901, processor 902, communication interface 903).

[0135] It should be noted that, although Figure 9 The illustrated electronic device 900 only shows a memory 901, a processor 902, and a communication interface 903. However, those skilled in the art should understand that in specific implementations, the electronic device 900 may also include other devices necessary for normal operation. Furthermore, depending on specific needs, those skilled in the art should understand that the electronic device 900 may also include hardware devices for implementing other additional functions. Moreover, those skilled in the art should understand that the electronic device 900 may only include the devices necessary for implementing the embodiments of this application, and may not necessarily include... Figure 9 All the devices shown.

[0136] This application also provides a chip, which includes a processor and a data interface. The processor reads instructions stored in a memory through the data interface to implement the fire alarm method based on fire risk scoring.

[0137] Optionally, as one implementation, the chip may further include a memory storing instructions, and the processor is used to execute the instructions stored in the memory. When the instructions are executed, the processor is used to execute the fire alarm method based on fire risk scoring.

[0138] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps of any of the above methods.

[0139] This application also provides a computer program product containing instructions. When the computer program product is run on a computer or processor, it causes the computer or processor to perform one or more steps of any of the methods described above.

[0140] Those skilled in the art will appreciate that the functionality described in conjunction with the various illustrative logic blocks, modules, and algorithmic steps disclosed herein can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality described by the various illustrative logic blocks, modules, and steps can be stored or transmitted as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may comprise a computer-readable storage medium, which corresponds to a tangible medium, such as a data storage medium, or a communication medium that includes any medium facilitating the transfer of a computer program from one place to another (e.g., based on a communication protocol). In this way, the computer-readable medium may substantially correspond to (1) a non-transitory tangible computer-readable storage medium, or (2) a communication medium, such as a signal or carrier wave. The data storage medium may be any available medium accessible by one or more computers or one or more processors to retrieve instructions, code, and / or data structures for implementing the techniques described in this application. A computer program product may comprise a computer-readable medium.

[0141] By way of example and not limitation, such computer-readable storage media may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, flash memory, or any other media that can be used to store desired program code in the form of instructions or data structures and is accessible by a computer. Furthermore, any connection is properly referred to as computer-readable media. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of media. However, it should be understood that the computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other temporary media, but are specifically addressed to non-temporary tangible storage media. As used herein, disks and optical discs include compact optical discs (CDs), laser optical discs, optical discs, digital versatile optical discs (DVDs), and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs reproduce data optically using lasers. The combination of the above items should also be included in the scope of computer-readable media.

[0142] Instructions can be executed by one or more processors, such as digital signal processors (DSPs), general-purpose microcontrollers, application-specific integrated circuits (ASICs), field-programmable arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Therefore, the term "processor" as used herein can refer to any of the foregoing structures or any other structures suitable for implementing the techniques described herein. Furthermore, in some aspects, the functionality described by the various illustrative logic blocks, modules, and steps described herein can be provided within dedicated hardware and / or software modules configured for encoding and decoding, or incorporated into combined codecs. Moreover, the techniques can be fully implemented within one or more circuit or logic elements.

[0143] The technology of this application can be implemented in a wide variety of devices or apparatuses, including wireless handheld devices, integrated circuits (ICs), or a set of ICs (e.g., chipsets). The various components, modules, or units described in this application are intended to emphasize functional aspects of the apparatus for performing the disclosed technology, but do not necessarily need to be implemented by different hardware units. In fact, as described above, the various units can be combined with suitable software and / or firmware within coded hardware units, or provided via interoperable hardware units (containing one or more processors as described above).

[0144] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the specific descriptions of the corresponding steps in the foregoing method embodiments, and will not be repeated here.

[0145] It should be understood that in the description of this application, unless otherwise stated, " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B can represent A or B; where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., do not necessarily imply difference. In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0146] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling, direct coupling, or communication connection shown or discussed between each other may be indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be read-only memory (ROM), random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as digital versatile discs (DVDs), or semiconductor media, such as solid-state disks (SSDs).

[0149] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

[0150] The device embodiments described above are merely illustrative. The units and modules described as separate components may or may not be physically separate. Furthermore, some or all of the units and modules can be selected to achieve the purpose of this embodiment, depending on actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0151] The above description is only a specific embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A fire alarm method based on fire risk scoring, characterized in that, The method includes: Acquire first environmental data of the tested environment, including smoke concentration; An oscillation coefficient is calculated based on the smoke concentration, and the oscillation coefficient is used to characterize the degree of fluctuation of the smoke concentration within a preset time window; A first fire risk score for the measured environment is determined based on the first environmental data and the oscillation coefficient. Whether to issue a fire alarm is determined based on the first fire risk score.

2. The method according to claim 1, characterized in that, The preset time window includes multiple time points, and the smoke concentration includes multiple smoke concentrations corresponding to each of the multiple time points; the calculation of the oscillation coefficient based on the smoke concentration includes: Calculate the average value of the multiple smoke concentrations; The oscillation coefficient is obtained by calculating the absolute value of the difference between each smoke concentration and the average value among the multiple smoke concentrations and summing them.

3. The method according to claim 2, characterized in that, The first environmental data also includes multiple environmental humidity levels corresponding to the multiple time points; the method further includes: If the target ambient humidity is greater than a preset humidity threshold, then it is determined whether the target smoke concentration corresponding to the target ambient humidity is greater than a preset concentration threshold; wherein, the target ambient humidity is one of the plurality of ambient humidity, and the target smoke concentration corresponds to the same time point as the target ambient humidity; If the target smoke concentration is greater than the preset concentration threshold, then the corresponding smoke concentration compensation value is determined based on the target ambient humidity. The target smoke concentration is attenuated and compensated based on the smoke concentration compensation value.

4. The method according to any one of claims 1-3, characterized in that, The first environmental data also includes temperature data, and determining the first fire risk score of the measured environment based on the first environmental data and the oscillation coefficient includes: The smoke concentration coefficient is determined based on the smoke concentration and the first weight; Smoke growth data is determined based on the smoke concentration, and a smoke growth coefficient is determined based on the smoke growth data and a second weight. The temperature coefficient is determined based on the temperature data and the third weighting. The first fire risk score of the tested environment is determined based on the smoke concentration coefficient, the smoke growth coefficient, the temperature coefficient, and the oscillation coefficient.

5. The method according to claim 4, characterized in that, The determination of the first fire risk score of the measured environment based on the smoke concentration coefficient, the smoke growth coefficient, the temperature coefficient, and the oscillation coefficient includes: The initial fire risk score of the measured environment is determined based on the smoke concentration coefficient, the smoke growth coefficient, and the temperature coefficient. The first fire risk score is obtained by correcting the initial fire risk score based on the oscillation coefficient.

6. The method according to claim 4, characterized in that, The method further includes: The first weight is corrected based on the oscillation coefficient, which is inversely correlated with the corrected first weight. The smoke concentration coefficient is determined based on the smoke concentration and the corrected first weight.

7. The method according to any one of claims 1-3, characterized in that, The step of determining whether to activate a fire alarm based on the first fire risk score includes: If the first fire risk score is greater than the first threshold, a fire alarm is triggered. If the first fire risk score is not greater than a first threshold but greater than a second threshold, then the user's reachability status is determined based on the first environmental data; wherein the second threshold is less than the first threshold, and the user reachability status indicates whether the user can receive warning information. If the user's reachable state is unreachable, then the first threshold is lowered.

8. A fire alarm device based on fire risk scoring, characterized in that, The device includes: An acquisition unit is used to acquire first environmental data of the measured environment, the first environmental data including smoke concentration; The calculation unit is used to calculate the oscillation coefficient based on the smoke concentration, and the oscillation coefficient is used to characterize the degree of fluctuation of the smoke concentration within a preset time window. A determining unit is configured to determine a first fire risk score for the measured environment based on the first environmental data and the oscillation coefficient. The judgment unit is used to determine whether to execute a fire alarm based on the first fire risk score.

9. An electronic device, characterized in that, The method includes a processor, a memory, a communication interface, and one or more programs, said one or more programs being stored in the memory and configured to be executed by the processor, said programs including instructions for performing the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform the method as described in any one of claims 1-7.

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