Smoke detector control method, device, equipment and medium

Abstract

The invention provides a control method, device and equipment of a smoke detector and a medium, and the control method comprises the steps: obtaining a signal sampling value and an environment parameter according to a preset sampling period; calculating and storing a corresponding initial compensation value according to the signal sampling value; calculating a corresponding adjustment coefficient through an environment correction model according to the environment parameters; the environment correction model represents a function relationship between an environment parameter and an adjustment coefficient; according to the initial compensation value, the adjustment coefficient, a historical initial compensation value and an initial alarm threshold value, calculating a real-time alarm threshold value, and giving an alarm according to the real-time alarm threshold value; the historical initial compensation value is calculated according to a historical signal sampling value. According to the smoke detector control method and device, the equipment and the medium provided by the invention, the technical problem that the alarm threshold cannot be effectively adjusted is solved.

Description

Technical Field

[0001] This invention relates to the field of sensors, and more particularly to a control method, apparatus, device, and medium for a smoke detector. Background Technology

[0002] Photoelectric smoke detectors provide fire early warning by detecting the scattering or attenuation of light by smoke particles, and have gradually replaced earlier ionization detectors. However, existing detectors still have significant limitations in practical applications. Photoelectric smoke detectors are susceptible to interference from non-fire particles such as dust, steam, and flying insects. Especially in complex environmental conditions, their detection performance and anti-interference capabilities face severe challenges, affecting the accuracy and timeliness of fire early warnings.

[0003] Traditional photoelectric smoke detectors generally suffer from insufficient sensitivity to certain types of smoke, susceptibility to environmental interference, and high false alarm rates. Especially in scenarios with fluctuating temperature and humidity or the presence of non-fire particulate matter, existing compensation mechanisms struggle to achieve rapid and effective threshold adjustments, leading to delayed warnings or false alarms and failing to meet the demands of high-reliability applications. Summary of the Invention

[0004] This invention provides a control method, device, equipment, and medium for a smoke detector to solve the technical problem that the alarm threshold cannot be effectively adjusted.

[0005] The present invention provides a control method for a smoke detector, comprising: According to the preset sampling period, acquire signal sample values ​​and environmental parameters; Calculate and save the corresponding initial compensation value based on the signal sampling value; Based on environmental parameters, corresponding adjustment coefficients are calculated using an environmental correction model; the environmental correction model characterizes the functional relationship between environmental parameters and adjustment coefficients. The real-time alarm threshold is calculated based on the initial compensation value, the adjustment coefficient, the historical initial compensation value, and the initial alarm threshold, so as to trigger an alarm based on the real-time alarm threshold; the historical initial compensation value is calculated based on historical signal sampling values.

[0006] In one embodiment of the present invention, the step of calculating and saving the corresponding initial compensation value based on the signal sampling value includes: Calculate the relative duration of the sampling time node of the signal sample value with respect to the preset initial time node; Calculate the initial drift value and drift rate based on the signal sampling value and the corresponding relative duration; Calculate and save the corresponding initial compensation value based on the initial drift value, the drift rate, and the relative duration.

[0007] In one embodiment of the present invention, the Nth initial compensation value Represented as: ,in, Represented as the i-th relative duration, This is represented as the Nth initial drift value. Represented as the Nth drift rate; The Nth drift rate is expressed as: ,in, This is represented as the i-th signal sample value; The Nth initial drift value is represented as: .

[0008] In one embodiment of the present invention, the step of calculating the real-time alarm threshold based on the initial compensation value, the adjustment coefficient, the historical initial compensation value, and the initial alarm threshold includes: The historical initial compensation values ​​are sorted in chronological order, and the last preset number of historical initial compensation values ​​are selected. Calculate the variance between the historical drift rate of the selected historical initial compensation value and the drift rate of the initial compensation value; Based on the comparison result between the variance and the preset first threshold, the initial compensation value is corrected, and the real-time alarm threshold is calculated based on the corrected initial compensation value, the corresponding adjustment coefficient, and the initial alarm threshold.

[0009] In one embodiment of the present invention, the step of correcting the initial compensation value based on the comparison result between the variance and a preset first threshold, and calculating the real-time alarm threshold based on the corrected initial compensation value, the corresponding adjustment coefficient, and the initial alarm threshold, includes: Determine the variance and the first threshold: When the variance is less than the first threshold, the initial compensation value is corrected according to the preset Kalman filter, the product of the corrected initial compensation value and the corresponding adjustment coefficient is calculated, and the sum of the product and the initial alarm threshold is calculated to obtain the corresponding real-time alarm threshold. Otherwise, calculate the product of the initial compensation value and the corresponding adjustment coefficient, and sum the product with the initial alarm threshold to obtain the corresponding real-time alarm threshold.

[0010] In one embodiment of the present invention, the step of triggering an alarm based on the real-time alarm threshold includes: Compare the real-time signal sampled value with the real-time alarm threshold: when the real-time signal sampled value is greater than the real-time alarm threshold, calculate the derivative of the real-time signal sampled value with respect to time based on multiple real-time signal sampled values ​​within a preset time period to obtain the rate of change; The rate of change is compared with a preset second threshold. When the rate of change is greater than the second threshold, a fire alarm command is generated, and the smoke detector sounds a fire alarm according to the fire alarm command.

[0011] In one embodiment of the present invention, the step of triggering an alarm based on the real-time alarm threshold includes: Calculate the ratio of the real-time alarm threshold to the initial alarm threshold, and determine the ratio relative to a preset third threshold: when the ratio is greater than the third threshold, generate a pollution alarm command, and the smoke detector triggers a pollution alarm based on the pollution alarm command.

[0012] The present invention also provides a control device for a smoke detector, comprising: The data acquisition module is used to acquire signal sample values ​​and environmental parameters according to a preset sampling period; The first calculation module is used to calculate and save the corresponding initial compensation value based on the signal sampling value; The second calculation module is used to calculate the corresponding adjustment coefficients based on environmental parameters using an environmental correction model; the environmental correction model represents the functional relationship between environmental parameters and adjustment coefficients. The alarm control module is used to calculate a real-time alarm threshold based on the initial compensation value, the adjustment coefficient, the historical initial compensation value, and the initial alarm threshold, so as to trigger an alarm based on the real-time alarm threshold; the historical initial compensation value is calculated based on historical signal sampling values.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the control method for the smoke detector.

[0014] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the control method for the smoke detector.

[0015] The beneficial effects of this invention are: It effectively solves the signal baseline drift problem of photoelectric smoke detectors in complex environments caused by factors such as dust accumulation, temperature and humidity changes, component aging, and power fluctuations through a dynamic linear drift compensation mechanism. An initial compensation value is calculated based on real-time acquired signal sampling values, and an adjustment coefficient is generated through an environmental correction model combined with environmental parameters. Finally, an adaptive real-time alarm threshold is generated by integrating historical initial compensation values ​​with the initial alarm threshold. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram: Figure 1 This is a flowchart of a control method for a smoke detector provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the control device for a smoke detector provided in one embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device provided in one embodiment of the present invention.

[0018] The reference numerals in the attached diagram are as follows: 100, data acquisition module; 200, first calculation module; 300, second calculation module; 400, alarm control module; 10, electronic device; 11, memory; 12, processor. Detailed Implementation

[0019] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0022] Please see Figure 1This invention discloses a control method for a smoke detector. The control method can compensate in real time for signal drift caused by environmental interference such as dust accumulation and component aging, thereby achieving adaptive adjustment of the alarm threshold. The control method may include the following steps: Step S10: Acquire signal sampling values ​​and environmental parameters according to a preset sampling period.

[0023] In some embodiments, the preset sampling period can be set to 1 minute, 5 minutes, 10 minutes, etc. At the end of each sampling period, the signal acquisition and environmental parameter reading operations of the smoke detector can be automatically performed. Specifically, the signal sampling value is the digital quantity obtained after analog-to-digital conversion of the photocurrent signal output by the photoelectric sensing unit in the smoke detector. The signal sampling value directly reflects the intensity of light scattering by smoke particles in the current detection cavity.

[0024] In some embodiments, environmental parameters are collected simultaneously, including real-time temperature data acquired by an integrated temperature sensor, real-time humidity data acquired by a humidity sensor, and an estimated value of the dust deposition thickness on the detector optical window based on an optical transmittance attenuation model.

[0025] In some embodiments, all collected data, including signal sample values ​​and their precise sampling time points, as well as environmental parameters, are packaged into a single data packet and timestamped to facilitate subsequent calculations. Each sampling must be performed at a preset sampling period interval to form equally spaced time series data, providing a basis for subsequent drift trend analysis.

[0026] In some embodiments, the control method may include the following steps: Step S20: Calculate and save the corresponding initial compensation value based on the signal sampling value.

[0027] In some embodiments, after data acquisition is completed in the above steps, the initial compensation value calculation process can proceed. The initial compensation value calculation process can use a linear regression model to quantify the baseline drift trend of the signal based on historical data within a preset time window (e.g., 24 hours). The sliding window means that each time this calculation is performed, all data sampled at preset sampling intervals within the previous 24 hours from the current moment is automatically included, and data earlier than 24 hours is removed, thus ensuring that the data used for analysis is always the latest data from the most recent day, dynamically tracking drift changes.

[0028] In some embodiments, when performing step S20, step S20 may specifically include the following steps: step S21, calculating the relative duration between the sampling time node of the signal sample value and the preset initial time node.

[0029] In some embodiments, an initial time node can be preset, i.e., the start time of the sliding window (e.g., the current time minus 24 hours). Then, for each signal sample value within the sliding window, the time difference between its sampling time node and this initial time node is calculated to obtain the corresponding relative duration. For example, a signal sample value collected 5 hours after the start of the sliding window has a relative duration of 5 hours. Converting absolute time to an offset relative to the starting point of the sliding window through normalization provides a basis for subsequent linear regression calculations.

[0030] In some embodiments, step S20 may further include the following steps: Step S22, calculate the initial drift value and drift rate based on the signal sampling value and the corresponding relative duration.

[0031] In some embodiments, the least squares method can be used to perform linear regression fitting on all data within the sliding window. Specifically, the relative duration of each data point is used as the independent variable, and the corresponding signal sample value is used as the dependent variable. The two parameters of the best-fit line are calculated using the least squares formula to obtain the initial drift value and drift rate. The calculated intercept can be defined as the initial drift value, representing the theoretical value of the signal baseline at the beginning of the sliding window, and its unit can be dB / m. The calculated slope can be defined as the drift rate, representing the average rate of change of the signal baseline over time, and its unit can be dB / m / h.

[0032] In some embodiments, the Nth drift rate is expressed as: The Nth initial drift value is represented as: .in, Let be the i-th signal sample value. It is represented as the i-th relative duration.

[0033] In some embodiments, step S20 may further include the following steps: Step S23, calculate and save the corresponding initial compensation value based on the initial drift value, drift rate and relative duration.

[0034] In some embodiments, the initial compensation value is calculated following a linear model, and its value is the initial drift value plus the product of the drift rate and the relative duration. The calculated initial compensation value represents the signal baseline value predicted by the linear regression model at a specific sampling time point, i.e., the long-term drift trend value after deducting the impact of short-term fluctuations. After calculation, the initial compensation value, its corresponding precise sampling timestamp, and the initial drift value and drift rate can be saved to the database to provide historical data for subsequent calculation of real-time alarm thresholds. The Nth initial compensation value... Represented as: .

[0035] In some embodiments, the control method may include the following steps: Step S30: Calculate the corresponding adjustment coefficients based on the environmental parameters using an environmental correction model; the environmental correction model characterizes the functional relationship between the environmental parameters and the adjustment coefficients.

[0036] In some embodiments, after obtaining the synchronously acquired estimates of temperature, humidity, and dust deposition thickness, a pre-set environmental correction model can be invoked to calculate a corresponding adjustment coefficient. This environmental correction model can be a multivariate function calibrated using a large amount of experimental data, with the following functional relationship: The environmental correction model can represent the combined effect of temperature T, humidity H, and dust deposition thickness estimate D on detector sensitivity, and quantify this effect using adjustment coefficients. .

[0037] In some embodiments, the specific functional form and parameters of the environmental correction model can be determined by experimental calibration. That is, in a laboratory or various typical real-world application environments, different temperature, humidity, and dust contamination conditions are artificially set, and the adjustment coefficients of the detector under these specific environmental combinations are recorded. Then, the least squares method is used on a large number of... By performing a fitting regression, we can finally obtain an environmental correction model that best reflects the intrinsic relationship between the two.

[0038] In some embodiments, the currently collected estimates of temperature, humidity, and dust deposition thickness can be used as inputs and substituted into a predetermined environmental correction model to directly obtain a preliminary adjustment coefficient. Subsequently, the adjustment coefficient can be compared with a preset baseline sensitivity value. Multiply by (for example, 0.5) to obtain an adjustment coefficient that reflects the degree of sensitivity change relative to the reference state. , .

[0039] In some embodiments, to ensure stability and reliability and prevent the calculation of unreasonable or potentially risky adjustment coefficients under extreme and abnormal environments, it is necessary to limit the range of values ​​for the adjustment coefficients. For example, the adjustment coefficients need to be limited to between 0.2 and 0.8. When the adjustment coefficients calculated above exceed this range, truncation is required; that is, if the adjustment coefficient is less than 0.2, the final adjustment coefficient is set to 0.2; if the adjustment coefficient is greater than 0.8, the final adjustment coefficient is set to 0.8.

[0040] In some embodiments, the control method may include the following steps: Step S40, calculating a real-time alarm threshold based on an initial compensation value, an adjustment coefficient, a historical initial compensation value, and an initial alarm threshold, so as to trigger an alarm based on the real-time alarm threshold; the historical initial compensation value is calculated based on historical signal sampling values.

[0041] In some embodiments, when performing step S40, step S40 may specifically include the following steps: Step S41: Sort the historical initial compensation values ​​in chronological order and select the last preset number of historical initial compensation values.

[0042] In some embodiments, the historical initial compensation values ​​can be the results calculated and saved using linear regression with a sliding window in each past sampling period. First, all historical initial compensation values ​​are strictly sorted in chronological order according to the sampling time node to ensure the continuity of the data's time series. After sorting, a preset number of historical initial compensation values ​​can be selected, for example, the three most recent historical initial compensation values, corresponding to all calculation results within the last three hours. This time window is selected to effectively reflect recent drift behavior characteristics, avoiding both overly short timeframes that fail to reflect trends and overly long timeframes that introduce outdated historical states that may no longer be applicable to the current environment.

[0043] In some embodiments, step S40 may further include the following steps: Step S42, calculating the variance between the historical drift rate of the selected historical initial compensation value and the drift rate of the initial compensation value.

[0044] In some embodiments, the drift rate corresponding to the latest initial compensation value calculated at the current latest sampling time is obtained. Then, the variance between this latest drift rate and the historical drift rate of the selected historical initial compensation value is calculated. The variance quantifies the degree of deviation between the latest calculated drift trend and the recent historical average trend. A smaller variance indicates that the current drift pattern is highly consistent with recent historical behavior, and the change is stable and predictable. A larger variance indicates that the drift behavior may have experienced abnormal abrupt changes or been strongly disturbed.

[0045] In some embodiments, step S40 may further include the following steps: step S43, based on the comparison result between the variance and the preset first threshold, the initial compensation value is corrected, and the real-time alarm threshold is calculated based on the corrected initial compensation value, the corresponding adjustment coefficient, and the initial alarm threshold.

[0046] In some embodiments, when performing step S43, step S43 may specifically include the following steps: step S431, determining the variance and the first threshold.

[0047] In some embodiments, the first threshold may be a threshold value determined after verification through a large number of experiments and field data, used to define the boundary between smooth drift and potential abnormal changes.

[0048] In some embodiments, step S43 may further include the following steps: Step S432: When the variance is less than the first threshold, the initial compensation value is corrected according to the preset Kalman filter, the product of the corrected initial compensation value and the corresponding adjustment coefficient is calculated, and the sum of the product and the initial alarm threshold is calculated to obtain the corresponding real-time alarm threshold.

[0049] In some embodiments, when the variance is less than a first threshold, it indicates that the current drift is in a stable and predictable state. At this time, a preset Kalman filter can be activated to optimally estimate and correct the currently calculated initial compensation value. The Kalman filter treats the drift itself as a state variable, and its internal model assumes that the drift changes slowly over time. The Kalman filter predicts the current drift based on its internal state equation and uses the latest calculated initial compensation value as an observation for data fusion and updating, ultimately outputting a corrected initial compensation value with less noise and more accurate estimation. Subsequently, this corrected initial compensation value can be multiplied by the aforementioned adjustment coefficient to obtain a compensation amount adjusted for environmental factors and optimal drift estimation. Finally, this compensation amount is added to the system's preset initial alarm threshold, representing the detector's initial alarm threshold in an absolutely clean environment, to obtain a highly adaptive real-time alarm threshold for the current moment.

[0050] In some embodiments, the Kalman filter can use the Kalman filtering algorithm to correct the initial compensation value. The entire correction process can be divided into an initialization stage, a prediction stage, and an update stage.

[0051] In some embodiments, during the initialization phase, it is assumed that the smoke detector does not experience any drift in the initial clean state, therefore the posterior estimate is... Setting it to 0 indicates that the initial best guess for the drift is zero drift; simultaneously, the initial posterior estimation error covariance is set to 0. The initial compensation value is set to 1.0, a relatively large value indicating a high degree of uncertainty regarding the initial state estimate. This initialization setting encourages the Kalman filter to place greater trust in the earliest input compensation values ​​during the initialization phase, thus promoting rapid convergence of the state estimate to the true drift level. The initial compensation value can be adjusted based on the specific application scenario; if the initial compensation value is relatively reliable, it can be appropriately reduced. The value.

[0052] In some embodiments, during the prediction phase, an established process model can be used to predict the current state and corresponding uncertainties. Based on the drift characteristics of the smoke detector, the drift can be modeled as a slowly changing process driven by process noise. The state prediction equation is expressed as follows: This means that the best prediction of the current state is the best posterior estimate of the previous state, where... Let this be the k-th initial compensation value. The error covariance prediction equation is expressed as: Where Q is the process noise variance, representing the uncertainty of the drift rate, such as the impact of temperature fluctuations or the randomness of dust accumulation. Through the prediction stage, a priori estimate of the current state can be obtained, i.e., the best prediction of the current state.

[0053] In some embodiments, during the update phase, the initial compensation value of the smoke detector can be fused with the prior estimate to obtain a more accurate state estimate. First, the Kalman gain is calculated. Where R is the observation noise variance, reflecting the measurement error level of the reference channel. The Kalman gain is a weighting coefficient between 0 and 1, used to balance the reliability of model predictions and observations. If the observation noise variance R is large (indicating low reliability of the smoke detector), the Kalman gain is calculated. It will become smaller, as the Kalman filter places more trust in its own predictions. If the prediction is uncertain... Larger (indicating inaccurate Kalman filter prediction), gain It will increase, and the Kalman filter will place more trust in the current observation. Subsequently, a state update can be performed. Using observations The initial compensation value (i.e., the currently calculated initial compensation value) is used to correct the prior estimate of the state at the current time. Finally, the error covariance is updated. This reduces the uncertainty of state estimation.

[0054] In some embodiments, the Kalman filter also handles abnormal states through dynamic parameter tuning and anomaly handling mechanisms. For example, when a sudden change in the environment is detected (such as a rapid temperature change or a sharp change in dust concentration), the Kalman filter increases the value of the process noise variance Q, enabling it to track drift changes more quickly. When the sensor channel noise of the smoke detector increases significantly (e.g., due to device aging), the observation noise variance R is increased, reducing the confidence in the prior estimates.

[0055] In some embodiments, when observing residuals If the current observation is an outlier, the update process is skipped, and the prior estimate of the current state is used directly as the current state estimate, thus avoiding the pollution of the filtered state by outlier observations.

[0056] In some embodiments, step S43 may further include the following steps: Step S433, otherwise, calculate the product of the initial compensation value and the corresponding adjustment coefficient, and calculate the sum of the product and the initial alarm threshold to obtain the corresponding real-time alarm threshold.

[0057] In some embodiments, when the variance is greater than or equal to a first threshold, it indicates a significant difference between the latest drift rate and recent historical trends, possibly stemming from sudden environmental changes or some kind of interference. In this case, the model assumptions of the Kalman filter may temporarily not hold, thus bypassing the filter correction stage to maintain normal response speed. At this point, the currently calculated initial compensation value (without Kalman filter processing) can be directly used, multiplied by the current adjustment coefficient, and then added to the initial alarm threshold to directly obtain the real-time alarm threshold. This approach ensures that the alarm threshold can quickly adapt to changes in drift behavior, avoiding temporary system sensitivity failure due to filter hysteresis.

[0058] In some embodiments, step S40 may further include the following step: step S44, triggering an alarm based on a real-time alarm threshold.

[0059] In some embodiments, when performing step S44, step S44 may include the following steps: comparing the real-time acquired signal sample value with a real-time alarm threshold: when the real-time signal sample value is greater than the real-time alarm threshold, calculating the derivative of the real-time signal sample value with respect to time based on multiple real-time signal sample values ​​within a preset time period to obtain the rate of change; comparing the rate of change with a preset second threshold: when the rate of change is greater than the second threshold, generating a fire alarm command, and the smoke detector alarms according to the fire alarm command.

[0060] In some embodiments, the real-time signal sample values ​​acquired in real time are compared with the calculated real-time alarm threshold. When the real-time signal sample value exceeds the real-time alarm threshold, it can be determined that the current signal strength has reached the potential fire alarm triggering condition. However, to further distinguish between a real fire alarm and environmental interference, a rate of change analysis process needs to be initiated. For example, a series of continuous real-time signal sample values ​​within a preset time period (e.g., the most recent 30 seconds) can be extracted from the memory cache. The differential method is used to calculate the derivative of these real-time signal sample values ​​with respect to time. Specifically, the instantaneous rate of change is calculated by the ratio of the difference between adjacent sampling points to the time interval. These instantaneous rates of change are then smoothed (e.g., by moving average) to suppress noise fluctuations, and finally, the rate of change is obtained. The rate of change can represent the speed at which the smoke concentration increases.

[0061] In some embodiments, the calculated rate of change can be compared with a preset second threshold, which is a threshold value pre-set based on typical fire smoke growth characteristics, for example, 5% per minute. If the calculated rate of change is greater than the second threshold, it indicates that the current smoke concentration not only exceeds the alarm threshold but is also growing rapidly at a typical fire spread rate, thus confirming a fire alarm status. At this time, a fire alarm command can be generated. Upon receiving the fire alarm command, the smoke detector immediately activates the audible and visual alarm and sends a fire alarm signal to the fire alarm controller via the communication interface, triggering a complete fire emergency response process. If the rate of change does not exceed the second threshold, it is determined to be a slowly changing interference signal (such as dust accumulation, steam effects, etc.), and no alarm is triggered; monitoring continues.

[0062] In some embodiments, when performing step S44, step S44 may further include the following steps: calculating the ratio of the real-time alarm threshold to the initial alarm threshold, and determining the ratio relative to a preset third threshold; when the ratio is greater than the third threshold, generating a pollution alarm command, and the smoke detector triggers a pollution alarm based on the pollution alarm command.

[0063] In some embodiments, the ratio of the current real-time alarm threshold to the initial alarm threshold calibrated at the factory of the smoke detector can be calculated. This ratio is compared to a preset third threshold, which is an upper limit set to protect the system from excessive drift, for example, 1.6. If the ratio is greater than the third threshold, it indicates that despite dynamic compensation, the detector's baseline drift has reached a point requiring maintenance, and its sensitivity has decreased excessively, possibly due to dust contamination, optical window blockage, or device aging. In this case, a contamination alarm command can be generated. Upon receiving the contamination alarm command, the smoke detector triggers a dedicated contamination alarm indication (such as a flashing yellow indicator light) and reports a maintenance signal to the monitoring system, prompting personnel to clean or repair the smoke detector to restore its normal detection performance.

[0064] As can be seen, the above scheme effectively solves the signal baseline drift problem of photoelectric smoke detectors caused by factors such as dust accumulation, temperature and humidity changes, component aging, and power fluctuations in complex environments through a dynamic linear drift compensation mechanism. An initial compensation value is calculated based on real-time collected signal sampling values, and an adjustment coefficient is generated through an environmental correction model combined with environmental parameters. Finally, an adaptive real-time alarm threshold is generated by integrating historical initial compensation values ​​with the initial alarm threshold. This scheme significantly improves the anti-interference capability of smoke detectors in industrial scenarios, effectively distinguishes between real fire alarms and environmental pollution interference, greatly reduces the false alarm rate and the risk of missed alarms, and simultaneously triggers pollution alarms in a timely manner through a compensation limiting protection mechanism, indicating maintenance needs, thereby comprehensively enhancing the accuracy and timeliness of the fire early warning system.

[0065] Please see Figure 2 The present invention also discloses a control device for a smoke detector, and the above-described control method can be applied to the control device. The control device may include a data acquisition module 100, a first calculation module 200, a second calculation module 300, and an alarm control module 400.

[0066] In some embodiments, the data acquisition module 100 can be used to acquire signal sample values ​​and environmental parameters according to a preset sampling period.

[0067] In some embodiments, the first calculation module 200 can be used to calculate and save the corresponding initial compensation value based on the signal sampling value.

[0068] In some embodiments, the second calculation module 300 can be used to calculate the corresponding adjustment coefficient based on the environmental parameters using an environmental correction model; the environmental correction model characterizes the functional relationship between the environmental parameters and the adjustment coefficient.

[0069] In some embodiments, the alarm control module 400 can be used to calculate a real-time alarm threshold based on an initial compensation value, an adjustment coefficient, a historical initial compensation value, and an initial alarm threshold, so as to trigger an alarm based on the real-time alarm threshold; the historical initial compensation value is calculated based on historical signal sampling values.

[0070] For specific limitations regarding the control device, please refer to the limitations of the control method above, which will not be repeated here. Each module in the aforementioned control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the memory in the electronic device, or stored in software form in the memory of the electronic device, so that the memory can call and execute the operations corresponding to each module.

[0071] Please see Figure 3 In some embodiments, the electronic device 10 may include a memory 11, a processor 12, and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 12, such as a program for a control method of a smoke detector.

[0072] In some embodiments, the memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 10, such as the portable hard drive of the electronic device 10. In other embodiments, the memory 11 can also be an external storage device of the electronic device 10, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 10. Furthermore, the memory 11 can include both internal and external storage units of the electronic device 10. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 10, such as the code for the control method of a smoke detector, but also to temporarily store data that has been output or will be output.

[0073] In some embodiments, the processor 12 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 12 is the control unit of the electronic device 10, connecting various components of the electronic device 10 via various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a control method program for a smoke detector) and calls data stored in the memory 11 to perform various functions of the electronic device 10 and process data.

[0074] In some embodiments, the processor 12 executes the operating system of the electronic device 10 and various installed applications. The processor 12 executes the applications to implement the steps in the control method for the smoke detector described above.

[0075] In some embodiments, the computer program may be divided into one or more modules, one or more of which are stored in the memory 11 and executed by the processor 12 to complete the present application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 10. For example, the computer program may be divided into a data acquisition module 100, a first calculation module 200, a second calculation module 300, an alarm control module 400, etc.

[0076] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A control method for a smoke detector, characterized in that, include: According to the preset sampling period, acquire signal sample values ​​and environmental parameters; Calculate and save the corresponding initial compensation value based on the signal sampling value; Based on environmental parameters, corresponding adjustment coefficients are calculated using an environmental correction model; the environmental correction model characterizes the functional relationship between environmental parameters and adjustment coefficients. The real-time alarm threshold is calculated based on the initial compensation value, the adjustment coefficient, the historical initial compensation value, and the initial alarm threshold, so as to trigger an alarm based on the real-time alarm threshold; the historical initial compensation value is calculated based on historical signal sampling values.

2. The control method for a smoke detector according to claim 1, characterized in that, The step of calculating and saving the corresponding initial compensation value based on the signal sample value includes: Calculate the relative duration of the sampling time node of the signal sample value with respect to the preset initial time node; Calculate the initial drift value and drift rate based on the signal sampling value and the corresponding relative duration; Calculate and save the corresponding initial compensation value based on the initial drift value, the drift rate, and the relative duration.

3. The control method for a smoke detector according to claim 2, characterized in that, The Nth initial compensation value Represented as: ,in, Represented as the i-th relative duration, This is represented as the Nth initial drift value. Represented as the Nth drift rate; The Nth drift rate is expressed as: ,in, This is represented as the i-th signal sample value; The Nth initial drift value is represented as: .

4. The control method for a smoke detector according to claim 2, characterized in that, The step of calculating the real-time alarm threshold based on the initial compensation value, the adjustment coefficient, the historical initial compensation value, and the initial alarm threshold includes: The historical initial compensation values ​​are sorted in chronological order, and the last preset number of historical initial compensation values ​​are selected. Calculate the variance between the historical drift rate of the selected historical initial compensation value and the drift rate of the initial compensation value; Based on the comparison result between the variance and the preset first threshold, the initial compensation value is corrected, and the real-time alarm threshold is calculated based on the corrected initial compensation value, the corresponding adjustment coefficient, and the initial alarm threshold.

5. The control method for a smoke detector according to claim 4, characterized in that, The step of correcting the initial compensation value based on the comparison result between the variance and the preset first threshold, and calculating the real-time alarm threshold based on the corrected initial compensation value, the corresponding adjustment coefficient, and the initial alarm threshold, includes: Determine the variance and the first threshold: When the variance is less than the first threshold, the initial compensation value is corrected according to the preset Kalman filter, the product of the corrected initial compensation value and the corresponding adjustment coefficient is calculated, and the sum of the product and the initial alarm threshold is calculated to obtain the corresponding real-time alarm threshold. Otherwise, calculate the product of the initial compensation value and the corresponding adjustment coefficient, and sum the product with the initial alarm threshold to obtain the corresponding real-time alarm threshold.

6. The control method for a smoke detector according to claim 1, characterized in that, The step of triggering an alarm based on the real-time alarm threshold includes: Compare the real-time signal sampled value with the real-time alarm threshold: when the real-time signal sampled value is greater than the real-time alarm threshold, calculate the derivative of the real-time signal sampled value with respect to time based on multiple real-time signal sampled values ​​within a preset time period to obtain the rate of change; The rate of change is compared with a preset second threshold. When the rate of change is greater than the second threshold, a fire alarm command is generated, and the smoke detector sounds a fire alarm according to the fire alarm command.

7. The control method for a smoke detector according to claim 1, characterized in that, The step of triggering an alarm based on the real-time alarm threshold includes: Calculate the ratio of the real-time alarm threshold to the initial alarm threshold, and determine the ratio relative to a preset third threshold: when the ratio is greater than the third threshold, generate a pollution alarm command, and the smoke detector triggers a pollution alarm based on the pollution alarm command.

8. A control device for a smoke detector, characterized in that, include: The data acquisition module is used to acquire signal sample values ​​and environmental parameters according to a preset sampling period; The first calculation module is used to calculate and save the corresponding initial compensation value based on the signal sampling value; The second calculation module is used to calculate the corresponding adjustment coefficients based on environmental parameters using an environmental correction model; the environmental correction model represents the functional relationship between environmental parameters and adjustment coefficients. The alarm control module is used to calculate a real-time alarm threshold based on the initial compensation value, the adjustment coefficient, the historical initial compensation value, and the initial alarm threshold, so as to trigger an alarm based on the real-time alarm threshold; the historical initial compensation value is calculated based on historical signal sampling values.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes a computer program, it implements the steps of the control method for the smoke detector as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the control method for the smoke detector as described in any one of claims 1 to 7.

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