Method and apparatus for compensating detection data of carbon monoxide detector

Abstract

This invention discloses a method and apparatus for compensating detection data of a carbon monoxide detector. The method includes: acquiring first operating data sent by a target detector, the first operating data including multiple first sensitivities and multiple first operating times of the target detector, with each first sensitivity corresponding to a specific first operating time; determining the operating stage of the target detector based on the first operating data sent by the target detector, the operating stage including at least one of an early stage, a middle stage, and a late attenuation stage; acquiring a sensitivity attenuation model corresponding to the target detector based on the operating stage of the target detector; and determining a target compensation value for the target detector based on the sensitivity attenuation model, the target compensation value being sent to the target detector to compensate for the detected carbon monoxide concentration. By implementing the method in this application, it is beneficial to improve the accuracy of carbon monoxide concentration detection.

Description

Technical Field

[0001] This invention relates to the field of alarm devices for fire safety, and more particularly to a method and device for compensating detection data of a carbon monoxide detector. Background Technology

[0002] Existing carbon monoxide detectors experience a natural decrease in sensor sensitivity over time during use, leading to discrepancies between the detected and actual concentrations. Traditional compensation methods typically involve periodically replacing the sensor or having professionals manually calibrate the CO sensor periodically with standard CO gas and calibration equipment. Alternatively, sensitivity compensation can be achieved by simply issuing compensation values. However, current technologies lack the ability to dynamically identify the actual decay patterns of the detectors, making it difficult to provide targeted compensation based on individual device differences and environmental factors. This results in the need to improve the accuracy of carbon monoxide concentration detection. Summary of the Invention

[0003] To address the aforementioned issues, this application provides a method and apparatus for compensating detection data of a carbon monoxide detector. The solution proposed in this application is beneficial for improving the accuracy of carbon monoxide concentration detection.

[0004] In a first aspect, embodiments of this application provide a method for compensating detection data of a carbon monoxide detector. The method includes: acquiring first operating data sent by a target detector, the first operating data including multiple first sensitivities and multiple first operating times of the target detector, wherein the first sensitivities and the first operating times correspond one-to-one; determining the operating stage of the target detector based on the first operating data sent by the target detector, the operating stage including at least one of an early stage, a middle stage, and a late attenuation stage; acquiring a sensitivity attenuation model corresponding to the target detector based on the operating stage of the target detector; and determining a target compensation value for the target detector based on the sensitivity attenuation model, the target compensation value being sent to the target detector to compensate for the detected carbon monoxide concentration.

[0005] As can be seen, in this embodiment of the application, by judging the operating stage of the target detector based on its own operating data, and selecting an appropriate sensitivity attenuation model according to different stages to determine the compensation value, the compensation process can be made to better fit the actual aging state of the detector, avoiding the problem of inaccurate compensation caused by using a single fixed compensation method, thereby improving the accuracy and reliability of carbon monoxide concentration detection.

[0006] In conjunction with the first aspect, in one possible embodiment, obtaining the sensitivity attenuation model corresponding to the target detector based on the operating phase of the target detector includes: obtaining multiple second operating data transmitted by multiple first detectors, the second operating data including multiple second sensitivities and multiple second operating times of the corresponding first detector, the second sensitivity and the second operating time being in one-to-one correspondence; obtaining third operating data matching the operating phase from the multiple second operating data, the third operating data including at least one second operating data; determining the corresponding initial attenuation model based on the operating phase; and constructing a sensitivity attenuation model based on the third operating data and the initial attenuation model.

[0007] As can be seen, in this embodiment of the application, by acquiring historical operating data of multiple detectors of the same type, samples that match the current operating stage of the target detector are selected, and the initial attenuation model of the corresponding stage is calibrated and constructed based on the samples. This makes the constructed sensitivity attenuation model not only conform to the typical attenuation pattern of the stage, but also incorporate the data characteristics of the actual detector, thereby improving the fitting accuracy of the model to the actual attenuation state of the target detector and providing a reliable basis for the accurate calculation of subsequent compensation values.

[0008] In conjunction with the first aspect, in one possible embodiment, the operation phase is an early phase, and a sensitivity attenuation model is constructed based on the third operation data and the initial attenuation model, including: determining a first preset model as the initial attenuation model, wherein the first preset model is a linear relationship model; generating an initial attenuation curve based on the third operation data and the initial attenuation model; if the absolute value of the slope of the initial attenuation curve is not greater than a first preset threshold, then determining the target sensitivity based on the initial attenuation curve; and constructing a sensitivity attenuation model based on the target sensitivity.

[0009] As can be seen in this embodiment, considering the extremely gradual sensitivity decay in the early stage, the slope of the initial decay curve is calculated and compared with a preset slope threshold to determine whether the sensitivity is in a stable range. When it is confirmed that the sensitivity is basically not decaying, a constant model is constructed using the target sensitivity, avoiding unnecessary dynamic compensation due to small fluctuations, reducing the data interaction frequency between the cloud server and the detector, and reducing computational overhead.

[0010] In conjunction with the first aspect, in one possible embodiment, the operation phase is an intermediate phase, and a sensitivity decay model is constructed based on the third operation data and the initial decay model, including: determining the first preset model as the initial decay model; determining the fitting weight of the corresponding third operation data based on the closeness between the second operation time and the first operation time in the third operation data; and fitting the sensitivity decay model based on the initial decay model, the fitting weight, and the third operation data.

[0011] As can be seen in this embodiment, considering the linear decay of sensitivity in the mid-term stage, by introducing fitting weights based on the similarity of running time, historical data that is more similar to the current state of the target detector plays a greater role in model construction, thereby constructing a sensitivity decay model that better fits the actual decay law of the target detector, improving the fitting accuracy of the model in the mid-term stage, and providing a more reliable basis for the accurate calculation of subsequent compensation values.

[0012] In conjunction with the first aspect, in one possible embodiment, the running phase is a late decay phase, and a sensitivity decay model is constructed based on the third running data and the initial decay model, including: determining a second preset model as the initial decay model, the second preset model being a nonlinear relationship model; obtaining a decay acceleration coefficient based on the third running data, the decay acceleration coefficient being used to characterize the magnitude of the accelerated decay of sensitivity as running time increases; and constructing a sensitivity decay model based on the decay acceleration coefficient and the exponential decay model.

[0013] As can be seen in the embodiments of this application, in view of the characteristics of accelerated sensitivity decay in the late decay stage, a nonlinear relationship model is used as the initial decay model. The decay acceleration coefficient is obtained by fitting, and a sensitivity decay model that can reflect the accelerated decay trend is constructed accordingly. This allows the model to maintain high fitting accuracy in the late stage of detector aging, thereby providing an accurate basis for the calculation of the target compensation value in the late stage and effectively making up for the problem of poor fitting effect of the linear model in the accelerated decay stage.

[0014] In conjunction with the first aspect, in one possible embodiment, before determining the operating stage of the target detector based on the first operating data sent by the target detector, the method further includes: determining the aging degree of the target detector based on the first operating data; determining a compensation time interval based on the aging degree of the target detector, wherein the aging degree is inversely correlated with the compensation time interval; and determining that the time interval for sending the detection compensation value of the target detector is not less than the compensation time interval.

[0015] As can be seen, in this embodiment, by dynamically determining the compensation time interval based on the aging degree of the target detector before determining the operation phase, and ensuring that the actual transmission time interval is not less than the compensation time interval, the adaptation of the compensation frequency to the aging state of the detector is achieved. A longer compensation interval is used for detectors with low aging degrees, reducing unnecessary communication interactions and computational resource consumption; a shorter compensation interval is used for detectors with high aging degrees, ensuring the timeliness of compensation and detection accuracy, thereby achieving an optimal balance between system resource consumption and detection accuracy.

[0016] In conjunction with the first aspect, in one possible embodiment, the first operating data further includes first temperature and humidity data, and the method further includes: if the first temperature and humidity data is greater than a first preset threshold, generating aging acceleration parameters for the target detector based on the first temperature and humidity data; and determining the aging degree of the target detector based on the aging acceleration parameters.

[0017] As can be seen, in this embodiment of the application, by introducing the first temperature and humidity data and generating aging acceleration parameters accordingly, the degree of aging is dynamically corrected, so that the detector operating in harsh environments such as high temperature and high humidity can be more accurately assessed for its true aging state, thereby providing a more reliable basis for subsequently determining the compensation time interval and operating stage, avoiding the problem of untimely or insufficient compensation due to underestimating the degree of aging, and further improving the detection accuracy and reliability of the detector in complex environments.

[0018] By implementing the methods in the above-mentioned embodiments, it can be seen that the impact of the target detector's operating stage on the compensation value is considered, thereby improving the accuracy and reliability of carbon monoxide concentration detection; by acquiring historical operating data from multiple detectors of the same type, the data characteristics of the actual detectors are incorporated into the sensitivity attenuation model, further improving the accuracy of carbon monoxide concentration detection; different model construction methods are adopted for different operating stages, improving compensation efficiency and accuracy; and different compensation cycles are adopted for target detectors with different aging degrees, ensuring the timeliness of compensation and detection accuracy.

[0019] Secondly, embodiments of this application provide a detection data compensation device for a carbon monoxide detector, comprising: The acquisition unit is used to acquire the first operating data sent by the target detector. The first operating data includes multiple first sensitivities and multiple first operating times of the target detector, and the first sensitivities and the first operating times correspond one-to-one. The transmitting unit is used to determine the operating phase of the target detector based on the first operating data transmitted by the target detector. The operating phase includes at least one of an early phase, a middle phase, and a late decay phase. The acquisition unit is also used to acquire the sensitivity attenuation model of the target detector according to the operating stage of the target detector; The determination unit is used to determine the target compensation value of the target detector according to the sensitivity attenuation model. The target compensation value is sent to the target detector so that the target detector compensates for the detected carbon monoxide concentration.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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

[0024] 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.

[0025] Figure 1 A schematic diagram illustrating an application scenario of a carbon monoxide detector detection data compensation method provided in this application embodiment; Figure 2 A flowchart illustrating a method for compensating detection data of a carbon monoxide detector provided in an embodiment of this application; Figure 3 A schematic diagram of an initial decay curve corresponding to an early stage provided in an embodiment of this application; Figure 4 This is a schematic diagram illustrating the determination of a sensitivity attenuation model provided in an embodiment of this application; Figure 5 A schematic flowchart illustrating another method for compensating detection data of a carbon monoxide detector provided in an embodiment of this application; Figure 6 A schematic diagram of an initial decay curve corresponding to the late stage provided in an embodiment of this application; Figure 7 A flowchart illustrating another method for compensating detection data of a carbon monoxide detector provided in an embodiment of this application; Figure 8 A schematic diagram of the structure of a detection data compensation device for a carbon monoxide detector provided in 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.

[0026] Explanation of reference numerals: 100: Application scenario; 101: Cloud server; 102: First detector; 103: Target detector; 800: Detection data compensation device for carbon monoxide detector; 801: Acquisition unit; 802: Transmission unit; 803: Determination unit; 900: Electronic device; 901: Memory; 902: Processor; 903: Communication interface; 904: Bus. Detailed Implementation

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

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

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

[0030] The embodiments of this application will now be described with reference to the accompanying drawings.

[0031] Example 1: Please refer to Figure 1 , Figure 1This is a schematic diagram of an application scenario for a carbon monoxide detector detection data compensation method provided in an embodiment of this application. In application scenario 100, a cloud server 101, a first detector 102, and a target detector 103 are included.

[0032] The cloud server 101 establishes communication connections with both the first detector 102 and the target detector 103. The first detector 102 is a carbon monoxide detector of the same model or type as the target detector 103, such as both being electrochemical carbon monoxide detectors. It periodically collects and uploads its own operating data to the cloud server 101 via its built-in self-test module, providing historical data samples for constructing a sensitivity decay model. The target detector 103 is the carbon monoxide detector currently requiring detection data compensation. It sends its own first operating data to the cloud server 101 through the same data channel and receives the target compensation value from the cloud server 101 to complete the compensation calibration of the detected carbon monoxide concentration.

[0033] In this embodiment, the cloud server 101 determines the current operating stage of the target detector 103 based on the first operating data sent by the target detector 103. The operating stage includes at least one of an early stage, a mid-stage, and a late decay stage, with different stages characterizing different trends in sensitivity changes over time.

[0034] Based on the determined operating stage, cloud server 101 obtains the sensitivity attenuation model corresponding to that stage. The sensitivity attenuation model describes the law of sensitivity attenuation of target detector 103 over time in the current operating stage. The attenuation models corresponding to different operating stages may be the same or different.

[0035] Based on the acquired sensitivity attenuation model, cloud server 101 calculates the target compensation value currently required by target detector 103. This target compensation value is sent to target detector 103, enabling it to compensate for the detected carbon monoxide concentration, thereby correcting the detection deviation caused by sensitivity attenuation.

[0036] As can be seen, in this embodiment of the application, by judging the operating stage of the target detector based on its own operating data, and selecting an appropriate sensitivity attenuation model according to different stages to determine the compensation value, the compensation process can be made to better fit the actual aging state of the detector, avoiding the problem of inaccurate compensation caused by using a single fixed compensation method, thereby improving the accuracy and reliability of carbon monoxide concentration detection.

[0037] Please see Figure 2 , Figure 2 This application provides a flowchart illustrating a method for compensating detection data of a carbon monoxide detector, which can be based on... Figure 1The application scenario 100 shown is implemented as follows: Figure 2 As shown, it includes steps S201-S204.

[0038] S201: The cloud server obtains the first operating data sent by the target detector. The first operating data includes multiple first sensitivities and multiple first operating times of the target detector, with each first sensitivity and first operating time corresponding one-to-one.

[0039] The cloud server acquires the first operational data sent by the target detector, which is used to reflect the operational status and performance changes of the target detector during actual use.

[0040] For example, the target detector is an electrochemical carbon monoxide detector. Its built-in self-test module automatically collects the current sensitivity value according to a preset cycle (such as every 24 hours) and records the cumulative running time corresponding to the collection time, forming multiple data pairs of first sensitivity and multiple first running time.

[0041] The first sensitivity is the actual sensitivity value measured by the target detector at the current operating time, and the first operating time is the cumulative duration from the initial use of the target detector to the current data acquisition time. The first operating data may further include ambient temperature and humidity data, baseline data, and sensor type identification at the time of acquisition. This data helps determine the true cause of sensitivity changes, such as distinguishing between short-term fluctuations caused by environmental factors and long-term degradation caused by aging factors. Through this method, the first operating data acquired by the cloud server includes both the original trajectory of sensitivity degradation over time and key environmental parameters affecting the degradation pattern, providing a data foundation for accurately dividing the operating phases subsequently.

[0042] S202: The cloud server determines the operating phase of the target detector based on the first operating data sent by the target detector. The operating phase includes at least one of the early phase, the middle phase, and the late decay phase.

[0043] Specifically, the cloud server analyzes the trend of the first sensitivity change over the first operating time based on the first operating data sent by the target detector, thereby determining the current operating stage of the target detector. The operating stage includes at least one of the early stage, the middle stage, and the late decay stage, where the early stage corresponds to a period of relatively slow sensitivity decay, the middle stage corresponds to a period of approximately linear sensitivity decay, and the late decay stage corresponds to a period of accelerated sensitivity decay until it approaches the failure threshold.

[0044] In some possible implementations, the cloud server calculates the sensitivity change rate of the target detector within different time periods based on multiple first sensitivities and corresponding multiple first running times. If the sensitivity change rate is less than or equal to a first preset threshold, the target detector is determined to be in the early stage; if the sensitivity change rate is greater than the first preset threshold and less than or equal to a second preset threshold, the target detector is determined to be in the middle stage; if the sensitivity change rate is greater than the second preset threshold, the target detector is determined to be in the late decay stage.

[0045] S203: The cloud server obtains the sensitivity attenuation model corresponding to the target detector based on the target detector's operating stage.

[0046] Specifically, the cloud server obtains the sensitivity attenuation model corresponding to the determined operating stage of the target detector. The sensitivity attenuation model describes the change in the sensitivity of the target detector over time during the current operating stage. The sensitivity attenuation models for different operating stages can be the same or different, and the sensitivity attenuation model for the same operating stage can be pre-established or determined in real time through various methods.

[0047] In some possible implementations, the cloud server pre-stores multiple sensitivity attenuation models, each corresponding to a specific operational phase, and each model is determined through experiments using the same type of detector. After determining the current operational phase of the target detector, the cloud server directly selects the sensitivity attenuation model matching that phase from the pre-stored models. For example, if the target detector is in an early phase, the cloud server selects the attenuation model corresponding to the early phase, which assumes that the sensitivity attenuates slowly at a fixed small margin during the early phase.

[0048] In this way, the cloud server obtains a sensitivity decay model that matches the decay pattern of the target detector at its current operating stage, providing a model basis that conforms to the actual aging characteristics for subsequent calculation of the target compensation value.

[0049] Optionally, obtaining the sensitivity attenuation model corresponding to the target detector based on the operation phase of the target detector includes: obtaining multiple second operation data transmitted by multiple first detectors, wherein the second operation data includes multiple second sensitivities and multiple second operation times of the corresponding first detector, and the second sensitivity and the second operation time correspond one-to-one; obtaining third operation data matching the operation phase from the multiple second operation data, wherein the third operation data includes at least one second operation data; determining the corresponding initial attenuation model based on the operation phase; and constructing a sensitivity attenuation model based on the third operation data and the initial attenuation model.

[0050] Specifically, when the cloud server obtains the corresponding sensitivity attenuation model based on the operating stage of the target detector, it first acquires multiple sets of second operating data sent by multiple first detectors. The first detectors are detectors of the same model or type as the target detector, such as both being electrochemical carbon monoxide detectors.

[0051] The second operational data includes the second sensitivity of each first detector collected at multiple time points and the second operational time corresponding to each second sensitivity. The second sensitivity and the second operational time are in one-to-one correspondence, which is used to reflect the overall distribution of the sensitivity of multiple detectors of the same type as the operational time during actual use.

[0052] The cloud server filters out third operational data from multiple second operational data sets that match the current operational phase of the target detector. For example, if the target detector is currently in the mid-stage, the cloud server extracts data from all the second operational data sets that are also in the mid-stage, i.e., data samples whose second sensitivity is within the mid-stage range and whose second operational time falls within the corresponding time interval of the mid-stage. These filtered third operational data sets constitute the sample set for constructing the sensitivity decay model.

[0053] The cloud server determines the initial attenuation model corresponding to the current operating stage of the target detector, and then calibrates or reconstructs the initial attenuation model based on the selected third operating data, thereby constructing a sensitivity attenuation model suitable for the current stage.

[0054] The initial attenuation model is used to describe the basic mathematical form of sensitivity attenuation in this stage, while the third set of running data is used to fit the parameters in the model or to verify and adjust the model structure.

[0055] For example, the initial attenuation model corresponding to this stage is determined to be a theoretical attenuation model constructed based on the detector's factory design parameters. This theoretical attenuation model is derived from the rated performance parameters calibrated by the detector manufacturer during the product design phase, including the initial sensitivity of the new sensor, the sensitivity at the end of the design life, and the theoretical attenuation trajectory.

[0056] As can be seen, in this embodiment of the application, by acquiring historical operating data of multiple detectors of the same type, samples that match the current operating stage of the target detector are selected, and the initial attenuation model of the corresponding stage is calibrated and constructed based on the samples. This makes the constructed sensitivity attenuation model not only conform to the typical attenuation pattern of the stage, but also incorporate the data characteristics of the actual detector, thereby improving the fitting accuracy of the model to the actual attenuation state of the target detector and providing a reliable basis for the accurate calculation of subsequent compensation values.

[0057] Optionally, the operation phase is an early stage, in which a sensitivity attenuation model is constructed based on the third operation data and the initial attenuation model, including: determining the first preset model as the initial attenuation model, wherein the first preset model is a linear relationship model; generating an initial attenuation curve based on the third operation data and the initial attenuation model; if the absolute value of the slope of the initial attenuation curve is not greater than the first preset threshold, then determining the target sensitivity based on the initial attenuation curve; and constructing a sensitivity attenuation model based on the target sensitivity.

[0058] Specifically, in this embodiment of the application, the target detector is in an early stage of operation, and the cloud server constructs a sensitivity attenuation model based on a preset linear relationship model.

[0059] The cloud server uses a first preset model as the initial decay model, which is a linear relationship model. In the early stages, the sensitivity decays relatively slowly over time, and using a linear relationship model as the initial decay model can concisely describe the decay trend in this stage.

[0060] For example, the first preset model satisfies the following equation (1).

[0061] (1) The first preset model S1(T) represents the sensitivity when the running time is T, S0 is the initial sensitivity, k is the first attenuation coefficient to be fitted, and T is the running time.

[0062] The cloud server acquires third operational data sent by multiple detectors of the same model in the early stages. This third operational data includes multiple second sensitivities and corresponding second operational times. The cloud server substitutes these data points into a linear relationship model and uses the least squares method to fit an initial attenuation curve that reflects the overall distribution trend of the data points, and calculates the absolute value of the slope of this curve.

[0063] The cloud server compares the calculated absolute value of the slope with a preset slope threshold. The preset slope threshold is a positive number close to zero, such as 0.01, used to determine whether the sensitivity decay is significant.

[0064] If the absolute value of the slope is less than or equal to the preset slope threshold, it indicates that the initial attenuation curve is relatively flat and the sensitivity decreases very little with running time. At this time, the cloud server determines that the early stage is the sensitivity stable range, that is, the sensitivity of the target detector does not change much in this range.

[0065] For example, please refer to Figure 3 , Figure 3This is a schematic diagram of an initial attenuation curve corresponding to an early stage provided in an embodiment of this application. It can be seen that in this image, the sensitivity decreases significantly with the increase of running time, that is, the absolute value of the slope is less than or equal to a preset slope threshold. Therefore, a target sensitivity can be determined based on this initial attenuation curve.

[0066] In this scenario, the cloud server determines the target sensitivity based on the stable sensitivity range. The target sensitivity is a fixed value; for example, the cloud server can use the difference between the average sensitivity of all samples within the stable sensitivity range and the initial sensitivity as the target sensitivity, or it can directly set the target sensitivity to zero, indicating that no dynamic compensation over time is required during this phase.

[0067] The cloud server constructs a sensitivity decay model based on the target sensitivity. This model is a constant model, and its output value is the target sensitivity, which does not change with runtime. For example, the constructed sensitivity decay model is C(T) = C0, where C0 is the target sensitivity and T is the runtime.

[0068] As can be seen in this embodiment, considering the extremely gradual sensitivity decay in the early stage, the slope of the initial decay curve is calculated and compared with a preset slope threshold to determine whether the sensitivity is in a stable range. When it is confirmed that the sensitivity is basically not decaying, a constant model is constructed using the target sensitivity, avoiding unnecessary dynamic compensation due to small fluctuations, reducing the data interaction frequency between the cloud server and the detector, and reducing computational overhead.

[0069] Optionally, the operation phase is the mid-term phase, in which a sensitivity decay model is constructed based on the third operation data and the initial decay model, including: determining the first preset model as the initial decay model; determining the fitting weight of the corresponding third operation data based on the closeness between the second operation time and the first operation time in the third operation data; and fitting the sensitivity decay model based on the initial decay model, the fitting weight, and the third operation data.

[0070] Specifically, in this embodiment of the application, the target detector is in the mid-stage of operation. The cloud server still constructs the sensitivity attenuation model according to the preset linear relationship model, but the specific construction process is different.

[0071] The cloud server uses a pre-defined model as the initial attenuation model and simultaneously acquires third operational data from multiple detectors of the same model during the mid-term phase. This third operational data includes multiple second sensitivities and corresponding second operational times. Based on the proximity of each second operational time in the third operational data to the target detector's current first operational time, the cloud server determines the fitting weight of each third operational data point in the subsequent fitting process. The closer the second operational time is to the first operational time, the higher the similarity between the historical data and the target detector's current state, and the higher the weight assigned when fitting the model; conversely, the greater the difference between the second and first operational times, the lower the weight assigned.

[0072] For example, cloud servers use the inverse time difference method to determine the fitting weights, assigning larger weights to samples with similar runtimes and smaller weights to samples with significantly different runtimes. Cloud servers can also use a Gaussian kernel function or other distance decay functions to calculate the weights, causing the weights to decay exponentially with increasing time difference.

[0073] Based on the determined initial attenuation model, the calculated fitting weights, and the third set of running data, the cloud server uses weighted least squares to fit the data, obtaining a sensitivity attenuation model suitable for the current state of the target detector. During the fitting process, the cloud server minimizes the weighted sum of squared residuals. By solving this optimization problem, the cloud server obtains the calibrated first attenuation coefficient k, thus constructing the final sensitivity attenuation model.

[0074] For example, please refer to Figure 4 , Figure 4 This is a schematic diagram illustrating the determination of a sensitivity attenuation model provided in an embodiment of this application. The sensitivity attenuation models for both the early and mid-stages use a first preset model as the initial attenuation model. For the early stage, an initial attenuation curve is first determined based on the third running data and the initial attenuation model. Then, the final initial sensitivity model is determined based on whether the absolute value of the slope of the initial attenuation curve is greater than a preset slope threshold. For the mid-stage, the sensitivity attenuation model is determined based on the initial attenuation model, the calculated fitting weights, and the third running data.

[0075] As can be seen in this embodiment, considering the linear decay of sensitivity in the mid-term stage, by introducing fitting weights based on the similarity of running time, historical data that is more similar to the current state of the target detector plays a greater role in model construction, thereby constructing a sensitivity decay model that better fits the actual decay law of the target detector, improving the fitting accuracy of the model in the mid-term stage, and providing a more reliable basis for the accurate calculation of subsequent compensation values.

[0076] Optionally, the first operating parameters also include the reference value of the corresponding first detector and the second temperature and humidity data of the detection environment. The method further includes: determining the validity of the second operating sensitivity based on the reference value and the second temperature and humidity data; and deleting the second operating data that is not valid.

[0077] Specifically, the second operating data sent by the first detector also includes a corresponding reference value and second temperature and humidity data of the detection environment. The reference value is used to characterize the output signal level of the first detector in a target gas environment, and the second temperature and humidity data includes temperature and humidity values, which are used to reflect the environmental conditions in which the first detector is located when acquiring the second sensitivity.

[0078] After acquiring multiple sets of second-running data, the cloud server determines the validity of each set of second-running data based on the baseline value and the second temperature and humidity data, and deletes invalid second-running data to ensure the quality of the sample data used to build the sensitivity decay model.

[0079] For example, the cloud server determines whether the first detector is in normal working condition based on a baseline value. The preset normal range of the baseline value is [B min B max ], where B min B is the minimum value of the benchmark. max This is the baseline maximum value. If the baseline value in some second set of data exceeds this range, for example, if the baseline value is much larger than B... max If this indicates that the detector's sensor has zero-point drift or abnormal data acquisition, then the second set of operating data is deemed invalid and deleted.

[0080] For example, the cloud server determines whether the first detector is in a suitable working environment based on the second temperature and humidity data. The preset temperature tolerance range is -10℃ to 60℃, and the preset humidity tolerance range is 10% to 90%. If the temperature value in a certain second operating data is lower than -10℃ or higher than 60℃, or the humidity value is lower than 10% or higher than 90%, it indicates that the data was collected under extreme environmental conditions. In such environments, the change in sensitivity is mainly driven by environmental factors rather than natural aging and has no statistical significance. Therefore, the second operating data is determined to be invalid and deleted.

[0081] Furthermore, the cloud server also combines the baseline value and the second temperature and humidity data for comprehensive judgment. When the second temperature and humidity data is within the normal range, but the baseline value deviates from the normal range, the data is deemed invalid; when the baseline value is within the normal range, but the second temperature and humidity data exceeds the tolerance range, the data is deemed invalid. Only when both the baseline value and the second temperature and humidity data meet the preset threshold conditions are the corresponding second sensitivity and second running time data recognized as valid data and retained for subsequent model building.

[0082] In this way, the cloud server uses benchmark values ​​and temperature and humidity data to screen the effectiveness of the collected second operating data, eliminating invalid samples caused by equipment failure, extreme environment or data anomalies, so that the third operating data participating in the subsequent fitting more realistically reflects the natural attenuation law of the detector under normal operating conditions, thereby improving the construction accuracy and reliability of the sensitivity attenuation model.

[0083] S204: The cloud server determines the target compensation value of the target detector based on the sensitivity attenuation model. The target compensation value is sent to the target detector to compensate for the detected carbon monoxide concentration.

[0084] Specifically, the cloud server determines the target compensation value required by the target detector based on the constructed sensitivity attenuation model. The sensitivity attenuation model describes the change in the sensitivity of the target detector over different operating times. Based on this model, the cloud server calculates the theoretical sensitivity of the target detector at the current operating time, and then determines the deviation that needs to be compensated as the target compensation value.

[0085] Optionally, the first operating data further includes first temperature and humidity data. After determining the operating stage of the target detector based on the first operating data sent by the target detector, if the operating stage does not match the second operating time of the target detector, the method further includes: determining the aging degree of the target detector based on the first temperature and humidity data; if the aging degree does not match the operating stage, then performing subsequent steps; if the aging degree does not match the operating stage, then calculating the rate of change of any two adjacent first sensitivities; if the rate of change exceeds a preset mutation threshold, then identifying the target detector as a faulty detector.

[0086] Specifically, after determining the operational phase of the target detector based on the initial operational data sent by the target detector, the cloud server needs to further determine whether the operational phase matches the target detector's second operational time. Here, the second operational time represents the cumulative operational time of the target detector from its initial use to the present. The matching between the operational phase and the second operational time is used to verify whether the currently determined operational phase conforms to the expected normal aging process of the detector.

[0087] If the operating phase does not match the second operating time, for example, the second operating time of the target detector shows that it is in the early stage of use, but the operating phase determined according to the first operating data is the late decay stage, it indicates that the sensitivity decay rate of the detector is much higher than expected, and there may be an abnormal situation.

[0088] At this point, the cloud server determines the aging level of the target detector based on the initial temperature and humidity data. The cloud server further determines whether this aging level matches the operational phase. If the aging level matches the operational phase, for example, if the aging level is significantly higher under high temperature and high humidity conditions, which can explain the mismatch between the operational phase and the operational time, then the abnormal performance of the detector can be explained by environmental factors, and the cloud server can continue to execute the subsequent compensation process.

[0089] If the aging level still does not match the operational phase, meaning the dominant influence of environmental factors has been ruled out, the cloud server calculates the rate of change between any two adjacent first sensitivities in the first operational data of the target detector. For example, the cloud server calculates the rate of change of sensitivity between adjacent time points sequentially according to the order of the first operational time.

[0090] The cloud server compares the calculated rate of change with a preset mutation threshold. The preset mutation threshold can be pre-set according to the detector model and normal aging process, for example, set to 30% or 50%. If the rate of change of any two adjacent first sensitivities exceeds the preset mutation threshold, it indicates that the detector's sensitivity has suddenly decreased or increased in a short period of time. In addition, if the above judgment has ruled out normal differences in operating time and interference from environmental factors, the cloud server will identify the target detector as a faulty detector.

[0091] For example, if the sensitivity of the target detector is stable at about 90% of the initial sensitivity during normal operation, and the sensitivity suddenly drops from 90% to 30% in two consecutive data acquisitions, far exceeding the preset mutation threshold of 30%, and the running time shows that the detector is still within the normal duration of the mid-stage, and the ambient temperature and humidity data are also within the normal range, then the cloud server determines that the detector has problems such as sensor sensitive material failure, internal circuit failure, or physical damage, and marks it as a faulty detector.

[0092] Once the cloud server determines that the target detector is faulty, it can stop sending compensation values ​​to the detector and generate a fault message, which is then pushed to the maintenance personnel's terminal to prompt on-site inspection or equipment replacement, in order to avoid the risk of detection blind spots or false alarms caused by detector failure.

[0093] As can be seen, in this embodiment of the application, by introducing temperature and humidity data to assess the degree of aging when the operation phase and operation time are mismatched, and further detecting sensitivity mutations after excluding environmental factors, it is possible to effectively distinguish between normal individual deviations caused by accelerated environmental aging and abnormal faults caused by physical damage to the equipment. This avoids misjudging compensable individual differences as equipment damage, and at the same time realizes the function of identifying faulty detectors, ensuring the reliability and safety of carbon monoxide detectors.

[0094] Example 2: The above application mainly describes a method for compensating the detection data of a carbon monoxide detector, including early and mid-stage target detectors. Based on this, this application also provides a method for compensating the detection data of a carbon monoxide detector, including a late-stage target detector. Please refer to... Figure 5 , Figure 5 This is a flowchart illustrating another method for compensating detection data of a carbon monoxide detector provided in an embodiment of this application. It can be based on... Figure 1 The application scenario 100 shown is implemented as follows: Figure 5 As shown, it includes steps S501-S506.

[0095] S501: The cloud server obtains the first operating data sent by the target detector. The first operating data includes multiple first sensitivities and multiple first operating times of the target detector, with each first sensitivity and first operating time corresponding one-to-one.

[0096] S502: The cloud server determines the operating phase of the target detector based on the first operating data sent by the target detector. The operating phase includes at least one of the early phase, the middle phase, and the late decay phase.

[0097] S503: The cloud server acquires multiple second operating data sent by multiple first detectors. The second operating data includes multiple second sensitivities and multiple second operating times of the corresponding first detectors, and the second sensitivities and second operating times correspond one-to-one.

[0098] S504: The cloud server obtains third operational data matching the operational phase from multiple second operational data, the third operational data including at least one second operational data; and determines the corresponding initial attenuation model according to the operational phase.

[0099] S505: The cloud server constructs a sensitivity attenuation model based on the third-party running data and the initial attenuation model.

[0100] Optionally, the operation phase is the late decay phase, and a sensitivity decay model is constructed based on the third operation data and the initial decay model, including: determining the second preset model as the initial decay model, the second preset model being a nonlinear relationship model; obtaining the decay acceleration coefficient by fitting the third operation data, the decay acceleration coefficient being used to characterize the magnitude of the accelerated decay of sensitivity as the operation time increases; and constructing a sensitivity decay model based on the decay acceleration coefficient and the exponential decay model.

[0101] Specifically, in this embodiment of the application, the target detector is in the late decay stage of operation, and the cloud server constructs a sensitivity decay model based on the second preset model.

[0102] The second preset model is a nonlinear relationship model. In the late decay stage, the sensitivity gradually decreases with running time, exhibiting accelerated decay characteristics. Using a nonlinear relationship model can more accurately describe the decay law in this stage.

[0103] In one possible implementation, the second preset model takes the form of a quadratic function and satisfies the following equation (2).

[0104] (2) The second preset model S2(T) characterizes the sensitivity when the running time is T; S0 represents the initial sensitivity; a is the decay acceleration coefficient to be fitted, and b is the second coefficient to be fitted.

[0105] It should be noted that the cloud server acquires third operational data transmitted by multiple detectors of the same model during the late decay phase. This third operational data includes multiple second sensitivities and corresponding second operational times. The cloud server substitutes these data points into a second preset model and uses a nonlinear regression method to fit and obtain the decay acceleration coefficient 'a', which is used to characterize the magnitude of the accelerated decay of sensitivity with increasing operational time. When 'a' is positive and has a large absolute value, it indicates a significant acceleration trend in decay; when 'a' is close to zero, it indicates that the decay in this stage is still close to linear.

[0106] During the fitting process, the cloud server uses the least squares method to calibrate the model parameters, minimizing the mean square error between the model's predicted values ​​and the second sensitivity in each group of the third running data, ensuring that the constructed sensitivity decay model can accurately reflect the actual decay trend in the late decay stage.

[0107] See, for example Figure 6 , Figure 6 This application provides a schematic diagram of an initial decay curve corresponding to the late stage, showing that in... Figure 6 In the sensitivity decay model shown, the sensitivity exhibits accelerated decay with increasing operating time. This is because, in the late decay stage, the electrochemical reaction materials or electrode structures of the internal sensor of the detector have undergone significant aging. The accumulation of aging products leads to a continuous decrease in reaction efficiency. Simultaneously, long-term operation may be accompanied by electrolyte consumption or electrode passivation, causing the sensitivity decay rate to accelerate with increasing operating time. Therefore, using a nonlinear relationship model as the initial decay model and constructing a sensitivity decay model by fitting the decay acceleration coefficient can accurately capture the changing trend of accelerated sensitivity decay in this stage. This ensures that the model output closely matches the actual decay trajectory, providing a more accurate basis for calculating the target compensation value in the late stage and avoiding the insufficient compensation problem that may occur in linear models during the accelerated decay stage.

[0108] As can be seen in the embodiments of this application, in view of the characteristics of accelerated sensitivity decay in the late decay stage, a nonlinear relationship model is used as the initial decay model. The decay acceleration coefficient is obtained by fitting, and a sensitivity decay model that can reflect the accelerated decay trend is constructed accordingly. This allows the model to maintain high fitting accuracy in the late stage of detector aging, thereby providing an accurate basis for the calculation of the target compensation value in the late stage and effectively making up for the problem of poor fitting effect of the linear model in the accelerated decay stage.

[0109] S506: The cloud server determines the target compensation value of the target detector based on the sensitivity attenuation model. The target compensation value is sent to the target detector to compensate for the detected carbon monoxide concentration.

[0110] For detailed explanations of steps S501-S506, please refer to the relevant content of steps S201-S204, which will not be repeated here.

[0111] Example 3 This application embodiment is based on the carbon monoxide detector detection data compensation method described in the above embodiments. This application embodiment also provides a more detailed carbon monoxide detector detection data compensation method; please refer to [link to relevant documentation]. Figure 7 , Figure 7 This is a flowchart illustrating another method for compensating detection data of a carbon monoxide detector provided in an embodiment of this application. It can be based on... Figure 1 The application scenario 100 shown is implemented as follows: Figure 7 As shown, it includes steps S701-S707.

[0112] S701: The cloud server obtains the first operating data sent by the target detector. The first operating data includes multiple first sensitivities and multiple first operating times of the target detector, with each first sensitivity and first operating time corresponding one-to-one.

[0113] S702: The cloud server determines the aging level of the target detector based on the first operating data.

[0114] Specifically, before determining the operating stage based on the first operating data sent by the target detector, the cloud server also determines the aging degree of the target detector based on the first operating data.

[0115] The aging level is used to characterize the current performance degradation level of the target detector, and it is related to multiple first sensitivities and multiple first operating times in the first operating data. For example, the cloud server determines the aging level based on the rate of change of the first sensitivity with the first operating time; the faster the sensitivity decreases, the higher the aging level. Alternatively, the cloud server determines the aging level based on the ratio of the absolute value of the first sensitivity to the initial sensitivity; the smaller the ratio of the current sensitivity to the initial sensitivity, the higher the aging level.

[0116] S703: The cloud server determines the compensation time interval based on the aging degree of the target detector, and the aging degree is inversely correlated with the compensation time interval.

[0117] Specifically, the cloud server determines the compensation time interval based on the aging degree of the target detector. The aging degree and the compensation time interval are inversely correlated; that is, the higher the aging degree, the shorter the compensation time interval; and the lower the aging degree, the longer the compensation time interval.

[0118] For example, when the target detector is in the early stage of low aging, the sensitivity decays slowly and the change in sensitivity is not obvious in a short period of time. Therefore, the compensation interval can be set to a longer time, such as once every 365 days. When the target detector is in the late stage of high aging, the sensitivity decays faster and more frequent compensation is required to maintain detection accuracy. Therefore, the compensation interval can be set to a shorter time, such as once every 180 days.

[0119] S704: The cloud server determines that the time interval for sending the detection compensation value of the target detector is not less than the compensation time interval.

[0120] Specifically, the target compensation value transmission interval refers to the actual time interval at which the cloud server sends the target compensation value to the target detector. This time interval is scheduled by the cloud server based on the compensation interval. For example, if the cloud server calculates the compensation interval to be 165 days based on the aging level, then the cloud server will ensure that the time interval between two consecutive transmissions of the target compensation value is not less than 165 days to avoid communication overhead and equipment power consumption caused by excessively frequent compensation transmissions. If the aging level of the target detector increases and the compensation interval is shortened to 180 days, then the cloud server will adjust the transmission frequency accordingly to ensure timely compensation.

[0121] Furthermore, the cloud server can establish a correspondence between aging levels and compensation time intervals using a segmented mapping method. For example, multiple aging levels can be preset, each corresponding to a compensation time interval; the higher the aging level, the higher the corresponding aging level and the shorter the compensation time interval. The cloud server matches the aging level of the target detector with the preset aging levels to determine the corresponding compensation time interval.

[0122] As can be seen, in this embodiment, by dynamically determining the compensation time interval based on the aging degree of the target detector before determining the operation phase, and ensuring that the actual transmission time interval is not less than the compensation time interval, the adaptation of the compensation frequency to the aging state of the detector is achieved. A longer compensation interval is used for detectors with low aging degrees, reducing unnecessary communication interactions and computational resource consumption; a shorter compensation interval is used for detectors with high aging degrees, ensuring the timeliness of compensation and detection accuracy, thereby achieving an optimal balance between system resource consumption and detection accuracy.

[0123] Optionally, the first operating data also includes first temperature and humidity data, and the method further includes: if the first temperature and humidity data is greater than a first preset threshold, generating aging acceleration parameters for the target detector based on the first temperature and humidity data; and determining the aging degree of the target detector based on the aging acceleration parameters.

[0124] Specifically, the first operating data also includes the first temperature and humidity data, which includes the ambient temperature and humidity values ​​of the target detector when it collects the first sensitivity data. In this embodiment, when the cloud server determines the aging degree of the target detector based on the first operating data, it also combines the first temperature and humidity data to make a correction judgment on the aging degree.

[0125] After the cloud server acquires the initial temperature and humidity data, it compares it with a first preset threshold. The first preset threshold includes a temperature threshold and a humidity threshold; for example, the temperature threshold is set to 40℃ and the humidity threshold is set to 80%RH. If the temperature value in the initial temperature and humidity data is greater than the temperature threshold, or the humidity value is greater than the humidity threshold, or both are greater than their respective thresholds, it indicates that the target detector is operating in a harsh environment with high temperature or high humidity. Such an environment will accelerate the aging process of the sensor.

[0126] When the initial temperature and humidity data exceed a preset threshold, the cloud server generates aging acceleration parameters for the target detector based on this data. These acceleration parameters characterize the additional impact of environmental factors on the detector's aging rate. For example, the cloud server calculates an acceleration factor greater than 1 based on the magnitude of temperature and humidity exceeding the threshold, combined with a preset acceleration coefficient mapping relationship. The greater the temperature and humidity exceed the threshold, the larger the acceleration factor, indicating more significant aging acceleration. For instance, when the temperature is between 40°C and 50°C, the acceleration factor is 1.2; when the temperature exceeds 50°C, the acceleration factor is 1.5; the same applies to humidity.

[0127] The cloud server determines the aging degree of the target detector based on aging acceleration parameters. Specifically, the cloud server first calculates the baseline aging degree based on multiple first sensitivities and multiple first operating times from the initial operating data, such as the sensitivity change rate or the ratio of the current sensitivity to the initial sensitivity. Then, the cloud server corrects this baseline aging degree using the aging acceleration parameters by multiplying the baseline aging degree by the aging acceleration parameters to obtain the corrected aging degree. For example, if the baseline aging degree is 20% and the aging acceleration parameter is 1.3, the corrected aging degree is 26%, indicating that under harsh environmental conditions, the actual aging degree of the target detector is more severe than the result determined solely by operating time.

[0128] In this way, when assessing the aging degree of the target detector, the cloud server not only considers the natural decay of sensitivity over time, but also introduces the accelerated effect of ambient temperature and humidity on the aging process, making the determination of the aging degree more consistent with the actual state of the detector under real working conditions.

[0129] As can be seen, in this embodiment of the application, by introducing the first temperature and humidity data and generating aging acceleration parameters accordingly, the degree of aging is dynamically corrected, so that the detector operating in harsh environments such as high temperature and high humidity can be more accurately assessed for its true aging state, thereby providing a more reliable basis for subsequently determining the compensation time interval and operating stage, avoiding the problem of untimely or insufficient compensation due to underestimating the degree of aging, and further improving the detection accuracy and reliability of the detector in complex environments.

[0130] S705: The cloud server determines the operating phase of the target detector based on the first operating data sent by the target detector. The operating phase includes at least one of the early phase, the middle phase, and the late decay phase.

[0131] S706: The cloud server obtains the sensitivity attenuation model corresponding to the target detector based on the target detector's operating phase.

[0132] S707: The cloud server determines the target compensation value of the target detector based on the sensitivity attenuation model. The target compensation value is sent to the target detector to compensate for the detected carbon monoxide concentration.

[0133] For detailed explanations of the other steps in steps S701-S707, please refer to the relevant content in steps S201-S204, which will not be repeated here.

[0134] By implementing the methods in the above-mentioned embodiments, it can be seen that the impact of the target detector's operating stage on the compensation value is considered, thereby improving the accuracy and reliability of carbon monoxide concentration detection; by acquiring historical operating data from multiple detectors of the same type, the data characteristics of the actual detectors are incorporated into the sensitivity attenuation model, further improving the accuracy of carbon monoxide concentration detection; different model construction methods are adopted for different operating stages, improving compensation efficiency and accuracy; and different compensation cycles are adopted for target detectors with different aging degrees, ensuring the timeliness of compensation and detection accuracy.

[0135] Based on the description of the above configuration method embodiments, this application also provides a carbon monoxide detector detection data compensation device 800, which can operate in... Figure 1 A computer program (including program code) is shown for executing the cloud server 101. Figure 2 , Figure 5 and Figure 7 The method shown. Please refer to [link / reference]. Figure 8 , Figure 8 This is a schematic diagram of the structure of a carbon monoxide detector detection data compensation device 800 provided in an embodiment of this application. The carbon monoxide detector detection data compensation device 800 includes: The acquisition unit 801 is used to acquire the first operating data sent by the target detector. The first operating data includes multiple first sensitivities and multiple first operating times of the target detector, and the first sensitivities and the first operating times correspond one-to-one. The transmitting unit 802 is used to determine the operating stage of the target detector based on the first operating data transmitted by the target detector. The operating stage includes at least one of an early stage, a middle stage, and a late decay stage. The acquisition unit 801 is also used to acquire the sensitivity attenuation model of the target detector according to the operating stage of the target detector; The determining unit 803 is used to determine the target compensation value of the target detector according to the sensitivity attenuation model. The target compensation value is sent to the target detector so that the target detector compensates for the detected carbon monoxide concentration.

[0136] In one possible embodiment, in obtaining the sensitivity attenuation model corresponding to the target detector based on the operating phase of the target detector, the acquisition unit 801 is further specifically configured to: acquire multiple second operating data sent by multiple first detectors, the second operating data including multiple second sensitivities and multiple second operating times of the corresponding first detector, the second sensitivity and the second operating time being in one-to-one correspondence; acquire third operating data matching the operating phase from the multiple second operating data, the third operating data including at least one second operating data; determine the corresponding initial attenuation model based on the operating phase; and construct a sensitivity attenuation model based on the third operating data and the initial attenuation model.

[0137] In one possible embodiment, the operation phase is an early phase. In constructing a sensitivity attenuation model based on the third operation data and the initial attenuation model, the determining unit 803 is further specifically used to: determine the first preset model as the initial attenuation model, wherein the first preset model is a linear relationship model; generate an initial attenuation curve based on the third operation data and the initial attenuation model; if the dispersion of the initial attenuation curve is less than a first preset threshold, determine the target sensitivity based on the initial attenuation curve; and construct a sensitivity attenuation model based on the target sensitivity.

[0138] In one possible embodiment, the running phase is an intermediate phase. In constructing the sensitivity decay model based on the third running data and the initial decay model, the determining unit 803 is further specifically used to: determine the first preset model as the initial decay model; determine the fitting weight of the corresponding third running data based on the closeness between the second running time and the first running time in the third running data; and fit the sensitivity decay model based on the initial decay model, the fitting weight, and the third running data.

[0139] In one possible embodiment, the running phase is a late decay phase. A sensitivity decay model is constructed based on the third running data and the initial decay model. The determining unit 803 is further specifically used to: determine the second preset model as the initial decay model, the second preset model being a nonlinear relationship model; obtain the decay acceleration coefficient by fitting the third running data, the decay acceleration coefficient being used to characterize the magnitude of the accelerated decay of sensitivity as the running time increases; and construct a sensitivity decay model based on the decay acceleration coefficient and the exponential decay model.

[0140] In one possible embodiment, before determining the operating stage of the target detector based on the first operating data sent by the target detector, the determining unit 803 is further specifically configured to: determine the aging degree of the target detector based on the first operating data; determine the compensation time interval based on the aging degree of the target detector, wherein the aging degree is inversely correlated with the compensation time interval; and determine that the detection compensation value transmission time interval of the target detector is not less than the compensation time interval.

[0141] In one possible embodiment, the first operating data further includes first temperature and humidity data, and the determining unit 803 is further specifically used to: if the first temperature and humidity data is greater than a first preset threshold, generate aging acceleration parameters for the target detector based on the first temperature and humidity data; and determine the aging degree of the target detector based on the aging acceleration parameters.

[0142] 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 cloud server 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.

[0143] The memory 901 may be a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM).

[0144] 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 carbon monoxide detector detection data compensation method of the present application embodiment.

[0145] 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 detection data compensation method of the carbon monoxide detector in the method embodiment of this application.

[0146] The processor 902 can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of the carbon monoxide detector detection data compensation method of this application can be completed by the integrated logic circuitry 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 performs the detection data compensation method of the carbon monoxide detector of this application method embodiment.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] This application embodiment also provides a chip, which includes a processor and a data interface. The processor reads instructions stored in the memory through the data interface to implement the detection data compensation method of the carbon monoxide detector.

[0151] 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 detection data compensation method of the carbon monoxide detector.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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 creative effort.

[0165] 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 method for compensating detection data of a carbon monoxide detector, characterized in that, The method includes: Acquire first operational data sent by the target detector, the first operational data including multiple first sensitivities and multiple first operational times of the target detector, the first sensitivity and the first operational time corresponding one-to-one; The operating phase of the target detector is determined based on the first operating data sent by the target detector, and the operating phase includes at least one of an early phase, a middle phase, and a late decay phase; The sensitivity attenuation model of the target detector is obtained according to the operating stage of the target detector; The target compensation value of the target detector is determined according to the sensitivity attenuation model. The target compensation value is sent to the target detector to compensate for the detected carbon monoxide concentration.

2. The method according to claim 1, characterized in that, The step of obtaining the sensitivity attenuation model corresponding to the target detector based on the operating phase of the target detector includes: Acquire multiple second operational data sent by multiple first detectors, wherein the second operational data includes multiple second sensitivities and multiple second operational times of the corresponding first detectors, and the second sensitivity and the second operational time correspond one-to-one; Obtain third running data that matches the running stage from the plurality of second running data, wherein the third running data includes at least one second running data; Determine the corresponding initial attenuation model based on the aforementioned operating phase; The sensitivity attenuation model is constructed based on the third operating data and the initial attenuation model.

3. The method according to claim 2, characterized in that, The operation phase is an early phase, and the construction of the sensitivity attenuation model based on the third operation data and the initial attenuation model includes: The first preset model is determined as the initial attenuation model, and the first preset model is a linear relationship model; An initial attenuation curve is generated based on the third operating data and the initial attenuation model; If the absolute value of the slope of the initial attenuation curve is not greater than the first preset threshold, then the target sensitivity is determined based on the initial attenuation curve. The sensitivity attenuation model is constructed based on the target sensitivity.

4. The method according to claim 2, characterized in that, The operation phase is the intermediate phase, and the construction of the sensitivity attenuation model based on the third operation data and the initial attenuation model includes: The first preset model is determined as the initial attenuation model; The fitting weight of the third running data is determined based on the degree of similarity between the second running time and the first running time in the third running data. The sensitivity attenuation model is obtained by fitting the initial attenuation model, the fitting weights, and the third running data.

5. The method according to claim 2, characterized in that, The operating phase is the late decay phase, and the construction of the sensitivity decay model based on the third operating data and the initial decay model includes: The second preset model is determined as the initial attenuation model, and the second preset model is a nonlinear relationship model; The attenuation acceleration coefficient is obtained by fitting the third operating data. The attenuation acceleration coefficient is used to characterize the magnitude of the accelerated attenuation of sensitivity as the operating time increases. The sensitivity decay model is constructed based on the decay acceleration coefficient and the exponential decay model.

6. The method according to any one of claims 1-5, characterized in that, Before determining the operational phase of the target detector based on the first operational data sent by the target detector, the method further includes: The aging degree of the target detector is determined based on the first operating data; The compensation time interval is determined based on the aging degree of the target detector, and the aging degree is inversely correlated with the compensation time interval; The time interval for transmitting the detection compensation value of the target detector is determined to be no less than the compensation time interval.

7. The method according to claim 6, characterized in that, The first operating data also includes first temperature and humidity data, and the method further includes: If the first temperature and humidity data is greater than the first preset threshold, then the aging acceleration parameters of the target detector are generated based on the first temperature and humidity data. The aging degree of the target detector is determined based on the aging acceleration parameters.

8. A detection data compensation device for a carbon monoxide detector, characterized in that, The device includes: The acquisition unit is used to acquire first operating data sent by the target detector. The first operating data includes multiple first sensitivities and multiple first operating times of the target detector, and the first sensitivities and the first operating times correspond one-to-one. The transmitting unit is configured to determine the operating phase of the target detector based on the first operating data transmitted by the target detector, wherein the operating phase includes at least one of an early phase, a mid-term phase, and a late decay phase; The acquisition unit is also used to acquire the sensitivity attenuation model corresponding to the target detector based on the operating stage of the target detector; The determining unit is used to determine the target compensation value of the target detector according to the sensitivity attenuation model, and the target compensation value is used to send to the target detector so that the target detector compensates for the detected carbon monoxide concentration.

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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