Method and related device for automatic tracking and correction of sensor signal drift

Abstract

The present disclosure provides a method for automatically tracking and correcting sensor signal drift and a related device. The method includes first determining a first average detection current of a sensor to be corrected within a first preset time period starting at a correction start time; then determining a second average detection current of the sensor to be corrected within a second preset time period ending at a current time; and obtaining a drift factor coefficient at the current time based on the first average detection current and the second average detection current; and correcting the sensitivity parameters of the sensor to be corrected based on the drift factor coefficient to achieve automatic tracking and correction of the signal drift of the sensor to be corrected, thereby avoiding correction errors caused by manual operation errors, improving the correction efficiency of signal drift, and reducing the workload of sensor users.

Description

Technical Field

[0001] The present disclosure relates to the technical field of sensor signal correction, and in particular to a method for automatically tracking and correcting sensor signal drift and a related device. Background Art

[0002] Sensor signal drift refers to the phenomenon in which the sensor output changes over time while the input remains constant. Drift is primarily caused by two factors: the influence of the sensor's structural parameters on the sensor signal; and the influence of the surrounding environment (such as temperature and humidity). While the influence of the sensor's structural parameters on the sensor signal can be eliminated by optimizing these parameters, the influence of the surrounding environment on the sensor signal is currently primarily addressed by manually correcting the sensor's sensitivity parameters using standard values detected in real time. This can lead to correction errors due to human error, is inefficient, and places an additional workload on the sensor user. Summary of the Invention

[0003] In view of this, the purpose of the present disclosure is to provide a sensor signal drift automatic tracking and correction method and related devices.

[0004] Based on the above objectives, the present disclosure provides a method for automatically tracking and correcting sensor signal drift, comprising:

[0005] Determining a first average detection current of the sensor to be calibrated within a first preset time period starting from the calibration start time;

[0006] Determining a second average detection current of the sensor to be corrected within a second preset time period with the current moment as the end moment;

[0007] Obtaining a drift factor coefficient at a current moment based on the first average detection current and the second average detection current;

[0008] The sensitivity parameter of the sensor to be corrected is corrected based on the drift factor coefficient, so as to achieve automatic tracking and correction of the signal drift of the sensor to be corrected.

[0009] Accordingly, the present disclosure also proposes a device for automatically tracking and correcting sensor signal drift, comprising:

[0010] A first determining module determines a first average detection current of the sensor to be calibrated within a first preset time period starting from the calibration start time;

[0011] a second determining module, determining a second average detection current of the sensor to be corrected within a second preset time period with the current moment as the end moment;

[0012] a drift calculation module, which obtains a drift factor coefficient at a current moment based on the first average detection current and the second average detection current;

[0013] The correction module corrects the sensitivity parameter of the sensor to be corrected based on the drift factor coefficient to achieve automatic tracking and correction of the signal drift of the sensor to be corrected.

[0014] Accordingly, the present disclosure also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein when the processor executes the program, the method for automatic tracking and correction of sensor signal drift as described above is implemented.

[0015] Accordingly, the present disclosure also proposes a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the method for automatic tracking and correction of sensor signal drift as described above.

[0016] As can be seen from the above description, the method for automatic tracking and correction of sensor signal drift provided by the present disclosure first determines the first average detection current of the sensor to be corrected within a first preset time period with the correction start time as the starting time; then determines the second average detection current of the sensor to be corrected within a second preset time period with the current time as the ending time; and obtains the drift factor coefficient at the current time based on the first average detection current and the second average detection current; corrects the sensitivity parameters of the sensor to be corrected based on the drift factor coefficient to achieve automatic tracking and correction of the signal drift of the sensor to be corrected, thereby avoiding correction errors caused by manual operation errors, improving the correction efficiency of signal drift, and reducing the workload of sensor users. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flowchart of a method for automatically tracking and correcting sensor signal drift according to an embodiment of the present disclosure is provided;

[0019] Figure 2 A schematic diagram of an automatic tracking and correction effect of sensor signal drift according to an embodiment of the present disclosure;

[0020] Figure 3Schematic diagram comparing the distribution of results of the automatic tracking correction method and the manual correction method according to an embodiment of the present disclosure;

[0021] Figure 4 Schematic diagram of the structure of a device for automatically tracking and correcting sensor signal drift according to an embodiment of the present disclosure;

[0022] Figure 5 The figure is a schematic structural diagram of a specific electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0025] As described in the background, prior art approaches to address the impact of the ambient environment on sensor signals primarily use standard values manually measured in real time to correct sensor sensitivity parameters. This can lead to correction errors due to human error, is inefficient, and places an additional workload on sensor users. For example, when a sensor is used to monitor changes in glucose concentration in human tissue fluid, the signal detected by the sensor electrodes results from glucose permeating through the outer membrane to the enzyme layer, where it is converted into hydrogen peroxide through an oxidation reaction. Hydrogen peroxide is electrochemically oxidized at the electrodes to generate a current signal. When the sensor is operating normally, the signal is solely related to the rate of glucose permeation through the outer membrane. When the outer membrane permeability is stable, the sensor sensitivity is also stable. In theory, this state is called steady state, and normal operation can be maintained without correction. However, when the sensor's environment or outer membrane state undergoes minor changes, the glucose permeability will slowly change over time, manifesting itself in the sensor signal as a gradual drift in the correlation between the signal and glucose concentration. To address sensitivity drift, correction is typically performed at regular intervals, when the deviation caused by this drift exceeds the accuracy threshold. This correction is performed to revise the sensor's concentration-signal relationship. This operation is called correction. Typically, the sensitivity parameter K, representing Y = KX + B, is corrected. The original algorithm, based on the sensitivity drift amplitude and patterns of clinical empirical data, required users to measure their fingertip blood glucose daily for correction to avoid excessive accumulation of deviations caused by drift. This approach is not only inefficient but also places an additional burden on the affected blood glucose level. Therefore, this disclosure proposes a method for automatically tracking and correcting sensor signal drift. This method eliminates the need for external secondary detection data, such as the fingertip blood test data mentioned above, and can directly analyze the sensor's own detection current value to achieve self-correction of sensor signal drift.

[0026] refer to Figure 1 , is a flow chart of a method for automatically tracking and correcting sensor signal drift according to an embodiment of the present disclosure, the method comprising the following steps:

[0027] S101 , determining a first average detection current of a sensor to be calibrated within a first preset time period starting from a calibration start time.

[0028] In a specific embodiment, the sensor to be corrected generally performs current detection at a fixed frequency. For example, a sensor used to detect the glucose concentration in human tissue fluid generally performs current detection every 3 minutes, and then calculates the blood glucose concentration in the human body based on the current value. When determining the first average detection current, it can be obtained by all the detection currents of the sensor within a first preset time period starting from the moment when the correction begins. The value range of the first preset time period can be set according to the stability of the sensor detection current, and the specific value can be set as needed and is not limited here. Optionally, the first preset time period of the sensor used to detect the glucose concentration in human tissue fluid can be set to 48 hours.

[0029] It should be noted that there is an initialization time when the sensor starts working. Generally, the correction start time is the time when the sensor initialization is completed. Of course, the sensor start time can also be directly used as the correction start time, which is not limited here.

[0030] In some embodiments, determining a first average detected current of the sensor to be calibrated within a first preset time period starting from the calibration start time specifically includes:

[0031] Acquiring multiple detection currents of the sensor to be corrected within the first preset time period;

[0032] The first average detection current is determined based on the multiple detection currents using a preset cumulative average formula.

[0033] In a specific embodiment, multiple detection currents of the correction sensor within the first preset time period are first obtained. Then, a first average detection current of the sensor to be corrected within the first preset time period starting at the start of correction is calculated using a preset cumulative averaging formula based on the multiple detection currents. Optionally, the preset cumulative averaging formula can be a simple summation formula, i.e., summing all detection currents and dividing the sum by the number of detections, or other averaging formulas, which are not limited here.

[0034] In order to simplify the method of calculating the first average detection current and save storage space, in some embodiments, the preset cumulative average formula includes:

[0035]

[0036] in, Indicates the cumulative average detection current corresponding to the nth detection current, Indicates the cumulative average detection current corresponding to the n-1th detection current, I n Indicates the nth detection current.

[0037] In specific implementation, when the above formula is used to calculate the first average detection current, the cumulative average detection current corresponding to each detection current can be calculated in sequence starting from the moment of starting correction, and then the cumulative average detection current corresponding to the last detection current in the first preset period is used as the first average detection current. When calculating the cumulative average detection current corresponding to the first detection current, that is, n is equal to 1, then

[0038] It should be noted that when using the above formula to calculate the first average detection current, since the calculation can be started from the cumulative average detection current corresponding to the first detection current, after calculating the cumulative average detection current corresponding to each detection current, only the cumulative average detection current corresponding to the detection current can be saved for calculating the cumulative average detection current corresponding to the next detection current, and other detection currents can be directly cleared, which can greatly save storage space, so that the method disclosed in the present invention can be implemented through the PCU (process control unit), and the PCU can be directly installed in the sensor, so that the sensor does not need to go through the cloud or external processor to perform the calculation.

[0039] In some embodiments, the preset cumulative average formula further includes:

[0040]

[0041] in, Indicates the cumulative average detection current corresponding to the nth detection current, represents the cumulative average detection current corresponding to the n-1th detection current, f represents the detection frequency of the sensor to be corrected, I n represents the nth detection current, and V1 represents the first preset period. Optionally, when the sensor to be calibrated is used to detect glucose concentration in human tissue fluid, V1 is 48 hours, or 2880 minutes. f is once every 3 minutes, or 1 / 3 of a minute, so V1*f is 960.

[0042] S102 , determining a second average detection current of the sensor to be corrected within a second preset time period with the current moment as the end moment.

[0043] In a specific embodiment, the second average detection current can be determined based on all detection currents of the sensor to be corrected within the second preset time period before the current moment. The second preset time period can be obtained based on experiments. Generally, the second preset time period should be able to represent the sensitivity drift state of the sensor as much as possible. If the time is too long, it cannot represent the implementation drift of the sensitivity, and if the time is too short, it cannot distinguish the fluctuation of the data detected by the sensor itself. Optionally, when the sensor to be corrected is used to detect the glucose concentration in human tissue fluid, the second preset time period can be set between 10 and 36 hours. For example, when the second preset time period is set to 10 hours, first obtain all detection currents within the 10 hours before the current moment, and then calculate the average value of all detection currents to serve as the second average detection current.

[0044] In some embodiments, the second average detection current can be determined by the following formula:

[0045]

[0046] in, Represents the second average current at the current moment, represents the second average current at the previous moment, f represents the detection frequency of the sensor to be corrected, I t represents the detection current of the sensor to be corrected at the current moment, and V2 represents the second preset threshold value. It should be noted that referring to the above-mentioned preset cumulative average formula similar to this formula, using this formula to calculate the second average detection current can save storage space.

[0047] In some embodiments, the process of determining the second preset time period includes:

[0048] A standard deviation of a detection current of the sensor to be calibrated within a third preset time period is determined, and the second preset time period is determined based on the standard deviation.

[0049] In a specific embodiment, the second preset period can be determined by the standard deviation of the detected current of the sensor to be corrected within a third preset period. Generally, the larger the standard deviation, the longer the corresponding second preset period. Optionally, the third preset period can be determined experimentally. Optionally, to further ensure the accuracy of the correction, the second preset period can be determined based on the standard deviation every third preset period, thereby dynamically adjusting the second preset period and further ensuring the accuracy of the correction.

[0050] S103 : Obtain a drift factor coefficient at a current moment based on the first average detection current and the second average detection current.

[0051] In a specific embodiment, after obtaining the first average detection current and the second average detection current, the drift factor coefficient at the current moment can be obtained according to the two average currents.

[0052] In some embodiments, the drift factor coefficient at the current moment may be determined by the following formula:

[0053]

[0054] Among them, S represents the drift factor at the current moment, represents the second average detection current, It should be noted that the drift factor coefficient in the present disclosure is dynamically adjusted. Generally, there is a drift factor coefficient corresponding to each moment. In order to reduce the computational burden and avoid useless calculations, the drift factor coefficient is generally not calculated during sensor initialization.

[0055] S104 , correcting the sensitivity parameter of the sensor to be corrected based on the drift factor coefficient, so as to achieve automatic tracking and correction of the signal drift of the sensor to be corrected.

[0056] In a specific embodiment, after obtaining the drift factor coefficient at the current moment, the sensitivity parameter of the sensor to be corrected is corrected according to the drift factor coefficient, thereby achieving automatic tracking and correction of the signal drift of the sensor to be corrected.

[0057] In some embodiments, the sensitivity parameter of the sensor to be corrected is corrected using the following formula:

[0058] K = KS + S * KS;

[0059] Wherein, K represents the sensitivity parameter after correction, and KS represents the sensitivity parameter before correction.

[0060] It should be noted that when the sensitivity parameters are first calibrated, KS is generally obtained through experiments or other testing methods to ensure that the initial KS is accurate. Each subsequent KS correction is based on the results of the previous correction.

[0061] The method for automatically tracking and correcting sensor signal drift provided by the present disclosure first determines a first average detection current of a sensor to be corrected within a first preset time period starting at a correction start time; then determines a second average detection current of the sensor to be corrected within a second preset time period ending at a current time; and obtains a drift factor coefficient at the current time based on the first average detection current and the second average detection current; and corrects the sensitivity parameters of the sensor to be corrected based on the drift factor coefficient to achieve automatic tracking and correction of the signal drift of the sensor to be corrected, thereby avoiding correction errors caused by manual operation errors, improving the correction efficiency of signal drift, and reducing the workload of sensor users.

[0062] In order to further verify the effect of the disclosed solution on automatic tracking and correction of sensor signal drift, the present disclosure conducted a comparative experiment on a sensor for detecting glucose concentration in human tissue fluid, referring to Figure 2 , where the horizontal axis represents time. The figure starts at midnight on July 25 and ends at midnight on August 4, with a total of 10 days of data collected. The vertical axis represents the quantitative value of the detection current, which can represent the change in the sensor detection current. CNO represents the quantitative value of the standard detection current obtained by performing blood glucose detection through finger blood, and then converting the blood glucose detection into the corresponding detection current. GLU-IR represents the quantitative value of the detection current after correction by the automatic correction method disclosed in this invention, and LW represents the quantitative value of the detection current before correction. Figure 2 It can be seen that LW gradually drifts upward with time, and is increasingly distant from the value of CNO. However, by correcting the original detection current, GLU-IR can make the quantized value of the corrected detection current basically coincide with CNO.

[0063] refer to Figure 3 , is a schematic diagram comparing the distribution of results of the automatic tracking and correction method and the manual correction method of the embodiment of the present disclosure. A consensus error grid distribution diagram is used to compare the distribution of results of the two methods. The consensus error grid distribution diagram (Consensus Error Grid) is widely used in endocrinology clinics to evaluate the results of continuous blood glucose monitoring. The horizontal axis is the reference blood glucose value and the vertical axis is the sensor-measured value. The ideal goal is to have all data points closely arranged along the diagonal. In real life, because the reference blood glucose and the tissue fluid glucose measured by the sensor have dynamic differences and are not strictly equal, the reference tissue fluid glucose and blood glucose will be inconsistent at the same time, and the data points are statistically distributed in a banded manner. Area A in the figure is considered clinically accurate, area B is clinically acceptable, and areas C and D have large errors, which may lead to incorrect clinical decisions.

[0064] It should be noted that Figure 3The original data used in this study are the original independent clinical data of the German MARD. Figure 3 In the figure, the left side shows the distribution of the original German MARD independent clinical raw data after manual correction using fingerstick blood test results. The right side shows the distribution of the original German MARD independent clinical raw data after correction using the automatic correction method disclosed herein. It can be seen that the distribution of results obtained using the disclosed method and the original manual correction method is not much different, and the results of both methods generally fall within areas A and B, meeting the requirements for blood glucose sensor calibration. This further demonstrates that the disclosed automatic correction method can improve detection efficiency while ensuring the accuracy of the correction results.

[0065] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0066] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0067] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides a device for automatically tracking and correcting sensor signal drift.

[0068] refer to Figure 4 The device for automatically tracking and correcting sensor signal drift comprises:

[0069] A first determining module 401 determines a first average detection current of the sensor to be calibrated within a first preset time period starting from the calibration start time;

[0070] A second determining module 402 determines a second average detection current of the sensor to be corrected within a second preset time period with the current moment as the end moment;

[0071] The drift calculation module 403 obtains a drift factor coefficient at a current moment based on the first average detection current and the second average detection current;

[0072] The correction module 404 corrects the sensitivity parameter of the sensor to be corrected based on the drift factor coefficient to achieve automatic tracking and correction of the signal drift of the sensor to be corrected.

[0073] In some embodiments, the first determining module is specifically configured to:

[0074] Acquiring multiple detection currents of the sensor to be corrected within the first preset time period;

[0075] The first average detection current is determined based on the multiple detection currents using a preset cumulative average formula.

[0076] In some embodiments, the second determining module is specifically configured to:

[0077] A standard deviation of a detection current of the sensor to be calibrated within a third preset time period is determined, and the second preset time period is determined based on the standard deviation.

[0078] In some embodiments, the preset cumulative average formula includes:

[0079]

[0080] in, Indicates the cumulative average detection current corresponding to the nth detection current, Indicates the cumulative average detection current corresponding to the n-1th detection current, I n Indicates the nth detection current.

[0081] In some embodiments, the preset cumulative average formula includes:

[0082]

[0083] in, Indicates the cumulative average detection current corresponding to the nth detection current, represents the cumulative average detection current corresponding to the n-1th detection current, f represents the detection frequency of the sensor to be corrected, I n represents the nth detection current, and V1 represents the first preset period.

[0084] In some embodiments, the drift factor coefficient at the current moment is determined by the following formula:

[0085]

[0086] Among them, S represents the drift factor at the current moment, represents the second average detection current, represents the first average detection current.

[0087] In some embodiments, the sensitivity parameter of the sensor to be corrected is corrected using the following formula:

[0088] K=K S +S*K S ;

[0089] Among them, K represents the sensitivity parameter after correction, K S Indicates the sensitivity parameter before correction.

[0090] For the convenience of description, the above devices are described as being functionally divided into various modules. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0091] The device of the above embodiment is used to implement the corresponding sensor signal drift automatic tracking and correction method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0092] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the method for automatic tracking and correction of sensor signal drift described in any of the above embodiments is implemented.

[0093] Figure 5 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0094] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0095] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0096] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0097] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0098] The bus 1050 comprises a path for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0099] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0100] The electronic device of the above embodiment is used to implement the corresponding sensor signal drift automatic tracking and correction method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0101] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the sensor signal drift automatic tracking and correction method as described in any of the above embodiments.

[0102] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0103] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the sensor signal drift automatic tracking and correction method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0104] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Within the scope of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0105] In addition, to simplify the description and discussion, and so as not to obscure the embodiments of the present disclosure, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, devices may be shown in the form of block diagrams to avoid obscuring the embodiments of the present disclosure, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be fully within the purview of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0106] Although the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0107] The embodiments of the present disclosure are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for automatically tracking and correcting sensor signal drift, comprising: Determining a first average detection current of the sensor to be calibrated within a first preset time period starting at the calibration start time specifically includes: Acquiring multiple detection currents of the sensor to be corrected within the first preset time period; The first average detection current is determined based on the multiple detection currents using a preset cumulative average formula, wherein the preset cumulative average formula includes: ； in, Indicates the cumulative average detection current corresponding to the nth detection current, Indicates the cumulative average detection current corresponding to the n-1th detection current, represents the detection frequency of the sensor to be corrected, Indicates the nth detection current, Indicates a first preset time period; Determining a second average detection current of the sensor to be corrected within a second preset time period with the current moment as the end moment; Based on the first average detection current and the second average detection current, a drift factor coefficient at a current moment is obtained; wherein the drift factor coefficient at a current moment is determined by the following formula: S = （ ） / ； Among them, S represents the drift factor at the current moment, represents the second average detection current, represents the first average detection current; Correcting the sensitivity parameter of the sensor to be corrected based on the drift factor coefficient to achieve automatic tracking and correction of the signal drift of the sensor to be corrected; The process of determining the second preset time period includes: Determine the standard deviation of the detection current of the sensor to be corrected within a third preset time period, and determine the second preset time period based on the standard deviation; the larger the standard deviation, the larger the corresponding second preset time period; and determine the second preset time period once every third preset time period based on the standard deviation.

2. The method according to claim 1, wherein The sensitivity parameter of the sensor to be corrected is corrected using the following formula: ； Among them, K represents the sensitivity parameter after correction, K S Indicates the sensitivity parameter before correction.

3. A device for automatically tracking and correcting sensor signal drift, comprising: The first determining module is configured to determine a first average detection current of the sensor to be calibrated within a first preset time period starting from the calibration start time, specifically for: Acquiring multiple detection currents of the sensor to be corrected within the first preset time period; The first average detection current is determined based on the multiple detection currents using a preset cumulative average formula, wherein the preset cumulative average formula includes: ； in, Indicates the cumulative average detection current corresponding to the nth detection current, Indicates the cumulative average detection current corresponding to the n-1th detection current, represents the detection frequency of the sensor to be corrected, Indicates the nth detection current, Indicates a first preset time period; a second determining module, determining a second average detection current of the sensor to be corrected within a second preset time period with the current moment as the end moment; The drift calculation module obtains a drift factor coefficient at a current moment based on the first average detection current and the second average detection current. The drift factor coefficient at the current moment is determined by the following formula: S = （ ） / ； Among them, S represents the drift factor at the current moment, represents the second average detection current, represents the first average detection current; a correction module, which corrects the sensitivity parameter of the sensor to be corrected based on the drift factor coefficient, so as to achieve automatic tracking and correction of the signal drift of the sensor to be corrected; The second determining module is specifically configured to: Determine the standard deviation of the detection current of the sensor to be corrected within a third preset time period, and determine the second preset time period based on the standard deviation; the larger the standard deviation, the larger the corresponding second preset time period; and determine the second preset time period once every third preset time period based on the standard deviation.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method according to any one of claims 1 to 2 when executing the program. 5 . A non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method according to claim 1 .

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