Data processing method and detection system, electronic equipment and storage medium

Abstract

The invention relates to a data processing method, a detection system, electronic equipment and a storage medium, and the method comprises the steps: determining at least one alternative sampling frequency of a sensing signal according to the fluctuation error of the sensing signal, historical record data determined by N (N > 1) sensing signals collected in a historical period, and user interaction information, the method comprises the steps of determining at least one of a first alternative sampling frequency determined by a fluctuation error, a second alternative sampling frequency determined by historical record data and a third alternative sampling frequency determined by user interaction information, and determining a target sampling frequency of a sensing signal according to the at least one alternative sampling frequency. According to the embodiment of the invention, the fluctuation error of the sensing signal, the historical record data and the user interaction information can be comprehensively considered, the sampling frequency of the sensing signal is adaptively adjusted, and the accuracy of data processing is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of medical technology, and in particular to a data processing method and a detection system, an electronic device and a storage medium. Background Art

[0002] In the related art, a continuous bioactive substance detection system (e.g., a continuous glucose detection system) is worn on the human body to be monitored in a minimally invasive manner. The bioactive substance data (e.g., blood sugar data) is collected by sampling the electrical signal of the sensor at a fixed time interval (i.e., a fixed sampling rate), and multiple sampled electrical signals within a single collection cycle (e.g., 5 minutes) are averaged to obtain a detection value (e.g., blood sugar value) that can reflect the changes in the bioactive substance within the collection cycle.

[0003] However, the above-mentioned measured values ​​(such as blood sugar values) can only represent the average value of the changes in the bioactive substances to be monitored (such as blood sugar) during the collection period, and cannot reflect the dynamic continuity of the changes in the bioactive substances during the collection period, especially when the user completes eating, exercising, taking medicine (such as injecting insulin), or the user's physical condition suddenly changes (such as dizziness, pain, etc.). When these factors that can affect the rapid change of the bioactive substance value appear, because the extreme values ​​of the bioactive substances (such as the maximum concentration of the bioactive substance and the minimum concentration of the bioactive substance) are difficult to obtain at a fixed sampling rate, the detection value calculated during the collection period will not be able to accurately feedback the situation when the actual bioactive substance value that appeared during the collection period reaches the extreme value, there is a great risk, which can easily lead to the occurrence of safety accidents. For example, in the process of using insulin, diabetic patients have inaccurate feedback on blood sugar values, resulting in excessive insulin dosage, causing hypoglycemia, and causing the user to fall into a coma. Summary of the invention

[0004] The present disclosure proposes a data processing method and a detection system, an electronic device and a storage medium.

[0005] According to one aspect of the present disclosure, a data processing method is provided, comprising: acquiring parameter information, the parameter information including a fluctuation error of a sensor signal, historical record data, and user interaction information, the historical record data being determined based on N sensor signals collected within a historical period, where N is an integer greater than 1; determining at least one alternative sampling frequency for the sensor signal based on the parameter information; and determining a target sampling frequency for the sensor signal based on the at least one alternative sampling frequency.

[0006] In one possible implementation, at least one alternative sampling frequency of the sensor signal is determined based on the parameter information, including: determining a first alternative sampling frequency based on the fluctuation error; and / or determining a second alternative sampling frequency based on the historical record data; and / or determining a third alternative sampling frequency based on the user interaction information.

[0007] In a possible implementation, a first alternative sampling frequency is determined based on the fluctuation error, including: when the fluctuation error is greater than a preset threshold, a dynamic sampling rate is determined based on the fluctuation error; when the dynamic sampling rate is greater than a preset maximum sampling rate, the maximum sampling rate is determined as the first alternative sampling frequency, or, when the dynamic sampling rate is less than a preset minimum sampling rate, the minimum sampling rate is determined as the first alternative sampling frequency, or, when the dynamic sampling rate is less than or equal to the maximum sampling rate and greater than or equal to the minimum sampling rate, the dynamic sampling rate is determined as the first alternative sampling frequency.

[0008] In a possible implementation, the method for obtaining the fluctuation error includes: obtaining M groups of window data in a sampling order, each group of window data includes P sensor signals, and P and M are integers greater than 1; determining the maximum and minimum values ​​of the P sensor signals in each group of window data; determining the average current fluctuation according to the average value of the maximum value of the M groups of window data, the average value of the minimum value of the M groups of window data, and the average value of the sensor signals of the M groups of window data; determining the fluctuation error according to the average current fluctuation and the average value of the sensor signals of the M groups of window data.

[0009] In one possible implementation, a second alternative sampling frequency is determined based on the historical record data, including: determining whether the historical record data is within a preset range to obtain a judgment result; when the judgment result indicates that the historical record data is within the preset range, determining the second alternative sampling frequency based on a preset reference frequency and an attenuation coefficient, wherein the attenuation coefficient is less than 1; or, when the judgment result indicates that the historical record data is outside the preset range, determining the second alternative sampling frequency based on the reference frequency and a growth coefficient, wherein the growth coefficient is greater than 1.

[0010] In one possible implementation, the method for obtaining the growth coefficient includes: when the historical record data is greater than the upper limit value of the preset range, determining a first proportional coefficient based on the historical record data and the upper limit value; determining the growth coefficient based on the first proportional system and a preset first piecewise function; or, when the historical record data is less than the lower limit value of the preset range, determining a second proportional coefficient based on the historical record data and the lower limit value; determining the growth coefficient based on the second proportional system and a preset second piecewise function.

[0011] In one possible implementation, the user interaction information includes at least one type of activity information and / or physical condition information of the user. Based on the user interaction information, a third alternative sampling frequency is determined, including: obtaining the gain coefficient, delay time, and duration corresponding to each type of activity information and / or physical condition information of the user; determining the maximum gain coefficient from the multiple gain coefficients obtained, determining the minimum delay time from the multiple delay times obtained, and determining the maximum duration from the multiple durations obtained; within the maximum duration after the minimum delay time, determining the third alternative sampling frequency based on a preset reference frequency and the maximum gain coefficient.

[0012] In one possible implementation, the target sampling frequency of the sensing signal is determined based on at least one alternative sampling frequency, including: comparing the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency to determine the maximum alternative sampling frequency; determining the target sampling frequency based on the maximum alternative sampling frequency; or, in accordance with a preset priority order, selecting the alternative sampling frequency with the highest priority as the target sampling frequency from the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency; or, averaging at least two of the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency, and determining the average operation result as the target sampling frequency.

[0013] According to one aspect of the present disclosure, a detection system is provided, which includes a sensor, an analog-to-digital converter, and a controller, wherein the controller is used to adaptively adjust the sampling frequency of the analog-to-digital converter for a sensor signal output by the sensor, and includes: a parameter information acquisition unit, used to acquire parameter information, wherein the parameter information includes a fluctuation error of the sensor signal, historical record data, and user interaction information, wherein the historical record data is determined based on N sensor signals collected within a historical period, where N is an integer greater than 1; an alternative sampling frequency determination unit, used to determine at least one alternative sampling frequency of the sensor signal based on the parameter information; and a target sampling frequency determination unit, used to determine the target sampling frequency of the sensor signal based on at least one alternative sampling frequency.

[0014] In one possible implementation, the alternative sampling frequency determination unit is used to: determine a first alternative sampling frequency based on the fluctuation error; and / or determine a second alternative sampling frequency based on the historical record data; and / or determine a third alternative sampling frequency based on the user interaction information.

[0015] In a possible implementation, a first alternative sampling frequency is determined based on the fluctuation error, including: when the fluctuation error is greater than a preset threshold, a dynamic sampling rate is determined based on the fluctuation error; when the dynamic sampling rate is greater than a preset maximum sampling rate, the maximum sampling rate is determined as the first alternative sampling frequency, or, when the dynamic sampling rate is less than a preset minimum sampling rate, the minimum sampling rate is determined as the first alternative sampling frequency, or, when the dynamic sampling rate is less than or equal to the maximum sampling rate and greater than or equal to the minimum sampling rate, the dynamic sampling rate is determined as the first alternative sampling frequency.

[0016] In one possible implementation, the parameter information acquisition unit is used to: acquire M groups of window data in a sampling order, each group of window data includes P sensor signals, and P and M are integers greater than 1; determine the maximum and minimum values ​​of the P sensor signals in each group of window data; determine the average current fluctuation based on the average value of the maximum value of the M groups of window data, the average value of the minimum value of the M groups of window data, and the average value of the sensor signals of the M groups of window data; determine the fluctuation error based on the average current fluctuation and the average value of the sensor signals of the M groups of window data.

[0017] In one possible implementation, a second alternative sampling frequency is determined based on the historical record data, including: determining whether the historical record data is within a preset range to obtain a judgment result; when the judgment result indicates that the historical record data is within the preset range, determining the second alternative sampling frequency based on a preset reference frequency and an attenuation coefficient, wherein the attenuation coefficient is less than 1; or, when the judgment result indicates that the historical record data is outside the preset range, determining the second alternative sampling frequency based on the reference frequency and a growth coefficient, wherein the growth coefficient is greater than 1.

[0018] In one possible implementation, the method for obtaining the growth coefficient includes: when the historical record data is greater than the upper limit value of the preset range, determining a first proportional coefficient based on the historical record data and the upper limit value; determining the growth coefficient based on the first proportional system and a preset first piecewise function; or, when the historical record data is less than the lower limit value of the preset range, determining a second proportional coefficient based on the historical record data and the lower limit value; determining the growth coefficient based on the second proportional system and a preset second piecewise function.

[0019] In one possible implementation, the user interaction information includes at least one type of activity information and / or physical condition information of the user. Based on the user interaction information, a third alternative sampling frequency is determined, including: obtaining the gain coefficient, delay time, and duration corresponding to each type of activity information and / or physical condition information of the user; determining the maximum gain coefficient from the multiple gain coefficients obtained, determining the minimum delay time from the multiple delay times obtained, and determining the maximum duration from the multiple durations obtained; within the maximum duration after the minimum delay time, determining the third alternative sampling frequency based on a preset reference frequency and the maximum gain coefficient.

[0020] In one possible implementation, the target sampling frequency determination unit is used to: compare the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency to determine the maximum alternative sampling frequency; determine the target sampling frequency based on the maximum alternative sampling frequency; or, in accordance with a preset priority order, select the alternative sampling frequency with the highest priority as the target sampling frequency from the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency; or, perform an average operation on at least two of the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency, and determine the average operation result as the target sampling frequency.

[0021] According to one aspect of the present disclosure, an electronic device is provided, comprising the detection system as described above.

[0022] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor.

[0023] In the embodiment of the present disclosure, at least one alternative sampling frequency of the sensor signal can be determined based on the fluctuation error of the sensor signal, the historical record data determined by N (N>1) sensor signals collected in the historical period, and the user interaction information, and the target sampling frequency of the sensor signal can be determined based on the at least one alternative sampling frequency. Among them, determining at least one alternative sampling frequency of the sensor signal based on the parameter information may include: determining a first alternative sampling frequency based on the fluctuation error, and / or determining a second alternative sampling frequency based on the historical record data, and / or determining a third alternative sampling frequency based on the user interaction information. In this way, the fluctuation error of the sensor signal, the historical record data, and the user interaction information can be comprehensively considered to adaptively adjust the sampling frequency of the sensor signal, which is conducive to improving the accuracy of data processing.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present disclosure. Other features and aspects of the present disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0026] Figure 1 A block diagram of a detection system according to an embodiment of the present disclosure is shown.

[0027] Figure 2 A flow chart of a data processing method according to an embodiment of the present disclosure is shown.

[0028] Figure 3 A schematic diagram of a continuous glucose monitoring system according to an embodiment of the present disclosure is shown.

[0029] Figure 4 A schematic diagram showing a method of determining a first candidate sampling frequency according to an embodiment of the present disclosure is shown.

[0030] Figure 5 A schematic diagram showing the effect of a blood sugar value curve in the related art.

[0031] Figure 6 A schematic diagram showing the effect of a blood glucose level curve according to an embodiment of the present disclosure.

[0032] Figure 7A schematic diagram showing a continuous blood glucose profile according to an embodiment of the present disclosure.

[0033] Figure 8 A schematic diagram showing another continuous blood glucose profile according to an embodiment of the present disclosure.

[0034] Fig. 9 A schematic diagram showing a method of determining a second candidate sampling frequency according to an embodiment of the present disclosure is shown.

[0035] Fig.10 A schematic diagram showing how to determine a third candidate sampling frequency according to an embodiment of the present disclosure.

[0036] Fig.11 A schematic diagram of a controller according to an embodiment of the present disclosure is shown.

[0037] Fig.12 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0038] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0039] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0040] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0041] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0042] The maximum and minimum values ​​of bioactive substances (such as blood sugar) are of great clinical significance. The detection systems in related technologies (such as continuous glucose detection systems) cannot effectively detect the maximum and minimum values ​​of bioactive substances (such as blood sugar). Therefore, it is necessary to increase the sampling rate of the electrical signal (such as the current value) of the sensor in the detection system, shorten the time interval between two adjacent electrical signal acquisitions, and obtain more intensive electrical signals, thereby increasing the probability of obtaining the electrical signal extreme value (i.e., the current value extreme value) of the bioactive substance.

[0043] In the related art, the sampling rate is increased by improving the hardware circuit, such as increasing the computing power of the transmitter's processing chip and the storage space of the memory. However, this method will not only increase the power consumption of the transmitter for collecting the electrical signals from the sensor, but also increase the data storage space of the electrical signals, resulting in an increase in cost. In addition, in order to ensure that the service life of the product is not shortened, the related art will increase the size of the transmitter's power supply battery, thereby increasing the overall size of the product, which is inconvenient for users to wear. Furthermore, the related art needs to filter the electrical signals collected by the sensor to reduce the interference of the external environment on the signal, which will cause a delay in the transmission time of the electrical signal and a delay in the detection value (blood sugar value) of the bioactive substance during the collection period, affecting the real-time feedback of the blood sugar value.

[0044] In view of this, the embodiment of the present disclosure provides a data processing method, which can determine at least one alternative sampling frequency of the sensor signal according to the fluctuation error of the sensor signal (the electrical signal output by the sensor of the detection system), the historical record data determined by N (N>1) sensor signals collected in the historical period, and the user interaction information, and determine the target sampling frequency of the sensor signal according to the at least one alternative sampling frequency. In this way, the fluctuation error of the sensor signal, the historical record data and the user interaction information can be comprehensively considered to adaptively adjust the sampling frequency of the sensor signal output by the analog-to-digital converter, thereby improving the accuracy, real-time and adaptability of the detection system.

[0045] Figure 1 A block diagram of a detection system according to an embodiment of the present disclosure is shown, which is used to execute the data processing method provided by the embodiment of the present disclosure, such as Figure 1 As shown, the detection system includes a sensor 1, an analog-to-digital converter 2, and a controller 3. The controller 3 adaptively adjusts the sampling frequency of the sensor signal output by the analog-to-digital converter 2 to the sensor 1 according to the data processing method.

[0046] In a possible implementation, the sensor 1 may include a biosensor, such as a thermosensitive biosensor, a field effect transistor biosensor, a piezoelectric biosensor, an optical biosensor, an acoustic channel biosensor, an enzyme electrode biosensor, etc., which is not limited in the embodiments of the present disclosure.

[0047] Among them, biosensors are sensitive to biological substances and can convert the concentration of biological substances into electrical signals (such as current signals, voltage signals, charge amounts, etc.), and use the electrical signals as output sensing signals. Biosensors can be composed of biological sensitive materials (such as enzymes, antibodies, antigens, microorganisms, cells, tissues, nucleic acids and other biologically active substances) as recognition elements and physical and chemical transducers (such as oxygen electrodes, photosensitive tubes, field effect tubes, piezoelectric crystals, etc.).

[0048] For example, a glucose sensor for detecting glucose concentration may be composed of an enzyme, an electrode, and a signal converter, and the glucose concentration may be detected by a current signal generated by an enzyme-catalyzed glucose reaction. It should be understood that different sensors 1 may be provided according to actual application scenarios, and the embodiments of the present disclosure do not limit the type and structure of the sensor 1.

[0049] In a possible implementation, the analog-to-digital converter 2 can discretize the analog signal with continuous time and continuous amplitude according to a certain sampling frequency, and convert it into a digital signal with discrete time and discrete amplitude, which can go through the sampling, holding, quantization and encoding process. Among them, the sampling frequency represents the number of times the analog-to-digital converter 2 collects the sensor signal per second, that is, the frequency at which the analog signal is discretized.

[0050] In a possible implementation, the controller 3 may include a processor having an execution instruction function in an electronic device, and the controller 3 may be implemented in any appropriate manner, for example, by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), central processing units (CPUs), single chip microcomputers (Single Chip Microcomputers), system-on-chips (SoCs), microprocessors or other electronic components. Inside the controller 3, the executable instructions may be executed by hardware circuits such as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, thereby controlling the sampling frequency of the sensor signal output by the analog-to-digital converter 2 to the sensor 1.

[0051] Figure 2 A flow chart of a data processing method according to an embodiment of the present disclosure is shown as follows: Figure 2 As shown, the data processing method includes:

[0052] In step S11, parameter information is obtained, the parameter information including fluctuation error of the sensor signal, historical record data, and user interaction information, the historical record data is determined based on N sensor signals collected in a historical period, where N is an integer greater than 1;

[0053] In step S12, at least one candidate sampling frequency of the sensing signal is determined according to the parameter information;

[0054] In step S13, a target sampling frequency of the sensing signal is determined according to at least one candidate sampling frequency.

[0055] In a possible implementation, the method can be applied to Figure 1 The detection system shown in the figure may be a terminal device, a server or other processing device, etc. The terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc.

[0056] In a possible implementation, the data processing method of the embodiment of the present disclosure may be implemented by the controller 3 in the detection system calling computer-readable instructions stored in a memory.

[0057] In one possible implementation, in step S11, the controller 3 may acquire parameter information in real time or periodically, and the parameter information is used to adjust the sampling frequency of the sensor signal output by the sensor 1 by the analog-to-digital converter 2. The parameter information may include at least one of the fluctuation error of the sensor signal, historical record data, and user interaction information.

[0058] The fluctuation error reflects the fluctuation of the sensor signal output by the sensor 1. The historical record data is determined based on N (N>1) sensor signals collected in a historical period. For example, the historical record data can be the mean (or deformation of the mean) of N sensor signals collected in a certain historical period, which is used to reflect the historical detection of the user's bioactive substances (such as blood sugar). User interaction information refers to the customized information input by the user to the detection system, which helps the detection system to obtain user needs more accurately, thereby adjusting the target sampling frequency to meet user needs.

[0059] Parameter information is obtained in step S11, and in step S12, at least one alternative sampling frequency of the sensor signal can be determined based on the parameter information, including: determining a first alternative sampling frequency based on the fluctuation error; and / or determining a second alternative sampling frequency based on the historical record data; and / or determining a third alternative sampling frequency based on the user interaction information.

[0060] Optionally, a first candidate sampling frequency may be determined based on the fluctuation error.

[0061] For example, when the fluctuation error is relatively small, the sampling frequency of the previous moment (or the system default sampling frequency) can be used as the first alternative sampling frequency; when the fluctuation error is relatively large, the preset filtering algorithm can be used to dynamically adjust the sampling frequency based on the fluctuation error and the sampling frequency range of the analog-to-digital converter 2, and the adjusted sampling frequency can be used as the first alternative sampling frequency.

[0062] For another example, the fluctuation error can be used as a judgment condition. When the fluctuation error of the sensor signal is large (for example, the fluctuation error is greater than a preset threshold), the maximum sampling frequency can be selected from the preset multiple sampling frequencies as the first candidate sampling frequency, so as to more effectively capture the details and changes of the sensor signal and improve the accuracy of the sensor signal. When the fluctuation error of the sensor signal is small (for example, the fluctuation error is less than or equal to the preset threshold), the minimum sampling frequency can be selected from the preset multiple sampling frequencies as the first candidate sampling frequency, so as to save computing resources and delay the service life of the detection system.

[0063] It should be understood that the first candidate sampling frequency can be dynamically adjusted by monitoring the fluctuation error of the sensor signal. The embodiment of the present disclosure does not limit the method for determining the first candidate sampling frequency, and it can be set according to the actual application scenario.

[0064] Optionally, a second candidate sampling frequency may be determined based on the historical record data;

[0065] For example, when the changes in multiple historical record data corresponding to multiple recent historical periods are relatively small (for example, the difference between the maximum and minimum values ​​in the historical record data is less than a fixed value), the sampling frequency of the previous historical period (or the system default sampling frequency) can be used as the second alternative sampling frequency; when the changes in multiple historical record data corresponding to multiple recent historical periods are relatively large (for example, the difference between the maximum and minimum values ​​in the historical record data is greater than a fixed value), the maximum sampling frequency supported by the analog-to-digital converter 2 can be used as the second alternative sampling frequency.

[0066] For another example, the historical record data can truly reflect the detection value of the user's bioactive substance at a historical moment (such as blood sugar value). The historical record data of the latest historical cycle (such as the previous historical cycle) can be used as a judgment condition. When the historical record data is outside the safety range, it means that the probability of the detection value of the user's bioactive substance being abnormal is relatively high. The largest sampling frequency can be selected from the preset multiple sampling frequencies as the second candidate sampling frequency, so as to more effectively capture the details and changes of the sensor signal and improve the accuracy of the sensor signal. When the historical record data is within the safety range, it means that the probability of the detection value of the user's bioactive substance being normal is relatively high. The smallest sampling frequency can be selected from the preset multiple sampling frequencies as the second candidate sampling frequency, thereby saving computing resources and delaying the service life of the detection system.

[0067] It should be understood that the second candidate sampling frequency can be dynamically adjusted by monitoring the historical record data. The embodiment of the present disclosure does not limit the method for determining the second candidate sampling frequency, and it can be set according to the actual application scenario.

[0068] Optionally, a third candidate sampling frequency may be determined based on the user interaction information. For example, the sampling frequency input by the user may be directly used as the third candidate sampling frequency; for another example, the gain coefficient, delay time, and duration corresponding to each type of activity information and / or physical condition information of the user may be obtained based on events input by the user, such as the user's diet information, exercise information, medication information, physical condition information, etc.; the maximum gain coefficient may be determined from the multiple acquired gain coefficients, the minimum delay time may be determined from the multiple acquired delay times, and the maximum duration may be determined from the multiple acquired durations; within the maximum duration after the minimum delay time, the third candidate sampling frequency may be determined based on the preset reference frequency and the maximum gain coefficient.

[0069] It should be understood that the third candidate sampling frequency can be adjusted through user interaction information. The embodiment of the present disclosure does not limit the method for determining the third candidate sampling frequency, and it can be set according to the actual application scenario.

[0070] In this way, the sampling frequency of the sensor signal can be adjusted according to at least one of the fluctuation error, historical record data, and user interaction information, which is conducive to the detection system adapting to different application scenarios and improving the flexibility and scalability of the detection system.

[0071] In a possible implementation, in step S12, only one candidate sampling frequency of the sensing signal may be determined according to the parameter information, and in step S13, the candidate sampling frequency may be determined as the target sampling frequency of the sensing signal.

[0072] Optionally, in step S12, a first candidate sampling frequency may be determined according to the fluctuation error, and in step S13, the first candidate sampling frequency may be directly used as the target sampling frequency of the sensing signal.

[0073] Optionally, in step S12, a second candidate sampling frequency may be determined according to historical record data, and in step S13, the second candidate sampling frequency may be directly used as the target sampling frequency of the sensing signal.

[0074] Optionally, in step S12, a third candidate sampling frequency may be determined according to the user interaction information, and in step S13, the third candidate sampling frequency may be directly used as the target sampling frequency of the sensing signal.

[0075] In one possible implementation, in step S12, two alternative sampling frequencies of the sensor signal can be determined based on at least two of the parameter information, historical record data, and user interaction information. In step S13, the target sampling frequency of the sensor signal can be determined based on the two alternative sampling frequencies.

[0076] Optionally, in step S12, a first candidate sampling frequency may be determined based on the fluctuation error; and a second candidate sampling frequency may be determined based on the historical record data. In step S13, a target sampling frequency of the sensing signal may be determined based on the first candidate sampling frequency and the second candidate sampling frequency. For example, the mean (or maximum value) of the first candidate sampling frequency and the second candidate sampling frequency may be used as the target sampling frequency of the sensing signal.

[0077] Optionally, in step S12, a first candidate sampling frequency may be determined based on the fluctuation error; and a third candidate sampling frequency may be determined based on the user interaction information. In step S13, a target sampling frequency of the sensing signal may be determined based on the first candidate sampling frequency and the third candidate sampling frequency. For example, the mean (or maximum value) of the first candidate sampling frequency and the third candidate sampling frequency may be used as the target sampling frequency of the sensing signal.

[0078] Optionally, in step S12, a second candidate sampling frequency may be determined based on historical record data; and a third candidate sampling frequency may be determined based on user interaction information. In step S13, a target sampling frequency of the sensing signal may be determined based on the second candidate sampling frequency and the third candidate sampling frequency. For example, the mean (or maximum value) of the second candidate sampling frequency and the third candidate sampling frequency may be used as the target sampling frequency of the sensing signal.

[0079] In a possible implementation, in step S12, three alternative sampling frequencies of the sensor signal may be determined based on parameter information, historical record data, and user interaction information. In step S13, a target sampling frequency of the sensor signal may be determined based on the three alternative sampling frequencies.

[0080] Optionally, according to different application scenarios, an alternative sampling frequency suitable for the current application scenario can be selected from the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency as the target sampling frequency of the sensing signal. For example, the first alternative sampling frequency can be selected as the target sampling frequency of the sensing signal; or, for another example, the second alternative sampling frequency can be selected as the target sampling frequency of the sensing signal; or, for another example, the third alternative sampling frequency can be selected as the target sampling frequency of the sensing signal.

[0081] Optionally, different candidate sampling frequencies may correspond to different priorities, and the candidate sampling frequency with the highest priority may be selected as the target sampling frequency from the first candidate sampling frequency, the second candidate sampling frequency, and the third candidate sampling frequency according to a preset priority order. Through the preset priority order, the detection system can quickly select the candidate sampling frequency with the highest priority from multiple candidate sampling frequencies as the target sampling frequency, which is conducive to improving the response speed of the detection system.

[0082] Optionally, at least two of the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency are averaged, and the target sampling frequency is determined by the average operation result. For example, the average of the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency can be used as the target sampling frequency of the sensing signal; or, for another example, the average of the first alternative sampling frequency and the second alternative sampling frequency can be used as the target sampling frequency of the sensing signal; or, for another example, the average of the first alternative sampling frequency and the third alternative sampling frequency can be used as the target sampling frequency of the sensing signal; or, for another example, the average of the second alternative sampling frequency and the third alternative sampling frequency can be used as the target sampling frequency of the sensing signal; through the average operation, multiple factors such as fluctuation error, historical record data, user interaction information, etc. can be comprehensively considered to obtain a more comprehensive and accurate sampling frequency.

[0083] Optionally, the first candidate sampling frequency, the second candidate sampling frequency, and the third candidate sampling frequency may be compared to determine the maximum candidate sampling frequency; and the target sampling frequency may be determined based on the maximum candidate sampling frequency. By comparing multiple candidate sampling frequencies and selecting the maximum candidate sampling frequency as the target sampling frequency, the target sampling frequency may be made high enough to more accurately capture the details and changes of the sensing signal, thereby reducing the distortion of the sensing signal due to insufficient frequency during the sampling process, and improving the integrity and accuracy of the sensing signal.

[0084] Through steps S11 to S13, at least one alternative sampling frequency of the sensor signal (electrical signal output by the sensor of the detection system) can be determined based on the fluctuation error of the sensor signal (electrical signal output by the sensor of the detection system), the historical record data determined by N (N>1) sensor signals collected in the historical period, and the user interaction information, such as at least one of the first alternative sampling frequency determined by the fluctuation error, the second alternative sampling frequency determined by the historical record data, and the third alternative sampling frequency determined by the user interaction information, and the target sampling frequency of the sensor signal can be determined based on at least one alternative sampling frequency. In this way, the fluctuation error of the sensor signal, the historical record data, and the user interaction information can be comprehensively considered to adaptively adjust the sampling frequency of the sensor signal output by the analog-to-digital converter 2 to the sensor 1, thereby improving the accuracy, real-time and adaptability of the detection system.

[0085] The data processing method of the embodiment of the present disclosure is exemplarily described below by taking a continuous glucose monitoring system as an example.

[0086] Figure 3 A schematic diagram of a continuous glucose monitoring system according to an embodiment of the present disclosure is shown. Figure 3 As shown, the glucose detection system includes not only a sensor 1 , an analog-to-digital converter 2 , and a controller 3 , but also a current amplification module 4 , a storage unit 5 , a data transmission interface 6 , and a user terminal 7 .

[0087] The sensor 1 may be a glucose sensor for detecting glucose concentration, which is immersed in the interstitial fluid of human tissues and can react with glucose to generate electric current.

[0088] The current amplification module 4 can amplify the weak reaction current of the sensor 1, and the amplified current signal can be transmitted to the analog-to-digital converter 2 so that the analog-to-digital converter 2 can collect the current signal and convert it into a digital sequence. The current amplification module 4 can also provide an excitation signal to the sensor 1 so that the sensor 1 enters a normal working state. It should be understood that in actual applications, the current amplification module 4 can be integrated into the sensor 1 or exist in the form of a discrete chip.

[0089] The analog-to-digital converter 2 can perform analog-to-digital conversion (converting analog quantity to digital quantity) on the current signal amplified by the current amplification module 4 to obtain a current digital signal.

[0090] The controller 3 can be used to receive the current digital signal, and calculate the average current value within the blood glucose collection period after filtering the current digital signal, and calculate the blood glucose value in the collection period based on the average current value.

[0091] The storage unit 5 is used to store historical blood sugar values ​​or other information as historical record data, and output the corresponding blood sugar value according to the control instruction issued by the controller 3.

[0092] The data transmission interface 6 can output the blood glucose value in the current collection cycle to the external user terminal 7 according to the control instructions of the controller 3. The data transmission interface 6 is a physical entity for the data of the blood glucose transmitter to exchange data with an external entity. The data transmission interface 6 may include a wireless interface such as a Bluetooth radio frequency interface (Bluetooth) and a near field communication interface (NearField Communication, NFC), and may also include a wired interface such as a serial port and a parallel port. The embodiment of the present disclosure does not limit the type of the data transmission interface 6.

[0093] The user terminal 7, for example, includes a human-computer interaction display terminal, a mobile phone or a tablet computer or a web page, which can display the blood sugar value and the blood sugar continuous graph to the user.

[0094] It should be understood that the analog-to-digital converter 2, storage unit 5, and data transmission interface 6 can exist in the form of discrete chips respectively, or can be integrated into the same chip with the controller 3. As a main control chip, the chip can trigger the analog-to-digital converter 2 to perform sampling conversion, read the digital signal output by the analog-to-digital converter 2, and perform filtering, calculation, and other processing on the digital signal.

[0095] In a possible implementation, the data processing method of the embodiment of the present disclosure can be applied to Figure 3 The continuous glucose monitoring system shown below is as follows Figure 3 Taking the continuous glucose monitoring system shown in the figure as an example, three schemes for determining alternative sampling frequencies are introduced respectively.

[0096] In the related art, the weak electrical signal generated by the electrochemical blood glucose sensor is collected through an analog-to-digital converter. Its current signal is often accompanied by noise, and the signal will have some large swings, which will interfere with the calculation of the final blood glucose value. Therefore, the current value of the blood glucose sensor generally needs to be digitally filtered. However, the use of filtering algorithms in digital filtering processing generally brings delays. The appearance of delays will cause the human body's blood sugar fluctuations to be displayed to the user after a period of time, reducing real-time performance.

[0097] The embodiments of the present disclosure can determine whether to start digital filtering processing of the current signal by monitoring the fluctuation error of the current signal output by the sensor 1, and dynamically adjust the first alternative sampling frequency after starting the digital filtering processing to reduce the delay caused by the current filtering, thereby adaptively enhancing the real-time and continuity of the blood glucose value.

[0098] Figure 4 A schematic diagram showing a method for determining a first candidate sampling frequency according to an embodiment of the present disclosure is shown as follows: Figure 4As shown, the analog-to-digital converter 2 samples the sensing signal (e.g., current) output by the sensor 1 at a certain time interval to obtain the current value. The sampling interval of the analog-to-digital converter 2 is controlled by the controller 3. The controller 3 can call the preset filter control and sampling adaptive program to determine whether to start the digital filtering process of the current signal, and dynamically adjust the first candidate sampling frequency after starting the digital filtering process. That is, the current value obtained after the analog-to-digital converter 2 samples can be digitally filtered or not, which can be determined by the preset filter control and sampling adaptive program. The first-in-first-out data cache 1 (First In First Out, FIFO) is used to store the current values ​​of multiple sampling points. The controller 3 executes the filter control and sampling adaptive program, and can determine the fluctuation error of the current value according to the current value read in the first-in-first-out data cache 1, and perform filter control and sampling adaptive processing based on the fluctuation error of the current value. The first-in-first-out data cache 2 can be used to cache the current values ​​of multiple sampling points after the filter control and sampling adaptive processing. Then, the average value of the multiple current values ​​collected under the first candidate sampling frequency can be calculated, and the blood glucose value output by the detection system can be determined according to the blood glucose algorithm.

[0099] Among them, in actual applications, the sampling of current values ​​is at the second level, and the generation of blood glucose values ​​is at the minute level, so multiple current values ​​need to be converted into one current value (such as an average value) and provided to the blood glucose algorithm. The blood glucose algorithm can perform blood glucose calculations based on the average value of the input current value. The blood glucose algorithm can perform weighted, linear, nonlinear and other deformation processing on the average value of the current value. The embodiments of the present disclosure do not specifically limit the blood glucose algorithm.

[0100] When the detection system is turned on, the current value may be subjected to digital filtering by default. After startup, it may be determined whether to perform digital filtering or no digital filtering according to a preset filtering control and sampling adaptive program.

[0101] The following is an exemplary introduction to the filtering control and sampling adaptation procedures of the embodiments of the present disclosure.

[0102] In a possible implementation, whether to start digital filtering processing can be determined by monitoring the fluctuation error of the sensor signal (for example, the current value sampled by the analog-to-digital converter 2), and the method for obtaining the fluctuation error includes: controlling the analog-to-digital converter 2 to obtain M groups of window data in a sampling order, each group of window data includes P sensor signals, and P and M are integers greater than 1; determining the maximum and minimum values ​​of the P sensor signals in each group of window data; determining the average current fluctuation according to the average value of the maximum value of the M groups of window data, the average value of the minimum value of the M groups of window data, and the average value of the sensor signals of the M groups of window data; determining the fluctuation error according to the average current fluctuation and the average value of the sensor signals of the M groups of window data.

[0103] For example, in the first-in-first-out data cache, the data is a sensor signal (such as a current value) arranged in a sampling order, and the time interval for each sensor signal to be generated is the sampling interval of the sensor signal at that time. Among them, the default sampling interval of the analog-to-digital converter 2 is 8 seconds, that is, the sampling frequency is 0.125 Hz. It should be understood that the sampling interval can be adjusted according to demand, and the embodiments of the present disclosure are not limited to this.

[0104] For example, assuming that the sensing signal is current data, the first-out data buffer can store n current values, such as current value I1, current value I2, current value I3, current value I4, ..., current value I n .

[0105] The controller 3 can control the analog-to-digital converter 2 to obtain M groups of window data in a sampling order, and each group of window data includes P sensing signals.

[0106] For example, assuming that the sensing signal is a current signal, the first set of window data is current values ​​I1 to I P , the second set of window data is the current value I P+1 ~Current value I 2P , the third group of window data is the current value I 2P+1 ~Current value I 3P , and so on, the Mth group of window data is the current value I (M-1)×P+1 ~Current value I M×P .

[0107] The controller 3 may determine the maximum value and the minimum value of the P sensing signals in each set of window data.

[0108] For example, the maximum value of the first set of window data Imax1=Max(I1, I2, ..., I P ), the minimum value of the first set of window data Imin1=Min(I1,I2,…,I P );

[0109] The maximum value of the second group of window data Imax2=Max(I P+1 , I P+2 ,…,I 2P ), the minimum value of the second set of window data Imin2=Min(I P+1 , I P+2 ,…,I 2P );

[0110] Similarly, the maximum value Imax of the Mth group of window data M =Max(I (M-1)×P+1 , I (M-1)×P+2 ,…,I M×P), the minimum value Imin of the Mth group of window data M =Min(I (M-1)×P+1 , I (M-1)×P+2 ,…,I M×P ).

[0111] For example, assuming that the sampling rate is 0.125 Hz, the analog-to-digital converter 2 can collect a current value once every 8 seconds. Within the preset period of blood glucose value display (e.g., 5 minutes), a total of 38 current values ​​can be collected. These 38 current values ​​are sequentially entered into the first-in-first-out data cache, and 6 groups of window data are generated with 6 widths as windows, and then the maximum and minimum values ​​of each group of these 6 groups of window data can be taken out. It should be understood that the embodiments of the present disclosure do not limit the specific values ​​of the window width and the sampling frequency.

[0112] Then, we can calculate the average value of the maximum value of M groups of window data. The average of the minimum values ​​of M groups of window data The average value of the sensor signal of M groups of window data Determine the average current fluctuation I wave =Max(|Imax avg -I avg |,|Imin avg -I avg |).

[0113] According to the average current fluctuation I wave and the average value I of the sensor signal of M groups of window data avg , determine the fluctuation error, that is:

[0114] Id= (|I avg -I wave |) / I avg ×100% (1)

[0115] Where, Id represents the fluctuation error of the sensor data (such as the current value collected by the analog-to-digital converter 2), I avg represents the average value of the sensor signal of M groups of window data, I wave Represents the average current fluctuation of M groups of window data.

[0116] In this way, the fluctuation error of the sensor data can be determined efficiently and accurately, so as to subsequently determine whether to start digital filtering processing based on the fluctuation error.

[0117] In a possible implementation, a first alternative sampling frequency is determined based on the fluctuation error, including: when the fluctuation error is greater than a preset threshold, a dynamic sampling rate is determined based on the fluctuation error; when the dynamic sampling rate is greater than a preset maximum sampling rate, the maximum sampling rate is determined as the first alternative sampling frequency, or, when the dynamic sampling rate is less than a preset minimum sampling rate, the minimum sampling rate is determined as the first alternative sampling frequency, or, when the dynamic sampling rate is less than or equal to the maximum sampling rate and greater than or equal to the minimum sampling rate, the dynamic sampling rate is determined as the first alternative sampling frequency.

[0118] Exemplarily, the fluctuation error Id can be compared with a preset threshold value Fter, and according to the comparison result of the fluctuation error Id and the preset threshold value Fter, it is determined whether to start the digital filtering process. For example, when the fluctuation error Id is greater than the preset threshold value Fter, the digital filtering process can be turned on, and the sampling rate of the sensor signal (such as the current signal) output by the analog-to-digital converter 2 to the sensor 1 can be dynamically adjusted to reduce the delay caused by the current filtering. Alternatively, when the fluctuation error Id is less than or equal to the preset threshold value Fter, it is considered that the quality of the sensor signal sampled by the analog-to-digital converter 2 is good, and no digital filtering process is required. The digital filtering process can be turned off without changing the current sampling rate of the sensor signal output by the analog-to-digital converter 2 to the sensor 1.

[0119] When the fluctuation error Id is greater than the preset threshold value of Fter, the dynamic sampling rate Fd can be determined according to the fluctuation error Id, that is:

[0120]

[0121] Wherein, Id represents the fluctuation error, as shown in formula (1), W represents the upper limit of the fluctuation error Id, and its value range is 20% to 40%, Fmax represents the preset maximum sampling rate, and Fmin represents the preset minimum sampling rate. The maximum sampling rate Fmax and the minimum sampling rate Fmin are both extreme values ​​for the detection system to maintain normal operation, which can be determined according to the electrode noise frequency of the detection system and the empirical value obtained in the actual application scenario. For example, the maximum sampling rate Fmax can be set to 0.5 Hz, and the minimum sampling rate Fmin can be set to 0.1 Hz; K f It represents the adjustment coefficient, and its value range is 0.8~1.2. For example, the adjustment coefficient K f Set to the default value of 1.

[0122] If the dynamic sampling rate Fd is greater than the maximum sampling rate Fmax, the maximum sampling rate Fmax can be determined as the first alternative sampling frequency; or, if the dynamic sampling rate Fd is less than the minimum sampling rate Fmin, the minimum sampling rate Fmin can be determined as the first alternative sampling frequency; or, if the dynamic sampling rate Fd is less than or equal to the maximum sampling rate Fmax and greater than or equal to the minimum sampling rate Fmin, the dynamic sampling rate Fd can be determined as the first alternative sampling frequency.

[0123] Figure 5 A schematic diagram showing the effect of a blood sugar value curve in the related art is shown in FIG. Figure 5 As shown in the figure, the upper curve is the blood glucose value curve without filtering, and the lower curve is the blood glucose value curve after filtering, that is, filtering with each point as a sequence. Point B is a noise point that deviates from the trend. The time interval of each point can be set to 2 minutes. It can be seen Figure 5 The noise point B1 on the lower curve is closer to the original trend line, and the curve is smoother and closer to the real situation of the human body. However, all points have a delay of time t, for example Figure 5 Point A1 is delayed by t compared to point A.

[0124] Figure 6 A schematic diagram showing the effect of a blood glucose level curve according to an embodiment of the present disclosure is shown in FIG. Figure 6 As shown, after using the first alternative sampling frequency of the embodiment of the present disclosure, 6 additional blood sugar values ​​will be generated between the two points A2 and B2 before and after point B, see Figure 6 The data points at positions 1 to 6 in the middle will increase in terms of blood glucose values. By increasing the sampling rate, the delay t caused by the filtering algorithm can be effectively reduced (for example Figure 6 The delay t between point A1 and A is less than Figure 5 The delay t between point A1 and A in the figure greatly shortens the delay time, and the B1 value can better maintain the overall trend of the curve, and the influence of noise on the trend is weakened. Further, before point B2 comes out, three blood glucose values ​​of small points 4, 5, and 6 are generated. The blood glucose algorithm can determine that point B is noise by comparing small points A2 to 6. It should be understood that in actual applications, the noise point can be deleted and replaced by two surrounding points, or the weighted average of the surrounding points can be used to replace the noise point. The embodiments of the present disclosure are not limited to this.

[0125] In one possible implementation, a second alternative sampling frequency is determined based on the historical record data, including: determining whether the historical record data is within a preset range to obtain a judgment result; when the judgment result indicates that the historical record data is within the preset range, determining the second alternative sampling frequency based on a preset reference frequency and an attenuation coefficient, wherein the attenuation coefficient is less than 1; or, when the judgment result indicates that the historical record data is outside the preset range, determining the second alternative sampling frequency based on the reference frequency and a growth coefficient, wherein the growth coefficient is greater than 1.

[0126] Assume that the detection system is a continuous glucose detection system (see Figure 3 ), the historical record data is the blood glucose value of the historical record, the continuous glucose detection system provides a blood glucose value to the user at a preset interval (for example, 5 minutes), and the blood glucose graph displayed on the user terminal 7 shows the continuous blood glucose graph to the user through plotted points or smooth curves. The continuous glucose detection system can detect the wearer's blood glucose changes, wherein the extreme values ​​of blood glucose (such as maximum and minimum values) are not easy to be captured when the blood glucose changes more drastically, so that the graph finally displayed to the user is "distorted". When blood glucose is stable, small fluctuations have little clinical significance, so the frequency of blood glucose value display can be reduced. When blood glucose produces a trend-type fluctuation or is about to fluctuate significantly, increasing the blood glucose value display frequency can more finely show the real-time changes of blood glucose.

[0127] Detect whether the short-term blood sugar fluctuation is drastic, so as to determine whether the blood sugar value calculation frequency needs to be adjusted, which targets the display frequency of the blood sugar value. When the blood sugar is about to reach an extreme value, the blood sugar value display frequency can be increased to obtain the extreme value. Accurate acquisition of the extreme value helps to accurately obtain the real blood sugar fluctuation. On the contrary, if the extreme value is not sampled, the auxiliary guidance of the continuous glucose monitoring system will be discounted for patients with high blood sugar and low blood sugar.

[0128] Therefore, for the continuous glucose monitoring system, the safe blood glucose range can be used as the preset range [Glu_max, Glu_min], and the lower limit Glu_min represents the observation point of clinically significant low blood glucose value (for example, Glu_min is set to 3.9mmol), or the lower limit observation point set by the user. The upper limit Glu_max represents the observation point of clinically significant high blood glucose value (for example, Glu_max is set to 8mmol), or the upper limit observation point set by the user.

[0129] Figure 7 A schematic diagram showing a continuous blood glucose profile according to an embodiment of the present disclosure is shown as follows: Figure 7As shown in the figure, if the blood glucose value recorded in the history fluctuates within the preset range [Glu_max, Glu_min], it means that the user's glucose level is within the safe range. The upper limit Glu_max and the lower limit Glu_min of the preset range [Glu_max, Glu_min] can be set according to the doctor's advice or by the user. The blood glucose value within the safe range is a range of low concern to the user, and the display frequency of the blood glucose value can be reduced, thereby saving system overhead, so as to avoid frequently reminding the user of blood glucose fluctuations and disturbing the user's normal life and work.

[0130] Figure 8 A schematic diagram showing another continuous blood glucose profile according to an embodiment of the present disclosure is shown as follows: Figure 8 As shown, when the blood glucose value exceeds the set upper limit Glu_max or lower limit Glu_min, it means that the user needs to pay special attention to the fluctuation of blood glucose. At this time, the blood glucose display interval can be appropriately increased to obtain a more detailed curve in the area concerned by the user.

[0131] Fig. 9 A schematic diagram showing a method for determining a second candidate sampling frequency according to an embodiment of the present disclosure is shown as follows: Fig. 9 As shown, it is assumed that the detection system is a continuous glucose detection system (see Figure 3 ), the historical record data is the blood glucose value of the historical record, see above, the preset range of safe blood glucose [Glu_max, Glu_min] can be set.

[0132] After setting the preset range of safe blood glucose [Glu_max, Glu_min], the historical record data of blood glucose can be read from the blood glucose value database, wherein the historical record data can be the blood glucose value Glu_d1 of the previous moment. For example, when the continuous glucose detection system is turned on, the blood glucose value closest to the current moment in time can be selected from the blood glucose value database as the blood glucose value Glu_d1, and the acquisition frequency at the current startup moment is determined according to the blood glucose value Glu_d1. For another example, after the continuous glucose detection system is turned on, the sampling frequency in the next blood glucose value acquisition cycle is adjusted according to the blood glucose value collected at the current moment.

[0133] After obtaining the blood sugar historical record data Glu_d1, it can be determined whether the blood sugar value Glu_d1 falls within the preset range [Glu_max, Glu_min]. If so, the calculation frequency of the blood sugar value can be reduced, otherwise, the calculation frequency of the blood sugar value can be increased.

[0134] Optionally, a second candidate sampling frequency may be determined according to a preset reference frequency Fdef and a damping coefficient Kd, and the second candidate sampling frequency is used to reduce the calculation frequency of the blood glucose value.

[0135] For example, Fnow=Fdef×Kd may be used to calculate the collection frequency of the reduced blood glucose value, wherein Fdef is a preset reference frequency, for example, the default blood glucose value calculation frequency may be used as the reference frequency Fdef, and Kd is the frequency attenuation coefficient (Kd<1);

[0136] The formula Fnow=Fdef*Kup can be used to calculate the increased blood glucose value collection frequency, where Fdef is the preset reference frequency. For example, the default blood glucose value calculation frequency can be used as the reference frequency Fdef, and Kup is the frequency growth coefficient (Kup>1).

[0137] Then, according to the blood sugar value collection frequency Fnow, the second candidate sampling frequency Fadc2 = Fnow × N1 of the analog-to-digital converter 2 for the current signal output by the sensor 1 can be determined; wherein N1 is the number of current signals collected within the preset cycle of blood sugar value display.

[0138] It should be understood that Fdef is the frequency of collecting blood sugar values ​​for users, for example, a blood sugar value is displayed to the user every 5 minutes. Fadc2 is the sampling rate of collecting the current value output by sensor 1, which has a multiple relationship, that is, collecting N1 Fadc2 values ​​to obtain a blood sugar value. The embodiment of the present disclosure does not limit the value of N1, for example, it can be set to 18, that is, a current value is collected every 10 seconds, and a blood sugar value is obtained every 3 minutes, and N1 = 180 seconds / 10 = 18.

[0139] In the example, the controller 3 may control the analog-to-digital converter 2 to perform current sampling on the current signal output by the sensor 1 at the second alternative sampling frequency Fadc2.

[0140] In this way, the second candidate sampling frequency can be obtained by determining whether to adjust the frequency of calculating the blood glucose value based on the user's historical record data (such as blood glucose value), that is, based on whether the short-term blood glucose fluctuation is severe.

[0141] In one possible implementation, the method for obtaining the growth coefficient includes: when the historical record data is greater than the upper limit value of the preset range, determining a first proportional coefficient based on the historical record data and the upper limit value; determining the growth coefficient based on the first proportional system and a preset first piecewise function; or, when the historical record data is less than the lower limit value of the preset range, determining a second proportional coefficient based on the historical record data and the lower limit value; determining the growth coefficient based on the second proportional system and a preset second piecewise function.

[0142] For example, the growth coefficient Kup can be obtained using a nonlinear strategy. Assuming the preset range is [Glu_max, Glu_min], the number of historical records of blood glucose values ​​is Glu_d1, when the number of historical records of blood glucose values ​​Glu_d1 is greater than the upper limit value Glu_max, the first proportional coefficient Kleve1 = (Glud1-Glu_max) / Glu_max can be determined based on the historical record data Glu_d1 and the upper limit value Glu_max.

[0143] Then, the growth coefficient Kup may be determined according to the first proportional system Kleve1 and the preset first piecewise function, namely:

[0144]

[0145] When the blood sugar value history record number Glu_d1 is less than the lower limit Glu_min, the second proportional coefficient Kleve 2 = (Glu_min-Glu_d1) / Glu_min can be determined according to the history record data Glu_d1 and the lower limit Glu_min.

[0146] Then, the growth coefficient Kup may be determined according to the second proportional system Kleve2 and the preset second piecewise function, namely:

[0147]

[0148] In this way, the growth factor Kup can be obtained accurately and quickly.

[0149] In a possible implementation, the detection system also includes a user terminal 7, and the user interaction information includes at least one type of activity information and / or physical condition information of the user based on input from the user terminal 7. According to the user interaction information, a third alternative sampling frequency is determined, including: obtaining the gain coefficient, delay time, and duration corresponding to each type of activity information and / or physical condition information of the user; determining the maximum gain coefficient from the multiple gain coefficients obtained, determining the minimum delay time from the multiple delay times obtained, and determining the maximum duration from the multiple durations obtained; within the maximum duration after the minimum delay time, the third alternative sampling frequency is determined according to the preset reference frequency and the maximum gain coefficient.

[0150] Assume that the detection system is a continuous glucose detection system (see Figure 3 ), the continuous glucose monitoring system can interact with the user through the human-computer interaction interface of the user terminal 7, including displaying blood glucose values ​​and continuous blood glucose graphs to the user, and the user can also input events to record the user's life situation.

[0151] Optionally, since the user's blood sugar may change after eating, the activity information input by the user terminal 7 may include diet information. Alternatively, since the user's exercise may accelerate sugar metabolism and blood sugar may change, the activity information input by the user terminal 7 may include exercise information. Alternatively, due to the use of glucose-control drugs (such as insulin), blood sugar may change dramatically, and the activity information input by the user terminal 7 may include medication information. Alternatively, when the user is physically unwell, such as dizziness, restlessness, etc., it may be caused by a change in blood sugar level, and the user terminal 7 may input physical condition information. It should be understood that the user interaction information input by the user terminal 7 may include activity information such as diet, exercise, and medication, and may also include physical condition information, and the embodiments of the present disclosure do not specifically limit this.

[0152] Fig.10 A schematic diagram showing a method of determining a third candidate sampling frequency according to an embodiment of the present disclosure is shown as follows: Fig.10 As shown, assuming that the user interaction information input by the user includes diet information, exercise information, medication information and physical condition information, each event may correspond to three control parameters, namely: gain coefficient, delay time and duration.

[0153] For example, the gain coefficient 1, delay time 1 and duration 1 corresponding to the dietary information can be obtained according to the dietary information input by the user; in actual applications, the value range of the gain coefficient 1 can be set to: 1 < gain coefficient 1 < 1.3; the value range of the delay time 1 can be set to: 7 minutes < delay time 1 < 15 minutes; the value range of the duration 1 can be set to: 45 minutes < duration 1 < 90 minutes. Among them, the dietary information input by the user may include the type and amount of food, such as 500 ml of milk, 200 grams of meat, 70 grams of eggs, etc. The embodiments of the present disclosure do not limit the specific content of the dietary information.

[0154] According to the motion information input by the user, the gain coefficient 2, delay time 2 and duration 2 corresponding to the motion information can be obtained; in actual applications, the value range of the gain coefficient 2 can be set to: 1.1 < gain coefficient 2 < 1.3; the value range of the delay time 2 can be set to: 2 minutes < delay time 2 < 7 minutes; the value range of the duration 2 can be set to: 10 minutes < duration 2 < 60 minutes. Among them, the motion information input by the user may include the type and duration of the sport, such as 15 minutes for badminton, 20 minutes for basketball, etc. The embodiments of the present disclosure do not limit the specific content of the motion information.

[0155] According to the medication information input by the user, the gain coefficient 3, delay time 3 and duration 3 corresponding to the medication information can be obtained; in actual applications, the value range of the gain coefficient 3 can be set to: 1.3 < gain coefficient 3 < 1.5; the value range of the delay time 3 can be set to: 0 minutes < delay time 3 < 5 minutes; the value range of the duration 3 can be set to: 20 minutes < duration 3 < 90 minutes. Among them, the medication information input by the user may include the type and dosage of the medication, such as 5 mg of insulin, and the embodiments of the present disclosure do not limit the specific content of the medication information.

[0156] According to the physical condition information input by the user, the gain coefficient 4, delay time 4 and duration 4 corresponding to the physical condition information can be obtained. In practical applications, the value range of the gain coefficient 4 can be set to: 1.3 < gain coefficient 4 < 1.8; the value range of the delay time 4 can be set to: 0 minutes < delay time 4 < 2 minutes; the value range of the duration 4 can be set to: 90 minutes < duration 4 < 180 minutes. Among them, the physical condition information input by the user can be characterized by symptoms, such as dizziness, headache, convulsion, vomiting, diarrhea, etc. The embodiments of the present disclosure do not limit the specific content of the physical condition information.

[0157] The gain coefficient is a coefficient for increasing the frequency of blood glucose calculation. When the gain coefficient changes, the frequency of blood glucose calculation changes. The delay time indicates that the frequency of blood glucose calculation starts to change after a period of time after receiving the user input. For example, the gain coefficient takes effect after the delay time. The duration indicates the period of time after receiving the event when the frequency of blood glucose calculation changes. For example, the duration is the period from when the gain coefficient takes effect to when it becomes invalid.

[0158] When multiple events occur within a duration, the integrator can integrate the gain coefficient, delay time, and duration corresponding to each type of activity information and / or physical condition information, determine the maximum gain coefficient from the multiple gain coefficients obtained, determine the minimum delay time from the multiple delay times obtained, and determine the maximum duration from the multiple durations obtained.

[0159] For example, assuming that diet information, exercise information, medication information and physical condition information all occur, the maximum gain coefficient Kevt = Max (gain coefficient 1, gain coefficient 2, gain coefficient 3, gain coefficient 4), the minimum delay time Td = Min (delay time 1, delay time 2, delay time 3, delay time 4), the maximum duration Ti = Max (duration 1, duration 2, duration 3, duration 4), Max() is the maximum value function.

[0160] Within the maximum duration Ti after the minimum delay time Td, the third candidate sampling frequency may be determined according to the preset reference frequency Fdef and the maximum gain coefficient Kevt.

[0161] For example, the target blood glucose value calculation frequency Fnow=Fdef×Kevt can be determined according to the preset reference frequency Fdef and the maximum gain coefficient Kevt, where Fdef is the preset reference frequency. For example, the default blood glucose value calculation frequency can be used as the reference frequency Fdef. Then, the third alternative sampling frequency Fadc3=Fnow×N1 of the current signal output by the analog-to-digital converter 2 to the sensor 1 can be determined according to the target blood glucose value calculation frequency Fnow; where N1 is the number of current signals collected within the preset cycle of the blood glucose value display.

[0162] In the example, the controller 3 may control the analog-to-digital converter 2 to perform current sampling on the current signal output by the sensor 1 at the third alternative sampling frequency Fadc3. For example, the target blood glucose value calculation frequency Fnow and the third alternative sampling frequency Fadc3 may be set and take effect after the minimum delay time Td through a time counter such as a timer, and restored to the value before adjustment after the maximum duration Ti.

[0163] In this way, a third candidate sampling frequency customized for the user can be determined according to the user's needs.

[0164] In summary, the data processing method of the disclosed example can determine at least one alternative sampling frequency of the sensor signal (electrical signal output by the sensor of the detection system) according to the fluctuation error of the sensor signal (electrical signal output by the sensor of the detection system), the historical record data determined by N (N>1) sensor signals collected in the historical period, and the user interaction information, such as at least one of the first alternative sampling frequency determined by the fluctuation error, the second alternative sampling frequency determined by the historical record data, and the third alternative sampling frequency determined by the user interaction information, and determine the target sampling frequency of the sensor signal according to at least one alternative sampling frequency. In this way, the fluctuation error of the sensor signal, the historical record data, and the user interaction information can be comprehensively considered to adaptively adjust the sampling frequency of the sensor signal output by the analog-to-digital converter 2 to the sensor 1, thereby improving the accuracy, real-time and adaptability of the detection system.

[0165] For example, for a continuous glucose monitoring system, the sampling rate of the blood glucose sensor can be automatically adjusted based on the current fluctuation error of the blood glucose sensor, historical blood glucose values, user dietary events, user exercise and other events. In this way, when blood glucose fluctuates rapidly, the extreme blood glucose values ​​can be well monitored; the time delay caused by digital filtering is greatly shortened, noise is better filtered out, and a more continuous blood glucose map can be provided to users.

[0166] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation method, the specific execution order of each step should be determined according to its function and possible internal logic.

[0167] In addition, the present disclosure also provides a detection system, an electronic device, a computer-readable storage medium, and a program, all of which can be used to implement any data processing method provided by the present disclosure. The corresponding technical solutions and descriptions are referred to the corresponding records in the method part and will not be repeated here.

[0168] In one possible implementation, Figure 1 As shown, the detection system provided by the embodiment of the present disclosure includes a sensor 1, an analog-to-digital converter 2, and a controller 3. The controller 3 is used to adaptively adjust the sampling frequency of the sensor signal output by the analog-to-digital converter 2 to the sensor 1.

[0169] Fig.11 A schematic diagram of a controller 3 according to an embodiment of the present disclosure is shown. Fig.11 As shown, the controller 3 includes:

[0170] A parameter information acquisition unit 31 is used to acquire parameter information, wherein the parameter information includes fluctuation error of the sensor signal, historical record data, and user interaction information, wherein the historical record data is determined based on N sensor signals collected in a historical period, where N is an integer greater than 1;

[0171] An alternative sampling frequency determination unit 32, configured to determine at least one alternative sampling frequency of the sensing signal according to the parameter information;

[0172] The target sampling frequency determination unit 33 is used to determine the target sampling frequency of the sensing signal according to at least one candidate sampling frequency.

[0173] In one possible implementation, the alternative sampling frequency determination unit 32 is used to: determine a first alternative sampling frequency based on the fluctuation error; and / or determine a second alternative sampling frequency based on the historical record data; and / or determine a third alternative sampling frequency based on the user interaction information.

[0174] In a possible implementation, a first alternative sampling frequency is determined based on the fluctuation error, including: when the fluctuation error is greater than a preset threshold, a dynamic sampling rate is determined based on the fluctuation error; when the dynamic sampling rate is greater than a preset maximum sampling rate, the maximum sampling rate is determined as the first alternative sampling frequency, or, when the dynamic sampling rate is less than a preset minimum sampling rate, the minimum sampling rate is determined as the first alternative sampling frequency, or, when the dynamic sampling rate is less than or equal to the maximum sampling rate and greater than or equal to the minimum sampling rate, the dynamic sampling rate is determined as the first alternative sampling frequency.

[0175] In a possible implementation, the parameter information acquisition unit 31 is used to: acquire M groups of window data in a sampling order, each group of window data includes P sensor signals, and P and M are integers greater than 1; determine the maximum and minimum values ​​of the P sensor signals in each group of window data; determine the average current fluctuation based on the average value of the maximum value of the M groups of window data, the average value of the minimum value of the M groups of window data, and the average value of the sensor signals of the M groups of window data; determine the fluctuation error based on the average current fluctuation and the average value of the sensor signals of the M groups of window data.

[0176] In one possible implementation, a second alternative sampling frequency is determined based on the historical record data, including: determining whether the historical record data is within a preset range to obtain a judgment result; when the judgment result indicates that the historical record data is within the preset range, determining the second alternative sampling frequency based on a preset reference frequency and an attenuation coefficient, wherein the attenuation coefficient is less than 1; or, when the judgment result indicates that the historical record data is outside the preset range, determining the second alternative sampling frequency based on the reference frequency and a growth coefficient, wherein the growth coefficient is greater than 1.

[0177] In one possible implementation, the method for obtaining the growth coefficient includes: when the historical record data is greater than the upper limit value of the preset range, determining a first proportional coefficient based on the historical record data and the upper limit value; determining the growth coefficient based on the first proportional system and a preset first piecewise function; or, when the historical record data is less than the lower limit value of the preset range, determining a second proportional coefficient based on the historical record data and the lower limit value; determining the growth coefficient based on the second proportional system and a preset second piecewise function.

[0178] In one possible implementation, the user interaction information includes at least one type of activity information and / or physical condition information of the user. Based on the user interaction information, a third alternative sampling frequency is determined, including: obtaining the gain coefficient, delay time, and duration corresponding to each type of activity information and / or physical condition information of the user; determining the maximum gain coefficient from the multiple gain coefficients obtained, determining the minimum delay time from the multiple delay times obtained, and determining the maximum duration from the multiple durations obtained; within the maximum duration after the minimum delay time, determining the third alternative sampling frequency based on a preset reference frequency and the maximum gain coefficient.

[0179] In one possible implementation, the target sampling frequency determination unit 33 is used to: compare the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency to determine the maximum alternative sampling frequency; determine the target sampling frequency based on the maximum alternative sampling frequency; or, in accordance with a preset priority order, select the alternative sampling frequency with the highest priority as the target sampling frequency from the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency; or, perform an average operation on at least two of the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency, and determine the average operation result as the target sampling frequency.

[0180] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0181] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0182] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

[0183] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0184] The electronic device may be provided as a terminal, a server, or a device in other forms.

[0185] Fig.12 1 is a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server or a terminal device, including the detection system described above. Fig.12 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0186] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as a Microsoft Server operating system (Windows Server 2003). TM ), a graphical user interface operating system launched by Apple (Mac OS X TM ), a multi-user, multi-process computer operating system (Unix TM ), a free and open source Unix-like operating system (Linux TM ), an open source Unix-like operating system (FreeBSD TM ) or similar.

[0187] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0188] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0189] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0190] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0191] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0192] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0193] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0194] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0195] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0196] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK) and the like.

[0197] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0198] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0199] If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, the personal information processing rules are notified by obvious signs / information, and the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0200] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire parameter information, where the parameter information includes fluctuation error of the sensor signal, historical record data, and user interaction information, where the historical record data is determined based on N sensor signals collected in a historical period, where N is an integer greater than 1; Determining at least one candidate sampling frequency of the sensing signal according to the parameter information; Determining a target sampling frequency of the sensing signal according to at least one alternative sampling frequency; Among them, according to the parameter information, at least one alternative sampling frequency of the sensor signal is determined, including: determining a first alternative sampling frequency according to the fluctuation error; and / or determining a second alternative sampling frequency according to the historical record data; and / or determining a third alternative sampling frequency according to the user interaction information.

2. The method according to claim 1, characterized in that Determining a first candidate sampling frequency according to the fluctuation error includes: When the fluctuation error is greater than a preset threshold, determining a dynamic sampling rate according to the fluctuation error; In the case where the dynamic sampling rate is greater than the preset maximum sampling rate, the maximum sampling rate is determined as the first candidate sampling frequency, or, In the case where the dynamic sampling rate is less than the preset minimum sampling rate, the minimum sampling rate is determined as the first candidate sampling frequency, or, When the dynamic sampling rate is less than or equal to the maximum sampling rate and greater than or equal to the minimum sampling rate, the dynamic sampling rate is determined as the first candidate sampling frequency.

3. The method according to claim 1, characterized in that The method for obtaining the fluctuation error includes: Acquire M groups of window data in a sampling order, each group of window data includes P sensor signals, where P and M are integers greater than 1; Determine the maximum and minimum values ​​of P sensor signals in each set of window data; Determine the current average fluctuation amount according to the average value of the maximum value of the M groups of window data, the average value of the minimum value of the M groups of window data, and the average value of the sensor signal of the M groups of window data; The fluctuation error is determined according to the average current fluctuation amount and the average value of the sensing signal of the M groups of window data.

4. The method according to claim 1, characterized in that: Determining a second candidate sampling frequency according to the historical record data includes: Determine whether the historical record data is within a preset range and obtain a determination result; In the case where the judgment result indicates that the historical record data is within the preset range, a second candidate sampling frequency is determined according to a preset reference frequency and an attenuation coefficient, and the attenuation coefficient is less than 1, or, When the judgment result indicates that the historical record data is outside the preset range, a second candidate sampling frequency is determined according to the reference frequency and a growth coefficient, and the growth coefficient is greater than 1.

5. The method according to claim 4, characterized in that The method for obtaining the growth coefficient includes: In the case where the historical record data is greater than an upper limit value of the preset range, determining a first proportionality coefficient according to the historical record data and the upper limit value; Determining the growth coefficient according to the first proportional system and a preset first piecewise function; or, In the case where the historical record data is less than a lower limit value of the preset range, determining a second proportionality coefficient according to the historical record data and the lower limit value; The growth coefficient is determined according to the second proportional system and a preset second piecewise function.

6. The method according to claim 1, characterized in that The user interaction information includes at least one type of activity information and / or physical condition information of the user, Determining a third candidate sampling frequency according to the user interaction information includes: Obtaining the gain coefficient, delay time, and duration corresponding to each type of activity information and / or physical condition information of the user; Determining a maximum gain coefficient from the acquired plurality of gain coefficients, determining a minimum delay time from the acquired plurality of delay times, and determining a maximum duration from the acquired plurality of durations; A third candidate sampling frequency is determined according to a preset reference frequency and a maximum gain coefficient within a maximum duration after the minimum delay time.

7. The method according to any one of claims 1 to 6, characterized in that Determining a target sampling frequency of the sensing signal according to at least one alternative sampling frequency includes: Compare the first candidate sampling frequency, the second candidate sampling frequency, and the third candidate sampling frequency to determine a maximum candidate sampling frequency; determine the target sampling frequency according to the maximum candidate sampling frequency; Alternatively, according to a preset priority order, from the first candidate sampling frequency, the second candidate sampling frequency, and the third candidate sampling frequency, the candidate sampling frequency with the highest priority is selected as the target sampling frequency; Alternatively, an average operation is performed on at least two of the first candidate sampling frequency, the second candidate sampling frequency, and the third candidate sampling frequency, and the average operation result is determined as the target sampling frequency.

8. A detection system, characterized in that: The detection system includes a sensor, an analog-to-digital converter, and a controller, wherein the controller is used to adaptively adjust the sampling frequency of the analog-to-digital converter for the sensor signal output by the sensor, including: A parameter information acquisition unit, used to acquire parameter information, wherein the parameter information includes a fluctuation error of the sensor signal, historical record data, and user interaction information, wherein the historical record data is determined based on N sensor signals collected in a historical period, where N is an integer greater than 1; An alternative sampling frequency determining unit, configured to determine at least one alternative sampling frequency of the sensing signal according to the parameter information; a target sampling frequency determination unit, configured to determine a target sampling frequency of the sensing signal according to at least one candidate sampling frequency; Wherein, the alternative sampling frequency determination unit is used to: determine a first alternative sampling frequency according to the fluctuation error; and / or determine a second alternative sampling frequency according to the historical record data; and / or determine a third alternative sampling frequency according to the user interaction information.

9. An electronic device, characterized in that: The electronic device comprises the detection system as claimed in claim 8.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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