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

Abstract

The present disclosure relates to a data processing method and detection system, an electronic device and a storage medium. The method comprises: determining at least one alternative sampling frequency of a sensing signal according to 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, such as at least one of a first alternative sampling frequency determined by the fluctuation error, a second alternative sampling frequency determined by the historical record data, and a third alternative sampling frequency determined by the user interaction information; and determining a target sampling frequency of the sensing signal according to the at least one alternative sampling frequency. The embodiments of the present disclosure can comprehensively consider the fluctuation error, the historical record data and the user interaction information of the sensing signal, and adaptively adjust the sampling frequency of the sensing signal, which is beneficial to improve the accuracy of data processing.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of medical treatment, and particularly relates to a data processing method and detection system, an electronic device and a storage medium. BACKGROUND

[0002] In the related art, a continuous biological activity substance detection system (for example, a continuous glucose detection system) is worn on a human body to be monitored in a minimally invasive manner. Biological activity substance data (for example, blood glucose data) is sampled at a fixed time interval (that is, a fixed sampling rate) to an electrical signal of a sensor, and a plurality of sampled electrical signals in a single acquisition period (for example, 5 minutes) are subjected to mean value processing to obtain a detection value (for example, a blood glucose value) that can reflect the change of the biological activity substance in the acquisition period.

[0003] However, the above measurement value (for example, a blood glucose value) can only represent the mean value of the change of the biological activity substance (for example, blood glucose) to be monitored in the acquisition period, and cannot reflect the dynamic continuity of the change of the biological activity substance in the acquisition period. In particular, when factors that can affect the rapid change of the biological activity substance value appear, such as the user completing eating, exercise, medication (such as injecting insulin), or the user's body condition suddenly changing (such as dizziness, pain, etc.), because the extreme value (for example, the maximum value of the biological activity substance concentration and the minimum value of the biological activity substance concentration) of the biological activity substance is difficult to obtain under the fixed sampling rate, the detection value calculated in the acquisition period cannot accurately reflect the situation when the actual extreme value of the biological activity substance value in the acquisition period appears, which has a great risk and is easy to cause safety accidents, such as the blood glucose value feedback inaccuracy of a diabetic patient in the process of using insulin, resulting in an excessive amount of insulin and causing hypoglycemia, causing the user to faint, etc. SUMMARY

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

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

[0006] In a possible implementation, the determining, according to the parameter information, of at least one alternative sampling frequency of the sensing signal comprises: 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.

[0007] In a possible implementation, the determining, according to the fluctuation error, of the first alternative sampling frequency comprises: determining a dynamic sampling rate according to the fluctuation error, in a case where the fluctuation error is greater than a preset threshold; determining the maximum sampling rate as the first alternative sampling frequency, in a case where the dynamic sampling rate is greater than a preset maximum sampling rate; or determining the minimum sampling rate as the first alternative sampling frequency, in a case where the dynamic sampling rate is less than a preset minimum sampling rate; or determining the dynamic sampling rate as the first alternative sampling frequency, in a case where the dynamic sampling rate is less than or equal to the maximum sampling rate and greater than or equal to the minimum sampling rate.

[0008] In a possible implementation, the fluctuation error is obtained in the following manner: M groups of window data are obtained in a sampling sequence, each group of window data comprising P sensing signals, P and M being integers greater than 1; the maximum value and the minimum value of the P sensing signals in each group of window data are determined; the current average fluctuation amount is determined according to the average value of the maximum values of the M groups of window data, the average value of the minimum values of the M groups of window data, and the average value of the sensing signals of the M groups of window data; and the fluctuation error is determined according to the current average fluctuation amount and the average value of the sensing signals of the M groups of window data.

[0009] In a possible implementation, the determining, according to the historical record data, of the second alternative sampling frequency comprises: determining whether the historical record data is in a preset range to obtain a determination result; determining the second alternative sampling frequency according to a preset reference frequency and a decay coefficient in a case where the determination result indicates that the historical record data is in the preset range, the decay coefficient being less than 1; or determining the second alternative sampling frequency according to the reference frequency and a growth coefficient in a case where the determination result indicates that the historical record data is out of the preset range, the growth coefficient being greater than 1.

[0010] In a possible implementation, the growth coefficient is determined in the following manner: when the historical record data is greater than an upper limit value of the preset range, a first proportional coefficient is determined according to the historical record data and the upper limit value; the growth coefficient is determined according to the first proportional coefficient and a preset first piecewise function; or when the historical record data is less than a lower limit value of the preset range, a second proportional coefficient is determined 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.

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

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

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

[0014] In a possible implementation, the alternative sampling frequency determination unit is configured 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.

[0015] In a possible implementation, determining the first alternative sampling frequency according to the fluctuation error includes: in a case where the fluctuation error is greater than a preset threshold, determining a dynamic sampling rate according to the fluctuation error; in a case where the dynamic sampling rate is greater than a preset maximum sampling rate, determining the maximum sampling rate as the first alternative sampling frequency, or in a case where the dynamic sampling rate is less than a preset minimum sampling rate, determining the minimum sampling rate as the first alternative sampling frequency, or in a case where the dynamic sampling rate is less than or equal to the maximum sampling rate and greater than or equal to the minimum sampling rate, determining the dynamic sampling rate as the first alternative sampling frequency.

[0016] In a possible implementation, the parameter information acquisition unit is configured to: acquire M groups of window data in a sampling order, each group of window data including P sensing signals, P and M being integers greater than 1; determine a maximum value and a minimum value of the P sensing signals in each group of window data; determine a current average fluctuation amount according to an average value of the maximum values of the M groups of window data, an average value of the minimum values of the M groups of window data, and an average value of the sensing signals of the M groups of window data; and determine the fluctuation error according to the current average fluctuation amount and the average value of the sensing signals of the M groups of window data.

[0017] In a possible implementation, determining the second candidate sampling frequency according to the historical record data comprises: judging whether the historical record data is in a preset range to obtain a judgment result; in a case where the judgment result indicates that the historical record data is in the preset range, determining the second candidate sampling frequency according to a preset reference frequency and an attenuation coefficient, the attenuation coefficient being less than 1, or in a case where the judgment result indicates that the historical record data is out of the preset range, determining the second candidate sampling frequency according to the reference frequency and a growth coefficient, the growth coefficient being greater than 1.

[0018] In a possible implementation, the growth coefficient is obtained in the following manner: in a case where the historical record data is greater than an upper limit value of the preset range, determining a first proportional coefficient according to the historical record data and the upper limit value; determining the growth coefficient according to the first proportional coefficient and a preset first piecewise function; or in a case where the historical record data is less than a lower limit value of the preset range, determining a second proportional coefficient according to the historical record data and the lower limit value; determining the growth coefficient according to the second proportional coefficient and a preset second piecewise function.

[0019] In a possible implementation, the user interaction information comprises at least one type of activity information and / or body condition information of the user, and determining the third candidate sampling frequency according to the user interaction information comprises: obtaining a gain coefficient, a delay time and a duration corresponding to each type of activity information and / or body condition information of the user; determining a maximum gain coefficient from the obtained multiple gain coefficients, a minimum delay time from the obtained multiple delay times, and a maximum duration from the obtained multiple durations; and determining the third candidate sampling frequency according to a preset reference frequency and the maximum gain coefficient within the maximum duration after the minimum delay time.

[0020] In a possible implementation, the target sampling frequency determination unit is configured to: 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; or select a candidate sampling frequency with the highest priority from the first candidate sampling frequency, the second candidate sampling frequency and the third candidate sampling frequency as the target sampling frequency according to a preset priority order; or perform an average operation on at least two of the first candidate sampling frequency, the second candidate sampling frequency and the third candidate sampling frequency, and determine the average operation result as the target sampling frequency.

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

[0022] According to an aspect of the present disclosure, a computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0023] In the embodiments of the present disclosure, at least one alternative sampling frequency of the sensing signal can be determined 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, and a target sampling frequency of the sensing signal can be determined according to the at least one alternative sampling frequency. Wherein, the at least one alternative sampling frequency of the sensing signal can be determined according to the parameter information, which can include: 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. In this way, the fluctuation error, the historical record data and the user interaction information of the sensing signal can be comprehensively considered, and the sampling frequency of the sensing signal can be adaptively adjusted, which is beneficial to improve the accuracy of data processing.

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

[0025] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the technical solutions of the present disclosure.

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

[0027] Figure 2 A flowchart 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 detection system according to an embodiment of the present disclosure is shown.

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

[0030] Figure 5 An effect schematic diagram of a blood glucose value curve in the related art is shown.

[0031] Figure 6 An effect schematic diagram of a blood glucose value curve according to an embodiment of the present disclosure is shown.

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

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

[0034] Figure 9 A schematic diagram illustrating determination of a second alternative sampling frequency according to an embodiment of the disclosure.

[0035] Figure 10 A schematic diagram illustrating determination of a third alternative sampling frequency according to an embodiment of the disclosure.

[0036] Figure 11 A schematic diagram illustrating a controller according to an embodiment of the disclosure.

[0037] Figure 12 A block diagram illustrating an electronic device according to an embodiment of the disclosure. DETAILED DESCRIPTION

[0038] Various exemplary embodiments, features, and aspects of the disclosure will be described hereinafter with reference to the accompanying drawings. The same reference numbers in different drawings denote the same or similar elements. Although various aspects of embodiments are illustrated in the drawings, the drawings are not necessarily drawn to scale unless specifically noted.

[0039] The term "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations.

[0040] The term "and / or" used herein only means an association relationship of the associated objects, and means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, at least one of A, B, and C includes any one or more elements selected from the set consisting of A, B, and C.

[0041] In addition, in order to better illustrate the disclosure, numerous specific details are given in the specific embodiments hereinafter. Those skilled in the art should understand that the disclosure can also be implemented without certain specific details. In some examples, methods, means, elements, and circuits that are well known to those skilled in the art are not described in detail in order to highlight the main idea of the disclosure.

[0042] The maximum and minimum values of a bioactive substance (e.g., blood glucose) are of great clinical significance, and the detection system (e.g., a continuous glucose detection system) in the related art cannot effectively detect the maximum and minimum values of the bioactive substance (e.g., blood glucose). Therefore, it is necessary to improve the sampling rate of the electrical signal (e.g., current value) of the sensor in the detection system, shorten the time interval between the collection of adjacent two electrical signals, and obtain more intensive electrical signals, so as to improve the acquisition probability of the electrical signal extreme value (i.e., current value extreme value) of the bioactive substance.

[0043] In the related art, the sampling rate is improved by improving the hardware circuit, such as increasing the computing power of the processing chip of the transmitter and the storage space of the memory, but this method not only increases the power consumption of the transmitter for collecting the electrical signal of the sensor, increases the data storage space of the electrical signal, and causes the cost to rise. Moreover, in order to ensure that the service life of the product is not shortened, the related art increases the size of the power supply battery of the transmitter, thereby increasing the overall size of the product, thereby being inconvenient for the user to wear. Further, the electrical signal collected on the sensor also needs to be filtered to reduce the interference of the external environment on the signal, thereby causing the delay of the electrical signal transmission time and the delay of the detection value (blood glucose value) of the bioactive substance in the collection period, affecting the real-time performance of the blood glucose value feedback.

[0044] Therefore, the data processing method provided by the embodiments of the present disclosure 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 the 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 considered comprehensively, and the sampling frequency of the sensor signal output by the analog-to-digital converter can be adaptively adjusted, thereby improving the accuracy, real-time performance, 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 embodiments of the present disclosure, as shown in 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 sensor 1 according to the data processing method.

[0046] In a possible implementation, the sensor 1 can include a biosensor, such as a thermal biosensor, a field effect transistor biosensor, a piezoelectric biosensor, a photoelectric biosensor, an acoustic channel biosensor, an enzyme electrode biosensor, etc., and the embodiments of the present disclosure do not limit this.

[0047] The biosensor is sensitive to biological substances and can convert the concentration of the biological substances into an electrical signal (for example, including a current signal, a voltage signal, a charge amount, etc.) and output the electrical signal as a sensing signal. The biosensor can be composed of a biosensitive material (for example, including an enzyme, an antibody, an antigen, a microorganism, a cell, a tissue, a nucleic acid, etc.) as a recognition element and a physical and chemical transducer (for example, including an oxygen electrode, a photosensitive tube, a field effect tube, a piezoelectric crystal, etc.).

[0048] For example, a glucose sensor for detecting the concentration of glucose can be composed of an enzyme, an electrode, and a signal converter, and the concentration of glucose is detected by a current signal generated by the enzyme catalyzing the glucose reaction. It should be understood that different sensors 1 can be set according to actual application scenarios, and 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 which is continuous in time and also continuous in amplitude at a certain sampling frequency, and convert it into a digital signal which is discrete in time and also discrete in amplitude, and can go through a sampling, holding, quantization, and encoding process. The sampling frequency represents the number of times per second that the analog-to-digital converter 2 collects the sensing signal, that is, the frequency at which the analog signal is discretized.

[0050] In a possible implementation, the controller 3 can include a processor having an execution instruction function in an electronic device, and the controller 3 can 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, system on chips (SoCs), microprocessors, or other electronic elements. Inside the controller 3, the executable instructions can be executed by hardware circuits such as logic gates, switches, application specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, so as to control the sampling frequency of the analog-to-digital converter 2 to the sensing signal output by the sensor 1.

[0051] Figure 2 A flowchart of a data processing method according to an embodiment of the present disclosure is shown in FIG. 1. Figure 2 As shown in FIG. 1, the data processing method includes:

[0052] In step S11, parameter information is acquired, the parameter information including fluctuation error of the sensing signal, historical record data, and user interaction information, the historical record data being determined according to N sensing signals collected in a historical period, N being an integer greater than 1;

[0053] In step S12, at least one alternative 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 the at least one alternative sampling frequency.

[0055] In a possible implementation, the method can be applied to a detection system as shown in Figure 1 The detection system can be a terminal device, a server, or other processing devices, etc. The terminal device can 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 embodiments of the present disclosure can be implemented by a controller 3 in the detection system calling computer readable instructions stored in a memory.

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

[0058] The fluctuation error reflects the fluctuation of the sensing signal output by the sensor 1. The historical record data is determined according to N (N > 1) sensing signals collected in a historical period, for example, the historical record data can be the mean value (or a deformation of the mean value) of N sensing signals collected in a certain historical period, and is used to reflect the historical detection situation of the biological active substance (such as blood glucose) of the user. The user interaction information refers to the customized information input by the user to the detection system, which helps the detection system to more accurately acquire the user's demand, so as to adjust the target sampling frequency to meet the user's demand.

[0059] In step S11, the parameter information is obtained, and in step S12, at least one alternative sampling frequency of the sensing signal is determined according to the parameter information, 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.

[0060] Optionally, the first alternative sampling frequency can be determined according to the fluctuation error.

[0061] For example, when the fluctuation error is relatively small, the sampling frequency of the last moment (or the default sampling frequency of the system) can be used as the first alternative sampling frequency; when the fluctuation error is relatively large, a 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 sensing signal is large (for example, greater than a preset threshold), the largest sampling frequency can be selected from a plurality of preset sampling frequencies as the first alternative sampling frequency, so as to more effectively capture the details and changes of the sensing signal and improve the accuracy of the sensing signal. When the fluctuation error of the sensing signal is small (for example, less than or equal to a preset threshold), the smallest sampling frequency can be selected from a plurality of preset sampling frequencies as the first alternative sampling frequency, so as to save computing resources and prolong the service life of the detection system.

[0063] It should be understood that by monitoring the fluctuation error of the sensing signal, the first alternative sampling frequency can be dynamically adjusted, and the embodiments of the present disclosure do not limit the determination manner of the first alternative sampling frequency, which can be set according to the actual application scenario.

[0064] Optionally, the second alternative sampling frequency can be determined according to the historical record data.

[0065] For example, when the changes of the plurality of historical record data corresponding to the plurality of historical periods in the recent period are relatively small (for example, the difference between the maximum value and the minimum value in the historical record data is less than a certain fixed value), the sampling frequency of the last historical period (or the default sampling frequency of the system) can be used as the second alternative sampling frequency; when the changes of the plurality of historical record data corresponding to the plurality of historical periods in the recent period are relatively large (for example, the difference between the maximum value and the minimum value in the historical record data is greater than a certain 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 example, the historical record data can truly reflect the detection value (e.g., blood glucose value) of the bioactive substance of the user at the historical time, and the historical record data of the latest historical period (e.g., the last historical period) can be taken as a judgment condition. When the historical record data is outside the safe range, it indicates that the probability of the detection value of the bioactive substance of the user being abnormal is relatively large. The maximum sampling frequency can be selected from the preset multiple sampling frequencies as the second alternative sampling frequency, so as to more effectively capture the details and changes of the sensing signal and improve the accuracy of the sensing signal. When the historical record data is within the safe range, it indicates that the probability of the detection value of the bioactive substance of the user being normal is relatively large. The minimum sampling frequency can be selected from the preset multiple sampling frequencies as the second alternative sampling frequency, so as to save computing resources and prolong the service life of the detection system.

[0067] It should be understood that the second alternative sampling frequency can be dynamically adjusted by monitoring the historical record data, and the embodiments of the present disclosure do not limit the determination manner of the second alternative sampling frequency, which can be set according to the actual application scenario.

[0068] Optionally, the third alternative sampling frequency can be determined according to the user interaction information. For example, the sampling frequency input by the user can be directly taken as the third alternative sampling frequency. For another example, the gain coefficient, delay time and duration corresponding to each type of activity information and / or body condition information of the user can be obtained according to the event input by the user, such as the diet information, exercise information, medication information and body condition information of the user. The maximum gain coefficient can be determined from the obtained multiple gain coefficients, the minimum delay time can be determined from the obtained multiple delay times, and the maximum duration can be determined from the obtained multiple durations. The third alternative sampling frequency can be determined according to the preset reference frequency and the maximum gain coefficient within the maximum duration after the minimum delay time.

[0069] It should be understood that the third alternative sampling frequency can be dynamically adjusted by the user interaction information, and the embodiments of the present disclosure do not limit the determination manner of the third alternative sampling frequency, which can be set according to the actual application scenario.

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

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

[0072] Optionally, in step S12, the first alternative sampling frequency can be determined according to the fluctuation error, and in step S13, the first alternative sampling frequency can be directly taken as the target sampling frequency of the sensing signal.

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

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

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

[0076] Optionally, in step S12, the first alternative sampling frequency can be determined according to the fluctuation error, and the second alternative sampling frequency can be determined according to the historical record data. In step S13, the target sampling frequency of the sensing signal can be determined according to the first alternative sampling frequency and the second alternative sampling frequency. For example, the mean (or maximum) of the first alternative sampling frequency and the second alternative sampling frequency can be taken as the target sampling frequency of the sensing signal.

[0077] Optionally, in step S12, the first alternative sampling frequency can be determined according to the fluctuation error, and the third alternative sampling frequency can be determined according to the user interaction information. In step S13, the target sampling frequency of the sensing signal can be determined according to the first alternative sampling frequency and the third alternative sampling frequency. For example, the mean (or maximum) of the first alternative sampling frequency and the third alternative sampling frequency can be taken as the target sampling frequency of the sensing signal.

[0078] Optionally, in step S12, the second alternative sampling frequency can be determined according to the historical record data, and the third alternative sampling frequency can be determined according to the user interaction information. In step S13, the target sampling frequency of the sensing signal can be determined according to the second alternative sampling frequency and the third alternative sampling frequency. For example, the mean (or maximum) of the second alternative sampling frequency and the third alternative sampling frequency can be taken as the target sampling frequency of the sensing signal.

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

[0080] Optionally, one of the first, second and third alternative sampling frequencies can be selected as the target sampling frequency of the sensing signal according to different application scenarios. 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 yet another example, the third alternative sampling frequency can be selected as the target sampling frequency of the sensing signal.

[0081] Optionally, different alternative sampling frequencies can correspond to different priorities, and the alternative sampling frequency with the highest priority can be selected as the target sampling frequency according to a preset priority order. By means of the preset priority order, the detection system can quickly select the alternative sampling frequency with the highest priority as the target sampling frequency from the plurality of alternative sampling frequencies, thereby facilitating improvement of the response speed of the detection system.

[0082] Optionally, at least two of the first, second and third alternative sampling frequencies can be subjected to average operation, and the average operation result can be used to determine the target sampling frequency. For example, the average of the first, second and third alternative sampling frequencies can be used as the target sampling frequency of the sensing signal; or, for another example, the average of the first and second alternative sampling frequencies can be used as the target sampling frequency of the sensing signal; or, for yet another example, the average of the first and third alternative sampling frequencies can be used as the target sampling frequency of the sensing signal; or, for yet another example, the average of the second and third alternative sampling frequencies can be used as the target sampling frequency of the sensing signal. By means of the average operation, the fluctuation error, historical record data, user interaction information and other factors can be comprehensively considered, thereby obtaining a more comprehensive and accurate sampling frequency.

[0083] Optionally, the first, second and third alternative sampling frequencies can be compared to determine the maximum alternative sampling frequency, and the target sampling frequency can be determined according to the maximum alternative sampling frequency. By comparing the plurality of alternative sampling frequencies and selecting the maximum alternative sampling frequency as the target sampling frequency, the target sampling frequency can be high enough to more accurately capture the details and changes of the sensing signal, thereby reducing the distortion of the sensing signal in the sampling process due to insufficient frequency and improving the integrity and accuracy of the sensing signal.

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

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

[0086] Figure 3 A schematic diagram of a continuous glucose detection system according to an embodiment of the present disclosure is shown as follows. Figure 3 As shown, the glucose detection system can include, in addition to the sensor 1, the analog-to-digital converter 2, and the controller 3, a current amplification module 4, a storage unit 5, a data transmission interface 6, and a user terminal 7.

[0087] The sensor 1 can be a glucose sensor for detecting the glucose concentration, which is immersed in the interstitial fluid of the human body and can react with glucose to generate an 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 and convert the current signal into a digital sequence. The current amplification module 4 can also provide an excitation signal for 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 in the sensor 1 or can exist in the form of a separate chip.

[0089] The analog-to-digital converter 2 can perform analog-to-digital conversion (analog-to-digital conversion) 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, perform filtering processing thereon, calculate the average current value in the blood glucose collection period, and calculate the blood glucose value in the collection period based on the average value.

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

[0092] The data transmission interface 6 can output the blood glucose value in the current collection period to the external user terminal 7 according to the indication of the control instruction of the controller 3. The data transmission interface 6 is a physical entity for data interaction between the blood glucose transmitter and the external entity. The data transmission interface 6 can include a Bluetooth radio frequency interface, a near field communication interface (NFC), and the like, and can also include a serial port, a parallel port, and the like. The type of the data transmission interface 6 is not limited in the embodiments of the present disclosure.

[0093] The user terminal 7, for example, includes a man-machine interactive display terminal, a mobile phone, a tablet computer, or a webpage, and can display the blood glucose value and the continuous blood glucose graph to the user.

[0094] It should be understood that the analog-to-digital converter 2, the storage unit 5, and the data transmission interface 6 can exist in the form of a discrete chip, or can be integrated into the same chip as the controller 3 as a master 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, operation, and the like on the digital signal.

[0095] In a possible implementation, the data processing method of the embodiments of the present disclosure can be applied to a continuous glucose detection system as shown in Figure 3 The following will take a continuous glucose detection system as shown in Figure 3 as an example to introduce three schemes for determining the first candidate sampling frequency.

[0096] In the related art, the weak electrical signal generated by the blood glucose electrochemical sensor is collected by the analog-to-digital converter. The current signal is often accompanied by noise, and the signal will swing with a large amplitude, 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 processed by digital filtering. However, the use of a filtering algorithm in digital filtering processing generally causes a delay, and the occurrence of the delay will cause the blood glucose fluctuation of the human body to be displayed to the user after a period of time, reducing the real-time performance.

[0097] The embodiments of the present disclosure can determine whether to start the 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 candidate sampling frequency after starting the digital filtering processing, so as to reduce the delay caused by the current filtering, thereby adaptively enhancing the real-time performance and continuity of the blood glucose value.

[0098] Figure 4 A schematic diagram for determining the first candidate sampling frequency according to the embodiments of the present disclosure is shown in FIG. 3. Figure 4As shown, the analog-to-digital converter 2 samples the sensor signal (e.g. current) output by the sensor 1 at a certain time interval to obtain a current value, the sampling interval of the analog-to-digital converter 2 is controlled by the controller 3, the controller 3 can call a preset filter control and sampling adaptive program to determine whether to start digital filter processing of the current signal, and after starting the digital filter processing, dynamically adjust the first candidate sampling frequency. That is, the current value obtained after sampling by the analog-to-digital converter 2 can be subjected to digital filter processing or not subjected to digital filter processing, which can be determined by the preset filter control and sampling adaptive program. The first in first out (FIFO) data buffer 1 is used to store current values of multiple sampling points. The controller 3 executes the filter control and sampling adaptive program, determines the fluctuation error of the current value according to the current value read from the first in first out data buffer 1, and performs filter control and sampling adaptive processing based on the fluctuation error of the current value. The first in first out data buffer 2 can be used to buffer the current values of multiple sampling points after filter control and sampling adaptive processing. Then the average of the multiple current values collected at 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] In actual application, the sampling of the current value is in seconds, and the generation of the blood glucose value is in minutes, so multiple current values need to be converted into one current value (e.g. average value) to be provided to the blood glucose algorithm, the blood glucose algorithm can perform blood glucose operation according to the input average of the current value, and the blood glucose algorithm can perform deformation processing such as weighting, linear transformation, and nonlinear transformation on the average of the current value. The embodiments of the present disclosure do not specifically limit the blood glucose algorithm.

[0100] When the detection system is started, the current value can be subjected to digital filter processing by default, and after starting, whether to perform digital filter processing or not can be determined according to the preset filter control and sampling adaptive program.

[0101] The filter control and sampling adaptive program of the embodiments of the present disclosure will be described below.

[0102] In a possible implementation, whether to start digital filter processing can be determined by monitoring the fluctuation error of the sensor signal (e.g. current value sampled by the analog-to-digital converter 2), and the fluctuation error can be obtained in the following manner: controlling the analog-to-digital converter 2 to obtain M groups of window data in a sampling order, each group of window data including P sensor signals, P and M being integers greater than 1; determining the maximum value and the minimum value of the P sensor signals in each group of window data; determining the average fluctuation amount of the current according to the average of the maximum values of the M groups of window data, the average of the minimum values of the M groups of window data, and the average of the sensor signals of the M groups of window data; and determining the fluctuation error according to the average fluctuation amount of the current and the average of the sensor signals of the M groups of window data.

[0103] Exemplarily, in the first-in first-out data buffer, the data is arranged in the sampling order of the sensing signals (e.g., current values), and the time interval of each sensing signal is the sampling interval at that time. 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 requirements, and the embodiments of the present disclosure do not limit this.

[0104] For example, assuming that the sensing signal is current data, the first-in data buffer can store n current values, for example: current value I1, current value I2, current value I3, current value I4, …, current value In. n .

[0105] The controller 3 can control the analog-to-digital converter 2 to obtain M groups of window data in the 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 group of window data is current value I1~current value I P , the second group of window data is current value I P+1 ~current value I 2P , the third group of window data is current value I 2P+1 ~current value I 3P , and so on. The Mth group of window data is current value I (M-1)×P+1 ~current value I M×P .

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

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

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

[0110] For example, the maximum value Imax M of the Mth group of window data is Max(I (M-1)×P+1 , I (M-1)×P+2 , …, I M×PMin(I M , I (M-1)×P+1 , …, I (M-1)×P+2 ). M×P ).

[0111] For example, assuming the sampling rate is 0.125hz, the current value can be collected by the analog-to-digital converter 2 once every 8 seconds, and in the preset period of blood glucose value display (for example, 5 minutes), a total of 38 current values can be collected, which are sequentially entered into the first-in-first-out data buffer, and 6 groups of window data are generated with a window width of 6, and then the maximum value and the minimum value of each group of the 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, the average value of the maximum values of the M groups of window data the average value of the minimum values of the M groups of window data the average value of the sensing signals of the M groups of window data determine the current average fluctuation I wave = Max(|Imax avg -I avg |, |Imin avg -I avg |).

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

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

[0115] wherein, Id represents the fluctuation error of the sensing data (for example, the current value collected by the analog-to-digital converter 2), I avg represents the average value of the sensing signals of the M groups of window data, and I wave represents the current average fluctuation of the M groups of window data.

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

[0117] In a possible implementation, the first candidate sampling frequency is determined according to the fluctuation error, including: in a case where the fluctuation error is greater than a preset threshold, determining a dynamic sampling rate according to the fluctuation error; in a case where the dynamic sampling rate is greater than a preset maximum sampling rate, determining the maximum sampling rate as the first candidate sampling frequency, or in a case where the dynamic sampling rate is less than a preset minimum sampling rate, determining the minimum sampling rate as the first candidate sampling frequency, or in a case where the dynamic sampling rate is less than or equal to the maximum sampling rate and greater than or equal to the minimum sampling rate, determining the dynamic sampling rate as the first candidate sampling frequency.

[0118] Exemplarily, the fluctuation error Id can be compared with the preset threshold Fter, and whether to start the digital filtering processing is determined according to a comparison result of the fluctuation error Id and the preset threshold Fter. For example, when the fluctuation error Id is greater than the preset threshold Fter, the digital filtering processing can be started, and the sampling rate of the analog-to-digital converter 2 to the sensing signal (for example, a current signal) output by the sensor 1 is dynamically adjusted to reduce the delay caused by the current filtering. Or, when the fluctuation error Id is less than or equal to the preset threshold Fter, it is considered that the quality of the sensing signal sampled by the analog-to-digital converter 2 is good, and the digital filtering processing is not needed, the digital filtering processing can be closed, and the sampling rate of the analog-to-digital converter 2 to the sensing signal output by the sensor 1 is not changed.

[0119] When the fluctuation error Id is greater than the preset threshold 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, which can be seen from the formula (1), W represents an upper limit value of the fluctuation error Id, and the value range of W 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 keeping the detection system working normally, and 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. f K represents an adjustment coefficient, and the value range of K is 0.8 to 1.2. For example, the adjustment coefficient K can be set to a default value 1. f

[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 An effect diagram of a blood glucose value curve in the related art is shown as follows: Figure 5 As shown in the diagram, the upper curve is a blood glucose value curve without filtering processing, and the lower curve is a blood glucose value curve after filtering processing, that is, filtering of each point as a sequence. Point B is a noise point deviating from the trend. The time interval of each point can be set to 2 minutes. It can be seen that Figure 5 the noise point B1 in the lower curve is closer to the original trend line, and the curve is also smoother and closer to the real situation of the human body. However, all points produce a time t delay, for example Figure 5 point A1 in the middle delays t from point A.

[0124] Figure 6 An effect diagram of a blood glucose value curve according to an embodiment of the present disclosure is shown as follows: Figure 6 As shown in the diagram, after using the first alternative sampling frequency of the embodiment of the present disclosure, six blood glucose values are additionally generated between the two points A2 and B2 before and after point B, see Figure 6 the small dots at positions 1-6, the data points in units of blood glucose values will increase, and the delay t (for example Figure 6 the delay t between point A1 and A in the middle is less than Figure 5 the delay t between point A1 and A in the middle), which 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, and the blood glucose algorithm can judge that point B is noise by comparing the small points A2-6. It should be understood that in actual application, the noise point can be deleted, and the surrounding two points can be used to replace the noise point, or the weighted average value of the surrounding points can be used to replace the noise point, and the embodiments of the present disclosure are not limited thereto.

[0125] In a possible implementation, determining the second candidate sampling frequency according to the historical record data comprises: judging whether the historical record data is in a preset range to obtain a judgment result; in a case where the judgment result indicates that the historical record data is in the preset range, determining the second candidate sampling frequency according to a preset reference frequency and an attenuation coefficient, the attenuation coefficient being less than 1, or in a case where the judgment result indicates that the historical record data is out of the preset range, determining the second candidate sampling frequency according to the reference frequency and a growth coefficient, the growth coefficient being greater than 1.

[0126] Suppose the detection system is a continuous glucose detection system (see Figure 3 ), the historical record data is a historical record of blood glucose values, and 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 by the user terminal 7 is displayed to the user in the form of a dot or a smooth curve. The continuous glucose detection system can detect the change in blood glucose of the wearer, and the extreme value (for example, the maximum value or the minimum value) of the blood glucose is not easy to capture in the case of a relatively sharp change in blood glucose, so that the graph ultimately displayed to the user is "distorted". In the case of stable blood glucose, small fluctuations have little clinical significance, and thus the display frequency of the blood glucose value can be reduced. When the blood glucose produces trend fluctuations or is about to fluctuate greatly, increasing the display frequency of the blood glucose value can more accurately reflect the real-time change in the blood glucose.

[0127] Detecting whether the short-term blood glucose fluctuation is sharp, so as to determine whether to adjust the blood glucose value calculation frequency, which is directed to the display frequency of the blood glucose value. In the case where the blood glucose is about to produce an extreme value, the display frequency of the blood glucose value can be increased to obtain the extreme value. Accurate acquisition of the extreme value helps to accurately obtain the real blood glucose fluctuation. On the contrary, if the extreme value is not sampled, the continuous continuous glucose detection system will have some discount in assisting the guidance of the high blood glucose and low blood glucose patients.

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

[0129] Figure 7 A schematic diagram of a continuous blood glucose graph according to an embodiment of the present disclosure is shown as Figure 7As shown, if the historical blood glucose value fluctuates within the preset range [Glu_max, Glu_min], it indicates that the user's glucose level is in a safe range, and the upper limit value Glu_max and the lower limit value Glu_min of the preset range [Glu_max, Glu_min] can be set according to the doctor's advice or by the user himself. The blood glucose value in the safe range is a less concerned interval for the user, and the display frequency of the blood glucose value can be reduced, thereby saving the system overhead, so as not to frequently prompt the user to check the blood glucose value and disturb the normal life and work of the user.

[0130] Figure 8 As shown in FIG. 6, another continuous blood glucose profile according to an embodiment of the present disclosure is shown. Figure 8 As shown, when the blood glucose value exceeds the set upper limit value Glu_max or lower limit value Glu_min, it indicates that the user needs to pay attention to the fluctuation of the blood glucose value, at which time the blood glucose display interval can be appropriately increased, so as to obtain a more detailed curve in the area of concern of the user.

[0131] Figure 9 As shown in FIG. 6, another continuous blood glucose profile according to an embodiment of the present disclosure is shown. Figure 9 As shown, assuming that the detection system is a continuous glucose detection system (see FIG. 1), and the historical record data is the historical record of the blood glucose value, see the above, the preset range [Glu_max, Glu_min] of the safe blood glucose can be set. Figure 3

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

[0133] After obtaining the historical record data Glu_d1 of the blood glucose, it can be judged whether the blood glucose value Glu_d1 falls within the preset range [Glu_max, Glu_min], if yes, the calculation frequency of the blood glucose value can be reduced, otherwise, the calculation frequency of the blood glucose value can be increased.

[0134] Optionally, the second alternative sampling frequency for reducing the calculation frequency of the blood glucose value can be determined according to the preset reference frequency Fdef and the decay coefficient Kd.

[0135] ​For example, the reduced blood glucose value acquisition frequency can be calculated as Fnow=Fdef*Kd, where Fdef is a preset reference frequency, for example, the default blood glucose value calculation frequency can be taken as the reference frequency Fdef, and Kd is a frequency attenuation coefficient (Kd<1) ;

[0136] The increased blood glucose value acquisition frequency can be calculated as Fnow=Fdef*Kup, where Fdef is a preset reference frequency, for example, the default blood glucose value calculation frequency can be taken as the reference frequency Fdef, and Kup is a frequency growth coefficient (Kup>1).

[0137] Then, the second alternative sampling frequency Fadc2 of the current signal output by the sensor 1 can be determined according to the blood glucose value acquisition frequency Fnow, that is, Fadc2=Fnow*N1, where N1 is the number of current signal acquisitions within a preset blood glucose value display period.

[0138] It should be understood that Fdef is the blood glucose value acquisition frequency for the user, for example, 5 minutes for one blood glucose value display for the user. Fadc2 is the sampling rate of the current value output by the sensor 1, and there is a multiple relationship, that is, N1 Fadc2 values are acquired to obtain one blood glucose value. The embodiments of the present disclosure do not limit the value of N1, for example, N1 can be set to 18, that is, one current value is acquired every 10 seconds, and one blood glucose value is displayed every 3 minutes, so N1=180 seconds / 10=18.

[0139] In the example, the controller 3 can 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, whether the acquisition frequency for calculating the blood glucose value needs to be adjusted can be determined according to the historical record data (for example, the blood glucose value) of the user, that is, based on whether the short-term blood glucose fluctuation is severe, and the second alternative sampling frequency is obtained.

[0141] In a possible implementation, the acquisition manner of the growth coefficient includes: in a case where the historical record data is greater than an upper limit value of the preset range, determining a first proportional coefficient according to the historical record data and the upper limit value; determining the growth coefficient according to the first proportional coefficient and a preset first segmented function; or in a case where the historical record data is less than a lower limit value of the preset range, determining a second proportional coefficient according to the historical record data and the lower limit value; and determining the growth coefficient according to the second proportional coefficient and a preset second segmented function.

[0142] For example, the growth coefficient Kupmay be obtained using a nonlinear strategy, assuming that the preset range is [Glu_max, Glu_min], the number of historical records of blood glucose values is Glu_d1, and the number of historical records of blood glucose values Glu_d1 is greater than the upper limit value Glu_max. The first proportional coefficient Kleve1may be determined according to the historical record data Glu_d1 and the upper limit value Glu_max, that is, Kleve1= (Glud1-Glu_max) / Glu_max.

[0143] Then, the growth coefficient Kupmay be determined according to the first proportional coefficient Kleve1and the preset first segmented function, that is,

[0144]

[0145] When the number of historical records of blood glucose values Glu_d1 is less than the lower limit value Glu_min, the second proportional coefficient Kleve2may be determined according to the historical record data Glu_d1 and the lower limit value Glu_min, that is, Kleve2= (Glu_min-Glu_d1) / Glu_min.

[0146] Then, the growth coefficient Kupmay be determined according to the second proportional system Kleve2and the preset second segmented function, that is,

[0147]

[0148] In this way, the growth coefficient Kupmay be accurately and quickly obtained.

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

[0150] Suppose the detection system is a continuous glucose detection system (see Figure 3 The continuous glucose detection system can interact with the user through the human-computer interaction interface of the user terminal 7, including displaying the blood glucose value and the continuous blood glucose graph to the user, and the user can also input the event to record the life situation of the user.

[0151] Optionally, since the blood sugar of the user will change after eating, the activity information input by the user terminal 7 can include diet information. Alternatively, since the user's movement will accelerate sugar metabolism, the blood sugar will change, and the activity information input by the user terminal 7 can include exercise information. Alternatively, since the user uses sugar-controlling drugs (for example, insulin), the blood sugar can change dramatically, and the activity information input by the user terminal 7 can include medication information. Alternatively, when the user is unwell, for example, dizzy, restless, etc., it can be caused by a change in blood sugar level, and the user terminal 7 can input body condition information. It should be understood that the user interaction information input by the user terminal 7 can include diet, exercise, medication, etc. activity information, and can also include body condition information, and embodiments of the present disclosure do not make specific limitations thereto.

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

[0153] For example, according to the diet information input by the user, the gain coefficient 1, the delay time 1, and the duration 1 corresponding to the diet information can be obtained; in actual application, the value range of the gain coefficient 1 can be set as: 1 < gain coefficient 1 < 1.3; the value range of the delay time 1 can be set as: 7 minutes < delay time 1 < 15 minutes; and the value range of the duration 1 can be set as: 45 minutes < duration 1 < 90 minutes. Wherein, the diet information input by the user can include the type and quantity of diet, such as 500 ml of milk, 200 g of meat, 70 g of egg, etc., and embodiments of the present disclosure do not make specific limitations on the specific content of the diet information.

[0154] According to the exercise information input by the user, the gain coefficient 2, the delay time 2, and the duration 2 corresponding to the exercise information can be obtained; in actual application, the value range of the gain coefficient 2 can be set as: 1.1 < gain coefficient 2 < 1.3; the value range of the delay time 2 can be set as: 2 minutes < delay time 2 < 7 minutes; and the value range of the duration 2 can be set as: 10 minutes < duration 2 < 60 minutes. Wherein, the exercise information input by the user can include the type and duration of exercise, such as 15 minutes of badminton and 20 minutes of basketball, etc., and embodiments of the present disclosure do not make specific limitations on the specific content of the exercise information.

[0155] The gain coefficient 3, the delay time 3 and the duration time 3 corresponding to the medication information can be obtained according to the medication information input by the user. In actual application, the value range of the gain coefficient 3 can be set as 1.3 < gain coefficient 3 < 1.5, the value range of the delay time 3 can be set as 0 minutes < delay time 3 < 5 minutes, and the value range of the duration time 3 can be set as 20 minutes < duration time 3 < 90 minutes. The medication information input by the user can include the type and dose of 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] The gain coefficient 4, the delay time 4 and the duration time 4 corresponding to the physical condition information can be obtained according to the physical condition information input by the user. In actual application, the value range of the gain coefficient 4 can be set as 1.3 < gain coefficient 4 < 1.8, the value range of the delay time 4 can be set as 0 minutes < delay time 4 < 2 minutes, and the value range of the duration time 4 can be set as 90 minutes < duration time 4 < 180 minutes. The physical condition information input by the user can be represented by symptoms such as dizziness, headache, convulsions, vomiting, diarrhea, etc., and the embodiments of the present disclosure do not limit the specific content of the physical condition information.

[0157] The gain coefficient is a calculation frequency improvement coefficient of the blood glucose value, and when the gain coefficient changes, the calculation frequency of the blood glucose value changes. The delay time indicates that after a period of time after receiving the user input, the calculation frequency of the blood glucose value starts to change. For example, the gain coefficient starts to take effect after the delay time. The duration time indicates a period of time during which the calculation frequency of the blood glucose value changes after receiving the event. For example, the duration time is the period of time during which the gain coefficient is effective to invalid.

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

[0159] For example, assuming that the diet information, the exercise information, the medication information and the 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), and the maximum duration time Ti = Max(duration time 1, duration time 2, duration time 3, duration time 4), and Max() is the maximum value function.

[0160] The third alternative sampling frequency can be determined according to the preset reference frequency Fdef and the maximum gain coefficient Kevt within the maximum duration Ti after the minimum delay time Td.

[0161] For example, the target blood glucose value calculation frequency Fnow = Fdef x 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 taken as the reference frequency Fdef. Then, the third alternative sampling frequency Fadc3 = Fnow x N1 of the current signal output by the sensor 1 can be determined by the analog-to-digital converter 2 according to the target blood glucose value calculation frequency Fnow, where N1 is the number of current signal acquisitions within the blood glucose value display preset period.

[0162] In the example, the controller 3 can control the analog-to-digital converter 2 to sample the current signal output by the sensor 1 at the third alternative sampling frequency Fadc3. For example, a timer or other time counter can be used to set and take effect the target blood glucose value calculation frequency Fnow and the third alternative sampling frequency Fadc3 after the minimum delay time Td, and restore to the values before adjustment after the maximum duration Ti.

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

[0164] In summary, the data processing method of the example of the present disclosure can determine at least one alternative sampling frequency of the sensing signal, 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, according to the fluctuation error of the sensing signal (the electrical signal output by the sensor of the detection system), the historical record data determined by the N (N > 1) sensing signals acquired within the historical period, and the user interaction information, and determine the target sampling frequency of the sensing signal according to the at least one alternative sampling frequency. In this way, the fluctuation error, the historical record data, and the user interaction information of the sensing signal can be considered comprehensively, and the sampling frequency of the analog-to-digital converter 2 to the sensing signal output by the sensor 1 can be adjusted adaptively, improving the accuracy, real-time performance, and adaptability of the detection system.

[0165] For example, for a continuous glucose detection system, the sampling rate of the blood glucose sensor can be automatically adjusted according to the current fluctuation error of the blood glucose sensor, the historical blood glucose value, the user's diet events, the user's exercise events, and other events. In this way, the blood glucose extreme value can be well monitored when the blood glucose fluctuates rapidly; the time delay caused by digital filtering processing is greatly shortened, and the noise is better filtered out, so that a more continuous and better blood glucose profile can be provided to the user.

[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 combined embodiments without deviating from the principle logic. Limited by the length of the present disclosure, the present disclosure will not be described again. Those skilled in the art can understand that in the above-mentioned method of the specific embodiment, 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, which can be used to implement any one of the data processing methods provided by the present disclosure. The corresponding technical solutions and descriptions are described in the method part and will not be described again.

[0168] In a possible implementation manner, as shown in Figure 1 The detection system provided by the embodiment of the present disclosure comprises a sensor 1, an analog-to-digital converter 2, and a controller 3. The controller 3 is configured to adaptively adjust the sampling frequency of the analog-to-digital converter 2 on the sensing signal output by the sensor 1.

[0169] Figure 11 A schematic diagram of the controller 3 according to the embodiment of the present disclosure is shown in FIG. 3. As shown in FIG. 3, the controller 3 comprises: Figure 11

[0170] A parameter information acquisition unit 31 is configured to acquire parameter information. The parameter information comprises fluctuation error of the sensing signal, historical record data, and user interaction information. The historical record data is determined according to N sensing signals collected in a historical period. N is an integer greater than 1.

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

[0172] A target sampling frequency determination unit 33 is configured to determine the target sampling frequency of the sensing signal according to the at least one alternative sampling frequency.

[0173] In a possible implementation manner, the alternative sampling frequency determination unit 32 is configured 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.

[0174] ​In a possible implementation, the first candidate sampling frequency is determined according to the fluctuation error, including: in a case where the fluctuation error is greater than a preset threshold, determining a dynamic sampling rate according to the fluctuation error; in a case where the dynamic sampling rate is greater than a preset maximum sampling rate, determining the maximum sampling rate as the first candidate sampling frequency, or in a case where the dynamic sampling rate is less than a preset minimum sampling rate, determining the minimum sampling rate as the first candidate sampling frequency, or in a case where the dynamic sampling rate is less than or equal to the maximum sampling rate and greater than or equal to the minimum sampling rate, determining the dynamic sampling rate as the first candidate sampling frequency.

[0175] In a possible implementation, the parameter information acquisition unit 31 is configured to: acquire M groups of window data in a sampling sequence, each group of window data including P sensing signals, P and M being integers greater than 1; determine maximum values and minimum values of the P sensing signals in each group of window data; determine an average current fluctuation amount according to an average value of the maximum values of the M groups of window data, an average value of the minimum values of the M groups of window data, and an average value of the sensing signals of the M groups of window data; and determine the fluctuation error according to the average current fluctuation amount and the average value of the sensing signals of the M groups of window data.

[0176] In a possible implementation, the second candidate sampling frequency is determined according to the historical record data, including: determining whether the historical record data is in a preset range to obtain a determination result; in a case where the determination result indicates that the historical record data is in the preset range, determining the second candidate sampling frequency according to a preset reference frequency and a decay coefficient, the decay coefficient being less than 1, or in a case where the determination result indicates that the historical record data is out of the preset range, determining the second candidate sampling frequency according to the reference frequency and a growth coefficient, the growth coefficient being greater than 1.

[0177] In a possible implementation, the growth coefficient is determined in the following manner: in a case where the historical record data is greater than an upper limit value of the preset range, determining a first proportional coefficient according to the historical record data and the upper limit value; determining the growth coefficient according to the first proportional coefficient and a preset first piecewise function; or in a case where the historical record data is less than a lower limit value of the preset range, determining a second proportional coefficient according to the historical record data and the lower limit value; and determining the growth coefficient according to the second proportional coefficient and a preset second piecewise function.

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

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

[0180] In some embodiments, the apparatus provided by the embodiments of the present disclosure has functions or includes modules that can be used to perform the methods described in the above method embodiments, and the specific implementation can refer to the description of the above method embodiments. For brevity, details are not repeated here.

[0181] The embodiments of the present disclosure also provide a computer-readable storage medium having computer program instructions stored therein, and the computer program instructions are executed by a processor to implement the above method. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0182] The embodiments of the present disclosure also provide an electronic device, including: a processor; a memory for storing processor-executable instructions; and wherein the processor is configured to invoke 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 computer readable code or a non-volatile computer readable storage medium carrying computer readable code, when the computer readable code is run in a processor of an electronic device, the processor in the electronic device executes the above method.

[0184] The electronic device can be provided as a terminal, a server, or other forms of devices.

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

[0186] The electronic device 1900 can 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 can operate based on an operating system stored in the memory 1932, such as Microsoft Windows Server operating system (Windows Server TM ), Apple's graphical user interface-based operating system (Mac OS X TM ), multi-user multi-process computer operating system (Unix TM ), free and open source Unix-like operating system (Linux TM ), open source Unix-like operating system (FreeBSD TM ) or the like.

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

[0188] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0189] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, 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 disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0190] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0191] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0192] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0193] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other data storage device. When the computer readable program instructions are loaded into the computer and other programmable data processing apparatus, a series of operational steps are implemented that provide processes such that the instructions which operate on the computer or other programmable data processing apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0194] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0195] The flow diagrams and the block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logic functions. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0196] The computer program product can be embodied in a tangible medium of

[0197] The above description of the various embodiments is intended to be illustrative in all aspects, rather than being restrictive. Those skilled in the art can refer to the description of the various embodiments to make modifications and / or improvements.

[0198] Those skilled in the art can understand that, in the above-described method of the specific embodiments, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible inherent logic.

[0199] If the technical solutions of the present application involve personal information, the product applying the technical solutions of the present application has been explicitly informed of the personal information processing rules before processing the personal information, and has obtained the personal independent consent. If the technical solutions of the present application involve sensitive personal information, the product applying the technical solutions of the present application has obtained the personal independent consent before processing the sensitive personal information, and at the same time meets the requirement of "explicit consent". For example, at the personal information collection device such as camera, a clear and prominent mark is set to inform that it has entered the personal information collection range and will collect personal information. If the individual voluntarily enters the collection range, it is considered to agree to collect personal information. Or on the device for processing personal information, through the pop-up information or by asking the individual to upload his personal information, the individual's authorization is obtained under the condition that the device uses obvious mark / information to inform the individual of the personal information processing rules. The personal information processing rules can include personal information processor, personal information processing purpose, processing method and personal information type, etc.

[0200] The above has described various embodiments of the present disclosure, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles, practical application or improvement of technology in the market of the embodiments, or to enable other ordinary skilled in the art to understand the embodiments disclosed herein.

Claims

1. A data processing method, characterized in that, The method includes: Obtain parameter information, which includes fluctuation error of sensor signal, historical data, and user interaction information. The historical data is determined based on N sensor signals collected within a historical period, where N is an integer greater than 1. Based on the parameter information, determine the alternative sampling frequency of the sensing signal; The target sampling frequency of the sensing signal is determined based on the alternative sampling frequencies. Specifically, determining the candidate sampling frequency of the sensing signal based on the parameter information includes: determining a first candidate sampling frequency based on the fluctuation error; determining a second candidate sampling frequency based on the historical data; and determining a third candidate sampling frequency based on the user interaction information. The determination of the second alternative sampling frequency based on the historical data includes: Determine whether the historical data is within a preset range and obtain the determination result; If the judgment result indicates that the historical data is within the preset range, a second alternative sampling frequency is determined based on a preset reference frequency and an attenuation coefficient, wherein the attenuation coefficient is less than 1, or... If the judgment result indicates that the historical data is outside the preset range, a second alternative sampling frequency is determined based on the reference frequency and the growth coefficient, wherein the growth coefficient is greater than 1. The growth coefficient is obtained in the following ways: If the historical data exceeds the upper limit of the preset range, a first proportional coefficient is determined based on the historical data and the upper limit. The growth coefficient is determined based on the first proportional coefficient and the preset first piecewise function; or, If the historical data is less than the lower limit of the preset range, a second proportional coefficient is determined based on the historical data and the lower limit. The growth coefficient is determined based on the second proportional coefficient and the preset second piecewise function.

2. The method according to claim 1, characterized in that, Based on the fluctuation error, a first candidate sampling frequency is determined, including: If the fluctuation error is greater than a preset threshold, the dynamic sampling rate is determined based on the fluctuation error. If the dynamic sampling rate is greater than the preset maximum sampling rate, the maximum sampling rate is determined as the first alternative sampling frequency, or... If the dynamic sampling rate is less than the preset minimum sampling rate, the minimum sampling rate is determined as the first alternative sampling frequency, or... If 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.

3. The method according to claim 1, characterized in that, The methods for obtaining the fluctuation error include: M sets of window data are acquired according to the sampling order. Each set 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 group of window data; The average current fluctuation is determined by the average of the maximum values ​​of the M groups of window data, the average of the minimum values ​​of the M groups of window data, and the average of the sensor signals of the M groups of window data. The fluctuation error is determined based on the average current fluctuation and the average value of the sensor signals from the M sets of window data.

4. The method according to claim 1, characterized in that, The user interaction information includes at least one type of user activity information and / or physical condition information. Based on the user interaction information, a third alternative sampling frequency is determined, including: Obtain the gain coefficient, latency, and duration corresponding to each type of user activity information and / or physical condition information; The maximum gain coefficient is determined from the multiple gain coefficients obtained, the minimum delay time is determined from the multiple delay times obtained, and the maximum duration is determined from the multiple durations obtained; Within the maximum duration following the minimum delay time, a third alternative sampling frequency is determined based on a preset reference frequency and a maximum gain coefficient.

5. The method according to any one of claims 1 to 4, characterized in that, Determining the target sampling frequency of the sensing signal based on the alternative sampling frequencies includes: The first candidate sampling frequency, the second candidate sampling frequency, and the third candidate sampling frequency are compared to determine the maximum candidate sampling frequency; the target sampling frequency is determined based on the maximum candidate sampling frequency. Alternatively, the target sampling frequency may be selected from the first, second, and third alternative sampling frequencies according to a preset priority order, with the highest priority among them. Alternatively, at least two of the first alternative sampling frequency, the second alternative sampling frequency, and the third alternative sampling frequency can be averaged, and the average result can be determined as the target sampling frequency.

6. A detection system, characterized in that, The detection system includes a sensor, an analog-to-digital converter, and a controller. The controller is used to adaptively adjust the sampling frequency of the sensor signal output by the analog-to-digital converter, including: The parameter information acquisition unit is used to acquire parameter information, which includes the fluctuation error of the sensing signal, historical data, and user interaction information. The historical data is determined based on N sensing signals collected within a historical period, where N is an integer greater than 1. The alternative sampling frequency determination unit is used to determine the alternative sampling frequency of the sensing signal based on the parameter information. The target sampling frequency determination unit is used to determine the target sampling frequency of the sensing signal based on the candidate sampling frequencies; The alternative sampling frequency determination unit is configured to: determine a first alternative sampling frequency based on the fluctuation error; determine a second alternative sampling frequency based on the historical data; and determine a third alternative sampling frequency based on the user interaction information. The determination of the second alternative sampling frequency based on the historical data includes: Determine whether the historical data is within a preset range and obtain the determination result; If the judgment result indicates that the historical data is within the preset range, a second alternative sampling frequency is determined based on a preset reference frequency and an attenuation coefficient, wherein the attenuation coefficient is less than 1, or... If the judgment result indicates that the historical data is outside the preset range, a second alternative sampling frequency is determined based on the reference frequency and the growth coefficient, wherein the growth coefficient is greater than 1. The growth coefficient is obtained in the following ways: If the historical data exceeds the upper limit of the preset range, a first proportional coefficient is determined based on the historical data and the upper limit. The growth coefficient is determined based on the first proportional coefficient and the preset first piecewise function; or, If the historical data is less than the lower limit of the preset range, a second proportional coefficient is determined based on the historical data and the lower limit. The growth coefficient is determined based on the second proportional coefficient and the preset second piecewise function.

7. An electronic device, characterized in that, The electronic device includes the detection system as described in claim 6.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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