A method, device, equipment and storage medium for detecting physiological indicators

Abstract

The present application discloses a physiological index detection method, device, equipment and storage medium, which can be applied to the artificial intelligence scenario. The method includes: obtaining static physiological parameters corresponding to a to-be-detected object in a static state, and determining the static physiological index of the to-be-detected object based on the static physiological parameters; obtaining dynamic physiological parameters corresponding to the to-be-detected object under a detection pressure, and determining a dynamic physiological index based on the dynamic physiological parameters corresponding to the detection pressure; adjusting the static physiological index and the dynamic physiological index based on a parameter adjustment coefficient to obtain a comprehensive physiological index of the to-be-detected object, and determining a physiological detection result of the to-be-detected object according to the comprehensive physiological index; the parameter adjustment coefficient is obtained by training with physiological parameters and actual physiological detection results of a sample object. By adopting the present application, the accuracy of physiological detection can be improved.

Description

Technical Field

[0001] The present application relates to the medical field, and particularly to a method, device, equipment and storage medium for detecting physiological indicators. Background Art

[0002] After a human body suffers from diabetes, it often causes other pathological changes, such as vascular lesions and diabetic neuropathy caused by vascular lesions. In order to more clearly determine the degree of damage to the patient's body, it is necessary to evaluate the neuropathy of the patient. At present, generally, a tuning fork for vibration sense, a percussion hammer for ankle reflex examination, a monofilament for pressure sense examination, and a thin needle for needle prick pain sense examination are integrated, and the neuropathy of the patient is determined by detecting the patient's vibration sense, pain sense, pressure sense and ankle reflex, etc. However, this method requires the use of a variety of tactile detection tools, the operation is complex, and the final neuropathy situation is determined directly through the detection results, without a clear standard for measurement, resulting in low detection accuracy and efficiency for the neuropathy caused by diabetes. Summary of the Invention

[0003] The embodiments of the present application provide a method, device, equipment and storage medium for detecting physiological indicators, which can improve the accuracy of physiological detection.

[0004] On the one hand, an embodiment of the present application provides a method for detecting physiological indicators, including:

[0005] Obtain the static physiological parameters corresponding to the object to be measured in the static state, and determine the static physiological indicators of the object to be measured based on the static physiological parameters;

[0006] Obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure, and determine the dynamic physiological indicators based on the dynamic physiological parameters corresponding to the detection pressure;

[0007] Adjust the static physiological indicators and dynamic physiological indicators based on the parameter adjustment coefficient to obtain the comprehensive physiological indicators of the object to be measured, and determine the physiological detection result of the object to be measured according to the comprehensive physiological indicators; the parameter adjustment coefficient is obtained by training with the physiological parameters and actual physiological detection results of the sample object.

[0008] On the other hand, an embodiment of the present application also provides a method for detecting physiological indicators, including:

[0009] Obtain the static sample parameters corresponding to the sample object in the static state, and determine the static sample indicators of the sample object based on the static sample parameters;

[0010] Obtain the dynamic sample parameters corresponding to the sample object under the detection pressure, and determine the dynamic sample indicators based on the dynamic sample parameters corresponding to the detection pressure;

[0011] Adjust the static sample index and the dynamic sample index based on the sample adjustment coefficient to obtain the comprehensive sample index of the sample object;

[0012] Obtain the actual physiological detection result of the sample object, and perform a selection process on the sample adjustment coefficient according to the actual physiological detection result and the comprehensive sample index to obtain the parameter adjustment coefficient; the parameter adjustment coefficient is used to determine the physiological detection result of the object to be measured.

[0013] On the one hand, an embodiment of the present application provides a physiological index detection device, including:

[0014] A static physiological parameter acquisition module, configured to acquire the static physiological parameters corresponding to the object to be measured in a static state;

[0015] A static physiological index determination module, configured to determine the static physiological index of the object to be measured based on the static physiological parameters;

[0016] A dynamic physiological parameter acquisition module, configured to acquire the dynamic physiological parameters corresponding to the object to be measured under the detection pressure;

[0017] A dynamic physiological index determination module, configured to determine the dynamic physiological index based on the dynamic physiological parameters corresponding to the detection pressure;

[0018] A detection result determination module, configured to adjust the static physiological index and the dynamic physiological index based on the parameter adjustment coefficient to obtain the comprehensive physiological index of the object to be measured, and determine the physiological detection result of the object to be measured according to the comprehensive physiological index; the parameter adjustment coefficient is obtained by training the physiological parameters and the actual physiological detection results of the sample object.

[0019] Among them, the physiological detection device further includes:

[0020] A static environment acquisition module, configured to acquire the static environment data for detecting the object to be measured through the detection device associated with the object to be measured.

[0021] Among them, the static physiological parameter acquisition module is specifically configured to splice the playback data at the first moment, the initial elimination data at the first moment, and the echo prediction increment at the second moment to obtain the target combined data for prediction processing.

[0022] Among them, the static environment data includes the first static environment data at the first wavelength and the second static environment data at the second wavelength; the first static environment data includes the first incident light intensity, the first outgoing light intensity, the first light absorption coefficient corresponding to the physiological substance of the object to be measured, and the first weight coefficient; the second static environment data includes the second incident light intensity, the second outgoing light intensity, the second light absorption coefficient corresponding to the physiological substance of the object to be measured, and the second weight coefficient;

[0023] Static physiological parameter acquisition module, including:

[0024] The first static equation construction unit is used to construct the first static equation at the first wavelength according to the data relationship among the first incident light intensity, the first outgoing light intensity, the first light absorption coefficient, and the first weight coefficient;

[0025] The second static equation construction unit is used to construct the second static equation at the second wavelength according to the data relationship among the second incident light intensity, the second outgoing light intensity, the second light absorption coefficient, and the second weight coefficient;

[0026] The static parameter determination unit is used to determine the static physiological parameter corresponding to the object to be measured in the static state based on the first static equation and the second static equation.

[0027] Among them, the first static equation includes the first distance static equation corresponding to the first light source distance and the first distance static equation corresponding to the second light source distance; the second static equation includes the second static equation corresponding to the first light source distance and the second static equation corresponding to the second light source distance;

[0028] The static parameter determination unit includes:

[0029] The first parameter elimination subunit is used to perform equation parameter elimination processing on the first distance static equation corresponding to the first light source distance and the first distance static equation corresponding to the second light source distance to obtain the first distance difference equation;

[0030] The second parameter elimination subunit is used to perform equation parameter elimination processing on the second static equation corresponding to the first light source distance and the second static equation corresponding to the second light source distance to obtain the second distance difference equation;

[0031] The equation solving subunit is used to solve the first distance difference equation and the second distance difference equation to obtain the static physiological parameter corresponding to the object to be measured in the static state.

[0032] Among them, the physiological detection device further includes:

[0033] The dynamic environment acquisition module is used to acquire the dynamic environment data for detecting the object to be measured through the detection device associated with the object to be measured; the dynamic environment data includes the first dynamic environment data corresponding to the first moment and the first dynamic environment data corresponding to the second moment at the first wavelength, and the second dynamic environment data corresponding to the first moment and the second dynamic environment data corresponding to the second moment at the second wavelength.

[0034] Among them, the dynamic physiological parameter acquisition module includes:

[0035] A first dynamic equation construction unit, configured to construct a first dynamic equation corresponding to a first moment at a first wavelength according to a data relationship of first dynamic environment data corresponding to the first moment, and construct a first dynamic equation corresponding to a second moment at the first wavelength according to a data relationship of first dynamic environment data corresponding to the second moment;

[0036] A second dynamic equation construction unit, configured to construct a second dynamic equation corresponding to the first moment at a second wavelength according to a data relationship of second dynamic environment data corresponding to the first moment, and construct a second dynamic equation corresponding to the second moment at the second wavelength according to a data relationship of second dynamic environment data corresponding to the second moment;

[0037] An equation parameter elimination unit, configured to perform equation parameter elimination processing on the first dynamic equation corresponding to the first moment and the first dynamic equation corresponding to the second moment to obtain a first change amount equation, and perform equation parameter elimination processing on the second dynamic equation corresponding to the first moment and the second dynamic equation corresponding to the second moment to obtain a second change amount equation;

[0038] An equation solving unit, configured to solve the first change amount equation and the second change amount equation to obtain dynamic physiological parameters corresponding to the object to be measured under the detected pressure.

[0039] Wherein, the dynamic physiological parameters include dynamic physiological parameters corresponding to T moment pairs respectively, and each moment pair includes a first moment and a second moment; T is a positive integer;

[0040] A dynamic physiological index determination module, including:

[0041] A method determination unit, configured to determine a slope detection method based on dynamic physiological index elements corresponding to the dynamic physiological parameters; the slope detection method includes an ascending slope detection method and a descending slope detection method;

[0042] A dynamic index determination unit, configured to determine the object slope corresponding to the dynamic physiological parameters corresponding to T moment pairs respectively under the slope detection method as the dynamic physiological index.

[0043] Wherein, the dynamic physiological parameter acquisition module further includes:

[0044] A pressure determination unit, configured to acquire dynamic physiological index elements to be detected, and determine a detection pressure based on the dynamic physiological index elements to be detected;

[0045] A dynamic parameter acquisition unit, configured to increase the detection pressure for the object to be measured and acquire the dynamic physiological parameters corresponding to the object to be measured under the detection pressure.

[0046] An embodiment of the present application provides a physiological index detection device on the one hand, including:

[0047] A static sample index determination module, configured to obtain static sample parameters corresponding to a sample object in a static state, and determine static sample indexes of the sample object based on the static sample parameters;

[0048] A dynamic sample index determination module, configured to obtain dynamic sample parameters corresponding to a sample object under a detection pressure, and determine dynamic sample indexes based on the dynamic sample parameters corresponding to the detection pressure;

[0049] A comprehensive sample index determination module, configured to adjust the static sample indexes and the dynamic sample indexes based on a sample adjustment coefficient to obtain comprehensive sample indexes of the sample object;

[0050] A selection module, configured to obtain an actual physiological detection result of a sample object, and perform a selection process on the sample adjustment coefficient according to the actual physiological detection result and the comprehensive sample indexes to obtain a parameter adjustment coefficient; the parameter adjustment coefficient is used to determine a physiological detection result of a to-be-detected object.

[0051] Wherein, the number of sample adjustment coefficients is S, and the comprehensive sample indexes include comprehensive sample indexes respectively corresponding to the S sample adjustment coefficients; S is a positive integer;

[0052] The selection module includes:

[0053] A sample detection result acquisition unit, configured to obtain sample detection results respectively indicated by the S comprehensive sample indexes, and determine detection precisions respectively corresponding to the S comprehensive sample indexes and accuracies respectively corresponding to the S comprehensive sample indexes according to the sample detection results respectively indicated by the S comprehensive sample indexes and the actual physiological detection result of the sample object;

[0054] A parameter adjustment coefficient determination unit, configured to determine a parameter adjustment coefficient from the sample adjustment coefficients corresponding to the S comprehensive sample indexes according to the distribution of the detection precisions respectively corresponding to the S comprehensive sample indexes and the accuracies respectively corresponding to the S comprehensive sample indexes.

[0055] On the one hand, the present application provides a computer device, including: a processor, a memory, and a network interface;

[0056] The above-mentioned processor is connected to the above-mentioned memory and the above-mentioned network interface. Wherein, the above-mentioned network interface is used to provide a data communication function, the above-mentioned memory is used to store a computer program, and the above-mentioned processor is used to call the above-mentioned computer program so that the computer device executes the method in the embodiment of the present application.

[0057] On the one hand, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program is adapted to be loaded and executed by a processor to execute the method in the embodiment of the present application.

[0058] In the embodiments of the present application, a computer device with a detection function can obtain the static physiological parameters corresponding to the object to be measured in a static state, and determine the static physiological indexes of the object to be measured through the static physiological parameters. Through the static physiological indexes, the static response characteristics of blood oxygen are reflected. At the same time, the computer device can also obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure, and determine the dynamic physiological indexes through the dynamic physiological parameters corresponding to the detection pressure. The dynamic physiological indexes can reflect the dynamic response characteristics of blood oxygen. Then, the computer device can adjust the static physiological indexes and the dynamic physiological indexes based on the parameter adjustment coefficient to obtain the comprehensive physiological indexes of the object to be measured. Among them, the comprehensive physiological indexes combine the static physiological indexes and the dynamic physiological indexes. That is to say, the comprehensive physiological indexes combine the static response characteristics and the dynamic response characteristics of blood oxygen, evaluate neuropathy from two dimensions of static and dynamic, increase the dimension of physiological detection, and then determine the physiological detection result of the object to be measured according to the comprehensive physiological indexes. It can be seen that in the embodiments of the present application, by quantitatively detecting the physiological conditions of the object to be measured (that is, the patient), physiological indexes in different dimensions are obtained, that is, the static physiological indexes and the dynamic physiological indexes are combined, which more comprehensively represents the physiological conditions of the object to be measured, thereby improving the accuracy of physiological detection. Description of the Drawings

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0060] Figure 1 It is a schematic diagram of a network architecture provided by an embodiment of the present application;

[0061] Figure 2 It is a schematic diagram of a structure for physiological detection provided by an embodiment of the present application;

[0062] Figure 3 It is a schematic flowchart of a physiological detection method provided by an embodiment of the present application;

[0063] Figure 4a It is a schematic diagram of a structure of a detection device provided by an embodiment of the present application;

[0064] Figure 4b It is a schematic diagram of a structure of a sensing array module provided by an embodiment of the present application;

[0065] Figure 4cIt is a characteristic absorption spectrum diagram corresponding to a physiological substance of a to-be-detected object provided by an embodiment of the present application;

[0066] Figure 4d It is a schematic diagram of a scenario of the change amount of blood oxygen concentration provided by an embodiment of the present application;

[0067] Figure 5 It is a schematic flowchart of a physiological index detection method provided by an embodiment of the present application;

[0068] Figure 6 It is a schematic diagram of a scenario of a comprehensive sample index changing with a sample adjustment coefficient provided by an embodiment of the present application;

[0069] Figure 7 It is a schematic structural diagram of a physiological detection device provided by an embodiment of the present application;

[0070] Figure 8 It is a schematic structural diagram of a physiological detection device provided by an embodiment of the present application;

[0071] Figure 9 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. Specific embodiments

[0072] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0073] Please refer to Figure 1 , which is a schematic diagram of a network architecture provided by an embodiment of the present invention. The network architecture may include a server 100, a wireless transceiver device 200, a data wireless relay device 300, and a detection device group 400 (such as Figure 1As shown, the number of the detection device group is taken as two here, specifically including detection devices 401, 402, etc.). In a system usage scenario, the server 100 can be communicatively connected to the wireless transceiver device 200, the wireless transceiver device 200 can be communicatively connected to the data wireless relay device 300, the data wireless relay device 300 can be communicatively connected to the detection device group, and the detection device group can be communicatively connected to each other. Among them, the above communication connection is not limited to the connection method, and can be directly or indirectly connected through a wired communication method, or can be directly or indirectly connected through a wireless communication method, or can also be connected through other methods, which are not limited in this application. Optionally, the server 100 can perform neuropathy detection with any one of the detection device group. In a system usage scenario, after the detection device 401 and the detection device 402 collect detection data by detecting the object to be measured, they can transmit the detection data to the data wireless relay device 300. After receiving the detection data, the data wireless relay device 300 can transmit the detection data to the wireless transceiver device 200. After receiving the detection data, the wireless transceiver device 200 transmits it to the server 100 by wire. After analyzing and processing the detection data, the server 100 obtains physiological data, and the physiological data here can include static physiological parameters and dynamic physiological parameters. Among them, the server mentioned above can be an electronic device, including but not limited to mobile phones, tablet computers, desktop computers, laptop computers, handheld computers, vehicle-mounted devices, augmented reality / virtual reality (AR / VR) devices, head-mounted displays, smart TVs, wearable devices, smart speakers, digital cameras, cameras, and other mobile internet devices (MID) with network access capabilities, or devices in scenarios such as trains, ships, and flights. The wireless transceiver device 200 can include routers, mobile phones, tablet computers, laptop computers, handheld computers, and mobile internet devices (MID). The data wireless relay device 300 can include base stations, etc.

[0074] Among them, the computer device mentioned in this application can be a server or a detection device, or a system composed of a server and a detection device.

[0075] Among them, there can be a communication connection between the detection device group. For example, there is a communication connection between the detection device 401 and the detection device 402. Among them, the above communication connection is not limited to the connection method, and can be directly or indirectly connected through a wired communication method, or can be directly or indirectly connected through a wireless communication method, or can also be connected through other methods, which are not limited in this application.

[0076] For ease of subsequent understanding and description, please also refer to Figure 2 , Figure 2 which is a schematic structural diagram of a physiological detection provided by an embodiment of the present application. Among them, in a feasible physiological detection scenario, a computer device can obtain detection data through a detection device. The detection data can be environmental data including static environmental data and dynamic environmental data. The static environmental data here can include the incident light intensity, the outgoing light intensity, the light absorption coefficient corresponding to the physiological substances of the object to be measured, and the weight coefficient, etc. at a certain wavelength. After analyzing and processing the detection data, the computer device obtains physiological data including static physiological parameters and dynamic physiological parameters. Then, the computer device can obtain static physiological indicators based on the static physiological parameters. At the same time, the computer device can obtain dynamic physiological indicators based on the dynamic physiological parameters. After that, the computer device can adjust the static physiological indicators and the dynamic physiological indicators based on a parameter adjustment coefficient to obtain the comprehensive physiological indicator of the object to be measured, and determine the physiological detection result of the object to be measured according to the comprehensive physiological indicator. Optionally, the computer device can be a Figure 1 shown server 100, and can obtain detection data from the detection device through the Figure 1 shown network architecture. At this time, the detection device can be any one of the detection devices in the Figure 1 shown detection device group or a sub-cluster composed of multiple detection devices; or, the computer device can be directly connected to the detection device (such as wired connection or wireless connection, etc.) to obtain the detection data sent by the detection device. Specifically, the connection method between the computer device and the detection device is not limited here. Of course, the computer device can also be any one of the detection devices in the detection device group, or a sub-cluster composed of multiple detection devices. The computer device can directly detect the object to be measured and obtain the detection data of the object to be measured.

[0077] In the embodiment of the present application, detection data can be obtained through a detection device, and then the detection data is processed to obtain static physiological parameters and dynamic physiological parameters, and further obtain static physiological indicators and dynamic physiological indicators. Then, the static physiological indicators and the dynamic physiological indicators are combined based on a parameter adjustment coefficient to obtain a comprehensive physiological indicator. The degree of neuropathy of neuro-oxygen can be evaluated from two dimensions of dynamic and static through the comprehensive physiological indicator, increasing the dimension of the detection data of physiological detection. Through the parameter adjustment coefficient, the indicators between the dynamic physiological indicators and the static physiological indicators are adjusted, so that the obtained comprehensive physiological indicator can take into account the detection fineness (i.e., sensitivity) and accuracy (i.e., specificity) of the detection result, thereby reducing the possibility of misjudgment of neuropathy and improving the accuracy of physiological detection.

[0078] Further, please refer to Figure 3 ,Figure 3 It is a schematic flowchart of a physiological index detection method provided by an embodiment of the present application. As Figure 3 shown, this method can be executed by a computer device, which can be any one of the detection devices in the detection device group, or the server 100 shown above Figure 1 and is not limited herein. For ease of understanding, an embodiment of the present application takes this method being executed by a computer device as an example for description. This physiological detection method can at least include the following steps S101-S103:

[0079] Step S101, obtain the static physiological parameters corresponding to the object to be measured in a static state, and determine the static physiological index of the object to be measured based on the static physiological parameters.

[0080] Specifically, the computer device can collect and process through a detection device, and then obtain the static physiological parameters of the object to be measured in a static state. Among them, the static state can refer to the state when the examined limb part of the object to be measured is stationary without applying a detection pressure. For example, under standard atmospheric pressure, the examined limb part of the object to be measured is in a stationary state. Here, the detection pressure can be an additional pressure applied on the basis of atmospheric pressure for detection requirements. Among them, the static physiological parameters can be detection data for the object to be measured. For example, the static physiological parameters can include the concentration of oxygenated hemoglobin (HbO2) and the concentration of deoxygenated hemoglobin (Hb). Further, the static physiological index can be a data standard for measuring the value of the static physiological parameters. For example, the static physiological index can include regional oxygenation saturation (RSO2). Among them, the computer device can emit near-infrared light to the physiological substances (that is, tissues, including oxygenated hemoglobin and deoxygenated hemoglobin, etc.) included in the object to be measured, or when the computer device is a server, it can emit near-infrared light to the physiological substances included in the object to be measured through a detection device associated with the object to be measured, and collect the static physiological parameters of the object to be measured under the near-infrared light based on the characteristic absorption spectrum of the physiological substances. Specifically, near-infrared light at a first wavelength can be emitted to the physiological substances included in the object to be measured, and based on the characteristic absorption spectrum of the physiological substances, the first static environment data of the object to be measured under the near-infrared light at the first wavelength is collected; near-infrared light at a second wavelength is emitted to the physiological substances included in the object to be measured, and based on the characteristic absorption spectrum of the physiological substances, the second static environment data of the object to be measured under the near-infrared light at the second wavelength is collected; the static physiological parameters are determined according to the first static environment data and the second static environment data.

[0081] Further, please refer to Figure 4a, Figure 4a is a schematic structural diagram of a detection device provided by an embodiment of the present application. As Figure 4a shown, the detection device may include a blood pressure cuff module, a sensing array module, etc. Among them, the computer device can apply a detection pressure to the object to be measured through the blood pressure cuff module, and the computer device can collect the original environmental data through the sensing array module, convert the original environmental data from an analog signal to a digital signal through the analog-to-digital conversion module, and perform feature extraction processing on the digital signal through the signal processing module. Finally, the computer device can display the digital signal (i.e., environmental data) after signal processing through the parameter display module.

[0082] Further, please refer to Figure 4b , Figure 4b is a schematic structural diagram of a sensing array module provided by an embodiment of the present application. As Figure 4b shown, among them, the computer device can detect the tissue oxygenation signal of the object to be measured through multiple detection channels (taking six detection channels as an example here) based on the principle of near-infrared spectroscopy (NIRS). The sensing array module here may include an analog-to-digital conversion module, a signal processing module, a parameter display module, etc. The computer device can obtain static physiological parameters and dynamic physiological parameters according to the environmental data based on the characteristic absorption spectra corresponding to oxyhemoglobin and deoxyhemoglobin in the tissue of the object to be measured. Specifically, the computer device can emit incident light through a light source (i.e., Figure 4b S in it), and detect the outgoing light through detection channel one (CH1), detection channel two (CH2), detection channel three (CH3), detection channel four (CH4), detection channel five (CH5), and detection channel six (CH6) respectively. In each detection channel, multiple detectors with different distances from the light source can be set, and the specific number is not limited here. In Figure 4b it takes two different detectors set in each detection channel as an example. The light source distance between the first detector and the light source can be r1, and the light source distance between the first detector and the light source can be r2. Detection data can be obtained through the sensing array as shown in Figure 4b . After being processed by the analog-to-digital conversion module, signal processing module, parameter display module, etc. in Figure 4a , environmental data including dynamic environmental data and static environmental data can be obtained, and the computer device can further obtain static physiological parameters and dynamic physiological parameters. Among them, the sensing array module can be a sensor or other devices with sensing functions.

[0083] In the case of obtaining static physiological parameters, a computer device can obtain static environment data for detecting a to-be-detected object through a detection device associated with the to-be-detected object. According to the data relationship between the static environment data, the static physiological parameters corresponding to the to-be-detected object in the static state are determined. Among them, the association method between the detection device and the to-be-detected object can be a direct wearing method or a data transmission method through a detection device worn by the to-be-detected object. Here, the static environment data can refer to the data for detection in the static state.

[0084] Specifically, the static environment data can include first static environment data at a first wavelength and second static environment data at a second wavelength. Both the first static environment data and the second static environment data can be data sets composed of various types of data. For example, the first static environment data can include a first incident light intensity, a first transmitted light intensity, a first light absorption coefficient corresponding to the physiological substance of the to-be-detected object, and a first weight coefficient. Correspondingly, the second static environment data can include a second incident light intensity, a second transmitted light intensity, a second light absorption coefficient corresponding to the physiological substance of the to-be-detected object, and a second weight coefficient. Further, the computer device can construct a first static equation at the first wavelength according to the data relationship between the first incident light intensity, the first transmitted light intensity, the first light absorption coefficient, and the first weight coefficient. At the same time, the computer device can construct a second static equation at the second wavelength according to the data relationship between the second incident light intensity, the second transmitted light intensity, the second light absorption coefficient, and the second weight coefficient. Then, the computer device can determine the static physiological parameters corresponding to the to-be-detected object in the static state based on the first static equation and the second static equation. Among them, the physiological substances of the to-be-detected object here can include oxyhemoglobin and deoxyhemoglobin, etc. Optionally, the first wavelength can be denoted as λ1, the second wavelength can be denoted as λ2, the incident light intensity can be denoted as I, the transmitted light intensity can be denoted as I′, the light absorption coefficient of oxyhemoglobin can be denoted as ε HbO2 , the light absorption coefficient of deoxyhemoglobin is denoted as ε Hb , the concentration of oxyhemoglobin is denoted as The concentration of deoxyhemoglobin is denoted as C Hb, denote the detection distance (i.e., the light source distance) between the light source and the detector in the detection device as r, denote the weight coefficient of the light source distance r (i.e., the differential path factor) as DFP, and denote the background absorption data of other substances in the object to be measured except oxyhemoglobin and deoxyhemoglobin as G. Among them, the first wavelength and the second wavelength are used to represent the wavelengths of the near-infrared light emitted to the physiological substances included in the object to be measured. That is to say, when emitting the near-infrared light of the first wavelength to the object to be measured, the first static physiological parameter of the object to be measured at the first wavelength can be collected; when emitting the near-infrared light of the second wavelength to the object to be measured, the second static physiological parameter of the object to be measured at the second wavelength can be collected.

[0085] For example, a possible first static equation can be seen as shown in Formula ①:

[0086]

[0087] Among them, in Formula ①, represents the first incident light intensity at the first wavelength, represents the first transmitted light intensity at the first wavelength, represents the first light absorption coefficient of oxyhemoglobin at the first wavelength, represents the first light absorption coefficient of deoxyhemoglobin at the first wavelength, represents the background absorption data at the first wavelength, represents the ratio of the first transmitted light intensity at the first wavelength to the first incident light intensity at the first wavelength.

[0088] For example, a possible second static equation can be seen as shown in Formula ②:

[0089]

[0090] Among them, in Formula ②, represents the second incident light intensity at the second wavelength, represents the second transmitted light intensity at the second wavelength, represents the second light absorption coefficient of oxyhemoglobin at the second wavelength, represents the second light absorption coefficient of deoxyhemoglobin at the second wavelength, represents the background absorption data at the second wavelength, represents the ratio of the second transmitted light intensity at the second wavelength to the second incident light intensity at the second wavelength.

[0091] It should be noted that the computer device can select the first wavelength and the second wavelength according to the characteristic absorption spectra corresponding to the physiological substances of the object to be measured. Further, please refer to Figure 4c , Figure 4cIt is a characteristic absorption spectrogram corresponding to a physiological substance of a to-be-detected object provided by an embodiment of the present application. As Figure 4c shown, the physiological substance of the to-be-detected object can be HbO2 and Hb. The abscissa of the characteristic absorption spectrogram represents the wavelength, and the ordinate represents the molar extinction coefficients corresponding to HbO2 and Hb respectively. This characteristic absorption spectrum represents the corresponding variation relationship between the wavelength of HbO2 and the molar extinction coefficient of HbO2, and the corresponding variation relationship between the wavelength of Hb and the molar extinction coefficient of Hb. The computer device can select the wavelengths corresponding to the characteristic peaks in the characteristic absorption spectrum as the first wavelength and the second wavelength respectively. For example, the computer device can select the characteristic peak of Hb as the first wavelength and the characteristic peak of HbO2 as the first wavelength. As Figure 4c shown in the characteristic absorption spectrum, 760 nm can be used as the first wavelength and 850 nm can be used as the second wavelength.

[0092] Furthermore, the first static equation includes the first distance static equation corresponding to the first light source distance and the first distance static equation corresponding to the second light source distance, while the second static equation includes the second static equation corresponding to the first light source distance and the second static equation corresponding to the second light source distance. Among them, the light source distance can be the distance between the light source and the detector in the detection device, and the numerical values of the first light source distance and the second light source distance can be different. The computer and device can perform equation parameter elimination processing on the first distance static equation corresponding to the first light source distance and the first distance static equation corresponding to the second light source distance to obtain the first distance difference equation. The computer device can also perform equation parameter elimination processing on the second static equation corresponding to the first light source distance and the second static equation corresponding to the second light source distance to obtain the second distance difference equation. Finally, the computer device can solve the first distance difference equation and the second distance difference equation to obtain the static physiological parameters corresponding to the to-be-detected object in the static state. In addition, the background absorption data at the first wavelength and the background absorption data at the second wavelength are not much different. In other words, it can be understood that and are equal.

[0093] Optionally, at the first wavelength, it can be considered that the first light absorption coefficient corresponding to the first light source distance and the first light absorption coefficient corresponding to the second light source distance are the same. This is mainly because the light absorption coefficient is related to the wavelength. When the wavelength remains unchanged, the light absorption coefficient can be considered the same. At this time, a possible first distance difference equation can be seen in Formula ③ as follows:

[0094]

[0095] Among them, in Formula ③, represents the first incident light intensity at the first wavelength, represents the first emitted light intensity at the first wavelength, represents the first light absorption coefficient of oxyhemoglobin at the first wavelength, represents the first light absorption coefficient of deoxyhemoglobin at the first wavelength, represents the change in the ratio of the first emitted light intensity at the first wavelength to the first incident light intensity at the first wavelength.

[0096] For example, a possible second distance difference equation is shown in Formula ④:

[0097]

[0098] where, represents the second incident light intensity at the second wavelength, represents the second emitted light intensity at the second wavelength, represents the second light absorption coefficient of oxyhemoglobin at the second wavelength, represents the second light absorption coefficient of deoxyhemoglobin at the second wavelength, represents the change in the ratio of the second emitted light intensity at the second wavelength to the second incident light intensity at the second wavelength.

[0099] It should be noted that the computer device can eliminate the background absorption data of other static physiological parameters of the object to be measured (i.e., G in the first static equation in the equation) by performing equation parameter elimination processing on the first distance static equation corresponding to the first light source distance and the first distance static equation corresponding to the second light source distance, which can reduce the process of obtaining the background absorption data G, thereby reducing the acquisition time for static physiological parameters and improving the efficiency of obtaining static physiological parameters. Similarly, the step of the computer device obtaining the second distance difference equation by performing equation parameter elimination processing on the second static equation corresponding to the first light source distance and the second static equation corresponding to the second light source distance has the same beneficial effects as the step of obtaining the first distance difference equation.

[0100] Further, since the first incident light intensity at the first wavelength, the first emitted light intensity at the first wavelength, the second incident light intensity at the second wavelength, the second emitted light intensity at the second wavelength, the first light absorption coefficient of oxyhemoglobin at the first wavelength, the first light absorption coefficient of deoxyhemoglobin at the first wavelength, the second light absorption coefficient of oxyhemoglobin at the second wavelength, the second light absorption coefficient of deoxyhemoglobin at the second wavelength, the first light source distance, the second light source distance, and the weight coefficient of the first light source distance and the second light source distance are all environmental data that can be obtained by the detection device, the computer device can solve equations based on the first distance difference equation and the second distance difference equation to directly obtain the static physiological parameters corresponding to the object to be measured in the static state. The static physiological parameters may include the concentration of oxyhemoglobin (i.e., ) and the concentration of deoxyhemoglobin (C Hb ).

[0101] In a possible case of obtaining static physiological parameters, the detection device can directly collect the static physiological parameters, and the computer device can collect the static physiological parameters corresponding to the object to be measured in the static state based on the detection device associated with the object to be measured; or, it can collect the static environmental data corresponding to the object to be measured in the static state based on the detection device associated with the object to be measured, and convert the static environmental data into static physiological parameters through the signal processing model in the detection device, etc.

[0102] Further, the computer device can determine the static physiological index of the object to be measured based on the static physiological parameters. Specifically, the computer device can determine the ratio of the concentration of oxyhemoglobin to the sum of the concentration of oxyhemoglobin and the concentration of deoxyhemoglobin as the static physiological index. For example, a possible static physiological index is shown in Formula ⑤:

[0103]

[0104] Among them, in Formula ⑤, represents the concentration of oxyhemoglobin, C Hb represents the concentration of deoxyhemoglobin, represents the total hemoglobin concentration, that is, the sum of oxyhemoglobin and deoxyhemoglobin, and RSO2 represents the ratio of the concentration of oxyhemoglobin to the total hemoglobin concentration, which refers to the static physiological index here and can be denoted as blood oxygen saturation here.

[0105] Optionally, the computer device can pass through such as Figure 4bThe sensing array module shown performs multi-detection channel detection. The computer device can obtain the static physiological parameters corresponding to the object to be measured under N detection channels. Based on the static physiological parameters corresponding to the N detection channels, the static physiological index of the object to be measured is determined. Specifically, based on the static physiological parameters corresponding to the N detection channels, the static sub-physiological indexes corresponding to the N detection channels can be determined, and the average value of the static sub-physiological indexes corresponding to the N detection channels is determined as the static physiological index of the object to be measured. The static sub-physiological index is used to represent the static physiological index of the object to be measured under a single detection channel. Specifically, it can be considered that the above formulas ① to ⑤ are examples of a detection channel. Through the above possible static sub-physiological index RSO2, the static sub-physiological indexes corresponding to the N detection channels are obtained. For example, by detecting the ratio of oxyhemoglobin concentration to total hemoglobin concentration through the N detection channels respectively, the N ratios of oxyhemoglobin concentration to total hemoglobin concentration corresponding to the N detection channels are obtained. N is a positive integer. Among them, the N ratios of oxyhemoglobin concentration to total hemoglobin concentration can be expressed as (RSO2)1, (RSO2)2, (RSO2)3, …, (RSO2) n , which is used to indicate the blood oxygen saturation corresponding to the N detection channels respectively. The computer device can calculate the average value of the blood oxygen saturation of the multi-detection channels. The specific calculation process can be seen in formula ⑥ shown below:

[0106]

[0107] Among them, in formula ⑥, (RSO2)1 represents the ratio of oxyhemoglobin concentration to total hemoglobin concentration of the first detection channel, (RSO2)2 represents the ratio of oxyhemoglobin concentration to total hemoglobin concentration of the second detection channel, …, (RSO2) n represents the ratio of oxyhemoglobin concentration to total hemoglobin concentration of the Nth detection channel, and (RSO2) m represents the average value of the blood oxygen saturation corresponding to the N detection channels respectively. Further, the computer device can determine the average value of the blood oxygen saturation corresponding to the N detection channels as the static physiological index.

[0108] Step S102: Obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure, and determine the dynamic physiological index based on the dynamic physiological parameters corresponding to the detection pressure.

[0109] Specifically, the computer device can obtain dynamic environment data for detecting the object to be measured through a detection device associated with the object to be measured. Then, the computer device can obtain dynamic physiological parameters based on the dynamic environment data. The dynamic environment data includes first dynamic environment data corresponding to a first wavelength and second dynamic environment data corresponding to a second wavelength. The first dynamic environment data may include a third incident light intensity, a third transmitted light intensity, a third light absorption coefficient corresponding to the physiological substance of the object to be measured, and a third weight coefficient. Correspondingly, the second dynamic environment data may include a fourth incident light intensity, a fourth transmitted light intensity, a fourth light absorption coefficient corresponding to the physiological substance of the object to be measured, and a fourth weight coefficient. Among them, the computer device can emit near-infrared light to the physiological substance (i.e., tissue, including oxyhemoglobin and deoxyhemoglobin, etc.) included in the object to be measured. Or when the computer device is a server, it can emit near-infrared light to the physiological substance included in the object to be measured through a detection device associated with the object to be measured. Based on the characteristic absorption spectrum of the physiological substance, the dynamic physiological parameters of the object to be measured under near-infrared light are collected. Specifically, near-infrared light of the first wavelength can be emitted to the physiological substance included in the object to be measured, and based on the characteristic absorption spectrum of the physiological substance, the first dynamic environment data of the object to be measured under the near-infrared light of the first wavelength is collected; near-infrared light of the second wavelength is emitted to the physiological substance included in the object to be measured, and based on the characteristic absorption spectrum of the physiological substance, the second dynamic environment data of the object to be measured under the near-infrared light of the second wavelength is collected; the dynamic physiological parameters are determined according to the first dynamic environment data and the second dynamic environment data.

[0110] In the case of obtaining dynamic physiological parameters, the first dynamic environment data at the first wavelength may include the first dynamic environment data at the first moment and the first dynamic environment data at the second moment collected at the first wavelength; the second dynamic environment data at the second wavelength may include the second dynamic environment data at the first moment and the second dynamic environment data at the second moment collected at the second wavelength. The computer device may construct a first dynamic equation corresponding to the first moment at the first wavelength according to the data relationship of the first dynamic environment data corresponding to the first moment, and construct a first dynamic equation corresponding to the second moment at the first wavelength according to the data relationship of the first dynamic environment data corresponding to the second moment. At the same time, the computer device may construct a second dynamic equation corresponding to the first moment at the second wavelength according to the data relationship of the second dynamic environment data corresponding to the first moment, and construct a second dynamic equation corresponding to the second moment at the second wavelength according to the data relationship of the second dynamic environment data corresponding to the second moment. Then, the computer device may perform equation parameter elimination processing on the first dynamic equation corresponding to the first moment and the first dynamic equation corresponding to the second moment to obtain a first change amount equation. Correspondingly, the computer device may perform equation parameter elimination processing on the second dynamic equation corresponding to the first moment and the second dynamic equation corresponding to the second moment to obtain a second change amount equation. Furthermore, the computer device may solve the first change amount equation and the second change amount equation to obtain the dynamic physiological parameters corresponding to the test object under the detected pressure. Among them, the physiological substances of the test object may include oxygenated hemoglobin, deoxyhemoglobin, etc. here. Optionally, when collecting the dynamic physiological parameters of the test object, the first wavelength may be denoted as The second wavelength is denoted as The incident light intensity is denoted as I, the transmitted light intensity is denoted as I′, and the light absorption coefficient of oxygenated hemoglobin is denoted as ε HbO2 , and the light absorption coefficient of deoxyhemoglobin is denoted as ε Hb , and the concentration of oxygenated hemoglobin is denoted as The concentration of deoxyhemoglobin is denoted as C Hb , the detection distance (i.e., the light source distance) between the light source and the detector in the detection device is denoted as r, the weight coefficient of the light source distance r (i.e., the differential path factor) is denoted as DFP, and the background absorption data of other substances in the test object except oxygenated hemoglobin and deoxyhemoglobin is denoted as G. The first wavelength and the second wavelength are used to represent the wavelengths of near-infrared light emitted to the physiological substances included in the test object. That is to say, when emitting near-infrared light of the first wavelength to the test object, the first dynamic physiological parameters of the test object at the first wavelength can be collected. When emitting near-infrared light of the second wavelength to the test object, the second dynamic physiological parameters of the test object at the second wavelength can be collected.

[0111] For example, a possible first dynamic equation can be seen as shown in Formula ⑦:

[0112]

[0113] Wherein, in Formula ⑦, represents the third incident light intensity at the first wavelength, represents the third transmitted light intensity at the first wavelength, represents the third light absorption coefficient of oxyhemoglobin at the first wavelength, represents the third light absorption coefficient of deoxyhemoglobin at the first wavelength, represents the background absorption data at the first wavelength, represents the ratio of the third transmitted light intensity at the first wavelength to the third incident light intensity at the first wavelength.

[0114] For example, a possible second dynamic equation can be seen as shown in Formula ⑧:

[0115]

[0116] Wherein, in Formula ⑧, represents the fourth incident light intensity at the second wavelength, represents the fourth transmitted light intensity at the second wavelength, represents the fourth light absorption coefficient of oxyhemoglobin at the second wavelength, represents the fourth light absorption coefficient of deoxyhemoglobin at the second wavelength, represents the background absorption data at the second wavelength, represents the change amount of the ratio of the fourth transmitted light intensity at the second wavelength to the fourth incident light intensity at the second wavelength.

[0117] It should be noted that the first static formula and the first dynamic formula have the same environmental data composition, but the specific numerical values of the environmental data are different. For example, the DFP of the first static formula and the DFP of the first dynamic formula can be different numerical values. Similarly, other environmental data can also have different numerical values in the first static formula and the first dynamic formula respectively. Similarly, the second static formula and the second dynamic formula have the same environmental data composition, but the specific numerical values of the environmental data are different.

[0118] Furthermore, for example, a possible first change amount equation can be seen as shown in Formula ⑨:

[0119]

[0120] Wherein, in Formula ⑨, represents the third incident light intensity at the first wavelength, represents the third emitted light intensity at the first wavelength, represents the third light absorption coefficient of oxyhemoglobin at the first wavelength, represents the third light absorption coefficient of deoxyhemoglobin at the first wavelength, ΔC HbO2 represents the change amount between the oxyhemoglobin concentration at the first moment and the oxyhemoglobin concentration at the second moment, ΔC Hb represents the change amount between the deoxyhemoglobin concentration at the first moment and the deoxyhemoglobin concentration at the second moment, represents the change amount of the ratio of the third emitted light intensity at the first wavelength to the third incident light intensity at the first wavelength.

[0121] For example, a possible equation for the second change amount is shown in Formula ⑩:

[0122]

[0123] where, in Formula ⑩, represents the fourth incident light intensity at the second wavelength, represents the fourth emitted light intensity at the second wavelength, represents the fourth light absorption coefficient of oxyhemoglobin at the second wavelength, represents the fourth light absorption coefficient of deoxyhemoglobin at the second wavelength, ΔC HbO2 represents the change amount between the oxyhemoglobin concentration at the first moment and the oxyhemoglobin concentration at the second moment, ΔC Hb represents the change amount between the deoxyhemoglobin concentration at the first moment and the deoxyhemoglobin concentration at the second moment, represents the change amount of the ratio of the fourth emitted light intensity at the second wavelength to the fourth incident light intensity at the second wavelength.

[0124] It should be noted that the computer device can eliminate the background absorption data of other static physiological parameters of the object to be measured (i.e., G in the first static equation in the equation) by performing equation parameter elimination processing on the first dynamic equation corresponding to the first moment and the first dynamic equation corresponding to the second moment to obtain the first change amount equation. This can reduce the process of obtaining the background absorption data G, and further reduce the acquisition time of static physiological parameters, improving the efficiency of obtaining static physiological parameters. Similarly, the computer device can obtain the second change amount equation by performing equation parameter elimination processing on the second dynamic equation corresponding to the first moment and the second dynamic equation corresponding to the second moment, which has the same beneficial effects as the steps of obtaining the first change amount equation.

[0125] Furthermore, since the third incident light intensity at the first wavelength, the third emitted light intensity at the first wavelength, the fourth incident light intensity at the second wavelength, the fourth emitted light intensity at the second wavelength, the third light absorption coefficient of oxyhemoglobin at the first wavelength, the third light absorption coefficient of deoxyhemoglobin at the first wavelength, the fourth light absorption coefficient of oxyhemoglobin at the second wavelength, the fourth light absorption coefficient of deoxyhemoglobin at the second wavelength, the first light source distance, the second light source distance, and the weight coefficient of the first light source distance and the second light source distance are all environmental data that can be obtained by the detection device, the computer device can solve the equations based on the first change equation and the second change equation to directly obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure, that is, the change amount between the oxyhemoglobin concentration at the first moment and the oxyhemoglobin concentration at the second moment, and the change amount between the deoxyhemoglobin concentration at the first moment and the deoxyhemoglobin concentration at the second moment.

[0126] Furthermore, the computer device can sum up the change amount between the oxyhemoglobin concentration at the first moment and the oxyhemoglobin concentration at the second moment, and the change amount between the deoxyhemoglobin concentration at the first moment and the deoxyhemoglobin concentration at the second moment, to obtain the change amount of the total hemoglobin concentration (including deoxyhemoglobin concentration and oxyhemoglobin concentration) at the first moment and the change amount of the total hemoglobin concentration (including deoxyhemoglobin concentration and oxyhemoglobin concentration) at the second moment. The change amount of the total hemoglobin concentration at the first moment and the total hemoglobin concentration at the second moment can be expressed as ΔC tHb = ΔC HbO2 + ΔC Hb . The change amount of the total hemoglobin concentration at the first moment and the total hemoglobin concentration at the second moment here can represent the change amount of blood oxygen saturation.

[0127] In a case of obtaining dynamic physiological parameters, the computer device can integrate the detection devices associated with the object to be measured into a dynamic physiological parameter acquisition module, and obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure based on the integrated dynamic parameter acquisition module.

[0128] Further, the computer device can determine the slope detection method based on the dynamic physiological index elements corresponding to the dynamic physiological parameters. The slope detection methods include the rising slope detection method and the falling slope detection method. The dynamic physiological index elements can include at least one of the blood flow volume (BV) or the blood consumption (BC). If the dynamic physiological index element is the blood flow volume, the computer device can obtain the blood flow volume by the change amount of the oxyhemoglobin concentration at a detection pressure greater than the diastolic arterial pressure and less than the systolic arterial pressure. At this time, the slope detection method corresponding to the blood flow volume obtained by the computer device is the rising slope detection method. In addition, if the dynamic physiological index element is the blood consumption, the computer device can obtain the blood consumption by the change amount of the total hemoglobin concentration (that is, the sum of the change amount of the oxyhemoglobin concentration and the change amount of the deoxyhemoglobin concentration) at a detection pressure greater than the systolic arterial pressure. At this time, the slope detection method corresponding to the blood consumption obtained by the computer device is the falling slope detection method. The object slopes corresponding to the dynamic physiological parameters at T moments under the slope detection method are determined as the dynamic physiological indexes, where T is a positive integer.

[0129] Further, obtain the dynamic physiological index elements to be detected, and determine the detection pressure based on the dynamic physiological index elements to be detected. Increase the detection pressure for the object to be measured, and obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure. Optionally, the dynamic physiological index element can include at least one of the first dynamic physiological index element or the second dynamic physiological index element. As Figure 4d shown, the first dynamic physiological index element can be the blood flow volume, and the second dynamic physiological index element can be the blood consumption. For the specific Figure 4d description, please refer to the following for Figure 4dSpecific description. If the dynamic physiological index element to be detected is the first dynamic physiological index element, determine the detection pressure as the first detection pressure, increase the first detection pressure for the object to be measured, and obtain the first dynamic physiological parameter of the object to be measured under the first detection pressure. Among them, the first detection pressure can be any pressure within the range greater than the diastolic arterial pressure and less than the systolic arterial pressure. Among them, if the dynamic physiological index element is the blood oxygen flow capacity, the computer device can pressurize the limb part to be detected to any pressure within the range greater than the diastolic arterial pressure and less than the systolic arterial pressure. At this time, the state of the limb part to be measured is the state of blocked venous return. The artery can deliver blood to tissues such as peripheral blood vessels to detect the microcirculation blood supply ability of the blood. For example, the detection pressure greater than the diastolic arterial pressure and less than the systolic arterial pressure can be 80 millimeters of mercury (mmHg). At the same time, if the dynamic physiological index element to be detected is the second dynamic physiological index element, determine the detection pressure as the second detection pressure, increase the second detection pressure for the object to be measured, and obtain the second dynamic physiological parameter of the object to be measured under the second detection pressure. Among them, the second detection pressure can be any pressure within the range greater than the systolic arterial pressure. Among them, if the dynamic physiological index element is the blood oxygen consumption rate, the computer device can pressurize the limb part to be detected to any pressure within the range greater than the systolic arterial pressure. At this time, the state of the limb part to be measured is the state of blocked venous return and blocked dynamic supply, so as to detect the metabolic ability of the blood. For example, the detection pressure greater than the systolic arterial pressure can be 220 millimeters of mercury (mmHg). Among them, after obtaining the required dynamic physiological parameter of the object to be measured, the pressure can be released, that is, stop increasing the detection pressure, so that the blood oxygen saturation value of the object to be measured returns to normal.

[0130] Further, please refer to Figure 4d , Figure 4d is a schematic diagram of the change in the amount of change in blood oxygen concentration provided by the embodiment of the present application. As Figure 4dAs shown, within the time period from 0s to 20s, if the detected pressure is zero, that is, the tissue part to be measured of the object to be measured is under standard atmospheric pressure and no additional detection pressure is applied. At this time, the change in oxyhemoglobin concentration and the change in deoxyhemoglobin concentration detected by the computer device can be regarded as unchanged. Therefore, the total hemoglobin concentration composed of oxyhemoglobin concentration and deoxyhemoglobin concentration detected by the computer device can also be regarded as unchanged. Within the time period from 20s to 50s, if the detected pressure is 80 mmHg (i.e., within the range greater than diastolic arterial pressure and less than systolic arterial pressure), the change in oxyhemoglobin concentration detected by the computer device can be regarded as unchanged, and the change in deoxyhemoglobin concentration increases with the passage of time. Therefore, the total hemoglobin concentration composed of oxyhemoglobin concentration and deoxyhemoglobin concentration detected by the computer device also increases with the passage of time. In addition, within the time period from 50s to 80s, if the detected pressure is 220 mmHg (i.e., within the range greater than systolic arterial pressure), the change in oxyhemoglobin concentration detected by the computer device can be regarded as decreasing with the passage of time, and the change in deoxyhemoglobin concentration increases with the passage of time, and the decreasing speed of oxyhemoglobin concentration is equal to the increasing speed of deoxyhemoglobin concentration. Therefore, the total hemoglobin concentration composed of oxyhemoglobin concentration and deoxyhemoglobin concentration detected by the computer device can be regarded as unchanged. Generally speaking, the rising slope of the total hemoglobin concentration change amount corresponds to the blood oxygen flow volume BV of the microcirculation in the tissue part of the object to be measured, reflecting the blood supply ability of the microcirculation in the tissue part of the object to be measured. And the falling slope of oxyhemoglobin corresponds to the blood oxygen consumption rate BC of the microcirculation in the tissue part of the object to be measured, reflecting the metabolic ability of the tissue part of the object to be measured. On this basis, the slope detection method for the computer device to obtain the blood oxygen flow volume can be the rising slope detection method, and the slope detection method for the computer device to obtain the blood oxygen consumption rate can be the falling slope detection method.

[0131] Optionally, the computer device can perform multi-detection channel detection through a sensing array module as Figure 4b shown. By detecting the change amount of oxyhemoglobin concentration under the detection pressure greater than diastolic arterial pressure and less than systolic arterial pressure through N detection channels, the blood oxygen flow volumes of N detection channels are obtained. Among them, the blood oxygen flow volumes of N detection channels can be expressed as BV1, BV2, BV3, …, BV n . The computer device can calculate the average value of the blood oxygen flow volumes of multiple detection channels. The specific calculation process can refer to the formula shown:

[0132]

[0133] Among them, in the formula Among them, BV1 represents the blood oxygen flow volume of the first detection channel, BV2 represents the blood oxygen flow volume of the second detection channel, …, BV n represents the blood oxygen flow volume of the Nth detection channel, and BV m represents the average value of the blood oxygen flow volumes of N detection channels.

[0134] In addition, the computer device can also perform multi-detection channel detection through a sensing array module as shown in Figure 4b . By detecting the total hemoglobin concentration change amount under the detection pressure greater than the arterial systolic pressure through N detection channels, the blood oxygen consumption rate of N detection channels is obtained. Among them, the blood oxygen consumption rates of N detection channels can be expressed as BC1, BC2, BC3, …, BC n . The computer device can calculate the average value of the blood oxygen consumption rates of multi-detection channels. The specific calculation process can refer to the formula shown in :

[0135]

[0136] Among them, in the formula , BC1 represents the blood oxygen consumption rate of the first detection channel, BC2 represents the blood oxygen consumption rate of the second detection channel, …, BC n represents the blood oxygen consumption rate of the Nth detection channel, and BC m represents the average value of the blood oxygen consumption rates of N detection channels. Further, the computer device can determine at least one of the two average values, namely the average value of the blood oxygen consumption rates of N detection channels and the average value of the blood oxygen consumption rates of N detection channels, as a dynamic physiological index.

[0137] Step S103, adjust the static physiological index and the dynamic physiological index based on the parameter adjustment coefficient to obtain the comprehensive physiological index of the object to be measured, and determine the physiological detection result of the object to be measured according to the comprehensive physiological index.

[0138] Specifically, the computer device can obtain the parameter adjustment coefficient through detecting and training the sample object. The computer device can adjust the static physiological index and the dynamic physiological index according to the parameter adjustment coefficient to obtain the comprehensive physiological index. Among them, the parameter adjustment coefficient is obtained through training with the physiological parameters and actual physiological detection results of the sample object. Among them, the comprehensive physiological index can be as shown in the formula :

[0139]

[0140] In the formula , (RSO2) m represents the static physiological index, and BV mIndicates the blood oxygen flow volume in dynamic physiological indicators, BC m Indicates the blood oxygen consumption rate in dynamic physiological indicators. a represents the coefficient of static physiological indicators, b represents the coefficient of blood oxygen flow volume in dynamic physiological indicators, c represents the coefficient of blood oxygen consumption rate in dynamic physiological indicators, d represents a constant coefficient. Then the parameter adjustment coefficient is the coefficient including a, b, c, and d, and Y is the comprehensive physiological indicator.

[0141] Among them, if b is 0, the blood oxygen consumption rate can be used as a dynamic physiological indicator alone and form a comprehensive physiological indicator with the static physiological indicator for physiological detection of the object to be measured. Optionally, if c is 0, the blood oxygen flow volume can be used as a dynamic physiological indicator alone and form a comprehensive physiological indicator with the static physiological indicator for physiological detection of the object to be measured. Optionally, if a is 0 and b and c are not 0 at the same time, the dynamic physiological indicator can be used as a comprehensive physiological indicator alone for physiological detection of the object to be measured. Optionally, if a, b, and c are not 0 at the same time, the static physiological indicator and the dynamic physiological indicators including blood oxygen flow volume and blood oxygen consumption rate can be jointly used to form a comprehensive physiological indicator for physiological detection of the object to be measured.

[0142] Among them, a result measurement standard can be obtained, and the comprehensive physiological indicator is mapped into this result measurement standard to determine the physiological detection result of the object to be measured. This physiological detection result can be used to represent the degree of diabetic peripheral neuropathy of the object to be measured. Among them, this result measurement standard is determined according to the predicted result and the actual physiological detection result of the sample object under the parameter adjustment coefficient. For example, this result measurement standard can be a lesion degree curve, which is composed of two dimensions: lesion degree and detection result. The lesion degree corresponding to the physiological detection result of this object to be measured in this lesion degree curve is determined as the physiological detection result of this object to be measured; or, this result measurement standard can be multiple lesion degree ranges, and the lesion degree range to which the physiological detection result of the object to be measured belongs is determined as the physiological detection result of this object to be measured, etc.

[0143] In the embodiments of the present application, a computer device may obtain the static physiological parameters corresponding to a to-be-detected object in a static state and the dynamic physiological parameters corresponding to the to-be-detected object under a detection pressure, and respectively determine the static physiological indexes and dynamic physiological indexes of the to-be-detected object based on the static physiological parameters and the dynamic physiological parameters corresponding to the detection pressure. At this time, the computer device may adjust the static physiological indexes and the dynamic physiological indexes based on a parameter adjustment coefficient to obtain the comprehensive physiological indexes of the to-be-detected object, and determine the physiological detection result of the to-be-detected object according to the comprehensive physiological indexes. It can be seen that the embodiments of the present application evaluate neuropathy from two dimensions of static and dynamic, increasing the dimension of physiological detection, and then determining the physiological detection result of the to-be-detected object according to the comprehensive physiological indexes. In other words, the embodiments of the present application can mutually supervise and verify the evaluation results of physiological indexes in different dimensions, reducing the probability that the physiological indexes with errors are determined as physiological detection results, and then improving the detection fineness and accuracy of the physiological detection results. All in all, the embodiments of the present application can improve the accuracy of physiological detection.

[0144] Further, please refer to Figure 5 , Figure 5 which is a schematic flowchart of a method for detecting physiological indexes provided by an embodiment of the present application. As Figure 5 shown, this method may be executed by a computer device, and the computer device may be any one of the detection devices in the detection device group shown above Figure 1 , for example, detection device 401, or may also be the server 100 shown above Figure 1 , which is not limited herein. For ease of understanding, the embodiments of the present application take this method being executed by a computer device as an example for description. This method may at least include the following steps S201-step S204:

[0145] Step S201, obtain the static sample parameters corresponding to a sample object in a static state, and determine the static sample indexes of the sample object based on the static sample parameters.

[0146] Specifically, the sample object may be an object with neuropathy. The static sample parameters may be detection data for the sample object. For example, the static sample parameters may be oxyhemoglobin and deoxyhemoglobin. In other words, the static sample parameters may be parameters similar to the static physiological parameters. For the specific description of the static sample parameters, reference may be made to Figure 3 the specific description of the static physiological parameters in step S101 therein, which will not be elaborated here. The determination method of the static sample indexes may be determined by the method of determining the static physiological indexes. For the specific description of the static sample indexes, reference may be made to Figure 3 the specific description of the static physiological indexes in step S101 therein, which will not be elaborated here.

[0147] Step S202: Obtain the dynamic sample parameters corresponding to the sample object under the detection pressure, and determine the dynamic sample indicators based on the dynamic sample parameters corresponding to the detection pressure.

[0148] Specifically, the computer device can obtain the dynamic sample parameters corresponding to the sample object under the detection pressure through the inspection device, and determine the dynamic sample indicators based on the dynamic sample parameters corresponding to the detection pressure. Among them, the process of obtaining the dynamic sample parameters is similar to the process of obtaining the dynamic physiological parameters. For the specific steps of obtaining the dynamic sample parameters, please refer to Figure 3 the specific steps of obtaining the dynamic physiological parameters in step S102 of Figure 3 which will not be elaborated here. Among them, the process of obtaining the dynamic sample indicators is similar to the process of obtaining the dynamic physiological indicators. For the specific steps of obtaining the dynamic sample indicators, please refer to

[0149] Step S203: Adjust the static sample indicators and dynamic sample indicators based on the sample adjustment coefficients to obtain the comprehensive sample indicators of the sample object.

[0150] Specifically, the computer device can obtain S groups of sample adjustment coefficients through random assignment, and adjust the static sample indicators and dynamic sample indicators through the S groups of sample adjustment coefficients to obtain the comprehensive sample indicators of Z groups of sample objects, where S is a positive integer. The format of the sample adjustment coefficients is similar to the format of the parameter adjustment coefficients. For the specific description of the sample adjustment coefficients, please refer to Figure 3 the specific description of the parameter adjustment coefficients in step S102 of

[0151] Step S204: Obtain the actual physiological detection result of the sample object, and perform a selection process on the sample adjustment coefficients according to the actual physiological detection result and the comprehensive sample indicators to obtain the parameter adjustment coefficients.

[0152] The parameter adjustment coefficients are used to determine the physiological detection result of the object to be measured. The number of sample adjustment coefficients is S, and the comprehensive sample indicators include the comprehensive sample indicators corresponding to the S sample adjustment coefficients respectively, where S is a positive integer. Obtain the sample detection results indicated by the S comprehensive sample indicators respectively, and determine the detection fineness corresponding to the S comprehensive sample indicators respectively and the accuracy corresponding to the S comprehensive sample indicators respectively according to the sample detection results indicated by the S comprehensive sample indicators respectively and the actual physiological detection result of the sample object. According to the distribution of the detection fineness corresponding to the S comprehensive sample indicators respectively and the accuracy corresponding to the S comprehensive sample indicators respectively, determine the parameter adjustment coefficients from the sample adjustment coefficients corresponding to the S comprehensive sample indicators.

[0153] Further, a result measurement criterion can be constructed based on the prediction result of the sample object under the parameter adjustment coefficient and the actual physiological detection result. The determination process of the prediction result can refer to the above Figure 3 determination process of the physiological detection result shown. The actual physiological detection result refers to the actual degree of diabetic peripheral neuropathy of the sample object. For example, the number of the sample objects is k. A result measurement criterion is constructed according to the mapping relationship between the prediction results respectively corresponding to the k sample objects and the actual physiological detection results respectively corresponding to the k sample objects, where k is a positive integer. For example, the prediction results respectively corresponding to the k sample objects can be used as the first-dimensional coordinates, and the actual physiological detection results respectively corresponding to the k sample objects can be used as the second-dimensional coordinates. A lesion degree curve is generated according to the first-dimensional coordinates and the second-dimensional coordinates, and the lesion degree curve is determined as the result measurement criterion; or, the actual physiological detection results respectively corresponding to the k sample objects can be divided into h lesion segments, the prediction results of the sample objects included in the h lesion segments are obtained, and h lesion degree ranges are determined according to the prediction results included in the h lesion segments, and the h lesion degree ranges are determined as the result measurement criterion, etc., where h is a positive integer.

[0154] Further, please refer to Figure 6 , Figure 6 which is a schematic diagram of a scenario of the comprehensive sample index varying with the sample adjustment coefficient provided by an embodiment of the present application. As shown in Figure 6 , it can be seen from the change curve graph of the comprehensive physiological index that there is an inverse contrast relationship between the accuracy and the detection fineness of the computer device for physiological detection. As the accuracy of physiological detection increases, the detection fineness of physiological detection will decrease. On the contrary, as the detection fineness of physiological detection increases, the accuracy of physiological detection will decrease. Therefore, the computer device can select Figure 6 the sample adjustment coefficient corresponding to the inflection point (i.e., point H) in Figure 6 as the parameter adjustment coefficient according to the change curve graph of the comprehensive sample index varying with the sample adjustment coefficient, so as to simultaneously ensure the accuracy of the physiological detection corresponding to the selected parameter adjustment coefficient and the balance of the detection fineness of the physiological detection, and ensure the good effect of the physiological detection corresponding to the selected parameter adjustment coefficient. The detection fineness of the physiological detection can represent the sensitivity of the physiological detection, and the accuracy of the physiological detection can represent the specificity of the physiological detection. In other words, the sensitivity refers to the probability that the computer device does not miss a diagnosis during physiological detection, and the specificity refers to the probability that the computer device does not make a misdiagnosis during physiological detection.

[0155] In the embodiments of the present application, by training the sample adjustment coefficient, the parameter adjustment coefficient is obtained, thereby improving the adaptability of the parameter adjustment coefficient for physiological detection and reducing the possibility of misjudging neuropathy. At the same time, the detection fineness and accuracy of the physiological detection results are improved. Therefore, by adopting the present application, the accuracy of physiological detection can be improved.

[0156] Further, please refer to Figure 7 , Figure 7 which is a schematic structural diagram of a physiological detection device provided by an embodiment of the present application. The above physiological detection device may be a computer program (including program code) running on a computer device. For example, the physiological detection device is an application software; the device can be used to execute the corresponding steps in the method provided by the embodiments of the present application. As Figure 7 shown, the physiological detection device 1 may include: a static physiological parameter acquisition module 11, a static physiological index determination module 12, a dynamic physiological parameter acquisition module 13, a dynamic physiological index determination module 14, and a detection result determination module 15.

[0157] The static physiological parameter acquisition module 11 is used to acquire the static physiological parameters corresponding to the object to be measured in a static state;

[0158] The static physiological index determination module 12 is used to determine the static physiological index of the object to be measured based on the static physiological parameters;

[0159] The dynamic physiological parameter acquisition module 13 is used to acquire the dynamic physiological parameters corresponding to the object to be measured under the detection pressure;

[0160] The dynamic physiological index determination module 14 is used to determine the dynamic physiological index based on the dynamic physiological parameters corresponding to the detection pressure;

[0161] The detection result determination module 15 is used to adjust the static physiological index and the dynamic physiological index based on the parameter adjustment coefficient to obtain the comprehensive physiological index of the object to be measured, and determine the physiological detection result of the object to be measured according to the comprehensive physiological index; the parameter adjustment coefficient is obtained by training the physiological parameters and the actual physiological detection results of the sample object.

[0162] Among them, for the specific functional implementation manners of the static physiological parameter acquisition module 11, the static physiological index determination module 12, the dynamic physiological parameter acquisition module 13, the dynamic physiological index determination module 14, and the detection result determination module 15, reference may be made to the steps S101 - step S103 in the corresponding embodiments above, which will not be elaborated here. Figure 3

[0163] Figure 7 Please refer to again, where the physiological detection device 1 further includes:

[0164] The static environment acquisition module 16 is used to acquire static environment data for detecting the object to be detected through a detection device associated with the object to be detected.

[0165] The specific functional implementation of the static environment acquisition module 16 can be found in the above Figure 3 The step S101 in the corresponding embodiment will not be described in detail here.

[0166] The static physiological parameter acquisition module 11 is specifically used to perform splicing processing on the playback data at the first moment, the initial elimination data at the first moment and the echo prediction increment at the second moment to obtain target combination data for prediction processing.

[0167] The specific functional implementation of the static physiological parameter acquisition module 11 can be found in the above Figure 3 The step S101 in the corresponding embodiment will not be described in detail here.

[0168] See also Figure 7 , wherein the static environment data includes first static environment data at a first wavelength and second static environment data at a second wavelength; the first static environment data includes a first incident light intensity, a first exit light intensity, a first light absorption coefficient corresponding to a physiological substance of the object to be measured, and a first weight coefficient; the second static environment data includes a second incident light intensity, a second exit light intensity, a second light absorption coefficient corresponding to a physiological substance of the object to be measured, and a second weight coefficient;

[0169] The static physiological parameter acquisition module 11 includes:

[0170] A first static equation constructing unit 111, configured to construct a first static equation at a first wavelength according to a data relationship among a first incident light intensity, a first emergent light intensity, a first light absorption coefficient, and a first weight coefficient;

[0171] A second static equation constructing unit 112, configured to construct a second static equation at a second wavelength according to a data relationship among a second incident light intensity, a second emergent light intensity, a second light absorption coefficient, and a second weight coefficient;

[0172] The static parameter determination unit 113 is used to determine the static physiological parameters corresponding to the object to be measured in a static state based on the first static equation and the second static equation.

[0173] The specific functional implementation of the first static equation construction unit 111, the second static equation construction unit 112 and the static parameter determination unit 113 can be found in the above Figure 3 The step S101 in the corresponding embodiment will not be described in detail here.

[0174] Please refer to again Figure 7 , where the first static equation includes a first distance static equation corresponding to the first light source distance and a first distance static equation corresponding to the second light source distance; the second static equation includes a second static equation corresponding to the first light source distance and a second static equation corresponding to the second light source distance;

[0175] The static parameter determination unit 113 includes:

[0176] The first parameter elimination subunit 1131 is configured to perform equation parameter elimination processing on the first distance static equation corresponding to the first light source distance and the first distance static equation corresponding to the second light source distance to obtain a first distance difference equation;

[0177] The second parameter elimination subunit 1132 is configured to perform equation parameter elimination processing on the second static equation corresponding to the first light source distance and the second static equation corresponding to the second light source distance to obtain a second distance difference equation;

[0178] The equation solving subunit 1133 is configured to solve the first distance difference equation and the second distance difference equation to obtain the static physiological parameters corresponding to the object to be measured in the static state.

[0179] Among them, the specific functional implementation manners of the first parameter elimination subunit 1131, the second parameter elimination subunit 1132, and the equation solving subunit 1133 can be referred to the above Figure 3 Step S101 in the corresponding embodiment will not be elaborated here.

[0180] Please refer to again Figure 7 , where the physiological detection device 1 further includes:

[0181] The dynamic environment acquisition module 17 is configured to acquire dynamic environment data for detecting the object to be measured through a detection device associated with the object to be measured; the dynamic environment data includes first dynamic environment data corresponding to the first moment and first dynamic environment data corresponding to the second moment at the first wavelength, and second dynamic environment data corresponding to the first moment and second dynamic environment data corresponding to the second moment at the second wavelength.

[0182] Among them, the specific functional implementation manner of the dynamic environment acquisition module 17 can be referred to the above Figure 3 Step S102 in the corresponding embodiment will not be elaborated here.

[0183] Among them, the dynamic physiological parameter acquisition module 13 includes:

[0184] The first dynamic equation construction unit 131 is configured to construct, according to the data relationship of the first dynamic environment data corresponding to the first moment, the first dynamic equation corresponding to the first moment at the first wavelength, and construct, according to the data relationship of the first dynamic environment data corresponding to the second moment, the first dynamic equation corresponding to the second moment at the first wavelength;

[0185] The second dynamic equation construction unit 132 is configured to construct, according to the data relationship of the second dynamic environment data corresponding to the first moment, the second dynamic equation corresponding to the first moment at the second wavelength, and construct, according to the data relationship of the second dynamic environment data corresponding to the second moment, the second dynamic equation corresponding to the second moment at the second wavelength;

[0186] The equation parameter elimination unit 133 is configured to perform equation parameter elimination processing on the first dynamic equation corresponding to the first moment and the first dynamic equation corresponding to the second moment to obtain a first change amount equation, and perform equation parameter elimination processing on the second dynamic equation corresponding to the first moment and the second dynamic equation corresponding to the second moment to obtain a second change amount equation;

[0187] The equation solving unit 134 is configured to solve the first change amount equation and the second change amount equation to obtain the dynamic physiological parameters corresponding to the object under the detected pressure.

[0188] Among them, the specific functional implementation manners of the first dynamic equation construction unit 131, the second dynamic equation construction unit 132, the equation parameter elimination unit 133, and the equation solving unit 134 can refer to step S102 in the corresponding embodiment above, Figure 3 which will not be elaborated here.

[0189] Please refer to Figure 7 again, where the dynamic physiological parameters include the dynamic physiological parameters corresponding to T moment pairs respectively, and each moment pair includes the first moment and the second moment; T is a positive integer;

[0190] The dynamic physiological index determination module 14 includes:

[0191] The method determination unit 141 is configured to determine a slope detection method based on the dynamic physiological index elements corresponding to the dynamic physiological parameters; the slope detection method includes an ascending slope detection method and a descending slope detection method;

[0192] The dynamic index determination unit 142 is configured to determine the object slope corresponding to the dynamic physiological parameters corresponding to T moment pairs respectively under the slope detection method as the dynamic physiological index.

[0193] Among them, the specific functional implementation manners of the method determination unit 141 and the dynamic index determination unit 142 can refer to the aboveFigure 3 Corresponding to step S102 in the embodiment, it will not be elaborated here.

[0194] Among them, the dynamic physiological parameter acquisition module 13 further includes:

[0195] A pressure determination unit 135, configured to obtain the dynamic physiological index elements to be detected, and determine the detection pressure based on the dynamic physiological index elements to be detected;

[0196] A dynamic parameter acquisition unit 136, configured to increase the detection pressure for the object to be measured and acquire the dynamic physiological parameters corresponding to the object to be measured under the detection pressure.

[0197] Among them, for the specific functional implementation manners of the pressure determination unit 135 and the dynamic parameter acquisition unit 136, reference can be made to step S102 in the above Figure 3 corresponding embodiment, and it will not be elaborated here.

[0198] Further, please refer to Figure 8 , Figure 8 which is a schematic structural diagram of a physiological detection device provided by an embodiment of the present application. The above physiological detection device may be a computer program (including program code) running in a computer device. For example, the physiological detection device is an application software; the device may be used to execute the corresponding steps in the method provided by the embodiment of the present application. As Figure 8 shown, the physiological detection device 2 may include: a static sample index determination module 21, a dynamic sample index determination module 22, a comprehensive sample index determination module 23, and a selection module 24.

[0199] The static sample index determination module 21 is configured to obtain the static sample parameters corresponding to the sample object in the static state, and determine the static sample index of the sample object based on the static sample parameters;

[0200] The dynamic sample index determination module 22 is configured to obtain the dynamic sample parameters corresponding to the sample object under the detection pressure, and determine the dynamic sample index based on the dynamic sample parameters corresponding to the detection pressure;

[0201] The comprehensive sample index determination module 23 is configured to adjust the static sample index and the dynamic sample index based on the sample adjustment coefficient to obtain the comprehensive sample index of the sample object;

[0202] The selection module 24 is configured to obtain the actual physiological detection result of the sample object, and perform a selection process on the sample adjustment coefficient according to the actual physiological detection result and the comprehensive sample index to obtain a parameter adjustment coefficient; the parameter adjustment coefficient is used to determine the physiological detection result of the object to be measured.

[0203] Among them, the specific functional implementation manners of the static sample index determination module 21, the dynamic sample index determination module 22, the comprehensive sample index determination module 23, and the selection module 24 can refer to the steps S201 - S204 in the corresponding embodiment above, which will not be elaborated here. Figure 5 Corresponding to the steps S201 - S204 in the corresponding embodiment, which will not be elaborated here.

[0204] Among them, the number of sample adjustment coefficients is S, and the comprehensive sample index includes the comprehensive sample indexes corresponding to the S sample adjustment coefficients respectively; S is a positive integer;

[0205] The selection module 24 includes:

[0206] A sample detection result acquisition unit 241, configured to acquire the sample detection results indicated by the S comprehensive sample indexes respectively, and determine the detection fineness corresponding to the S comprehensive sample indexes respectively and the accuracy corresponding to the S comprehensive sample indexes respectively according to the sample detection results indicated by the S comprehensive sample indexes respectively and the actual physiological detection results of the sample object;

[0207] A parameter adjustment coefficient determination unit 242, configured to determine a parameter adjustment coefficient from the sample adjustment coefficients corresponding to the S comprehensive sample indexes according to the distribution of the detection fineness corresponding to the S comprehensive sample indexes respectively and the accuracy corresponding to the S comprehensive sample indexes respectively.

[0208] Among them, the specific functional implementation manners of the sample detection result acquisition unit 241 and the parameter adjustment coefficient determination unit 242 can refer to the step S204 in the corresponding embodiment above, which will not be elaborated here. In addition, the description of the beneficial effects of adopting the same method will not be elaborated either. Figure 5 Corresponding to the step S204 in the corresponding embodiment, which will not be elaborated here. In addition, the description of the beneficial effects of adopting the same method will not be elaborated either.

[0209] Further, please refer to Figure 9 , Figure 9 is a schematic structural diagram of a computer device provided by an embodiment of the present application. As Figure 9 shown, the computer device 1000 may include: at least one processor 1001, such as a CPU, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. Among them, the user interface 1003 may include a display screen (Display) and a keyboard (Keyboard), and the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI - FI interface). The memory 1005 may be a high - speed RAM memory or a non - volatile memory, such as at least one disk memory. The memory 1005 may optionally further be at least one storage device located far from the foregoing processor 1001. AsFigure 9 As shown in Figure 9 , the memory 1005, which is a computer storage medium, may include an operating system, a network communication module, a user interface module, and a device control application program.

[0210] In Figure 9 In the computer device 1000 shown in Figure 9 , the network interface 1004 can provide network communication functions; the user interface 1003 is mainly used to provide an interface for users to input; and the processor 1001 can be used to call the device control application program stored in the memory 1005 to achieve:

[0211] Obtain the static physiological parameters corresponding to the object to be measured in the static state, and based on the static physiological parameters, determine the static physiological index of the object to be measured;

[0212] Obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure, and based on the dynamic physiological parameters corresponding to the detection pressure, determine the dynamic physiological index;

[0213] Adjust the static physiological index and the dynamic physiological index based on the parameter adjustment coefficient to obtain the comprehensive physiological index of the object to be measured, and determine the physiological detection result of the object to be measured according to the comprehensive physiological index; the parameter adjustment coefficient is obtained by training with the physiological parameters and actual physiological detection results of the sample object.

[0214] The processor 1001 can also be used to call the device control application program stored in the memory 1005 to achieve:

[0215] Obtain the static sample parameters corresponding to the sample object in the static state, and based on the static sample parameters, determine the static sample index of the sample object;

[0216] Obtain the dynamic sample parameters corresponding to the sample object under the detection pressure, and based on the dynamic sample parameters corresponding to the detection pressure, determine the dynamic sample index;

[0217] Adjust the static sample index and the dynamic sample index based on the sample adjustment coefficient to obtain the comprehensive sample index of the sample object;

[0218] Obtain the actual physiological detection result of the sample object, and based on the actual physiological detection result and the comprehensive sample index, perform a selection process on the sample adjustment coefficient to obtain the parameter adjustment coefficient; the parameter adjustment coefficient is used to determine the physiological detection result of the object to be measured.

[0219] It should be understood that the computer device 1000 described in the embodiments of the present application can execute the foregoing Figure 2 , Figure 3 , Figure 4a , Figure 4b , Figure 4c , Figure 4d ,Figure 5 and Figure 6 For the description of the physiological detection method in the corresponding embodiment, the foregoing Figure 7 For the description of the physiological detection device 1 in the corresponding embodiment, the foregoing Figure 8 For the description of the physiological detection device 2 in the corresponding embodiment, it will not be elaborated here. In addition, the description of the beneficial effects of adopting the same method will not be elaborated either.

[0220] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions, and when the program instructions are executed by a processor, they implement Figure 2 , Figure 3 , Figure 4a , Figure 4b , Figure 4c , Figure 4d , Figure 5 and Figure 6 the physiological detection methods provided by each step in Figure 2 , Figure 3 , Figure 4a , Figure 4b , Figure 4c , Figure 4d , Figure 5 and Figure 6 The implementation manners provided by each step. It will not be elaborated here. In addition, the description of the beneficial effects of adopting the same method will not be elaborated either.

[0221] The above computer-readable storage medium may be the internal storage unit of the physiological detection device or the above computer device provided in any foregoing embodiment, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Further, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the computer device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store the data that has been output or will be output.

[0222] The embodiments of the present application also provide a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device can execute the descriptions of the physiological detection methods in the foregoing Figure 2 and Figure 3 、 Figure 4a 、 Figure 4b 、 Figure 4c 、 Figure 4d 、 Figure 5 and Figure 6 corresponding embodiments, which will not be elaborated herein. In addition, the descriptions of the beneficial effects of adopting the same method will not be elaborated either.

[0223] The terms "including" and any variations thereof in the specification, claims and drawings of the embodiments of the present application are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or modules, but optionally further includes steps or modules not listed, or optionally further includes other step units inherent to these processes, methods, devices, products or equipment.

[0224] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0225] The methods and related devices provided by the embodiments of the present application are described with reference to the method flowcharts and / or structural schematic diagrams provided by the embodiments of the present application. Specifically, each process and / or block of the method flowchart and / or structural schematic diagram, and the combination of the processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable physiological detection devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable physiological detection devices generate for implementing in the process Figure 1 one process or multiple processes and / or structural schematic Figure 1means for the functions specified in one or more boxes. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable physiological detection device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements in the process Figure 1 one process or more processes and / or structural schematic Figure 1 means for the functions specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable physiological detection device, such that a series of operating steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 one process or more processes and / or structural schematic steps for the functions specified in one or more boxes.

[0226] The foregoing disclosure is only for the preferred embodiments of the present application. Of course, the scope of the rights of the present application cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present application still fall within the scope covered by the present application.

Claims

1. A method for detecting physiological indicators, characterized in that, Including: Obtain the static physiological parameters corresponding to the object to be measured in a static state, and based on the static physiological parameters, determine the static physiological index of the object to be measured; Obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure, and based on the dynamic physiological parameters corresponding to the detection pressure, determine the dynamic physiological index; Adjust the static physiological index and the dynamic physiological index based on the parameter adjustment coefficient to obtain the comprehensive physiological index of the object to be measured, and determine the physiological detection result of the object to be measured according to the comprehensive physiological index; The parameter adjustment coefficient is obtained by training with the physiological parameters of the sample object and the actual physiological detection result corresponding to the comprehensive physiological index, and the sample object is an object with neuropathy; The parameter adjustment coefficient is obtained by training with the physiological parameters of the sample object and the actual physiological detection result corresponding to the comprehensive physiological index, including: determining the static sample index and the dynamic sample index of the sample object according to the physiological parameters of the sample object, and adjusting the static sample index and the dynamic sample index based on a plurality of sample adjustment coefficients to obtain a plurality of comprehensive sample indexes of the sample object; according to the actual physiological detection result of the sample object and the plurality of comprehensive sample indexes, determine the detection fineness and the detection accuracy corresponding to the plurality of comprehensive sample indexes respectively, so as to perform a selection process on the plurality of sample adjustment coefficients to obtain the parameter adjustment coefficient.

2. The method according to claim 1, wherein The method further includes: Obtain the static environment data for detecting the object to be measured through a detection device associated with the object to be measured; The obtaining the static physiological parameters corresponding to the object to be measured in a static state includes: Determine the static physiological parameters corresponding to the object to be measured in a static state according to the data relationship between the static environment data.

3. The method according to claim 2, wherein The static environment data includes first static environment data at a first wavelength and second static environment data at a second wavelength; the first static environment data includes a first incident light intensity, a first outgoing light intensity, a first light absorption coefficient corresponding to the physiological substance of the object to be measured, and a first weight coefficient; the second static environment data includes a second incident light intensity, a second outgoing light intensity, a second light absorption coefficient corresponding to the physiological substance of the object to be measured, and a second weight coefficient; The determining the static physiological parameters corresponding to the object to be measured in a static state according to the data relationship between the static environment data includes: Construct a first static equation at the first wavelength according to the data relationship between the first incident light intensity, the first outgoing light intensity, the first light absorption coefficient, and the first weight coefficient; Construct a second static equation at the second wavelength according to the data relationship between the second incident light intensity, the second outgoing light intensity, the second light absorption coefficient, and the second weight coefficient; Based on the first static equation and the second static equation, determine the static physiological parameters corresponding to the object to be measured in a static state.

4. The method according to claim 3, wherein The first static equation includes a first distance static equation corresponding to the first light source distance and a first distance static equation corresponding to the second light source distance; the second static equation includes a second static equation corresponding to the first light source distance and a second static equation corresponding to the second light source distance; Based on the first static equation and the second static equation, determine the static physiological parameters corresponding to the object to be measured in the static state, including: Perform equation parameter elimination processing on the first distance static equation corresponding to the first light source distance and the first distance static equation corresponding to the second light source distance to obtain a first distance difference equation; Perform equation parameter elimination processing on the second static equation corresponding to the first light source distance and the second static equation corresponding to the second light source distance to obtain a second distance difference equation; Solve the first distance difference equation and the second distance difference equation to obtain the static physiological parameters corresponding to the object to be measured in the static state.

5. The method according to claim 1, wherein The method further includes: Obtain dynamic environment data for detecting the object to be measured through a detection device associated with the object to be measured; the dynamic environment data includes first dynamic environment data corresponding to the first moment and first dynamic environment data corresponding to the second moment at the first wavelength, and second dynamic environment data corresponding to the first moment and second dynamic environment data corresponding to the second moment at the second wavelength; The obtaining of the dynamic physiological parameters corresponding to the object to be measured under the detection pressure includes: According to the data relationship of the first dynamic environment data corresponding to the first moment, construct a first dynamic equation corresponding to the first moment at the first wavelength, and according to the data relationship of the first dynamic environment data corresponding to the second moment, construct a first dynamic equation corresponding to the second moment at the first wavelength; According to the data relationship of the second dynamic environment data corresponding to the first moment, construct a second dynamic equation corresponding to the first moment at the second wavelength, and according to the data relationship of the second dynamic environment data corresponding to the second moment, construct a second dynamic equation corresponding to the second moment at the second wavelength; Perform equation parameter elimination processing on the first dynamic equation corresponding to the first moment and the first dynamic equation corresponding to the second moment to obtain a first change amount equation, and perform equation parameter elimination processing on the second dynamic equation corresponding to the first moment and the second dynamic equation corresponding to the second moment to obtain a second change amount equation; Solve the first change amount equation and the second change amount equation to obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure.

6. The method according to claim 5, wherein The dynamic physiological parameters include dynamic physiological parameters corresponding to T time pairs respectively, and each time pair includes a first moment and a second moment; T is a positive integer; The determining of the dynamic physiological index based on the dynamic physiological parameters corresponding to the detection pressure includes: Determine a slope detection method based on the dynamic physiological index elements corresponding to the dynamic physiological parameters; the slope detection method includes an ascending slope detection method and a descending slope detection method; Determine the object slopes corresponding to the dynamic physiological parameters at the T moments under the slope detection method as the dynamic physiological indexes.

7. The method according to claim 1, wherein The obtaining of the dynamic physiological parameters corresponding to the object to be measured under the detection pressure includes: Obtain the dynamic physiological index elements to be detected, and determine the detection pressure based on the dynamic physiological index elements to be detected; Apply the detection pressure to the object to be measured, and obtain the dynamic physiological parameters corresponding to the object to be measured under the detection pressure.

8. A physiological index detection method, characterized in that, including: Obtain the static sample parameters corresponding to the sample object in the static state, and determine the static sample index of the sample object based on the static sample parameters, where the sample object is an object with neuropathy; Obtain the dynamic sample parameters corresponding to the sample object under the detection pressure, and determine the dynamic sample index based on the dynamic sample parameters corresponding to the detection pressure; Adjust the static sample index and the dynamic sample index based on a plurality of sample adjustment coefficients to obtain a plurality of comprehensive sample indexes of the sample object; Obtain the actual physiological detection result of the sample object, and determine the detection fineness and detection accuracy corresponding to the plurality of comprehensive sample indexes according to the actual physiological detection result and the plurality of comprehensive sample indexes, so as to perform a selection process on the plurality of sample adjustment coefficients to obtain a parameter adjustment coefficient; the parameter adjustment coefficient is used to determine the physiological detection result of the object to be measured.

9. The method according to claim 8, wherein The number of the sample adjustment coefficients is S, and the comprehensive sample indexes include the comprehensive sample indexes corresponding to the S sample adjustment coefficients respectively; S is a positive integer; The determining the detection fineness and detection accuracy corresponding to the plurality of comprehensive sample indexes according to the actual physiological detection result and the plurality of comprehensive sample indexes, so as to perform a selection process on the plurality of sample adjustment coefficients to obtain a parameter adjustment coefficient includes: Obtain the sample detection results indicated by the S comprehensive sample indexes respectively, and determine the detection fineness corresponding to the S comprehensive sample indexes respectively and the accuracy corresponding to the S comprehensive sample indexes respectively according to the sample detection results indicated by the S comprehensive sample indexes respectively and the actual physiological detection result of the sample object; Determine the parameter adjustment coefficient from the sample adjustment coefficients corresponding to the S comprehensive sample indexes according to the distribution of the detection fineness corresponding to the S comprehensive sample indexes respectively and the accuracy corresponding to the S comprehensive sample indexes respectively.

10. A physiological index detection device, characterized in that, including: A static physiological parameter acquisition module, configured to acquire the static physiological parameters corresponding to the object to be measured in the static state; A static physiological index determination module, configured to determine the static physiological index of the object to be measured based on the static physiological parameters; A dynamic physiological parameter acquisition module, configured to acquire the dynamic physiological parameters corresponding to the object to be measured under the detection pressure; A dynamic physiological index determination module, configured to determine the dynamic physiological index based on the dynamic physiological parameters corresponding to the detection pressure; A detection result determination module, configured to adjust static physiological indicators and dynamic physiological indicators based on a parameter adjustment coefficient to obtain a comprehensive physiological indicator of the object to be measured, and determine a physiological detection result of the object to be measured according to the comprehensive physiological indicator; The parameter adjustment coefficient is obtained by training with physiological parameters and actual physiological detection results of sample objects, and the sample objects are objects with neuropathy; A selection module, configured to determine a static sample indicator and a dynamic sample indicator of the sample object according to the physiological parameters of the sample object, adjust the static sample indicator and the dynamic sample indicator based on a plurality of sample adjustment coefficients to obtain a plurality of comprehensive sample indicators of the sample object; according to the actual physiological detection result of the sample object and the plurality of comprehensive sample indicators, determine the detection fineness and detection accuracy corresponding to the plurality of comprehensive sample indicators respectively, so as to perform a selection process on the plurality of sample adjustment coefficients to obtain a parameter adjustment coefficient.

11. A physiological index detection device, characterized in that, It includes: A static sample indicator determination module, configured to obtain static sample parameters corresponding to a sample object in a static state, and determine a static sample indicator of the sample object based on the static sample parameters, where the sample object is an object with neuropathy; A dynamic sample indicator determination module, configured to obtain dynamic sample parameters corresponding to a sample object under a detection pressure, and determine a dynamic sample indicator based on the dynamic sample parameters corresponding to the detection pressure; A comprehensive sample indicator determination module, configured to adjust a static sample indicator and a dynamic sample indicator based on a plurality of sample adjustment coefficients to obtain a plurality of comprehensive sample indicators of the sample object; A selection module, configured to obtain the actual physiological detection result of the sample object, determine the detection fineness and detection accuracy corresponding to the plurality of comprehensive sample indicators respectively according to the actual physiological detection result and the plurality of comprehensive sample indicators, so as to perform a selection process on the plurality of sample adjustment coefficients to obtain a parameter adjustment coefficient; The parameter adjustment coefficient is used to determine the physiological detection result of the object to be measured.

12. A computer device, characterized in that, It includes: A processor, a memory, and a network interface; The processor is connected to the memory and the network interface. Among them, the network interface is used to provide a data communication function, the memory is used to store a computer program, and the processor is used to call the computer program so that the computer device executes the method according to any one of claims 1-7 or the method according to any one of claims 8-9.

13. A computer-readable storage medium, characterized in that, A computer program is stored in a computer-readable storage medium, and the computer program is suitable for being loaded and executed by a processor so that a computer device with a processor executes the method according to any one of claims 1-7 or the method according to any one of claims 8-9.

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