Detection method and related device

Abstract

The invention provides a detection method and a related device, and the method comprises the steps: obtaining first data; then, according to the first data, determining items to be detected, and / or, according to the first data, determining a detection sequence of each item to be detected; then, acquiring at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine target physiological data; and finally, determining a detection result according to the target physiological data. The disease detection process can be optimized, the detection time is greatly shortened while the detection accuracy is guaranteed, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a detection method and related devices. Background Art

[0002] With the development of technology, preliminary detection methods for various diseases are becoming more and more convenient. For example, combining the user's lung X-rays, cough, fever, lung rales, blood test results, etc. to analyze whether the lungs are likely to be infected. However, the current detection process is very cumbersome, the initial data collection process is long, and the user experience is not good. Summary of the invention

[0003] In view of this, the present application provides a detection method and related devices, which can greatly reduce the detection time while ensuring the detection accuracy and improve the user experience.

[0004] In a first aspect, an embodiment of the present application provides a detection method, the method comprising:

[0005] Acquire first data;

[0006] Determine the items to be detected according to the first data, and / or determine the detection order of each item to be detected according to the first data;

[0007] Acquire at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine target physiological data;

[0008] A detection result is determined according to the target physiological data.

[0009] In a second aspect, an embodiment of the present application provides a detection method, the method comprising:

[0010] Detecting the current item to be detected according to the initial detection strategy corresponding to the current item to be detected;

[0011] If the confidence level of the physiological parameter corresponding to the current item to be detected that has been obtained meets the preset confidence range, the initial detection strategy corresponding to the current item to be detected is adjusted to the target detection strategy corresponding to the current item to be detected, and the detection of the current item to be detected is completed with the target detection strategy corresponding to the current item to be detected, and at least one physiological parameter corresponding to the current item to be detected is determined;

[0012] The test result is determined based on the tested data of this test.

[0013] In a third aspect, an embodiment of the present application provides a detection device, the device comprising:

[0014] An acquiring unit, configured to acquire first data;

[0015] A first determining unit, configured to determine an item to be detected according to the first data, and / or to determine a detection order of each item to be detected according to the first data;

[0016] A second determining unit, configured to obtain at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine target physiological data;

[0017] A detection unit is used to determine a detection result according to the target physiological data.

[0018] In a fourth aspect, an embodiment of the present application provides a detection device, the detection device comprising a processing unit, the processing unit being configured to:

[0019] Detecting the current item to be detected according to the initial detection strategy corresponding to the current item to be detected;

[0020] If the confidence level of the physiological parameter corresponding to the current item to be detected that has been obtained meets the preset confidence range, the initial detection strategy corresponding to the current item to be detected is adjusted to the target detection strategy corresponding to the current item to be detected, and the detection of the current item to be detected is completed with the target detection strategy corresponding to the current item to be detected, and at least one physiological parameter corresponding to the current item to be detected is determined;

[0021] The test results are determined based on the data tested in this test.

[0022] In a fifth aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps of any method in the first aspect or the second aspect of the embodiment of the present application.

[0023] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in any method of the first aspect or the second aspect of the embodiment of the present application.

[0024] In a seventh aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect or the second aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0025] It can be seen that through the above detection method and related devices, first, the first data is obtained; then, the items to be detected are determined according to the first data, and / or the detection order of each item to be detected is determined according to the first data; then, at least one physiological parameter corresponding to each item to be detected is obtained according to the detection order to determine the target physiological data; finally, the detection result is determined according to the target physiological data. The detection process can be optimized, and the detection time can be greatly reduced while ensuring the detection accuracy, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0027] Figure 1 A system architecture diagram of a detection method provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0029] Figure 3 A schematic diagram of a detection method provided in an embodiment of the present application;

[0030] Figure 4 A schematic diagram for comparing a first detection position set and a second detection position set provided in an embodiment of the present application;

[0031] Figure 5a A schematic diagram of an interactive control interface provided in an embodiment of the present application;

[0032] Figure 5b A schematic diagram of another interactive control interface provided in an embodiment of the present application;

[0033] Figure 6 A schematic diagram of another detection method provided in an embodiment of the present application;

[0034] Figure 7 A schematic diagram of another detection method provided in an embodiment of the present application;

[0035] Figure 8 A block diagram of the functional units of a detection device provided in an embodiment of the present application;

[0036] Fig. 9 A block diagram of the functional units of another detection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0039] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship. The "plurality" appearing in the embodiments of the present application refers to two or more.

[0040] In the embodiments of the present application, "at least one item" or similar expressions refer to any combination of these items, including any combination of single items or plural items, and refer to one or more, and multiple refers to two or more. For example, at least one item of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0041] The "connection" that appears in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0042] The embodiments, implementation methods and features of the present application may be replaced or combined with each other unless there is any conflict.

[0043] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0044] In related technologies, infection assessment of diseases generally requires the combination of multiple indicators to ensure the accuracy of the assessment. For example, the risk assessment of lung infection requires the combination of user information, cardiopulmonary auscultation information, cough information, body temperature and other physiological parameters that are highly correlated with lung infection to ensure the specificity and sensitivity of the infection assessment. When users are in a non-hospital environment, if they want to conduct a preliminary self-test, they need to go through cumbersome collection steps to obtain the test results, which results in a poor user experience.

[0045] To solve the above problems, the embodiments of the present application provide a detection method and related devices, which can combine user data and historical data to personalize and optimize the user's detection process, ensure detection accuracy, greatly reduce detection time, and improve user experience.

[0046] First, combine Figure 1 The system architecture of a detection method in an embodiment of the present application is described. Figure 1 1 is a system architecture diagram of a detection method in an embodiment of the present application. The system architecture 100 includes a processing device 110 and a collection device 120.

[0047] The processing device 110 may be a device with data processing capabilities, and the processing device 110 may communicate with the acquisition device 120 to obtain data related to the detection of the target disease and perform analysis.

[0048] There may be multiple acquisition devices 120, and each acquisition device 120 may include multiple sensors for acquiring user information and physiological parameters of the user, wherein the user information may include but is not limited to age, gender, height, weight, BMI, family medical history, symptom questionnaire information, etc., and the physiological parameters may include but are not limited to body temperature, heart rate, respiratory rate, lung sounds, blood oxygen, daytime active cough sounds, daytime passive cough sounds, nighttime active cough sounds, nighttime passive cough sounds, etc., which are not specifically limited here.

[0049] Among them, the processing device 110 can obtain user information and physiological parameters from the acquisition device 120, and optimize the detection process of the target disease that currently needs to be detected in combination with the user information and / or physiological parameters to obtain an optimized detection process for the target disease.

[0050] In one possible embodiment, the processing device 110 may have a built-in detection model. After the detection process is completed, the physiological data finally obtained is input into the detection model to determine the infection risk of the target disease. The detection model may be a machine learning or deep learning algorithm model, which will not be elaborated here.

[0051] In a possible embodiment, the processing device 110 and the acquisition device 120 may be integrated into the same electronic device.

[0052] In a possible embodiment, the processing device 110 and the acquisition device 120 may be different electronic devices, and the user information and physiological parameters on the acquisition device 120 may be synchronized to the processing device 110 in a timely manner, so that the processing device 110 can accurately optimize the detection process of the target disease and obtain accurate infection risk detection results of the target disease.

[0053] Combine the following Figure 2 The electronic device in the embodiment of the present application is described. Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the electronic device includes one or more application processors 220, a memory 230, a communication module 240 and one or more programs 231. The application processor 220 is communicatively connected with the memory 230 and the communication module 240 via an internal communication bus.

[0054] The one or more programs 231 are stored in the memory 230 and configured to be executed by the application processor 220 . The one or more programs 231 include instructions for executing any step in the method embodiment.

[0055] Among them, the application processor 220 can be, for example, a microcontroller unit (MCU), a central processing unit (CPU), a neural network processing unit (NPU), an application processor (AP), a modem, a graphics processor (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, units and circuits described in conjunction with the disclosure of this application. The application processor 220 can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication unit can be a communication module 240, a transceiver, a transceiver circuit, etc., and the storage unit can be a memory 230.

[0056] The memory 230 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM).

[0057] It is understandable that the electronic device 20 may include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, a physical button, a Wi-Fi module, a speaker, a Bluetooth module, a sensor, a display module, etc., which are not limited here. It is understandable that the electronic device 20 may be a collection device in the embodiment of the present application, or a processing device in the embodiment of the present application, or a combination of a processing device and a collection device in the embodiment of the present application.

[0058] After understanding the system architecture of the embodiment of the present application, Figure 3 A detection method in the embodiment of the present application is described. Figure 3 A flow chart of a detection method provided in an embodiment of the present application specifically includes the following steps:

[0059] Step 301, obtaining first data.

[0060] Among them, the first data may include user input information, which may include basic information and symptom questionnaire information filled in by the user, and the basic information may include but is not limited to age, gender, height, weight, BMI, family medical history, personal medical history, etc. The symptom questionnaire information is strongly related to the target disease, including but not limited to the course of disease, number of fever days, maximum fever temperature, whether there is nasal congestion and runny nose, headache, dyspnea or wheezing, cough, itchy throat, sore throat, etc. It can be understood that the detection method of the embodiment of the present application is used to detect the infection risk of the target disease. For example, the target disease may be pneumonia or viral infection.

[0061] In a possible embodiment, the first data may include historical detection information, which may include historical physiological data of the user in the recent period, and the historical physiological data may include but is not limited to body temperature, heart rate, respiratory rate, lung sounds, blood oxygen, daytime active cough sounds, daytime passive cough sounds, nighttime active cough sounds, nighttime passive cough sounds, etc., which are not specifically limited here.

[0062] The historical detection information may be obtained by synchronizing data with other devices, or by searching a database of the current device, and the database may store the user's recent historical physiological data, which is not specifically limited here.

[0063] In a possible embodiment, the first data may include the processing time of each item to be detected, and the processing time may be the total time required to complete the corresponding item to be detected.

[0064] In a possible embodiment, the first data may include the detected data of this detection, and the detected data of this detection may include the detected item, at least one physiological parameter corresponding to the detected item, and at least one of the confidence levels of the detection results obtained based on the at least one physiological parameter corresponding to the detected item, without specific limitation herein.

[0065] It should be noted that, in some embodiments, this detection may refer to the detection required for assessing the infection risk of the target disease. Taking pneumonia as an example, the physiological parameters that need to be detected in this detection may include, but are not limited to, one or more of body temperature, heart rate, respiratory rate, lung sounds, blood oxygen, active cough sounds during the day, passive cough sounds during the day, active cough sounds at night, and passive cough sounds at night. Among them, active cough (daytime or nighttime) and passive cough (daytime or nighttime) can be used as separate physiological parameters to participate in the determination of the test results. In some embodiments, this detection can be understood as the detection of a single physiological data, wherein a single physiological data may include, but is not limited to, any one of body temperature, heart rate, respiratory rate, lung sounds, blood oxygen, active cough sounds during the day, passive cough sounds during the day, active cough sounds at night, and passive cough sounds at night. It can be understood that this detection of the electronic device may include multiple items to be detected, may have only one item to be detected, and can be selected according to user needs.

[0066] It can be understood that the first data may include at least one of user input information, historical detection information, processing time of each item to be detected, and detected data of this detection.

[0067] It can be seen that by acquiring the first data, data reference can be provided for optimizing subsequent detection items and / or detection sequence.

[0068] Step 302: determining the items to be detected according to the first data, and / or determining the detection order of each item to be detected according to the first data.

[0069] The items to be detected may be preset or determined according to the first data, and the detection order of each item to be detected may be preset or updated in real time according to the first data, which is not specifically limited here.

[0070] It is understandable that determining the items to be detected based on the first data, and / or determining the detection order of each item to be detected based on the first data includes but is not limited to:

[0071] The items to be detected are preset items to be detected (for example, several items to be detected for a certain disease are preset), and the detection order of the preset items to be detected is determined according to the first data; or,

[0072] The items to be detected are determined according to the first data (for example, the items to be detected for a certain disease are determined according to the first data, and the items to be detected may be different if the first data is different), and the detection order of the items to be detected adopts a preset order (for example, a fixed order).

[0073] Among them, since the physiological parameters required for the detection of different diseases are different, the detection items corresponding to different diseases are also different. The preset detection item set can be determined according to each preset detection item corresponding to the target disease. For example, if the target disease is lung disease, it can be determined that its preset detection item set includes: body temperature detection, heart sound detection, lung sound detection, blood oxygen detection, daytime active cough sound detection, daytime passive cough sound detection, nighttime active cough sound detection, nighttime passive cough sound detection, etc. It can be understood that the physiological parameters collected by the complete preset detection items can more accurately determine the infection risk of the target disease.

[0074] Among them, each item to be detected that is adapted to the above-mentioned first data can be screened out from the preset detection item set to obtain the item set to be detected. It can be understood that the item set to be detected includes at least one item to be detected and the item set to be detected is a subset of the preset detection item set. In a possible embodiment, each item to be detected for this detection can be determined according to the user input information in the first data. For example, the detection difficulty of each preset detection item for the user can be determined according to one or more information such as the user's age, gender, height, weight, BMI, family medical history, personal medical history, etc., and the preset detection items with detection difficulty lower than the preset difficulty threshold are used as the items to be detected, and the preset detection items with detection difficulty higher than or equal to the preset difficulty threshold are deleted. For example, the detection weight of the relevant items to be detected can also be increased according to the family medical history or personal medical history, such as increasing its detection time, number of detections, etc.

[0075] For example, in a possible embodiment, the user's cognitive ability level can be determined in combination with user information, and then the user's cooperation and / or acquisition adaptability of each preset detection item under the cognitive ability level can be determined, and the detection difficulty of each preset detection item for the user can be determined in combination with the user's cooperation and / or acquisition adaptability. It can be understood that the cognitive ability level is positively correlated with the user's cooperation, and the cognitive ability level is positively correlated with the acquisition adaptability. For example, if the user is a child under three years old, his cognitive ability level is low, and his user cooperation and acquisition adaptability are low for some preset detection items that require active cooperation, so active cough sound detection and / or blood oxygen detection and other items can be deleted. In this way, the determined items to be detected can be made suitable for the user as much as possible, and some preset detection items with low user cooperation and low acquisition adaptability can be deleted, which greatly reduces the detection time. At the same time, the credibility of the physiological parameters obtained by these preset detection items with high detection difficulty is low, and the detection accuracy can be improved after deletion.

[0076] In a possible embodiment, each item to be detected can be determined in combination with the historical detection information in the first data. For example, since the historical physiological data in the historical detection information is the physiological parameters of the user on the most recent date, the preset detection items involving the historical physiological data can be deleted, and the preset detection items not involving the historical physiological data are used as the items to be detected. In this way, repeated detection of some detection items can be avoided, which greatly saves the detection time and does not affect the accuracy of subsequent detection results. It is understandable that some physiological parameters may not fluctuate much in a short period of time. If there is historical data that meets the requirements (for example, within a preset time) that can be used, there is no need to repeat the measurement to save measurement time.

[0077] In a possible embodiment, it is also possible to first determine the preset detection items that meet the user input information, and then filter out the items to be detected from the preset detection items based on the historical detection information, so as to avoid repeated detection of detection items that have been detected recently, thereby greatly saving detection time and not affecting the accuracy of subsequent detection results.

[0078] In a possible embodiment, if the first data includes the detected data of the current detection, the items to be detected in the preset detection items can be screened out according to the detected data of the current detection, for example, items other than the detected items in the preset detection items are screened out as the items to be detected. It can be understood that the preset detection items can be detection items corresponding to the detection function of the electronic device, or can be detection items determined according to other first data such as user input information.

[0079] Among them, in a possible embodiment, the priority of each item to be detected can be determined according to the first data to obtain the detection order, and the physiological parameters that the user needs to focus on collecting can be determined in combination with the user input information and / or historical detection information in the first data, and sorted according to the importance, and the priority of each item to be detected is determined by the order of importance. For example, it has been determined that the user is an elderly person over 70 years old, has undergone heart surgery, and has coughed frequently recently. Then it can be determined that the physiological parameters that the user most needs to focus on collecting are heart sounds and lung sounds, followed by passive cough sounds at night. Then it can be determined that the priority of heart and lung sound detection in the set of items to be detected is the highest, and the priority of passive cough sounds at night is second, and so on, which will not be repeated here.

[0080] In a possible embodiment, the priority of each item to be detected can be determined according to the correlation between the physiological parameters collected by each item to be detected and the target disease, that is, the priority of each item to be detected is positively correlated with the above correlation.

[0081] Among them, in a possible embodiment, the priority of each item to be detected can be determined according to the detection time of each item to be detected, that is, the detection time is inversely correlated with the above correlation, so that more physiological parameters can be obtained within the same time, thereby improving the accuracy of the detection results.

[0082] In a possible embodiment, the priority of each item to be detected can be determined according to the processing time of each item to be detected. Since different physiological parameters require different processing times, the priority of the items to be detected that require a long time to calculate can be increased. For example, the processing time of each item to be detected is positively correlated with the priority of the item to be detected. The detection order of at least some of the items to be detected can be set to be inversely correlated with the processing time. For example, the longer the processing time, the higher the detection order, so that in the subsequent detection process, the items to be detected with longer processing times can be processed in parallel synchronously, which can shorten the data processing time, thereby shortening the duration of the overall detection process and improving the user experience. Of course, the order of all items to be detected can be sorted according to the above-mentioned processing time, or some of the items to be detected can be sorted according to the above-mentioned processing time.

[0083] In a possible embodiment, if the first data includes the detected data of the current detection, and if the detected data of the current detection does not meet the preset conditions, the preset conditions can be used to indicate that the detection results that meet the result output conditions have been determined, for example, the historical detection results corresponding to the detected items do not meet the result output conditions. At this time, the necessary detection items among the items to be detected can be determined based on the detected items, and there is a mapping relationship between the detected items and the necessary detection items. The mapping relationship is used to indicate that the detection results obtained based on at least one physiological parameter corresponding to the detected items (for example, part or all of the detected data of this detection) and at least one physiological parameter corresponding to the necessary detection items meet the result output conditions, and the detection order is determined based on the necessary detection items, and the order of the necessary detection items precedes any item to be detected other than the necessary detection items. Among them, the result output condition may be that the confidence of the historical test results is greater than or equal to the preset confidence, and the necessary test item may be any one of the items to be tested. For example, the tested item is body temperature, and the items to be tested are heart sound detection, lung sound detection, and cough sound detection. Since there is a mapping relationship between body temperature and lung sounds, lung sound detection can be directly used as a necessary test item and its detection order is put at the front. In this way, after the lung sound detection, it is likely that a test result with a confidence greater than or equal to the preset confidence can be obtained, and there is no need for subsequent testing, which can reduce the overall detection time.

[0084] In some embodiments, if the detected data of this detection does not meet the preset conditions, the items to be detected in the preset detection items are screened out according to the detected data of this detection, wherein the preset conditions are used to indicate that the detection results that meet the result output conditions have been determined. It is understandable that if the detected data is not sufficient to generate a detection result that meets the requirements, in order to further improve the detection efficiency and ensure accuracy, the optimal items to be detected can be derived based on the detected data, and the detection order and the number of detections of these items to be detected can be preset or determined based on other first data. It can be seen that in some embodiments, the first data can be multiple.

[0085] In a possible embodiment, if the first data includes the detected data of the current detection, and the detected data of the current detection meets the preset conditions, the current detection is stopped to determine the detection result based on the detected data of the current detection, and the preset conditions are used to indicate that the detection result that meets the result output condition has been determined. For example, if the confidence of the detection result obtained according to at least one physiological parameter corresponding to the detected item is higher than or equal to the preset confidence, subsequent detection may be stopped, and the detection result obtained according to at least one physiological parameter corresponding to the detected item may be directly output. Alternatively, when the value of at least one physiological parameter corresponding to the detected item meets the preset value range, subsequent detection may be stopped, and the detection result may be directly obtained and output according to at least one physiological parameter corresponding to the detected item, which will not be elaborated here. In this way, accurate detection results can be obtained with a minimum number of items to be detected, reducing the overall detection time.

[0086] It should be noted that the above embodiments of determining the items to be detected based on the first data and / or determining the detection order of each item to be detected based on the first data can be implemented separately or in combination as appropriate.

[0087] It can be understood that since the infection analysis of the target disease is a real-time analysis, determining the detection order of the set of items to be detected can first obtain physiological parameters with higher priority for analysis, thereby improving the accuracy of the detection results.

[0088] Step 303: acquiring at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine target physiological data.

[0089] The target physiological data is a set of acquired physiological parameters, which can be used to perform infection risk analysis of the target disease. The initial detection strategy corresponding to each item to be detected can be determined first, and the initial detection strategy can include at least one of the detection number strategy, the detection time strategy, and the detection location strategy. Then, the following steps are performed on each item to be detected according to the above detection sequence to determine the target physiological data:

[0090] S1, detecting the first item to be detected according to an initial detection strategy corresponding to the first item to be detected, where the first item to be detected is any item to be detected;

[0091] S2. If the confidence level of the acquired physiological parameters corresponding to the first item to be detected is greater than or equal to the preset confidence level, the initial detection strategy corresponding to the first item to be detected is adjusted to the target detection strategy corresponding to the first item to be detected, and the detection of the first item to be detected is completed with the target detection strategy corresponding to the first item to be detected, and at least one physiological parameter corresponding to the first item to be detected is determined.

[0092] S3, if the confidence level of the acquired physiological parameter corresponding to the first item to be detected is less than the preset confidence level, completing the detection of the first item to be detected with the initial detection strategy, and determining at least one physiological parameter corresponding to the first item to be detected.

[0093] For example, the target physiological data may be determined based on part or all (at least one) of the physiological parameters corresponding to the first item to be detected.

[0094] It should be noted that the confidence level in the embodiments of the present application can be an indicator used to evaluate the credibility of output results in models such as machine learning and deep learning. It can be an indicator that determines the credibility of the current measurement based on prior knowledge. It can be an indicator that determines the credibility of the current measurement based on the previous measurements of an item to be tested that requires multiple measurements. It can also be an indicator that determines the credibility of the current measurement based on the quality of the sensor signal. It reflects the degree of credibility of the data and will not be elaborated here.

[0095] In a possible embodiment, if the initial detection strategy corresponding to the first item to be detected is the first detection number and the confidence of the physiological parameter corresponding to the first item to be detected that has been obtained is greater than or equal to the preset confidence, the first detection number is reduced to the second detection number, so that the detection is completed when the number of detections of the first item to be detected reaches the second detection number, and at least one physiological parameter corresponding to the first item to be detected is determined. For example, the first item to be detected is body temperature detection, and its corresponding first detection number is 3 times, that is, the body temperature needs to be detected 3 times. When the confidence of the first two body temperature parameters that have been obtained is greater than or equal to the preset confidence, the first detection number can be adjusted to the second detection number. At this time, the second detection number can be determined as 2 times. Since 2 times have been detected at this time, the body temperature detection is completed. For example, the confidence can be determined based on the difference between the physiological parameters obtained by multiple detections, for example, the confidence is inversely correlated with the difference between the physiological parameters obtained multiple times. In this way, the detection times of the items to be detected that need to be detected multiple times can be reduced while ensuring the accuracy of subsequent detection results.

[0096] In a possible embodiment, if the initial detection strategy corresponding to the first item to be detected is the first detection duration and the confidence of the acquired physiological parameter corresponding to the first item to be detected is greater than or equal to the preset confidence, the first detection duration is reduced to the second detection duration, so that the detection is completed when the detected duration of the first item to be detected reaches the second detection duration, and at least one physiological parameter corresponding to the first item to be detected is determined. For example, the first item to be detected is heart sound detection, and the first detection duration is 30 seconds. The acquired heart sounds can be analyzed in real time to obtain heart rate data. When the confidence of the acquired heart rate is greater than or equal to the preset confidence, the first detection duration can be reduced to the second detection duration. For example, if the second detection duration is reduced to 20 seconds, the detection can be completed when the detected duration reaches 20 seconds. In this way, the detection duration of each item to be detected can be reduced while ensuring the accuracy of subsequent test results.

[0097] In a possible embodiment, if the initial detection strategy corresponding to the first item to be detected is the first detection position set and the confidence of the acquired physiological parameter corresponding to the first item to be detected is greater than or equal to the preset confidence, the first detection position set is adjusted to the second detection position set, so that the detection is completed when the detected position set of the first item to be detected is the second detection position set, and at least one physiological parameter corresponding to the first item to be detected is determined. For example, the first item to be detected is heart sound detection, and the first detection position set includes 5 points in the heart area. For details, see Figure 4 , Figure 4 A schematic diagram of a comparison between a first detection position set and a second detection position set provided in an embodiment of the present application shows that if the heart sound of point a has been acquired and the heart rate data has been calculated, and the confidence of the heart rate data is higher than the preset confidence, the first detection position set [a, b, c, d, e] can be adjusted to the second detection position set [a]. At this time, the detection of point a has been completed, so the detection is completed. In this way, the detection positions of each item to be detected can be reduced while ensuring the accuracy of subsequent detection results.

[0098] It can be seen that by acquiring at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine the target physiological data, the detection times, detection time and detection location of the items to be detected can be reduced as much as possible while ensuring the accuracy of subsequent detection results.

[0099] Step 304: determining a detection result according to the target physiological data.

[0100] Wherein, the test results are used to indicate the infection risk of the target disease.

[0101] In a possible embodiment, the detection result may be presented in an interactive control manner, as shown in Table 1:

[0102] Table 1

[0103]

[0104] It can be seen that the test results can present a specific risk assessment of the target disease, and can also include tested items and corresponding values, and can also include untested items. Users can conduct further testing by clicking on untested items such as "blood oxygen", "lung sound point 3", "lung sound point 4", etc.

[0105] In a possible embodiment, during the process of obtaining user information and actual detection, corresponding interactive controls may be displayed to interact with the user, such as Figure 5a As shown, Figure 5a This is a schematic diagram of an interface of an interactive control provided in an embodiment of the present application. After starting the detection process, the initial interface of the interactive control related to the detection process can be displayed, such as the "user information filling" control, the "symptom questionnaire filling" control and other detection pre-information feedback entrances, the "body temperature measurement" control, the "cardiopulmonary auscultation measurement" control, the "blood oxygen measurement" control, the "active cough measurement" control, the "night cough measurement" control and other physiological parameter detection entrances required for actual detection. The user can jump to the corresponding interactive interface by clicking any interactive control, such as Figure 5b As shown in the figure, if you click the "User Information Fill" control, you will be redirected to the "User Information Fill" interface, where the user can enter information such as gender, age, and family medical history. If you click the "Temperature Measurement" control, you will be redirected to the temperature detection interface, where the specific operation process of temperature detection is displayed, and the results of each temperature measurement are displayed in a timely manner for the user to perceive. This will enhance the user's interactive experience during the detection process.

[0106] It is understandable that the detection results can be determined by performing feature extraction, feature selection and other steps on the target physiological data through machine learning algorithms such as decision trees and support vector machines, or by deep learning models, etc., which will not be elaborated here.

[0107] It can be seen that through the above detection method, the user's detection process can be personalized and optimized by combining user data and historical data, thereby ensuring the accuracy of detection while greatly reducing the detection time and improving the user experience.

[0108] Combine the following Figure 6 Another detection method in the embodiment of the present application is described. Figure 6 Another detection method provided in the embodiment of the present application specifically comprises the following steps:

[0109] Step 601, obtaining first data.

[0110] Step 602: determining a set of items to be detected in a preset set of detection items according to the first data, and determining a detection order of the set of items to be detected.

[0111] Step 603: determine the initial detection strategy corresponding to the current item to be detected.

[0112] Step 604: Detect the current item to be detected according to the initial detection strategy corresponding to the current item to be detected.

[0113] Step 605: If the obtained confidence level of the physiological parameter corresponding to the current item to be detected is greater than or equal to the preset confidence level, the initial detection strategy corresponding to the current item to be detected is adjusted to the target detection strategy corresponding to the current item to be detected.

[0114] Step 606: Complete the detection of the current item to be detected using the target detection strategy corresponding to the current item to be detected.

[0115] Step 607, determining the test result based on the tested data of this test.

[0116] If the confidence level of the detection result is higher than or equal to the preset confidence level, step 608 is executed.

[0117] Step 608, end the detection and output the detection result.

[0118] If the confidence level of the detection result is lower than or equal to the preset confidence level and there are items to be detected, the process returns to step 603 .

[0119] If the confidence level of the detection result is lower than or equal to the preset confidence level and there is no item to be detected, step 608 is executed.

[0120] In this way, a test result can be output once each item to be tested is completed, and the test can be stopped when the confidence level of the test result is higher than the preset confidence level, which can greatly reduce the test time while ensuring the accuracy of the test result.

[0121] The steps not described in detail above can be found in Figure 3 The description of the steps of the method will not be repeated here.

[0122] Combine the following Figure 7 Another detection method in the embodiment of the present application is described. Figure 7 A schematic flow chart of another detection method provided in an embodiment of the present application specifically comprises the following steps:

[0123] Step 701 : detecting the current item to be detected according to the initial detection strategy corresponding to the current item to be detected.

[0124] The current item to be detected may be any one of the items to be detected. It can be understood that the number of items to be detected may be one or more.

[0125] Step 702: If the confidence level of the physiological parameters corresponding to the current item to be detected is within the preset confidence range, the initial detection strategy corresponding to the current item to be detected is adjusted to the target detection strategy corresponding to the current item to be detected, and the detection number of the current item to be detected is completed with the target detection strategy corresponding to the current item to be detected.

[0126] Step 703, determining the detection result according to the detected data of this detection.

[0127] In this way, the detection strategy for each item to be detected can be adjusted in time, further reducing the number of detections, detection time, detection location, etc. for each item to be detected, which can greatly reduce the detection time while ensuring the accuracy of the test results.

[0128] The steps not described in detail above can be found in Figure 3 , Figure 6 The description of the steps of the method will not be repeated here.

[0129] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0130] The embodiment of the present application can divide the electronic device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0131] In the case of dividing each functional module into corresponding functional modules, Figure 8 This is a block diagram of the functional units of a detection device provided in an embodiment of the present application. The detection device 800 includes:

[0132] An acquisition unit 810, configured to acquire first data;

[0133] A first determining unit 820, configured to determine an item to be detected according to the first data, and / or determine a detection order of each item to be detected according to the first data;

[0134] A second determining unit 830 is used to obtain at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine target physiological data;

[0135] The detection unit 840 is used to determine the detection result according to the target physiological data.

[0136] It can be seen that through the above detection method and related devices, first, the first data is obtained; then, the items to be detected are determined according to the first data, and / or the detection order of each item to be detected is determined according to the first data; then, at least one physiological parameter corresponding to each item to be detected is obtained according to the detection order to determine the target physiological data; finally, the detection result is determined according to the target physiological data. The process of disease detection can be optimized, and the detection time can be greatly reduced while ensuring the detection accuracy, thereby improving the user experience.

[0137] It should be noted that the specific implementation of each operation can adopt the corresponding description of the method embodiment shown above, and the detection device 800 can be used to execute the above method embodiment of the present application, which will not be described in detail.

[0138] In the case of an integrated unit, the following combination Fig. 9 Another detection device 900 in an embodiment of the present application is described in detail, and the detection device 900 includes a processing unit 901 and a communication unit 902, wherein the processing unit 901 is used to execute any step in the above method embodiment, and when performing data transmission such as sending, the communication unit 902 can be selectively called to complete the corresponding operation.

[0139] The detection device 900 may further include a storage unit 903 for storing program codes and data. The processing unit 901 may be a processor, the communication unit 902 may be a wireless communication module, the storage unit 903 may be a memory, and the processing unit 901 is specifically used for:

[0140] Detecting the current item to be detected according to the initial detection strategy corresponding to the current item to be detected;

[0141] If the confidence level of the physiological parameter corresponding to the current item to be detected that has been obtained meets the preset confidence range, the initial detection strategy corresponding to the current item to be detected is adjusted to the target detection strategy corresponding to the current item to be detected, and the detection of the current item to be detected is completed with the target detection strategy corresponding to the current item to be detected, and at least one physiological parameter corresponding to the current item to be detected is determined;

[0142] The detection result is determined based on the detected data of this detection.

[0143] It can be seen that through the above detection method and related devices, first, the current item to be detected is detected according to the initial detection strategy corresponding to the current item to be detected; if the confidence of the physiological parameters corresponding to the current item to be detected that have been obtained meets the preset confidence range, the initial detection strategy corresponding to the current item to be detected is adjusted to the target detection strategy corresponding to the current item to be detected, and the detection of the current item to be detected is completed with the target detection strategy corresponding to the current item to be detected, and at least one physiological parameter corresponding to the current item to be detected is determined; the detection result is determined according to the detected data of this detection. The detection strategy for each item to be detected can be adjusted in real time to optimize the process of disease detection, greatly reduce the detection time while ensuring the accuracy of the detection, and improve the user experience.

[0144] It should be noted that the specific implementation of each operation can adopt the corresponding description of the method embodiment shown above, and the detection device 900 can be used to execute the above method embodiment of the present application, which will not be described in detail.

[0145] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiment, and the above computer includes an electronic device.

[0146] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0147] It should be noted that, for the above-mentioned various embodiments, for the sake of simple description, they are all expressed as a series of action combinations. Those skilled in the art should be aware that the present application is not limited by the described order of actions, because some steps in the embodiments of the present application can be performed in other orders or simultaneously. In addition, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily required by the embodiments of the present application.

[0148] In the above embodiments, the embodiments of the present application have different focuses on the description of each embodiment. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0149] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by executing software instructions by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, read-only compact disks (CD-ROMs) or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also be present in a terminal device or a management device as discrete components.

[0150] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiments of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0151] The modules / units included in the devices and products described in the above embodiments may be software modules / units or hardware modules / units, or may be partially software modules / units and partially hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or in different components of the chip module, or at least some of the modules / units may be implemented in the form of software programs. The software programs run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits. It is implemented in the form of a software program, which runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or in different components in the terminal equipment, or, at least some modules / units can be implemented in the form of a software program, which runs on a processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in hardware such as circuits.

[0152] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific implementation method of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A detection method, characterized in that: The method comprises: Acquire first data; Determine the items to be detected according to the first data, and / or determine the detection order of each item to be detected according to the first data; Acquire at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine target physiological data; A detection result is determined according to the target physiological data.

2. The method according to claim 1, characterized in that The first data includes at least one of the following: user input information, historical detection information, processing time of each item to be detected, and detected data of this detection.

3. The method according to claim 2, characterized in that If the first data includes the processing time of each item to be detected, determining the detection order of each item to be detected according to the first data includes: The detection order of each of the items to be detected is determined according to the processing time of each of the items to be detected, wherein the detection order of at least some of the items to be detected is inversely correlated with the processing time.

4. The method according to claim 2, characterized in that: If the first data includes the detected data of the current detection, determining the detection order of each item to be detected according to the first data includes: If the detected data of the current detection does not meet the preset condition, the necessary detection items among the items to be detected are determined based on the detected items, the preset condition is used to indicate that the detection result that meets the result output condition has been determined, and there is a mapping relationship between the detected items and the necessary detection items, and the mapping relationship is used to indicate that the detection result obtained based on part or all of the detected data of the current detection and at least one physiological parameter corresponding to the necessary detection item meets the result output condition; The inspection order is determined according to the necessary inspection items, and the order of the necessary inspection items is prior to any item to be inspected other than the necessary inspection items.

5. The method according to claim 2, characterized in that: If the first data includes the user input information and the historical detection information, determining the items to be detected according to the first data includes: Determine a preset test item that matches the user input information; The items to be detected in the preset detection items are screened out according to the historical detection information.

6. The method according to claim 2, characterized in that If the first data includes the detected data of the current detection, determining the items to be detected according to the first data includes: If the detected data of this detection does not meet the preset conditions, the items to be detected in the preset detection items are screened out according to the detected data of this detection, and the preset conditions are used to indicate that the detection results that meet the result output conditions have been determined.

7. The method according to claim 2, if the first data includes the detected data of the current detection, the method further comprises: If the detected data of this detection meets the preset condition, stop this detection to determine the detection result according to the detected data of this detection, and the preset condition is used to indicate that the detection result that meets the result output condition has been determined.

8. The method according to any one of claims 1 to 7, characterized in that: The acquiring at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine target physiological data includes: Determine an initial detection strategy corresponding to each item to be detected; According to the detection sequence, the following steps are performed on each of the items to be detected to determine the target physiological data: Detecting the first item to be detected according to an initial detection strategy corresponding to the first item to be detected, where the first item to be detected is any item to be detected; If the confidence level of the acquired physiological parameters corresponding to the first item to be detected is greater than or equal to the preset confidence level, the initial detection strategy corresponding to the first item to be detected is adjusted to the target detection strategy corresponding to the first item to be detected, and the detection of the first item to be detected is completed with the target detection strategy corresponding to the first item to be detected, and at least one physiological parameter corresponding to the first item to be detected is determined. The target physiological data can be determined based on the at least one physiological parameter corresponding to the first item to be detected.

9. The method according to claim 8, characterized in that If the confidence level of the acquired physiological parameter corresponding to the first item to be detected is greater than or equal to the preset confidence level, the initial detection strategy corresponding to the first item to be detected is adjusted to the target detection strategy corresponding to the first item to be detected, and the detection of the first item to be detected is completed with the target detection strategy corresponding to the first item to be detected, and at least one physiological parameter corresponding to the first item to be detected is determined, including: If the initial detection strategy corresponding to the first item to be detected is the first number of detections and the confidence level of the acquired physiological parameters corresponding to the first item to be detected is greater than or equal to the preset confidence level, the first number of detections is reduced to a second number of detections, so that the detection is completed when the number of detections of the first item to be detected reaches the second number of detections, and at least one physiological parameter corresponding to the first item to be detected is determined.

10. The method according to claim 8, characterized in that If the confidence level of the acquired physiological parameter corresponding to the first item to be detected is greater than or equal to the preset confidence level, the initial detection strategy corresponding to the first item to be detected is adjusted to the target detection strategy corresponding to the first item to be detected, and the detection of the first item to be detected is completed with the target detection strategy corresponding to the first item to be detected, and at least one physiological parameter corresponding to the first item to be detected is determined, including: If the initial detection strategy corresponding to the first item to be detected is the first detection duration and the confidence level of the acquired physiological parameters corresponding to the first item to be detected is greater than or equal to the preset confidence level, the first detection duration is reduced to a second detection duration, so that the detection is completed when the detected duration of the first item to be detected reaches the second detection duration, and at least one physiological parameter corresponding to the first item to be detected is determined.

11. The method according to claim 8, characterized in that If the confidence level of the acquired physiological parameter corresponding to the first item to be detected is greater than or equal to the preset confidence level, the initial detection strategy corresponding to the first item to be detected is adjusted to the target detection strategy corresponding to the first item to be detected, and the detection of the first item to be detected is completed with the target detection strategy corresponding to the first item to be detected, and at least one physiological parameter corresponding to the first item to be detected is determined, including: If the initial detection strategy corresponding to the first item to be detected is the first detection position set and the confidence of the acquired physiological parameters corresponding to the first item to be detected is greater than or equal to the preset confidence, the first detection position set is adjusted to the second detection position set, so that the detection is completed when the detected position set of the first item to be detected is the second detection position set, and at least one physiological parameter corresponding to the first item to be detected is determined.

12. The method according to claim 8, characterized in that After the first item to be detected is detected according to the initial detection strategy corresponding to the first item to be detected, the method further includes: If the confidence level of the acquired physiological parameter corresponding to the first item to be detected is less than the preset confidence level, the detection of the first item to be detected is completed using the initial detection strategy to determine at least one physiological parameter corresponding to the first item to be detected.

13. A detection method, characterized in that: The method comprises: Detecting the current item to be detected according to the initial detection strategy corresponding to the current item to be detected; If the confidence level of the physiological parameter corresponding to the current item to be detected that has been obtained meets the preset confidence range, the initial detection strategy corresponding to the current item to be detected is adjusted to the target detection strategy corresponding to the current item to be detected, and the detection of the current item to be detected is completed with the target detection strategy corresponding to the current item to be detected; The test result is determined based on the tested data of this test.

14. A detection device, characterized in that: The device comprises: An acquiring unit, configured to acquire first data; A first determining unit, configured to determine an item to be detected according to the first data, and / or to determine a detection order of each item to be detected according to the first data; A second determining unit, configured to obtain at least one physiological parameter corresponding to each item to be detected according to the detection sequence to determine target physiological data; A detection unit is used to determine a detection result according to the target physiological data.

15. A detection device, characterized in that: The apparatus comprises a processing unit, wherein the processing unit is configured to: Detecting the current item to be detected according to the initial detection strategy corresponding to the current item to be detected; If the confidence level of the physiological parameter corresponding to the current item to be detected that has been obtained meets the preset confidence range, the initial detection strategy corresponding to the current item to be detected is adjusted to the target detection strategy corresponding to the current item to be detected, and the detection of the current item to be detected is completed with the target detection strategy corresponding to the current item to be detected; The test result is determined based on the tested data of this test.

16. An electronic device, characterized in that: include: A processor, a memory, and one or more programs; the one or more programs are stored in the memory and are configured to be executed by the processor, the programs comprising instructions for executing the steps in the method of any one of claims 1 to 12 or claim 13.

17. A computer storage medium, characterized in that: The computer storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method according to any one of claims 1 to 12 or claim 13.

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