Remote urine index analysis method

Abstract

The invention provides a remote urine index analysis method, and relates to the technical field of medical detection. The method is applied to a cloud server, and comprises the following steps: receiving test paper detection data for a target user sent by a urine analysis terminal, the test paper detection data being detection data obtained by the urine analysis terminal by using preset detection test paper to detect a urine sample of the target user, the test paper detection data comprises color block information of a plurality of detection areas on preset detection test paper; performing urine analysis on the color block information of the plurality of detection areas by adopting a first preset urine analysis model to obtain current index parameters of the plurality of urine index items; generating a health analysis report for the target user according to the current index parameters of the urine index items and the personal characteristics of the target user; and sending a health analysis result of the target user to the mobile application device, wherein the health analysis result at least comprises a health analysis report. And finally, a personalized health analysis result is provided for the target user.

Description

Technical Field

[0001] The present invention relates to the field of medical detection technology, and in particular to a remote urine index analysis method. Background Art

[0002] As a non-invasive and convenient testing method, urinalysis plays an important role in health management. Traditionally, urinalysis relies primarily on medical laboratory departments, requiring patients to visit medical institutions in person for testing. This method is time-consuming and infrequent, hindering patient health monitoring. Summary of the Invention

[0003] The purpose of the present invention is to provide a remote urine index analysis to address the deficiencies in the above-mentioned existing technologies, so as to collect test data through a urine analysis terminal, use a cloud server to perform efficient data analysis and processing, and ultimately provide users with personalized health analysis results, thereby achieving the convenience and accuracy of remote health monitoring.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0005] In a first aspect, an embodiment of the present application provides a remote urine index analysis method, which is applied to a cloud server, and the method includes:

[0006] Receiving test strip test data for a target user sent by a urine analysis terminal, wherein the test strip test data is test data obtained by the urine analysis terminal using a preset test strip to test a urine sample of the target user, and the test strip test data includes: color block information of multiple detection areas on the preset test strip;

[0007] Using a first preset urine analysis model, performing urine analysis on the color block information of the multiple detection areas to obtain current indicator parameters of multiple urine indicator items;

[0008] generating a health analysis report for the target user based on the current indicator parameters of the plurality of urine indicator items and the personal characteristics of the target user;

[0009] The health analysis result of the target user is sent to a mobile application device, where the health analysis result at least includes: the health analysis report.

[0010] In an optional embodiment, the method further comprises:

[0011] performing linear classification on the current indicator parameters of the multiple urine indicator items using a second preset urine analysis model based on the preset indicator reference ranges of the multiple urine indicator items to obtain a detection category of the target user for the multiple urine indicator items, wherein the detection category of each urine indicator item is used to indicate whether each urine indicator item of the target user is normal or the severity of the abnormality;

[0012] The health analysis result also includes: detection categories of the multiple urine indicator items.

[0013] In an optional embodiment, the preset indicator reference ranges of the multiple urine indicator items are preset standard indicator reference ranges, or individual indicator reference ranges of the target user.

[0014] In an optional embodiment, the method further comprises:

[0015] Using a preset decision tree model, generating auxiliary disease diagnosis information for the target user based on current indicator parameters of the multiple urine indicator items;

[0016] The health analysis result also includes: the auxiliary disease diagnosis information.

[0017] In an optional embodiment, the method further comprises:

[0018] Obtaining historical indicator parameters of the target user for the target urine indicator item;

[0019] Generate time series change information of the target urine indicator item for the target user using a preset time series analysis model according to the historical indicator parameters of the target urine indicator item and the current indicator parameters of the target urine indicator item;

[0020] The health analysis result also includes: time series change information of the target urine indicator item.

[0021] In an optional embodiment, the method further comprises:

[0022] Obtaining historical indicator parameters of the target user for the multiple urine indicator items;

[0023] Analyzing the correlation between the urine indicator items using a third preset urine analysis model based on the historical indicator parameters of the multiple urine indicator items and the current indicator parameters of each urine indicator item to obtain an abnormal urine indicator combination for the target user;

[0024] The health analysis result also includes: the abnormal urine indicator combination.

[0025] In a second aspect, an embodiment of the present application further provides a remote urine index analysis method, which is applied to a processing unit in a urine analysis terminal, and the method includes:

[0026] Controlling the microfluidic module to distribute the urine sample of the target user to multiple detection areas on the preset test strip;

[0027] Controlling the light source system to emit light toward the preset test paper;

[0028] Acquiring spectral data of the plurality of detection areas collected by a spectral sensor;

[0029] Obtaining color block information of the plurality of detection areas according to the spectral data of the plurality of detection areas;

[0030] Sending the test paper detection data of the target user to the cloud server, the test paper detection data including: color block information of the multiple detection areas.

[0031] In an optional embodiment, the method further comprises:

[0032] Acquiring temperature data of the detection chamber where the preset detection test strip is located collected by a temperature sensor;

[0033] According to the temperature data, a preset temperature compensation algorithm is used to obtain compensation parameters of multiple color channels respectively;

[0034] The color block information of the multiple detection areas is supplemented and corrected according to the supplementary parameters of the multiple color channels.

[0035] In an optional embodiment, before obtaining the color block information of the multiple detection areas based on the spectral data of the multiple detection areas, the method further includes:

[0036] Acquiring humidity data of the detection chamber where the preset detection test strip is located collected by a humidity sensor;

[0037] If the humidity data is greater than or equal to a preset humidity threshold, a preset refractive index correction model is used to correct the spectral data of the multiple detection areas;

[0038] The obtaining, based on the spectral data of the plurality of detection areas, the color block information of the plurality of detection areas comprises:

[0039] Color block information of the multiple detection areas is obtained according to the corrected spectral data of the multiple detection areas.

[0040] In an optional embodiment, the method further comprises:

[0041] Acquiring spectral data of a preset identification area on the preset detection test paper;

[0042] Perform feature matching based on the spectral data of the preset identification area to obtain the target manufacturer information corresponding to the preset test strip;

[0043] According to the target manufacturer information, obtaining the color calibration parameters of the preset test paper;

[0044] The obtaining, based on the spectral data of the plurality of detection areas, the color block information of the plurality of detection areas comprises:

[0045] Color block information of the multiple detection areas is obtained according to the spectral data of the multiple detection areas and the color calibration parameters.

[0046] In a third aspect, an embodiment of the present application further provides a remote urine index analysis device, which is applied to a cloud server, and the device includes:

[0047] a receiving module, configured to receive test strip detection data for a target user sent by a urine analysis terminal, wherein the test strip detection data is detection data obtained by the urine analysis terminal using a preset detection strip to detect a urine sample of the target user, and the test strip detection data includes: color block information of multiple detection areas on the preset detection strip;

[0048] an analysis module, configured to perform urine analysis on the color block information of the plurality of detection areas using a first preset urine analysis model to obtain current indicator parameters of a plurality of urine indicator items;

[0049] A first generating module is configured to generate a health analysis report for the target user based on the current indicator parameters of the plurality of urine indicator items and the personal characteristics of the target user;

[0050] The first sending module is configured to send the health analysis result of the target user to a mobile application device, where the health analysis result at least includes the health analysis report.

[0051] In a fourth aspect, an embodiment of the present application further provides a remote urine index analysis device, which is applied to a processing unit in a urine analysis terminal, and the device includes:

[0052] A control module, configured to control the microfluidic module to distribute the urine sample of the target user to a plurality of detection areas on a preset test strip;

[0053] The control module is further used to control the light source system to emit light toward the preset test paper;

[0054] An acquisition module, configured to acquire spectral data of the plurality of detection areas collected by the spectral sensor;

[0055] A second generating module is configured to obtain color block information of the plurality of detection areas according to the spectral data of the plurality of detection areas;

[0056] The second sending module is used to send the test paper detection data of the target user to the cloud server, where the test paper detection data includes: color block information of the multiple detection areas.

[0057] In the fifth aspect, an embodiment of the present application also provides a cloud server, comprising: a processor, a storage medium and a bus, wherein the storage medium stores program instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the program instructions to perform the steps of the remote urine index analysis method as described in any one of the first aspects.

[0058] In a sixth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the remote urine index analysis method as described in any one of the first aspects are executed.

[0059] The beneficial effects of this application are:

[0060] An embodiment of the present application provides a remote urine index analysis method, which is applied to a cloud server, and the method includes: receiving test strip detection data for a target user sent by a urine analysis terminal, wherein the test strip detection data is detection data obtained by the urine analysis terminal using a preset detection test strip to detect the urine sample of the target user, and the test strip detection data includes: color block information of multiple detection areas on the preset detection test strip; using a first preset urine analysis model, performing urine analysis on the color block information of multiple detection areas to obtain current indicator parameters of multiple urine indicator items; generating a health analysis report for the target user based on the current indicator parameters of multiple urine indicator items and the personal characteristics of the target user; and sending the health analysis results of the target user to a mobile application device, the health analysis results at least including: a health analysis report. The method of the present application collects detection data through a urine analysis terminal, uses a cloud server to perform efficient data analysis and processing, and ultimately provides users with personalized health analysis results, thereby achieving the convenience and accuracy of remote health monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1This is a flow chart of a remote urine index analysis method provided in an embodiment of the present application;

[0063] Figure 2 This is a second flow chart of a remote urine index analysis method provided in an embodiment of the present application;

[0064] Figure 3 The third flow chart of a remote urine index analysis method provided in an embodiment of the present application;

[0065] Figure 4 This is a fourth flow chart of a remote urine index analysis method provided in an embodiment of the present application;

[0066] Figure 5 This is a fifth flow chart of a remote urine index analysis method provided in an embodiment of the present application;

[0067] Figure 6 Flowchart 6 of a remote urine index analysis method provided in an embodiment of the present application;

[0068] Figure 7 Schematic diagram of the seventh process of a remote urine index analysis method provided in an embodiment of the present application;

[0069] Figure 8 A schematic diagram of the functional modules of a remote urine index analysis device provided in an embodiment of the present application;

[0070] Figure 9 A schematic diagram of the functional modules of another remote urine index analysis device provided in an embodiment of the present application;

[0071] Figure 10 A schematic diagram of a cloud server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0073] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0074] In the description of this application, it should be noted that if the terms "upper", "lower", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the accompanying drawings, or is the orientation or position relationship in which the product of the application is usually placed when in use. It is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on this application.

[0075] In addition, the terms "first," "second," and the like in the description and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0076] It should be noted that, in the absence of conflict, the features in the embodiments of this application can be combined with each other.

[0077] In order to remotely analyze urine indicators so that the target user can receive a health analysis report based on a mobile application device, an embodiment of the present application provides a remote urine indicator analysis system, which includes: a cloud server, a urine analysis terminal, and a mobile application device, wherein the cloud server is wirelessly connected to the urine analysis terminal and the mobile application device respectively; the urine analysis terminal is used to use a preset test strip to test the target user's urine sample to obtain test strip test data, and then send the test strip test data to the cloud server. The cloud server is used to use a first preset urine analysis model to analyze the test strip test data, obtain current indicator parameters of multiple urine indicator items, and generate a health analysis report for the target user in combination with the personal characteristics of the target user, and then send the health analysis report to the mobile application device.

[0078] The mobile application device can be a device terminal held by the target user or a device terminal held by medical staff, so that the target user can view his or her health analysis report on the mobile application device held by him or her, and the medical staff can also view the health analysis report of the target user on the mobile application device held by him or her, and provide remote guidance to the target user based on the health analysis report to realize the sharing of health analysis reports.

[0079] The remote urine index analysis method provided in the embodiment of the present application is explained in detail below with reference to specific examples in conjunction with the accompanying drawings.

[0080] Figure 1 This is one of the flow charts of a remote urine index analysis method provided in the embodiment of the present application. Figure 1 As shown, the method includes:

[0081] S101: Receive test paper detection data for a target user sent by a urine analysis terminal.

[0082] Among them, the test paper detection data is the detection data obtained by the urine analysis terminal using a preset detection test paper to detect the urine sample of the target user. The test paper detection data includes: color block information of multiple detection areas on the preset detection test paper.

[0083] In this embodiment, a wireless communication connection is established between the cloud server and the urinalysis terminal. Therefore, after the urinalysis terminal uses a preset test strip to test the target user's urine sample and obtain the test strip test data, the test strip test data is transmitted to the cloud server via the urinalysis terminal's wireless communication module (such as WiFi, Bluetooth, etc.). During the transmission process, the test strip test data uses the Transport Layer Security version 1.3 (TLS1.3) protocol to ensure the security of the test strip test data during transmission.

[0084] Since the preset test strip has multiple detection areas and the target user's urine sample is located in multiple detection areas, after performing spectral detection on the target user's urine sample using the preset test strip, color block information of multiple detection areas will be obtained.

[0085] S102: Using a first preset urine analysis model, perform urine analysis on the color block information of multiple detection areas to obtain current indicator parameters of multiple urine indicator items.

[0086] The first preset urine analysis model is constructed based on a large amount of test strip test data and a machine learning algorithm. During the model training phase, a vast amount of known urine sample data was collected. This known urine sample data contained color block information from different test areas and the corresponding true indicator parameters for multiple urine indicators. Using deep learning algorithms, such as convolutional neural networks, the urine sample data was trained, enabling the first preset urine analysis model to learn the mapping relationship between color block information and urine indicators. After repeated training and optimization, the first preset urine analysis model was able to accurately analyze the input color block information. The first preset urine analysis model was then used to analyze the color block information from multiple test areas, obtaining the current indicator parameters, or precise levels, for multiple urine indicators. These indicators may include: white blood cells, carcass, nitrite, urobilinogen, bilirubin, glucose, protein, specific gravity, pH, occult blood, vitamins, microalbumin, calcium, creatinine, ACR*, and calcium / creatinine.

[0087] Specifically, after the cloud server receives the color block information of multiple detection areas sent by the urine analysis terminal, it inputs it into the first preset urine analysis model. The first preset urine analysis model first pre-processes the color block information, such as image noise reduction, normalization, etc., to eliminate noise and interference that may exist in the acquisition process. Then, the pre-processed color block information is feature extracted and analyzed by the multi-layer neural network inside the first preset urine analysis model to identify the specific color value of each detection area and the concentration gradient corresponding to the color depth. According to the mapping relationship obtained by model training, these color features are converted into the current indicator parameters of the corresponding urine indicator items. For example, for the glucose detection area, the concentration value of glucose in the urine is calculated based on the degree of color change of the color block; for the protein detection area, the presence or absence of protein in the urine and the approximate content are determined, thereby determining the current indicator parameters of multiple urine indicator items.

[0088] S103: Generate a health analysis report for the target user based on the current indicator parameters of the multiple urine indicator items and the personal characteristics of the target user.

[0089] The target user's personal characteristics are an important basis for generating health analysis reports. These characteristics include, but are not limited to, the user's age, gender, height, weight, history of underlying medical conditions, family medical history, and medication use. If the target user is female, personal characteristics may also include special physiological conditions such as the female menstrual cycle and pregnancy status. While receiving the test strip test data, the cloud server will retrieve the target user's personal characteristic data from databases such as user registration information and historical health records to ensure the completeness and accuracy of all relevant information.

[0090] Based on the current parameters of multiple urine indicators, combined with the target user's personal characteristics, a comprehensive assessment is conducted using medical knowledge and data analysis methods. For example, the normal ranges of certain urine indicators for adolescent users may differ from those of the elderly, necessitating a targeted analysis based on age and stage of physical development. For users with a history of diabetes, changes in glucose indicators will be closely monitored, and their blood sugar control status will be assessed based on medication use.

[0091] It should be noted that the lifestyle input by the target user can also be obtained, such as diet diary (protein / salt intake), exercise data (frequency / intensity), sleep quality (duration / score), stress level, etc., and a health analysis report for the target user can be generated based on the current indicator parameters of multiple urine indicator items, the personal characteristics of the target user, and the lifestyle of the target user. Based on the health analysis report, personalized health advice can also be provided to the target user, such as diet adjustment, exercise plan, medication guidance, further examination advice, etc. For example, if the test finds that the protein content in the urine is high, the user is advised to reduce high-protein diet, pay attention to rest and other information.

[0092] S104: Send the health analysis result of the target user to the mobile application device.

[0093] Among them, the health analysis results include at least: health analysis report.

[0094] Specifically, the health analysis results sent to the mobile application device include at least a health analysis report. Other relevant information, such as the test time and test device number, may also be attached based on user needs and settings. The health analysis report is presented on the mobile application device in a clear and easy-to-read format, such as a combination of text and images, making it easier for the target user or medical staff to view and understand it.

[0095] In summary, an embodiment of the present application provides a remote urine index analysis method, which is applied to a cloud server, and the method includes: receiving test paper detection data for a target user sent by a urine analysis terminal, wherein the test paper detection data is detection data obtained by the urine analysis terminal using a preset detection test paper to detect the urine sample of the target user, and the test paper detection data includes: color block information of multiple detection areas on the preset detection test paper; using a first preset urine analysis model, urine analysis is performed on the color block information of multiple detection areas to obtain current indicator parameters of multiple urine indicator items; based on the current indicator parameters of multiple urine indicator items and the personal characteristics of the target user, a health analysis report is generated for the target user; and the health analysis results of the target user are sent to a mobile application device, and the health analysis results at least include: a health analysis report. The method of the present application collects detection data through a urine analysis terminal, uses a cloud server to perform efficient data analysis and processing, and ultimately provides users with personalized health analysis results, thereby achieving the convenience and accuracy of remote health monitoring.

[0096] The present application embodiment also provides another possible implementation of the remote urine index analysis method, which further includes:

[0097] Based on the preset indicator reference ranges of multiple urine indicator items, a second preset urine analysis model is used to perform linear classification on the current indicator parameters of the multiple urine indicator items to obtain detection categories of the target user for the multiple urine indicator items. The detection category of each urine indicator item is used to indicate whether each urine indicator item of the target user is normal or the severity of the abnormality.

[0098] The health analysis results also include: detection categories of multiple urine indicators.

[0099] In this embodiment, the second preset urine analysis model can be a support vector machine, which performs linear classification on the current indicator parameters of multiple urine indicator items, that is, compares the current indicator parameters of multiple urine indicator items with the medical standard reference range, so as to determine the detection category of each urine indicator item, wherein the detection category may include normal, level one abnormality, level two abnormality, and level three abnormality, level one abnormality represents mild abnormality, level two abnormality represents moderate abnormality, and level three abnormality represents severe abnormality.

[0100] At the same time, warning information for multiple urine indicator items can be generated according to the detection categories of multiple urine indicator items. For example, if the detection category of urine protein is level three abnormality, warning information for urine protein is generated.

[0101] Optionally, the preset indicator reference ranges of the plurality of urine indicator items are preset standard indicator reference ranges, or individual indicator reference ranges of the target user.

[0102] Among them, the preset standard indicator reference range is the medical standard reference range, and the individual indicator reference range of the target user can be the individual indicator reference range pre-set by medical staff based on the personal characteristics of the target user. The current indicator parameters of multiple urine indicator items are compared with the individual indicator reference range to determine the detection category of each urine indicator item.

[0103] In the method provided in the embodiment of the present application, based on the preset indicator reference ranges of multiple urine indicator items, a second preset urine analysis model is used to perform linear classification on the current indicator parameters of multiple urine indicator items, and obtain the detection category of the target user for multiple urine indicator items, providing the target user and medical staff with an intuitive judgment of the degree of abnormality, so that the target user can pay attention to the abnormal urine indicator items.

[0104] The present application embodiment also provides another possible implementation of the remote urine index analysis method, which further includes:

[0105] A preset decision tree model is used to generate auxiliary disease diagnosis information for the target user based on the current indicator parameters of multiple urine indicator items.

[0106] Health analysis results also include: auxiliary disease diagnosis information.

[0107] In this embodiment, the preset decision tree model can determine the disease diagnosis set corresponding to the current indicator parameters of multiple urine indicator items based on the current indicator parameters of multiple urine indicator items. For example, if the indicator parameter of glucose exceeds the preset indicator reference range and is determined to be a level 3 abnormality, the probability that the target user has diabetes can be determined, thereby determining the auxiliary disease diagnosis information of the target user and sending it to the mobile application device held by the target user and medical staff.

[0108] In the method provided in the embodiment of the present application, the auxiliary diagnostic information not only provides the target user with a potential health risk warning, but also provides medical staff with structured diagnostic clues, which is conducive to remote health monitoring.

[0109] The present application also provides another possible implementation of the remote urine index analysis method. Figure 2 This is a second flow chart of a remote urine index analysis method provided in an embodiment of the present application, as shown in FIG. Figure 2 As shown, the method further includes:

[0110] S201: Obtain historical indicator parameters of a target user for a target urine indicator item.

[0111] S202 : Generate time series change information of the target urine indicator item for the target user using a preset time series analysis model according to the historical indicator parameters of the target urine indicator item and the current indicator parameters of the target urine indicator item.

[0112] The health analysis results also include: time series change information of target urine indicators.

[0113] In this embodiment, the cloud server database stores historical indicator parameters of multiple urine indicator items of the target user within a historical time period. According to the identifier of the target user, the historical indicator parameters of multiple urine indicator items of the target user within a historical time period can be obtained from the database. For example, the historical indicator parameters of multiple urine indicator items of the target user within the historical 12 months can be obtained.

[0114] The preset time series analysis model can be a long short-term memory network (LSTM) model. The preset time series analysis model is used to generate time series change information of the target urine indicator item for the target user based on the historical indicator parameters of the target urine indicator item and the current indicator parameters of the target urine indicator item, that is, change trend information. The time series change information of the target urine indicator item can be displayed in the form of a line graph, so that the target user and medical staff can intuitively view the change trend of the current indicator parameters of the target urine indicator item.

[0115] The present application also provides another possible implementation of the remote urine index analysis method. Figure 3 The third flow chart of a remote urine index analysis method provided in the embodiment of the present application is as follows: Figure 3 As shown, the method further includes:

[0116] S301: Obtain historical indicator parameters of a target user for multiple urine indicator items.

[0117] S302: Analyze the correlation between the urine indicator items using a third preset urine analysis model based on historical indicator parameters of the plurality of urine indicator items and current indicator parameters of each urine indicator item to obtain an abnormal urine indicator combination for the target user.

[0118] Health analysis results also include: abnormal urine indicator combination.

[0119] In this embodiment, historical indicator parameters for multiple urine indicator items are obtained from a database, and a third preset urine analysis model is used to analyze the historical indicator parameters for the multiple urine indicator items and the current indicator parameters of each urine indicator item to determine the correlation between the urine indicator items. For example, when the historical indicator parameter for glucose and the current indicator parameter show a gradually increasing trend, and the historical indicator parameter for protein and the current indicator parameter also show a gradually increasing trend, it is determined that glucose and protein are correlated. When the current indicator parameters of glucose and protein are both abnormal, glucose and protein are determined to be an abnormal urine indicator combination for the target user. The abnormal urine indicator combination can also provide auxiliary disease diagnosis information for the target user.

[0120] The present application embodiment also provides another possible implementation of the remote urine index analysis method, which is applied to the processing unit in the urine analysis terminal. Figure 4 This is a fourth flow chart of a remote urine index analysis method provided in an embodiment of the present application, as shown in FIG. Figure 4 As shown, the method further includes:

[0121] S401 , controlling the microfluidic module to distribute the urine sample of the target user to multiple detection areas on a preset detection test strip.

[0122] In this embodiment, the target user first obtains a urine sample according to the urine sample collection instructions, and places the collected urine sample into the sample inlet of the urine analysis terminal. The sample inlet is an ergonomically designed and convenient opening to ensure the correct input of the urine sample.

[0123] The microfluidic module is controlled to accurately distribute the urine sample to each area on the preset test strip, and the flow of the urine sample is controlled by capillary action or a micropump to ensure the consistency and stability of the test and avoid deviations in the test data due to uneven flow rate of the urine sample.

[0124] S402: Control the light source system to emit light toward the preset test paper.

[0125] S403: Acquire spectral data of multiple detection areas collected by the spectral sensor.

[0126] Specifically, the light source system is controlled to use a three-wavelength LED array (455nm blue / 530nm green / 650nm red ±5nm) to emit light to a preset test paper.

[0127] The spectral sensor is an integrated AMS TCS34725 RGB sensor array, which divides the preset test paper into 16 detection areas (1.5mm intervals), each area corresponding to a pixel unit of the spectral sensor.

[0128] In addition, the spectral sensor can ensure accurate detection of light of different wavelengths to capture subtle color changes in multiple detection areas on the preset test strips, and then obtain spectral data of multiple detection areas. Since different chemical components in urine samples may exhibit different absorbance at different wavelengths, full-band scanning is used to obtain the most comprehensive data to improve the credibility of the analysis results.

[0129] S404 , obtaining color block information of the multiple detection areas according to the spectral data of the multiple detection areas.

[0130] Specifically, weights are assigned to multiple detection areas according to their locations. For example, the center area has a higher weight and the edge area has a decreasing weight. Then, the 3σ principle is used to remove outliers (for example, if the deviation of the data in a certain area from the mean exceeds 3 times the standard deviation, it is removed). Finally, a comprehensive data set containing 16 groups of RGB values is obtained, which is the color block information of multiple detection areas.

[0131] S405: Send the test paper detection data of the target user to the cloud server.

[0132] The test paper detection data includes: color block information of multiple detection areas.

[0133] Specifically, the control wireless transmission module uses the TLS1.3 encryption protocol to send the target user's test paper detection data to the cloud server to ensure the security of data transmission.

[0134] In summary, the embodiment of the present application provides a remote urine index analysis method, which is applied to the processing unit in the urine analysis terminal. The method includes: controlling the microfluidic module to distribute the urine sample of the target user to multiple detection areas on the preset test strip; controlling the light source system to emit light toward the preset test strip; obtaining the spectral data of the multiple detection areas collected by the spectral sensor; obtaining the color block information of the multiple detection areas based on the spectral data of the multiple detection areas; and sending the test strip detection data of the target user to the cloud server. The test strip detection data includes: the color block information of the multiple detection areas. The spectral data of the multiple detection areas collected by the spectral sensor and the color block information of the multiple detection areas based on the spectral data of the multiple detection areas are used to improve the detection accuracy of the test strip detection data, so that the cloud server generates accurate health analysis results.

[0135] The present application also provides another possible implementation of the remote urine index analysis method. Figure 5 This is a flow chart of a remote urine index analysis method provided in an embodiment of the present application, as shown in FIG. Figure 5 As shown, the method further includes:

[0136] S501: Acquire temperature data of a test chamber where a preset test strip is located, collected by a temperature sensor.

[0137] S502 : According to the temperature data, a preset temperature compensation algorithm is used to obtain compensation parameters of multiple color channels respectively.

[0138] S503 : supplement and correct the color block information of the multiple detection areas according to the supplementary parameters of the multiple color channels.

[0139] In this embodiment, a temperature sensor is used to detect the temperature data of the detection chamber in real time, and a preset temperature compensation algorithm is used to calculate the compensation parameters of multiple color channels respectively. The preset temperature compensation algorithm is expressed as: ΔRGB = α·T + β·T 2 , where α=0.12% / ℃, β=0.002% / ℃ 2 .

[0140] Based on the supplementary parameters of multiple color channels, the color block information of multiple detection areas is supplemented and corrected. For example, if the collected temperature rises by 5°C, the color block information of multiple detection areas is adjusted according to the preset temperature compensation algorithm.

[0141] The temperature compensation correction module effectively solves the problem of ambient temperature interfering with the color rendering of preset test strips through precise temperature monitoring and dynamic parameter adjustment, ensuring the reliability of test data in different scenarios.

[0142] The present application also provides another possible implementation of the remote urine index analysis method. Figure 6 This is a flow chart of a remote urine index analysis method provided in an embodiment of the present application, as shown in FIG. Figure 6 As shown, before obtaining the color block information of the multiple detection areas based on the spectral data of the multiple detection areas, the method further includes:

[0143] S601: Obtain humidity data of a test chamber where a preset test strip is located, collected by a humidity sensor.

[0144] S602: If the humidity data is greater than or equal to a preset humidity threshold, a preset refractive index correction model is used to correct the spectral data of the multiple detection areas.

[0145] Based on the spectral data of the multiple detection areas, the color block information of the multiple detection areas is obtained, including:

[0146] S603 : Obtain color block information of the multiple detection areas according to the corrected spectral data of the multiple detection areas.

[0147] In this embodiment, a humidity sensor is used to detect the humidity data of the detection chamber in real time. The preset humidity threshold can be 70%. When the relative humidity (RH) is greater than 70%, the refractive index correction model is activated. The refractive index correction model is used to adjust the current spectral data n of multiple detection areas. base Correction is performed to obtain the corrected spectral data n corrected , where the correction formula is expressed as: n corrected =n base (1+k(RH-70%)), where k represents the humidity compensation coefficient.

[0148] The light intensity data of multiple detection areas are normalized to eliminate the scattering interference caused by humidity. Finally, the color block information of multiple detection areas is obtained based on the corrected spectral data of the multiple detection areas.

[0149] The present application also provides another possible implementation of the remote urine index analysis method. Figure 7 This is a flow chart of a remote urine index analysis method provided in an embodiment of the present application, as shown in FIG. Figure 7 As shown, the method further includes:

[0150] S701: Acquire spectral data of a preset identification area on a preset test strip.

[0151] S702: Perform feature matching based on the spectral data of the preset identification area to obtain target manufacturer information corresponding to the preset test strip.

[0152] S703: Obtain color calibration parameters of the preset test paper according to the target manufacturer information.

[0153] Based on the spectral data of the multiple detection areas, the color block information of the multiple detection areas is obtained, including:

[0154] S704 : Obtain color block information of the multiple detection areas according to the spectral data of the multiple detection areas and the color calibration parameters.

[0155] In this embodiment, a characteristic color developing reagent is added to a preset identification area on a preset test paper. For example, manufacturer A uses bromocresol purple and manufacturer B uses phenol red. The spectral data of the preset identification area is obtained. Then, feature similarity matching is performed based on the spectral data of the preset identification area and the manufacturer feature library stored in this city to obtain the target manufacturer information corresponding to the preset test paper.

[0156] Then, according to the target manufacturer information, the color calibration parameters of the preset test paper are obtained, which are used to calibrate the spectral data of multiple detection areas, and finally the color block information of multiple detection areas is obtained.

[0157] In the method provided in this embodiment, automatic adaptation of test strips from different manufacturers is achieved through spectral feature analysis of preset identification areas, solving the problem of consistency of cross-manufacturer test data.

[0158] The following continues to explain the remote urine index analysis device and cloud server provided by any of the above embodiments of the present application. The specific implementation process and the technical effects produced are the same as those of the corresponding method embodiments mentioned above. For the sake of brief description, the parts not mentioned in this embodiment can be referred to the corresponding content in the method embodiment.

[0159] Figure 8 This is a functional module diagram of a remote urine index analysis device provided in an embodiment of the present application. Figure 8 As shown, the remote urine index analysis device 100 includes:

[0160] The receiving module 110 is configured to receive test strip test data for a target user sent by the urine analysis terminal, wherein the test strip test data is test data obtained by the urine analysis terminal using a preset test strip to test a urine sample of the target user, and the test strip test data includes: color block information of multiple detection areas on the preset test strip;

[0161] An analysis module 120 is configured to perform urine analysis on the color block information of the multiple detection areas using a first preset urine analysis model to obtain current indicator parameters of multiple urine indicator items;

[0162] A first generating module 130 is configured to generate a health analysis report for a target user based on current indicator parameters of a plurality of urine indicator items and personal characteristics of the target user;

[0163] The first sending module 140 is configured to send the health analysis result of the target user to the mobile application device, where the health analysis result at least includes a health analysis report.

[0164] Optionally, the device further comprises:

[0165] A classification module is used to perform linear classification on the current indicator parameters of multiple urine indicator items based on the preset indicator reference ranges of multiple urine indicator items using a second preset urine analysis model to obtain the detection category of the target user for the multiple urine indicator items. The detection category of each urine indicator item is used to indicate whether each urine indicator item of the target user is normal or the severity of the abnormality; the health analysis result also includes: the detection category of multiple urine indicator items.

[0166] Optionally, the preset indicator reference ranges of the plurality of urine indicator items are preset standard indicator reference ranges, or individual indicator reference ranges of the target user.

[0167] Optionally, the first generating module 130 is further configured to generate auxiliary disease diagnosis information of the target user based on current indicator parameters of multiple urine indicator items using a preset decision tree model; the health analysis result also includes: auxiliary disease diagnosis information.

[0168] Optionally, the first generation module 130 is further used to obtain historical indicator parameters of the target user for the target urine indicator item; based on the historical indicator parameters of the target urine indicator item and the current indicator parameters of the target urine indicator item, a preset time series analysis model is used to generate time series change information of the target user for the target urine indicator item; the health analysis result also includes: time series change information of the target urine indicator item.

[0169] Optionally, the first generation module 130 is further used to obtain historical indicator parameters of the target user for multiple urine indicator items; based on the historical indicator parameters of the multiple urine indicator items and the current indicator parameters of each urine indicator item, a third preset urine analysis model is used to analyze the correlation between the urine indicator items to obtain an abnormal urine indicator combination for the target user; the health analysis result also includes: an abnormal urine indicator combination.

[0170] Figure 9 This is a functional module diagram of another remote urine index analysis device provided in an embodiment of the present application. The processing unit used in the urine analysis terminal, such as Figure 9 As shown, the remote urine index analysis device 200 includes:

[0171] The control module 210 is used to control the microfluidic module to distribute the urine sample of the target user to multiple detection areas on the preset detection test strip;

[0172] The control module 210 is further used to control the light source system to emit light toward the preset test paper;

[0173] An acquisition module 220 is configured to acquire spectral data of multiple detection areas collected by a spectral sensor;

[0174] A second generating module 230 is configured to obtain color block information of the plurality of detection areas based on the spectral data of the plurality of detection areas;

[0175] The second sending module 240 is used to send the test paper detection data of the target user to the cloud server, where the test paper detection data includes: color block information of multiple detection areas.

[0176] Optionally, the device further comprises:

[0177] The correction module is used to obtain the temperature data of the detection chamber where the preset detection test strip is located, which is collected by the temperature sensor; based on the temperature data, a preset temperature compensation algorithm is used to obtain the compensation parameters of multiple color channels respectively; and based on the supplementary parameters of the multiple color channels, the color block information of multiple detection areas is supplemented and corrected.

[0178] Optionally, the correction module is further configured to obtain humidity data of a detection chamber where a preset detection test strip is located, collected by a humidity sensor; if the humidity data is greater than or equal to a preset humidity threshold, a preset refractive index correction model is used to correct the spectral data of the multiple detection areas;

[0179] The second generating module 230 is further configured to obtain color block information of the multiple detection areas according to the corrected spectral data of the multiple detection areas.

[0180] Optionally, the acquisition module 220 is further configured to acquire spectral data of a preset identification area on a preset test strip; perform feature matching based on the spectral data of the preset identification area to obtain target manufacturer information corresponding to the preset test strip; and acquire color calibration parameters of the preset test strip based on the target manufacturer information.

[0181] The second generating module 230 is further configured to obtain color block information of the multiple detection areas according to the spectral data of the multiple detection areas and the color calibration parameters.

[0182] The above-mentioned device is used to execute the method provided in the above-mentioned embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0183] The above modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more microprocessors, or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code through a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0184] Figure 10 This is a schematic diagram of a cloud server provided in an embodiment of the present application, which can be used for remote urine index analysis. Figure 10As shown, the cloud server includes: a processor 310 , a storage medium 320 , and a bus 330 .

[0185] Storage medium 320 stores machine-readable instructions executable by processor 310. When the cloud server is running, processor 310 communicates with storage medium 320 via bus 330, and processor 310 executes the machine-readable instructions to perform the steps of the above method embodiment. The specific implementation methods and technical effects are similar and will not be repeated here.

[0186] Optionally, the present application further provides a storage medium 320 on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method embodiment are executed. The specific implementation and technical effects are similar and will not be repeated here.

[0187] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0188] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0189] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional units.

[0190] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor (English: processor) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (English: Read-Only Memory, abbreviated: ROM), a random access memory (English: Random Access Memory, abbreviated: RAM), a magnetic disk or an optical disk, and other media that can store program code.

[0191] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A remote urine index analysis method, characterized in that: Applied to a cloud server, the method includes: Receiving test strip test data for a target user sent by a urine analysis terminal, wherein the test strip test data is test data obtained by the urine analysis terminal using a preset test strip to test a urine sample of the target user, and the test strip test data includes: color block information of multiple detection areas on the preset test strip; Using a first preset urine analysis model, performing urine analysis on the color block information of the multiple detection areas to obtain current indicator parameters of multiple urine indicator items; generating a health analysis report for the target user based on the current indicator parameters of the plurality of urine indicator items and the personal characteristics of the target user; The health analysis result of the target user is sent to a mobile application device, where the health analysis result at least includes: the health analysis report.

2. The method according to claim 1, wherein The method further comprises: performing linear classification on the current indicator parameters of the multiple urine indicator items using a second preset urine analysis model based on the preset indicator reference ranges of the multiple urine indicator items to obtain a detection category of the target user for the multiple urine indicator items, wherein the detection category of each urine indicator item is used to indicate whether each urine indicator item of the target user is normal or the severity of the abnormality; The health analysis result also includes: detection categories of the multiple urine indicator items.

3. The method according to claim 2, wherein The preset indicator reference ranges of the multiple urine indicator items are preset standard indicator reference ranges, or individual indicator reference ranges of the target user.

4. The method according to claim 1, wherein The method further comprises: Using a preset decision tree model, generating auxiliary disease diagnosis information for the target user based on current indicator parameters of the multiple urine indicator items; The health analysis result also includes: the auxiliary disease diagnosis information.

5. The method according to claim 1, wherein The method further comprises: Obtaining historical indicator parameters of the target user for the target urine indicator item; Generate time series change information of the target urine indicator item for the target user using a preset time series analysis model according to the historical indicator parameters of the target urine indicator item and the current indicator parameters of the target urine indicator item; The health analysis result also includes: time series change information of the target urine indicator item.

6. The method according to claim 1, wherein The method further comprises: Obtaining historical indicator parameters of the target user for the multiple urine indicator items; Analyzing the correlation between the urine indicator items using a third preset urine analysis model based on the historical indicator parameters of the multiple urine indicator items and the current indicator parameters of each urine indicator item to obtain an abnormal urine indicator combination for the target user; The health analysis result also includes: the abnormal urine indicator combination.

7. A remote urine index analysis method, characterized in that: The method is applied to a processing unit in a urine analysis terminal, and includes: Controlling the microfluidic module to distribute the urine sample of the target user to multiple detection areas on the preset test strip; Controlling the light source system to emit light toward the preset test paper; Acquiring spectral data of the plurality of detection areas collected by a spectral sensor; Obtaining color block information of the plurality of detection areas according to the spectral data of the plurality of detection areas; Sending the test paper detection data of the target user to the cloud server, the test paper detection data including: color block information of the multiple detection areas.

8. The method according to claim 7, wherein The method further comprises: Acquiring temperature data of the detection chamber where the preset detection test strip is located collected by a temperature sensor; According to the temperature data, a preset temperature compensation algorithm is used to obtain compensation parameters of multiple color channels respectively; The color block information of the multiple detection areas is supplemented and corrected according to the supplementary parameters of the multiple color channels.

9. The method according to claim 7, wherein: Before obtaining the color block information of the multiple detection areas based on the spectral data of the multiple detection areas, the method further includes: Acquiring humidity data of the detection chamber where the preset detection test strip is located collected by a humidity sensor; If the humidity data is greater than or equal to a preset humidity threshold, a preset refractive index correction model is used to correct the spectral data of the multiple detection areas; The obtaining, based on the spectral data of the plurality of detection areas, the color block information of the plurality of detection areas comprises: Color block information of the multiple detection areas is obtained according to the corrected spectral data of the multiple detection areas.

10. The method according to claim 7, wherein: The method further comprises: Acquiring spectral data of a preset identification area on the preset detection test paper; Perform feature matching based on the spectral data of the preset identification area to obtain the target manufacturer information corresponding to the preset test strip; According to the target manufacturer information, obtaining the color calibration parameters of the preset test paper; The obtaining, based on the spectral data of the plurality of detection areas, the color block information of the plurality of detection areas comprises: Color block information of the multiple detection areas is obtained according to the spectral data of the multiple detection areas and the color calibration parameters.

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