Data management method based on physical examination machine and related equipment

Abstract

The invention discloses a data management method based on a physical examination machine and related equipment, and relates to the technical field of data management, and the method comprises the steps: obtaining the basic information of a target user, creating a basic information sub-data set of the target user, obtaining the historical information of the target user, creating a historical information sub-data set of the target user, acquiring a plurality of physical examination data of the target user using the physical examination machine for physical examination, creating a target user physical examination sub-data set, judging whether an error exists or not according to the target user historical information sub-data set, and if the error exists, re-detecting and updating the target user physical examination sub-data set; comparing each physical examination data with a preset physical examination data standard range according to the target user physical examination sub-data set, judging the abnormal degree of each physical examination data, and further analyzing the effectiveness of each physical examination data to obtain a first physical examination data effective coefficient W1; the effect of improving the data management efficiency based on the physical examination machine is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of data management, and in particular to a data management method and related equipment based on a health examination machine. Background Art

[0002] At present, the health check-up machine is a device used to detect the health status of the human body. It usually includes a series of sensors and measuring instruments to measure various physiological indicators of the body, such as blood pressure, heart rate, body temperature, weight, height, etc. Through these data, the health status of the individual can be evaluated and analyzed, so as to provide relevant health advice and guidance. Data management is the process of effectively collecting, storing, processing and applying data using computer hardware and software technology. Data management based on health check-up machines is crucial to improving the convenience of human life.

[0003] The existing data management method based on health examination machines is to collect and enter, store and encrypt, as well as integrate and analyze the data of health examination machines. However, the existing data management method based on health examination machines may have measurement errors when performing measurements of the health examination machines. The data accuracy based on health examination machines is low. At the same time, the impact of users' basic information and historical medical conditions on users' physical examinations is not taken into account. The personalized service of data management based on health examination machines is insufficient, resulting in low efficiency of data management based on health examination machines. There is room for improvement. Summary of the invention

[0004] In order to improve the efficiency of data management based on a health examination machine, the present application provides a data management method and related equipment based on a health examination machine.

[0005] In the first aspect, the data management method based on the health examination machine provided by the present application adopts the following technical solution: The data management method based on the health examination machine includes the following steps: Obtain the target user's basic information and create a target user's basic information sub-dataset, obtain the target user's historical information and create a target user's historical information sub-dataset, obtain multiple physical examination data of the target user using a health examination machine for physical examination and create a target user's physical examination sub-dataset, determine whether there is an error based on the target user's historical information sub-dataset, and if there is an error, re-detect and update the target user's physical examination sub-dataset; According to the target user's physical examination sub-data set, each physical examination data is compared with the preset physical examination data standard range to determine the abnormality of each physical examination data, and then the validity of each physical examination data itself is analyzed to obtain the first physical examination data validity coefficient W1; According to the target user basic information sub-dataset and the target user historical information sub-dataset, the validity of the target user's physical examination data in the target user physical examination sub-dataset is judged to obtain a second physical examination data validity coefficient W2 of each physical examination data in the target user physical examination sub-dataset; Based on the first physical examination data validity coefficient W1 and the second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-data set of the target user, each physical examination data in the physical examination sub-data set of the target user is divided to obtain a physical examination data classification result; Processing multiple physical examination data of the target user according to the physical examination data classification result, and determining the priority of the physical examination items of each user to be tested according to the physical examination data classification result, the first physical examination data validity coefficient W1, and the second physical examination data validity coefficient W2 to obtain the physical examination priority determination result of the target user; The target user's physical examination priority result is sent to the user terminal.

[0006] Preferably, basic information of the target user is obtained, the basic information of the target user including age information of the target user, gender information of the target user and occupation information of the target user, and a sub-dataset of basic information of the target user is constructed according to the age information of the target user, the gender information of the target user and the occupation information of the target user; Acquire the target user's historical information, wherein the target user's historical information includes the target user's historical physical examination information and the target user's historical disease information, wherein the target user's historical disease information includes the target user's historical disease type information and the target user's historical disease severity information for each time the target user was ill, and the target user's historical physical examination information, the target user's historical disease type information, and the target user's historical disease severity information constitute a target user's historical information sub-dataset; Obtaining the physical examination data of the target user when undergoing a physical examination at the health examination machine and summarizing and processing it to obtain a target user physical examination sub-dataset, wherein the target user physical examination sub-dataset includes the target user's basic physiological parameter information, the target user's body composition distribution information, the target user's electrocardiogram information, and the target user's biochemical index information; The target user's historical physical examination information includes historical basic physiological parameter information, historical human body composition distribution information, historical electrocardiogram information, and historical biochemical index information.

[0007] Preferably, the basic physiological parameter information of the target user is compared with the historical basic physiological parameter information to obtain a basic physiological parameter comparison result, the body composition distribution information of the target user is compared with the historical body composition distribution information to obtain a body composition distribution comparison result, the electrocardiogram information of the target user is compared with the historical electrocardiogram information to obtain an electrocardiogram comparison result, and the biochemical index information of the target user is compared with the historical biochemical index information to obtain a biochemical index comparison result; Based on the basic physiological parameter comparison result, determine whether the basic physiological parameter information of the target user is consistent with the historical basic physiological parameter information. If not, determine whether the target user's operation is standardized when performing the basic physiological parameter detection. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-detect the target user's basic physiological parameters, and update the target user's basic physiological parameter information; Based on the comparison result of the human body composition distribution, determine whether the human body composition distribution information of the target user is consistent with the historical human body composition distribution information. If not, determine whether the target user's operation is standardized when performing the human body composition distribution detection. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-detect the target user's human body composition distribution, and update the target user's human body composition distribution information. Based on the ECG comparison result, determine whether the target user's ECG information is consistent with the historical ECG information. If not, determine whether the target user's operation is standardized during the ECG test. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, retest the target user's ECG, and update the target user's ECG information. Based on the biochemical index comparison results, determine whether the target user's biochemical index information is consistent with the historical biochemical index information. If not, determine whether the target user's operation is standardized when performing the biochemical index test. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-test the target user's biochemical index, and update the target user's biochemical index information; Based on the updated basic physiological parameter information of the target user, the updated human body composition distribution information of the target user, the updated electrocardiogram information of the target user, and the updated biochemical index information of the target user, the target user physical examination sub-dataset is updated; The target user physical examination sub-dataset, the target user basic information sub-dataset and the target user historical information sub-dataset are cached.

[0008] Preferably, the standard range of each physical examination data is pre-set and cached; Match each physical examination data in the physical examination sub-data set of the target user with the preset physical examination data standard, and determine whether the physical examination data in the physical examination sub-data set of the target user is within the range of the physical examination data standard; If the physical examination data is within the physical examination data standard range, the first physical examination data validity coefficient W1 of the physical examination data is 0; If the physical examination data is not within the physical examination data standard range, determine whether the physical examination data is above or below the physical examination data standard range; If the physical examination data is higher than the physical examination data standard range, the difference between the physical examination data and the maximum value in the physical examination data standard range is calculated to obtain a high-order physical examination abnormal difference, and a first physical examination data effective coefficient W1 of the physical examination data is obtained based on the high-order physical examination abnormal difference; If the physical examination data is lower than the physical examination data standard range, the difference between the physical examination data and the small value in the physical examination data standard range is calculated to obtain a low-order physical examination abnormal difference, and the first physical examination data effective coefficient W1 of the physical examination data is obtained based on the low-order physical examination abnormal difference; The first physical examination data validity coefficient W1 of each physical examination data in the target user physical examination sub-data set is aggregated and stored.

[0009] Preferably, a user disease information database is obtained, wherein the user disease information database includes age disease types of users of different ages and the age disease probability of users of each type of disease, gender disease types of users of different genders and the gender disease probability of users of each type of disease, and occupational disease types of users of different occupations and the occupational disease probability of users of each type of disease; Match the target user's basic information sub-dataset with the user's disease information database, and select the most common user age disease type of the target user according to the user's age disease probability to obtain the first basic information disease type; According to the disease probability of user gender, the most common disease type of the target user gender is selected to obtain the second basic information disease type; According to the probability of occupational diseases of users, the most common occupational disease types of target users are screened to obtain the third basic information disease type; The first basic information disease type, the second basic information disease type and the third basic information disease type are combined to form the target user's predicted disease type information.

[0010] Preferably, the influence of the target user's basic information on the validity of each physical examination data in the target user's physical examination sub-dataset is determined according to the target user's predicted disease type information to obtain the target user's basic information validity coefficient p of each physical examination data; According to the target user's historical disease type information in the target user's historical information sub-dataset, determine the impact of the target user's historical disease type on the validity of each physical examination data in the target user's physical examination sub-dataset to obtain the historical disease type validity coefficient q of each physical examination data; Determine the target user's predicted disease type value based on the target user's predicted disease type information, and adjust the first proportional factor based on the target user's predicted disease type value. The more the target user predicts the disease type, the greater the first proportional factor The bigger; Determine the target user's historical disease type value based on the target user's historical disease type information, and adjust the second proportional factor based on the target user's historical disease type value. The second proportional factor is determined, where the more the target user's historical disease type value is, the greater the second proportional factor is. The bigger; According to the target user's basic information effective coefficient p, historical disease type effective coefficient q, and the first proportional factor of each physical examination data in the target user's physical examination sub-dataset and the second scaling factor , based on data validity relationship function Calculation is performed to obtain the physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-data set of the target user.

[0011] Preferably, a valid physical examination data set for a target user is created, and the physical examination data whose first physical examination data validity coefficient W1 is not 0 in the physical examination sub-data set for the target user is screened for the first time and stored in the valid physical examination data set for the target user; Create a relatively invalid data set for the physical examination of the target user, compare the second physical examination data validity coefficient W2 of each physical examination data after the first screening of the physical examination sub-data set of the target user with the preset physical examination data side validity coefficient threshold, and perform a second screening on the physical examination data whose second physical examination data validity coefficient W2 after the first screening of the physical examination sub-data set of the target user is greater than the preset physical examination data side validity coefficient threshold and store it in the relatively invalid data set for the physical examination of the target user; Create a target user's absolutely invalid physical examination data set, and aggregate and store each physical examination data after the second screening of the target user's physical examination sub-data set into the target user's absolutely invalid physical examination data set; The target user physical examination valid data set, the target user physical examination relatively invalid data set and the target user physical examination absolutely invalid data set are combined to form a physical examination data classification result.

[0012] Preferably, the data in the absolutely invalid data set of the target user's physical examination is deleted; Preliminarily determining the priority of the physical examination items of the target user according to the target user physical examination valid data set and the target user physical examination relatively invalid data set to obtain a first priority determination result, wherein the priority of the physical examination items corresponding to each physical examination data in the target user physical examination valid data set is higher than the priority of the physical examination items corresponding to each physical examination data in the target user physical examination relatively invalid data set; The priority of the physical examination item corresponding to each physical examination data in the physical examination valid data set of the target user is determined according to the first physical examination data validity coefficient W1 to obtain a second priority determination result, wherein the larger the first physical examination data validity coefficient W1 of the physical examination data is, the higher the priority of the physical examination item corresponding to the physical examination data is; The priority of each physical examination data corresponding to the physical examination item in the physical examination relative invalid data set of the target user is determined according to the second physical examination data validity coefficient W2 to obtain a third priority determination result, wherein the larger the second physical examination data validity coefficient W2 of the physical examination data is, the higher the priority of the physical examination item corresponding to the physical examination data is; The first priority determination result, the second priority determination result, and the third priority determination result are combined to form a target user physical examination priority determination result.

[0013] Preferably, a wireless communication module is obtained and a signal connection link is established between the wireless communication module and a health examination machine; Obtain the priority determination result of the target user's physical examination and cache it; The priority determination result of the target user's physical examination is sent to the user terminal based on the wireless communication module.

[0014] In the second aspect, the present application provides data management related equipment based on a health examination machine, adopting the following technical solutions: Data management related equipment based on health examination machines, including: A user information acquisition module is configured to acquire basic information of a target user and create a target user basic information sub-dataset, acquire historical information of a target user and create a target user historical information sub-dataset, acquire multiple physical examination data of a target user using a health examination machine for a physical examination and create a target user physical examination sub-dataset, determine whether there is an error based on the target user historical information sub-dataset, and if there is an error, re-detect and update the target user physical examination sub-dataset; The data validity analysis module is configured to compare each physical examination data with a preset physical examination data standard range according to the physical examination sub-data set of the target user, determine the abnormality of each physical examination data, and then analyze the validity of each physical examination data itself to obtain the first physical examination data validity coefficient W1; A data validity side analysis module is configured to judge the validity of the physical examination data of the target user in the physical examination sub-dataset of the target user according to the basic information sub-dataset of the target user and the historical information sub-dataset of the target user, and obtain a second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-dataset of the target user; A physical examination data classification module is configured to classify each physical examination data in the physical examination sub-data set of the target user based on the first physical examination data validity coefficient W1 and the second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-data set of the target user to obtain a physical examination data classification result; The physical examination item priority analysis module is configured to process multiple physical examination data of the target user according to the physical examination data classification result, and to determine the priority of the physical examination items of each user to be tested according to the physical examination data classification result, the first physical examination data validity coefficient W1, and the second physical examination data validity coefficient W2 to obtain the target user physical examination priority determination result; The wireless communication module is configured to send the target user's physical examination priority result to the user terminal.

[0015] In summary, the present application includes at least one of the following beneficial technical effects: 1. By acquiring the basic information of the target user and the historical information of the target user, the physical examination data of the target user when undergoing a physical examination on the health examination machine is obtained and summarized to obtain the target user physical examination sub-dataset, and the basic physiological parameter information of the target user is compared with the historical basic physiological parameter information, the human body composition distribution information of the target user is compared with the historical human body composition distribution information, the electrocardiogram information of the target user is compared with the historical electrocardiogram information, and the biochemical index information of the target user is compared with the historical biochemical index information, to determine whether the physical examination data of the target user on the health examination machine is reasonable compared with the historical physical examination data. If it is not reasonable, determine whether the physical examination operation of the target user on the health examination machine is standardized. If it is not standardized, standardize the operation, re-measure, and update the target user physical examination sub-dataset, thereby improving the data accuracy of the target user when undergoing a physical examination on the health examination machine, and thus improving the data management efficiency based on the health examination machine; 2. By matching the target user's basic information sub-dataset with the user's disease information database, determine the disease type with the highest probability corresponding to the target user's age information, target user's gender information, and target user's occupation information to obtain the target user's predicted disease type information, and obtain the target user's basic information validity coefficient p of each physical examination data according to the target user's predicted disease type information, and obtain the historical disease type validity coefficient q of each physical examination data according to the target user's historical disease type information of the target user's historical information sub-dataset, and calculate the first proportional factor according to the target user's predicted disease type information. Preset the second proportional factor based on the target user's historical disease type information Preset, according to the target user's basic information effective coefficient p of each physical examination data in the target user's physical examination sub-dataset, the historical disease type effective coefficient q, the first proportional factor and the second scaling factor , the effective coefficient W2 of each physical examination data is obtained, which improves the detection accuracy of the effective coefficient W2 of each physical examination data, provides data support for subsequent data management based on the health examination machine, and further improves the data management efficiency based on the health examination machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a data management method based on a health examination machine, which is mainly reflected in this embodiment; Figure 2 This is a module diagram of data management related equipment based on a health examination machine, which is mainly reflected in this embodiment.

[0017] Figure numerals: 1. User information acquisition module; 2. Data validity analysis module; 3. Data validity side analysis module; 4. Physical examination data division module; 5. Physical examination item priority analysis module; 6. Wireless communication module. DETAILED DESCRIPTION

[0018] The present application is further described in detail below in conjunction with the accompanying drawings.

[0019] The embodiment of the present application discloses a data management method based on a health examination machine.

[0020] The data management method based on the health examination machine includes the following steps: Reference Figure 1 , step S1, obtain the basic information of the target user and create a sub-dataset of the basic information of the target user, obtain the historical information of the target user and create a sub-dataset of the historical information of the target user, obtain multiple physical examination data of the target user using the health examination machine for physical examination and create a sub-dataset of the physical examination of the target user, and determine whether there is an error based on the sub-dataset of the historical information of the target user. If there is an error, re-detect and update the sub-dataset of the physical examination of the target user. Step S1 specifically includes the following sub-steps: Step A1, acquiring the target user's basic information, including the target user's age information, target user's gender information and target user's occupation information, and constructing a target user's basic information sub-dataset based on the target user's age information, target user's gender information and target user's occupation information.

[0021] Step A2, acquiring the target user's historical information, including the target user's historical physical examination information and the target user's historical disease information. The target user's historical disease information includes the target user's historical disease type information and the target user's historical disease severity information for each time the target user was ill. The target user's historical physical examination information, the target user's historical disease type information and the target user's historical disease severity information construct a target user historical information sub-dataset.

[0022] Step A3, obtain the physical examination data of the target user when undergoing a physical examination on the health examination machine and summarize and process it to obtain the target user physical examination sub-dataset, which includes the target user's basic physiological parameter information, the target user's body composition distribution information, the target user's electrocardiogram information and the target user's biochemical indicator information.

[0023] Among them, the target user's basic physiological parameter information includes the target user's height value, target user's weight value, target user's body temperature value, target user's blood pressure value and target user's blood oxygen saturation; the target user's body composition distribution information includes the target user's fat content, target user's fat distribution, target user's water content, target user's water distribution, target user's muscle content and target user's muscle distribution; the target user's biochemical indicator information includes the target user's blood sugar value, target user's uric acid level and target user's cholesterol level.

[0024] Step A4, the target user's historical physical examination information includes historical basic physiological parameter information, historical human body composition distribution information, historical electrocardiogram information, and historical biochemical index information.

[0025] Among them, historical basic physiological parameter information includes historical height values, historical weight values, historical body temperature values, historical blood pressure values ​​and historical blood oxygen saturation; historical human body composition distribution information includes historical fat content, historical fat distribution, historical water content, historical water distribution, historical muscle content and historical muscle distribution; historical biochemical indicator information includes historical blood sugar values, historical uric acid levels and historical cholesterol levels.

[0026] Step S1 also includes the following sub-steps: Step B1, compare the target user's basic physiological parameter information with the historical basic physiological parameter information to obtain the basic physiological parameter comparison result, compare the target user's body composition distribution information with the historical body composition distribution information to obtain the body composition distribution comparison result, compare the target user's electrocardiogram information with the historical electrocardiogram information to obtain the electrocardiogram comparison result, and compare the target user's biochemical indicator information with the historical biochemical indicator information to obtain the biochemical indicator comparison result.

[0027] Step B2, based on the basic physiological parameter comparison results, determine whether the basic physiological parameter information of the target user is consistent with the historical basic physiological parameter information. If not, determine whether the target user's operation is standardized when performing basic physiological parameter detection. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-detect the target user's basic physiological parameters, and update the target user's basic physiological parameter information.

[0028] Step B3, based on the comparison result of the human body composition distribution, determine whether the human body composition distribution information of the target user is consistent with the historical human body composition distribution information. If they are inconsistent, determine whether the target user's operation is standardized when performing the human body composition distribution detection. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-detect the target user's human body composition distribution, and update the target user's human body composition distribution information.

[0029] Step B4, based on the ECG comparison result, determine whether the target user's ECG information is consistent with the historical ECG information. If not, determine whether the target user's operation is standardized during the ECG test. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-test the target user's ECG, and update the target user's ECG information.

[0030] Step B5, based on the biochemical index comparison result, determine whether the target user's biochemical index information is consistent with the historical biochemical index information. If not, determine whether the target user's operation is standardized when performing the biochemical index test. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-test the target user's biochemical index, and update the target user's biochemical index information.

[0031] Step B6, updating the target user's physical examination sub-dataset based on the updated target user's basic physiological parameter information, the updated target user's body composition distribution information, the updated target user's electrocardiogram information, and the updated target user's biochemical index information.

[0032] Step B7, caching the target user's physical examination sub-dataset, the target user's basic information sub-dataset, and the target user's historical information sub-dataset.

[0033] In specific applications, by obtaining the basic information of the target user and the target user's historical information, the physical examination data of the target user when undergoing a physical examination on a health examination machine is obtained and summarized to obtain a target user physical examination sub-dataset, and the target user's basic physiological parameter information is compared with the historical basic physiological parameter information, the target user's body composition distribution information is compared with the historical body composition distribution information, the target user's electrocardiogram information is compared with the historical electrocardiogram information, and the target user's biochemical indicator information is compared with the historical biochemical indicator information, to determine whether the data of the target user's physical examination on the health examination machine is reasonable compared with the historical physical examination data; if not, to determine whether the target user's physical examination operation on the health examination machine is standardized; if not, to standardize the operation, to re-measure and update the target user's physical examination sub-dataset, thereby improving the data accuracy of the target user when undergoing a physical examination on the health examination machine, and thereby improving the data management efficiency based on the health examination machine.

[0034] Reference Figure 1 , step S2, according to the target user physical examination sub-data set, compare each physical examination data with the preset physical examination data standard range, determine the abnormality of each physical examination data, and then analyze the validity of each physical examination data itself to obtain the first physical examination data validity coefficient W1. Step S2 specifically includes the following sub-steps: Step S21, presetting the standard range of each physical examination data and performing cache processing.

[0035] Step S22, matching each physical examination data in the physical examination sub-data set of the target user with a preset physical examination data standard, and determining whether the physical examination data in the physical examination sub-data set of the target user is within the range of the physical examination data standard.

[0036] Step S23: If the physical examination data is within the standard range of the physical examination data, the first physical examination data validity coefficient W1 of the physical examination data is 0.

[0037] Step S24: if the physical examination data is not within the physical examination data standard range, determine whether the physical examination data is higher than or lower than the physical examination data standard range.

[0038] Step S25, if the physical examination data is higher than the physical examination data standard range, the difference between the physical examination data and the maximum value in the physical examination data standard range is calculated to obtain a high physical examination abnormal difference, and the first physical examination data effective coefficient W1 of the physical examination data is obtained based on the high physical examination abnormal difference.

[0039] Step S26, if the physical examination data is lower than the physical examination data standard range, the difference between the physical examination data and the small value in the physical examination data standard range is calculated to obtain a low-order physical examination abnormal difference, and the first physical examination data effective coefficient W1 of the physical examination data is obtained based on the low-order physical examination abnormal difference.

[0040] Step S27, summarizing and storing the first physical examination data validity coefficient W1 of each physical examination data in the physical examination sub-data set of the target user.

[0041] Reference Figure 1 Step S3, based on the target user basic information sub-dataset and the target user historical information sub-dataset, the validity of the target user's physical examination data in the target user physical examination sub-dataset is judged to obtain the second physical examination data validity coefficient W2 of each physical examination data in the target user physical examination sub-dataset. Step S3 specifically includes the following sub-steps: Step C1, obtaining a user disease information database, which includes age disease types of users of different ages and the age disease probability of each type of disease, gender disease types of users of different genders and the gender disease probability of each type of disease, and occupational disease types of users of different occupations and the occupational disease probability of each type of disease.

[0042] Step C2, matching the target user basic information sub-dataset with the user disease information database, and filtering the most common user age disease type of the target user according to the user age disease probability to obtain the first basic information disease type.

[0043] Step C3, filtering the most common disease types of the target user's gender according to the disease probability of the user's gender to obtain the disease type of the second basic information.

[0044] Step C4, filtering the most common occupational disease types of the target user's occupation according to the user's occupational disease probability to obtain the third basic information disease type.

[0045] Step C5, the first basic information disease type, the second basic information disease type and the third basic information disease type are combined to form the target user's predicted disease type information.

[0046] Step S3 also includes the following sub-steps: Step D1, judging the influence of the target user's basic information on the validity of each physical examination data in the target user's physical examination sub-dataset according to the target user's predicted disease type information, and obtaining the target user's basic information validity coefficient p of each physical examination data.

[0047] For example, the predicted disease type information of the target user is diabetes, and the effectiveness of the target user's blood sugar value in the target user physical examination sub-dataset is the largest, that is, the target user's basic information effectiveness coefficient p of the target user's blood sugar value in the target user physical examination sub-dataset is the largest, and the effective influence of diabetes on the target user's height value in the target user physical examination sub-dataset is 0, then the target user's basic information effectiveness coefficient p of the target user's height value in the target user physical examination sub-dataset is 0.

[0048] Step D2, based on the target user's historical disease type information in the target user's historical information sub-dataset, determine the impact of the target user's historical disease type on the validity of each physical examination data in the target user's physical examination sub-dataset to obtain the historical disease type validity coefficient q of each physical examination data.

[0049] For example, the historical disease type information of the target user is hyperlipidemia, which has the greatest effectiveness on the target user's fat content and target user's fat distribution in the target user's physical examination sub-dataset, that is, the historical disease type effectiveness coefficient q of the target user's fat content and target user's fat distribution in the target user's physical examination sub-dataset is the largest, and the effective influence of hyperlipidemia on the target user's height value in the target user's physical examination sub-dataset is 0, that is, the historical disease type effectiveness coefficient q of the target user's height value in the target user's physical examination sub-dataset is 0.

[0050] Step D3, determining the target user's predicted disease type value based on the target user's predicted disease type information, and calculating the first proportionality factor based on the target user's predicted disease type value. The more the target user predicts the disease type, the greater the first proportional factor The bigger.

[0051] Step D4, determining the target user's historical disease type value based on the target user's historical disease type information, and calculating the second proportional factor based on the target user's historical disease type value. The second proportional factor is determined, where the more the target user's historical disease type value is, the greater the second proportional factor is. The bigger.

[0052] Step D5: according to the target user basic information effective coefficient p, historical disease type effective coefficient q, and first proportional factor of each physical examination data in the target user physical examination sub-dataset, and the second scaling factor , based on data validity relationship function Calculation is performed to obtain the physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-data set of the target user.

[0053] In specific applications, by matching the target user's basic information sub-dataset with the user's disease information database, the disease type with the highest probability corresponding to the target user's age information, target user's gender information, and target user's occupation information is determined to obtain the target user's predicted disease type information, and the target user's basic information validity coefficient p of each physical examination data is obtained according to the target user's predicted disease type information, and the historical disease type validity coefficient q of each physical examination data is obtained according to the target user's historical disease type information of the target user's historical information sub-dataset. Preset the second proportional factor based on the target user's historical disease type information Preset, according to the target user's basic information effective coefficient p of each physical examination data in the target user's physical examination sub-dataset, the historical disease type effective coefficient q, the first proportional factor and the second scaling factor , the effective coefficient W2 of each physical examination data is obtained, which improves the detection accuracy of the effective coefficient W2 of each physical examination data, provides data support for subsequent data management based on the health examination machine, and thus improves the data management efficiency based on the health examination machine.

[0054] Reference Figure 1 Step S4, based on the first physical examination data validity coefficient W1 and the second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-data set of the target user, divide each physical examination data in the physical examination sub-data set of the target user to obtain a physical examination data classification result. Step S4 specifically includes the following sub-steps: Step S41 , creating a target user physical examination valid data set, performing a first screening on the physical examination data whose first physical examination data validity coefficient W1 of each physical examination data in the target user physical examination sub-data set is not 0 and storing the same in the target user physical examination valid data set.

[0055] Step S42, create a relatively invalid data set of physical examination for the target user, compare the second physical examination data validity coefficient W2 of each physical examination data after the first screening of the target user physical examination sub-data set with the preset physical examination data side validity coefficient threshold, and perform a second screening on the physical examination data whose second physical examination data validity coefficient W2 after the first screening of the target user physical examination sub-data set is greater than the preset physical examination data side validity coefficient threshold and store it in the relatively invalid data set of physical examination for the target user.

[0056] Step S43, creating a target user's absolutely invalid physical examination data set, and aggregating and storing each physical examination data after the second screening of the target user's physical examination sub-data set into the target user's absolutely invalid physical examination data set.

[0057] Step S44, the target user's physical examination valid data set, the target user's physical examination relatively invalid data set and the target user's physical examination absolutely invalid data set are combined to form a physical examination data classification result.

[0058] Reference Figure 1 Step S5, according to the physical examination data classification result, multiple physical examination data of the target user are processed, and the priority of the physical examination items of each user to be tested is determined according to the physical examination data classification result, the first physical examination data validity coefficient W1 and the second physical examination data validity coefficient W2 to obtain the target user physical examination priority determination result. Step S5 specifically includes the following sub-steps: Step S51, deleting the data in the absolutely invalid data set of the target user's physical examination.

[0059] Step S52, preliminarily judge the priority of the physical examination items of the target user based on the target user's valid physical examination data set and the target user's relatively invalid physical examination data set to obtain a first priority determination result, wherein the priority of the physical examination items corresponding to each physical examination data in the target user's valid physical examination data set is higher than the priority of the physical examination items corresponding to each physical examination data in the target user's relatively invalid physical examination data set.

[0060] Step S53, judging the priority of the physical examination items corresponding to each physical examination data in the target user's physical examination valid data set according to the first physical examination data validity coefficient W1 to obtain a second priority determination result, wherein the larger the first physical examination data validity coefficient W1 of the physical examination data, the higher the priority of the physical examination item corresponding to the physical examination data.

[0061] Step S54, judging the priority of the physical examination items corresponding to each physical examination data in the target user's physical examination relative to the invalid data set according to the second physical examination data validity coefficient W2, to obtain a third priority determination result, wherein the larger the second physical examination data validity coefficient W2 of the physical examination data, the higher the priority of the physical examination item corresponding to the physical examination data.

[0062] Step S55: The first priority determination result, the second priority determination result and the third priority determination result are combined to form a target user physical examination priority determination result.

[0063] Reference Figure 1 , step S6, sending the target user's physical examination priority result to the user terminal. Step S6 specifically includes the following sub-steps: Step S61, obtaining the wireless communication module 6 and establishing a signal connection link between the wireless communication module 6 and the health examination machine.

[0064] Step S62, obtaining the result of the target user's physical examination priority determination and caching it.

[0065] Step S63, based on the wireless communication module 6, the target user's physical examination priority determination result is sent to the user terminal, so that the target user can understand the priority of the physical examination items at the user terminal, thereby improving the target user's subsequent physical examination efficiency. It should be noted that the wireless communication module 6 in the embodiment of the present application refers to a wireless communication module 6 based on wireless Bluetooth technology.

[0066] The embodiment of the present application also discloses data management related equipment based on the health examination machine.

[0067] Reference Figure 2 , data management related equipment based on health examination machine includes: The user information acquisition module 1 is configured to obtain the basic information of the target user and create a target user basic information sub-dataset, obtain the target user historical information and create a target user historical information sub-dataset, obtain multiple physical examination data of the target user using a health examination machine for physical examination and create a target user physical examination sub-dataset, determine whether there is an error based on the target user historical information sub-dataset, and if there is an error, re-detect and update the target user physical examination sub-dataset.

[0068] The data validity analysis module 2 is configured to compare each physical examination data with a preset physical examination data standard range according to the physical examination sub-data set of the target user, determine the abnormality of each physical examination data, and then analyze the validity of each physical examination data itself to obtain the first physical examination data validity coefficient W1.

[0069] The data validity side analysis module 3 is configured to judge the validity of the physical examination data of the target user in the target user physical examination sub-dataset based on the target user basic information sub-dataset and the target user historical information sub-dataset, and obtain the second physical examination data validity coefficient W2 of each physical examination data in the target user physical examination sub-dataset.

[0070] The physical examination data classification module 4 is configured to classify each physical examination data in the physical examination sub-dataset of the target user based on the first physical examination data validity coefficient W1 and the second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-dataset of the target user to obtain a physical examination data classification result.

[0071] The physical examination item priority analysis module 5 is configured to process multiple physical examination data of the target user according to the physical examination data classification result, and to make priority judgments on the physical examination items of each user to be tested according to the physical examination data classification result, the first physical examination data validity coefficient W1 and the second physical examination data validity coefficient W2 to obtain the target user physical examination priority judgment result.

[0072] The wireless communication module 6 is configured to send the target user's physical examination priority result to the user terminal.

[0073] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A data management method based on a health examination machine, characterized in that: The following steps are involved: Obtain the target user's basic information and create a target user's basic information sub-dataset, obtain the target user's historical information and create a target user's historical information sub-dataset, obtain multiple physical examination data of the target user using a health examination machine for physical examination and create a target user's physical examination sub-dataset, determine whether there is an error based on the target user's historical information sub-dataset, and if there is an error, re-detect and update the target user's physical examination sub-dataset; According to the target user's physical examination sub-data set, each physical examination data is compared with the preset physical examination data standard range to determine the abnormality of each physical examination data, and then the validity of each physical examination data itself is analyzed to obtain the first physical examination data validity coefficient W1; According to the target user basic information sub-dataset and the target user historical information sub-dataset, the validity of the target user's physical examination data in the target user physical examination sub-dataset is judged to obtain a second physical examination data validity coefficient W2 of each physical examination data in the target user physical examination sub-dataset; Based on the first physical examination data validity coefficient W1 and the second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-data set of the target user, each physical examination data in the physical examination sub-data set of the target user is divided to obtain a physical examination data classification result; Processing multiple physical examination data of the target user according to the physical examination data classification result, and determining the priority of the physical examination items of each user to be tested according to the physical examination data classification result, the first physical examination data validity coefficient W1, and the second physical examination data validity coefficient W2 to obtain the physical examination priority determination result of the target user; The target user's physical examination priority result is sent to the user terminal.

2. The data management method based on the health examination machine according to claim 1 is characterized in that: The steps of obtaining basic information of a target user and creating a target user basic information sub-dataset, obtaining historical information of a target user and creating a target user historical information sub-dataset, obtaining multiple physical examination data of a target user using a health examination machine for a physical examination and creating a target user physical examination sub-dataset, and judging whether there is an error according to the target user historical information sub-dataset, and if there is an error, re-detecting and updating the target user physical examination sub-dataset specifically include: Acquiring basic information of a target user, wherein the basic information of the target user includes age information of the target user, gender information of the target user, and occupation information of the target user, and constructing a sub-dataset of basic information of the target user according to the age information of the target user, the gender information of the target user, and the occupation information of the target user; Acquire the target user's historical information, wherein the target user's historical information includes the target user's historical physical examination information and the target user's historical disease information, wherein the target user's historical disease information includes the target user's historical disease type information and the target user's historical disease severity information for each time the target user was ill, and the target user's historical physical examination information, the target user's historical disease type information, and the target user's historical disease severity information constitute a target user's historical information sub-dataset; Obtaining the physical examination data of the target user when undergoing a physical examination at the health examination machine and summarizing and processing it to obtain a target user physical examination sub-dataset, wherein the target user physical examination sub-dataset includes the target user's basic physiological parameter information, the target user's body composition distribution information, the target user's electrocardiogram information, and the target user's biochemical index information; The target user's historical physical examination information includes historical basic physiological parameter information, historical human body composition distribution information, historical electrocardiogram information, and historical biochemical index information.

3. The data management method based on the health examination machine according to claim 2 is characterized in that: The steps of obtaining basic information of a target user and creating a target user basic information sub-dataset, obtaining historical information of a target user and creating a target user historical information sub-dataset, obtaining a plurality of physical examination data of a target user using a health examination machine for a physical examination and creating a target user physical examination sub-dataset, and judging whether there is an error according to the target user historical information sub-dataset, and if there is an error, re-detecting and updating the target user physical examination sub-dataset, further include: Compare the target user's basic physiological parameter information with the historical basic physiological parameter information to obtain a basic physiological parameter comparison result, compare the target user's body composition distribution information with the historical body composition distribution information to obtain a body composition distribution comparison result, compare the target user's electrocardiogram information with the historical electrocardiogram information to obtain an electrocardiogram comparison result, and compare the target user's biochemical index information with the historical biochemical index information to obtain a biochemical index comparison result; Based on the basic physiological parameter comparison result, determine whether the basic physiological parameter information of the target user is consistent with the historical basic physiological parameter information. If not, determine whether the target user's operation is standardized when performing the basic physiological parameter detection. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-detect the target user's basic physiological parameters, and update the target user's basic physiological parameter information; Based on the comparison result of the human body composition distribution, determine whether the human body composition distribution information of the target user is consistent with the historical human body composition distribution information. If not, determine whether the target user's operation is standardized when performing the human body composition distribution detection. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-detect the target user's human body composition distribution, and update the target user's human body composition distribution information. Based on the ECG comparison result, determine whether the target user's ECG information is consistent with the historical ECG information. If not, determine whether the target user's operation is standardized during the ECG test. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, retest the target user's ECG, and update the target user's ECG information. Based on the biochemical index comparison results, determine whether the target user's biochemical index information is consistent with the historical biochemical index information. If not, determine whether the target user's operation is standardized when performing the biochemical index test. If the target user's operation is not standardized, adjust the target user's operation until the target user's operation is standardized, re-test the target user's biochemical index, and update the target user's biochemical index information; Based on the updated basic physiological parameter information of the target user, the updated human body composition distribution information of the target user, the updated electrocardiogram information of the target user, and the updated biochemical index information of the target user, the target user physical examination sub-dataset is updated; The target user physical examination sub-dataset, the target user basic information sub-dataset and the target user historical information sub-dataset are cached.

4. The data management method based on the health examination machine according to claim 3 is characterized in that: According to the target user's physical examination sub-data set, each physical examination data is compared with a preset physical examination data standard range, the abnormality of each physical examination data is determined, and then the validity of each physical examination data itself is analyzed to obtain the first physical examination data validity coefficient W1, which specifically includes: Pre-set the standard range of each physical examination data and perform cache processing; Match each physical examination data in the physical examination sub-data set of the target user with the preset physical examination data standard, and determine whether the physical examination data in the physical examination sub-data set of the target user is within the range of the physical examination data standard; If the physical examination data is within the physical examination data standard range, the first physical examination data validity coefficient W1 of the physical examination data is 0; If the physical examination data is not within the physical examination data standard range, determine whether the physical examination data is above or below the physical examination data standard range; If the physical examination data is higher than the physical examination data standard range, the difference between the physical examination data and the maximum value in the physical examination data standard range is calculated to obtain a high-order physical examination abnormal difference, and a first physical examination data effective coefficient W1 of the physical examination data is obtained based on the high-order physical examination abnormal difference; If the physical examination data is lower than the physical examination data standard range, the difference between the physical examination data and the small value in the physical examination data standard range is calculated to obtain a low-order physical examination abnormal difference, and the first physical examination data effective coefficient W1 of the physical examination data is obtained based on the low-order physical examination abnormal difference; The first physical examination data validity coefficient W1 of each physical examination data in the target user physical examination sub-data set is aggregated and stored.

5. The data management method based on the health examination machine according to claim 4 is characterized in that: The step of judging the validity of the physical examination data of the target user in the physical examination sub-dataset of the target user according to the basic information sub-dataset of the target user and the historical information sub-dataset of the target user to obtain the second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-dataset of the target user specifically includes: Acquire a user disease information database, wherein the user disease information database includes age disease types of users of different ages and the age disease probability of users of each type of disease, gender disease types of users of different genders and the gender disease probability of users of each type of disease, and occupational disease types of users of different occupations and the occupational disease probability of users of each type of disease; Match the target user's basic information sub-dataset with the user's disease information database, and select the most common user age disease type of the target user according to the user's age disease probability to obtain the first basic information disease type; According to the disease probability of user gender, the most common disease type of the target user gender is selected to obtain the second basic information disease type; According to the probability of occupational diseases of users, the most common occupational disease types of target users are screened to obtain the third basic information disease type; The first basic information disease type, the second basic information disease type and the third basic information disease type are combined to form the target user's predicted disease type information.

6. The data management method based on the health examination machine according to claim 5 is characterized in that: The step of judging the validity of the physical examination data of the target user in the physical examination sub-dataset of the target user according to the basic information sub-dataset of the target user and the historical information sub-dataset of the target user to obtain the second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-dataset of the target user specifically includes: According to the target user's predicted disease type information, determine the effect of the target user's basic information on the validity of each physical examination data in the target user's physical examination sub-dataset to obtain the target user's basic information validity coefficient p of each physical examination data; According to the target user's historical disease type information in the target user's historical information sub-dataset, determine the impact of the target user's historical disease type on the validity of each physical examination data in the target user's physical examination sub-dataset to obtain the historical disease type validity coefficient q of each physical examination data; Determine the target user's predicted disease type value based on the target user's predicted disease type information, and adjust the first proportional factor based on the target user's predicted disease type value. The more the target user predicts the disease type, the greater the first proportional factor The bigger; Determine the target user's historical disease type value based on the target user's historical disease type information, and adjust the second proportional factor based on the target user's historical disease type value. The second proportional factor is determined, where the more the target user's historical disease type value is, the greater the second proportional factor is. The bigger; According to the target user's basic information effective coefficient p, historical disease type effective coefficient q, and the first proportional factor of each physical examination data in the target user's physical examination sub-dataset and the second scaling factor , based on data validity relationship function Calculation is performed to obtain the physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-data set of the target user.

7. The data management method based on the health examination machine according to claim 6 is characterized in that: The step of dividing each physical examination data in the target user physical examination sub-data set to obtain a physical examination data classification result based on the first physical examination data validity coefficient W1 and the second physical examination data validity coefficient W2 of each physical examination data in the target user physical examination sub-data set specifically includes: Create a valid physical examination data set for the target user, perform a first screening on the physical examination data whose first physical examination data validity coefficient W1 is not 0 in the physical examination sub-data set for the target user, and store them in the valid physical examination data set for the target user; Create a relatively invalid data set for the physical examination of the target user, compare the second physical examination data validity coefficient W2 of each physical examination data after the first screening of the physical examination sub-data set of the target user with the preset physical examination data side validity coefficient threshold, and perform a second screening on the physical examination data whose second physical examination data validity coefficient W2 after the first screening of the physical examination sub-data set of the target user is greater than the preset physical examination data side validity coefficient threshold and store it in the relatively invalid data set for the physical examination of the target user; Create a target user's absolutely invalid physical examination data set, and aggregate and store each physical examination data after the second screening of the target user's physical examination sub-data set into the target user's absolutely invalid physical examination data set; The target user physical examination valid data set, the target user physical examination relatively invalid data set and the target user physical examination absolutely invalid data set are combined to form a physical examination data classification result.

8. The data management method based on the health examination machine according to claim 7 is characterized in that: The steps of processing the multiple physical examination data of the target user according to the physical examination data classification result, and determining the priority of the physical examination items of each user to be tested according to the physical examination data classification result, the first physical examination data validity coefficient W1, and the second physical examination data validity coefficient W2 to obtain the physical examination priority determination result of the target user specifically include: Delete the data in the absolutely invalid data set of the target user's physical examination; Preliminarily determining the priority of the physical examination items of the target user according to the target user physical examination valid data set and the target user physical examination relatively invalid data set to obtain a first priority determination result, wherein the priority of the physical examination items corresponding to each physical examination data in the target user physical examination valid data set is higher than the priority of the physical examination items corresponding to each physical examination data in the target user physical examination relatively invalid data set; The priority of the physical examination item corresponding to each physical examination data in the physical examination valid data set of the target user is determined according to the first physical examination data validity coefficient W1 to obtain a second priority determination result, wherein the larger the first physical examination data validity coefficient W1 of the physical examination data is, the higher the priority of the physical examination item corresponding to the physical examination data is; The priority of each physical examination data corresponding to the physical examination item in the physical examination relative invalid data set of the target user is determined according to the second physical examination data validity coefficient W2 to obtain a third priority determination result, wherein the larger the second physical examination data validity coefficient W2 of the physical examination data is, the higher the priority of the physical examination item corresponding to the physical examination data is; The first priority determination result, the second priority determination result, and the third priority determination result are combined to form a target user physical examination priority determination result.

9. The data management method based on the health examination machine according to claim 8, characterized in that: The step of sending the target user physical examination priority result to the user terminal specifically includes: Acquiring a wireless communication module (6) and establishing a signal connection link between the wireless communication module (6) and a health examination machine; Obtain the priority determination result of the target user's physical examination and cache it; Based on the wireless communication module (6), the result of the target user's physical examination priority determination is sent to the user terminal.

10. Data management related equipment based on health examination machine, characterized in that: The data management related equipment based on the health examination machine is used to implement the data management method based on the health examination machine described in any one of claims 1 to 9, including: The user information acquisition module (1) is configured to acquire basic information of a target user and create a target user basic information sub-dataset, acquire historical information of a target user and create a target user historical information sub-dataset, acquire multiple physical examination data of a target user using a health examination machine to perform a physical examination and create a target user physical examination sub-dataset, and determine whether there is an error based on the target user historical information sub-dataset, and if there is an error, re-detect and update the target user physical examination sub-dataset; The data validity analysis module (2) is configured to compare each physical examination data with a preset physical examination data standard range according to the physical examination sub-data set of the target user, determine the abnormality of each physical examination data, and then analyze the validity of each physical examination data itself to obtain a first physical examination data validity coefficient W1; A data validity side analysis module (3) is configured to judge the validity of the physical examination data of the target user in the physical examination sub-dataset of the target user based on the basic information sub-dataset of the target user and the historical information sub-dataset of the target user, and obtain a second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-dataset of the target user; A physical examination data classification module (4) is configured to classify each physical examination data in the physical examination sub-data set of the target user based on a first physical examination data validity coefficient W1 and a second physical examination data validity coefficient W2 of each physical examination data in the physical examination sub-data set of the target user to obtain a physical examination data classification result; A physical examination item priority analysis module (5) is configured to process multiple physical examination data of a target user according to the physical examination data classification result, and to determine the priority of the physical examination items of each user to be tested according to the physical examination data classification result, the first physical examination data validity coefficient W1, and the second physical examination data validity coefficient W2 to obtain a physical examination priority determination result of the target user; The wireless communication module (6) is configured to send the target user's physical examination priority result to the user terminal.

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