Whole-process tracking management method and system for medical test sample

Abstract

The invention is suitable for the technical field of medical sample traceability, and particularly relates to a full-process tracking management method and system for medical test samples, and the method comprises the steps: constructing a sample code sharing database; obtaining medical examination category data, and constructing a standardized examination category database based on the medical examination category data; obtaining medical sample inspection data, performing normalization processing on the medical sample inspection data based on the standardized inspection category database, and constructing normalized coordinates; and determining the number of layers of the hierarchical fitting function according to the number of the standardized inspection category databases, performing hierarchical fitting based on the number of layers to obtain a plurality of inspection data fitting function groups, and storing the inspection data fitting function groups. According to the method, distributed storage of the medical sample inspection data is realized, the security of the medical sample inspection data is improved, the data security is ensured, the circulation path of the medical sample can be determined according to the traceability fitting function, and the user privacy is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical sample traceability, and in particular relates to a full-process tracking management method and system for medical test samples. Background Art

[0002] Medical sample traceability refers to the use of information technology to comprehensively track and record the collection, processing, storage, and transportation of various biological samples generated during medical procedures, such as blood and tissue sections. Its purpose is to ensure that every step of the sample, from collection to final use, is traceable, thereby ensuring its accuracy and safety. This also helps improve the quality and efficiency of medical services, supporting clinical diagnosis, treatment, and medical research. The application of modern technologies such as barcoding, radio frequency identification (RFID), and blockchain can effectively enhance the transparency and reliability of sample management, reduce human error, protect patient privacy, and comply with relevant laws and regulations.

[0003] Current traceability management systems are usually independent, and data cannot be shared between different traceability management systems. When medical samples flow between different entities, it is easy to cause the traceability chain to be disconnected and the traceability link to be fragmented. Summary of the Invention

[0004] The purpose of the present invention is to provide a full-process tracking and management method for medical test samples, aiming to solve the problem that current traceability management systems are usually independent, and data cannot be shared between different traceability management systems. When medical samples flow between different entities, it is easy to cause the traceability chain to be disconnected, resulting in the fragmentation of the traceability link.

[0005] The present invention is implemented as follows: a full-process tracking and management method for medical test samples, the method comprising:

[0006] Building a sample code sharing database, wherein the sample code sharing database is used to store unique codes of medical samples and basic information of samples;

[0007] Acquire medical examination category data, and build a standardized examination category database based on the medical examination category data, wherein the standardized examination category database is used to store numbers of different examination categories and normalized parameter ranges of corresponding examination categories;

[0008] Obtain medical sample test data, normalize the medical sample test data based on the standardized test category database, and construct normalized coordinates;

[0009] The number of layers of the hierarchical fitting function is determined according to the number of standardized inspection category databases, and hierarchical fitting is performed based on the number of layers to obtain multiple inspection data fitting function groups, which are stored. Each inspection data fitting function group contains a traceability fitting function and a data fitting function.

[0010] Preferably, the step of obtaining medical examination category data and building a standardized examination category database based on the medical examination category data includes:

[0011] Obtain medical examination category data, conduct statistics on medical examination categories, and determine their total number;

[0012] Set a number for each medical examination category, and the numbers of medical examination categories are generated continuously;

[0013] Obtain the standard parameter range of each medical examination category, normalize the standard parameter range to obtain normalized parameters, store the normalized parameters, and obtain a standardized examination category database.

[0014] Preferably, the step of obtaining medical sample test data, normalizing the medical sample test data based on a standardized test category database, and constructing normalized coordinates specifically includes:

[0015] Obtain medical sample test data, query the standardized test category database, and extract the normalization parameters corresponding to the medical sample test data;

[0016] Processing the medical sample test data according to the normalization parameters, completing the normalization process, converting it into a range corresponding to the normalization parameters, and obtaining normalized test data;

[0017] Record the batch number and medical test category number of each normalized test data, and generate normalized coordinates. The normalized coordinates contain three sets of coordinate values, namely batch coordinates, test category number coordinates, and normalized data coordinates.

[0018] Preferably, the step of determining the number of layers of the hierarchical fitting function according to the number of standardized test category databases, performing hierarchical fitting based on the number of layers, obtaining multiple test data fitting function groups, and storing the groups includes:

[0019] Statistics: Determine the number of layers of the hierarchical fitting function according to the number of standardized inspection category databases, retrieve the normalized coordinates and decompose them into traceability data coordinates and inspection data coordinates;

[0020] Function fitting is performed based on the coordinates of the traceability data and the coordinates of the inspection data to obtain a set of traceability fitting functions and data fitting functions respectively;

[0021] According to the fitting results and normalized coordinates, the traceability fitting difference coordinates and the inspection data difference coordinates are constructed, and the fitting is repeated multiple times to obtain multiple sets of traceability fitting functions and data fitting functions. Each standardized inspection category database is assigned a traceability fitting function and a data fitting function to form an inspection data fitting function group, which is stored.

[0022] Preferably, a QR code is provided on the medical sample, and the QR code is bound to the unique code of the medical sample.

[0023] Another object of the present invention is to provide a full-process tracking and management system for medical test samples, the system comprising:

[0024] A shared database construction module is used to construct a sample code shared database, wherein the sample code shared database is used to store the unique code of the medical sample and the basic information of the sample;

[0025] A test category database construction module is used to obtain medical test category data and construct a standardized test category database based on the medical test category data. The standardized test category database is used to store the numbers of different test categories and the normalized parameter ranges of the corresponding test categories;

[0026] The data normalization module is used to obtain medical sample test data, normalize the medical sample test data based on the standardized test category database, and construct normalized coordinates;

[0027] The traceability function construction module is used to determine the number of layers of the hierarchical fitting function according to the number of standardized inspection category databases, perform hierarchical fitting based on the number of layers, obtain multiple inspection data fitting function groups, and store them. Each inspection data fitting function group contains a traceability fitting function and a data fitting function.

[0028] Preferably, the inspection category database construction module includes:

[0029] Category statistics unit, used to obtain medical examination category data, count the medical examination categories, and determine their total number;

[0030] A number generation unit is used to set a number for each medical examination category. The numbers of the medical examination categories are generated continuously.

[0031] The standard normalization unit is used to obtain the standard parameter range of each medical examination category, normalize the standard parameter range to obtain normalized parameters, store the normalized parameters, and obtain a standardized examination category database.

[0032] Preferably, the data normalization module includes:

[0033] A parameter extraction unit is used to obtain medical sample test data, query the standardized test category database, and extract normalized parameters corresponding to the medical sample test data;

[0034] A normalization processing unit is used to process the medical sample test data according to the normalization parameters, complete the normalization processing, convert it into a range corresponding to the normalization parameters, and obtain normalized test data;

[0035] The normalized coordinate generation unit is used to record the batch number and medical examination category number of each normalized test data and generate normalized coordinates. The normalized coordinates contain three sets of coordinate values, namely batch coordinates, examination category number coordinates and normalized data coordinates.

[0036] Preferably, the traceability function building module includes:

[0037] A hierarchical layer number statistics unit is used to determine the number of layers of the hierarchical fitting function according to the number of standardized inspection category databases, and to retrieve the normalized coordinates and decompose them into traceability data coordinates and inspection data coordinates;

[0038] A basic fitting unit is used to perform function fitting based on the traceability data coordinates and the inspection data coordinates, respectively, to obtain a set of traceability fitting functions and data fitting functions;

[0039] The repeated fitting unit is used to construct the traceability fitting difference coordinates and the inspection data difference coordinates based on the fitting results and the normalized coordinates. The fitting is repeated multiple times to obtain multiple sets of traceability fitting functions and data fitting functions. Each standardized inspection category database is assigned a traceability fitting function and a data fitting function to form an inspection data fitting function group, which is stored.

[0040] Preferably, a QR code is provided on the medical sample, and the QR code is bound to the unique code of the medical sample.

[0041] The full-process tracking and management method for medical test samples provided by the present invention can unify the test results of different medical units by normalizing the medical test category data, and construct multiple sets of traceability fitting functions and data fitting functions based on the unified test results, thereby realizing distributed storage of medical sample test data, improving the security of medical sample test data, ensuring data security, and being able to determine the circulation path of the medical sample according to the traceability fitting function, and restore the test results corresponding to each circulation node according to the data fitting function, thereby ensuring user privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of a full-process tracking and management method for medical test samples provided by an embodiment of the present invention;

[0043] Figure 2 A flowchart of the steps of obtaining medical examination category data and building a standardized examination category database based on the medical examination category data provided by an embodiment of the present invention;

[0044] Figure 3 A flowchart of the steps of obtaining medical sample test data, normalizing the medical sample test data based on a standardized test category database, and constructing normalized coordinates provided by an embodiment of the present invention;

[0045] Figure 4 A flowchart of the steps of determining the number of layers of a hierarchical fitting function based on the number of standardized test category databases, performing hierarchical fitting based on the number of layers, obtaining multiple test data fitting function groups, and storing the groups, provided by an embodiment of the present invention;

[0046] Figure 5 This is an architectural diagram of a full-process tracking and management system for medical test samples provided by an embodiment of the present invention;

[0047] Figure 6 This is an architectural diagram of the inspection category database construction module provided by an embodiment of the present invention;

[0048] Figure 7 This is an architecture diagram of the data normalization module provided in an embodiment of the present invention;

[0049] Figure 8 This is an architectural diagram of the traceability function building module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] like Figure 1 FIG. 1 is a flowchart of a full-process tracking and management method for medical test samples provided by an embodiment of the present invention, the method comprising:

[0052] S100: Build a sample code sharing database, where the sample code sharing database is used to store unique codes of medical samples and basic sample information.

[0053] In this step, a sample code sharing database is constructed. During the flow of medical test samples, they will pass through multiple different medical entities (medical units) and undergo different tests in different medical entities to produce different results. The data between different medical entities are not interoperable. A sample code sharing database is constructed between medical entities. When a medical test sample involves multiple medical entities, the unique code of the medical test sample is imported into the sample code sharing database. The unique code is bound to the QR code of the medical test sample. The sample basic information is used to record relevant information of the medical test sample, such as the source of the sample, the collection method, the storage method, the content information of the sample, etc.

[0054] S200 , obtaining medical examination category data, and constructing a standardized examination category database based on the medical examination category data, wherein the standardized examination category database is used to store numbers of different examination categories and normalized parameter ranges of corresponding examination categories.

[0055] In this step, medical examination category data is obtained. Each medical entity has its own internal medical examination category, such as different test items. Each test item corresponds to a medical examination category. For example, the blood test department includes the red blood cell count test and the hemoglobin count test in the blood. Each test has a corresponding detection range, such as the platelet count 0-1000*10 9 / L, each inspection item is a medical inspection category, and the inspection range of each medical inspection category is normalized, and the inspection ranges of all medical inspection categories are normalized to the same range interval, such as 0-1, that is, the results of each medical inspection category are represented by a number between 0 and 1 to achieve normalization. In the standardized inspection category database, the name, number, initial inspection range and normalized inspection range of each medical inspection category are stored. The numbers of the medical inspection categories in the standardized inspection category database corresponding to different medical subjects are not repeated and are numbered continuously.

[0056] S300 , obtaining medical sample test data, normalizing the medical sample test data based on a standardized test category database, and constructing normalized coordinates.

[0057] In this step, medical sample inspection data is obtained. The medical sample inspection data is the inspection results of the medical inspection sample conducted in different medical subjects. For example, three tests are conducted on medical subject A and two tests are conducted on medical subject B. That is, the medical sample inspection data includes the inspection results conducted on all medical subjects. The medical sample inspection data is divided. Since different medical subjects have different standards for the same inspection items, the same inspection items corresponding to different medical subjects are regarded as two independent inspection items. When normalizing, the corresponding standardized inspection category database is queried according to the medical subject corresponding to the medical sample inspection data, and the relevant information of the corresponding medical inspection category is retrieved therefrom. The normalization processing of the medical sample inspection data is completed according to the standardized inspection category database. According to the normalization processing result, the normalized coordinates are constructed, and the batch inspected, the inspection category number and the corresponding normalized data are recorded in the normalized coordinates.

[0058] S400, determine the number of layers of the hierarchical fitting function according to the number of standardized inspection category databases, perform hierarchical fitting based on the number of layers, obtain multiple inspection data fitting function groups, and store them. Each inspection data fitting function group includes a traceability fitting function and a data fitting function.

[0059] In this step, the number of layers of the hierarchical fitting function is determined according to the number of standardized test category databases. The number of standardized test category databases is the number of layers of the hierarchical fitting function. A traceability fitting function and a data fitting function are constructed for the medical sample test data, wherein the traceability fitting function is used to determine the flow path of the medical sample, namely, path tracing, and the data fitting function is used to restore the test results of each test, namely, result tracing. When fitting, the normalized coordinates are split into two parts, one part is used to fit to obtain the traceability fitting function, and the other part is used to fit to obtain the data fitting function. When fitting, multiple fittings are performed according to the set accuracy, and each fitting obtains a set of fitting functions, namely, the traceability fitting function and the data fitting function. After a single fitting, the fitting error is calculated, and fitting is performed again according to the fitting error to obtain a new round of traceability fitting functions and data fitting functions. According to the number of layers of the determined hierarchical fitting function, the corresponding number of fittings is performed to obtain a corresponding number of traceability fitting functions and data fitting functions. A traceability fitting function and a data fitting function are assigned to each medical subject, and the combination obtains a test data fitting function group, and each medical subject stores the test data fitting function group separately.

[0060] In an embodiment of the present invention, since each medical subject stores a test data fitting function group, when tracing the source, the corresponding medical subject is required to provide the corresponding test data fitting function group in order to obtain all traceability fitting functions and data fitting functions. By importing the test batch to output the items tested each time and the corresponding test results, traceability is achieved, and the medical subjects involved all have independent traceability fitting functions and data fitting functions, which can avoid data leakage or unauthorized data sharing, ensure data security, and the data is not stored in plain text, which further improves data security and protects the privacy of sample data.

[0061] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of obtaining medical examination category data and building a standardized examination category database based on the medical examination category data includes:

[0062] S201, obtaining medical examination category data, counting the medical examination categories, and determining their total number.

[0063] In this step, medical examination category data is obtained. For each medical subject, all medical examination category data of the medical subject are obtained. Each medical examination category is an examination item, and the total number of medical examination categories included in all medical subjects is counted.

[0064] S202: Set a number for each medical examination category. The numbers of the medical examination categories are generated continuously.

[0065] In this step, a number is set for each medical examination category. When numbering, continuous numbering is performed. Specifically, natural numbers are used as numbers to randomly sort the medical examination categories. After sorting, a number is assigned to each medical examination category in sequence.

[0066] S203, obtaining a standard parameter range for each medical examination category, normalizing the standard parameter range to obtain normalized parameters, storing the normalized parameters, and obtaining a standardized examination category database.

[0067] In this step, the standard parameter range of each medical examination category is obtained. The standard parameter range is the maximum detection range of the examination item. The maximum detection range includes not only the normal reference range, but also the abnormal range, that is, the lower limit and upper limit of the examination item. By normalization processing, they are integrated into the same range, such as between 0 and 1. The original maximum detection range and the corresponding normalization parameters of each medical examination category are stored to construct a standardized examination category database.

[0068] like Figure 3As shown, as a preferred embodiment of the present invention, the steps of obtaining medical sample test data, normalizing the medical sample test data based on a standardized test category database, and constructing normalized coordinates specifically include:

[0069] S301, obtaining medical sample test data, querying a standardized test category database, and extracting normalization parameters corresponding to the medical sample test data.

[0070] S302 , processing the medical sample test data according to the normalization parameters, completing the normalization processing, converting it into a range corresponding to the normalization parameters, and obtaining normalized test data.

[0071] In this step, medical sample inspection data is obtained. The medical sample inspection data at least includes the inspection items that have been performed and the corresponding inspection result data. The corresponding standardized inspection category database is retrieved according to the different inspection items performed on the medical sample. According to the maximum detection range and corresponding normalization parameters recorded in the standardized inspection category database, each inspection result is converted into normalized inspection data, that is, it is also converted into the 0-1 range.

[0072] S303, recording the batch and medical examination category number of each normalized test data, and generating normalized coordinates. The normalized coordinates include three sets of coordinate values, namely batch coordinates, examination category number coordinates, and normalized data coordinates.

[0073] In this step, the batch number and medical test category number of each normalized test data are recorded. The normalized coordinates include X-coordinate, Y-coordinate and Z-coordinate. The X-coordinate is used to record the batch of the current test item. For example, for a medical sample, the first test is the first batch, batch 1, and the nth test is n. The Y-coordinate is used to record the test category number coordinate, the number of the medical test category corresponding to the nth test, and the Z-coordinate is used to record the normalized test data of the test.

[0074] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of determining the number of layers of the hierarchical fitting function according to the number of standardized test category databases, performing hierarchical fitting based on the number of layers, obtaining multiple test data fitting function groups, and storing the groups includes:

[0075] S401, statistically determining the number of layers of the hierarchical fitting function according to the number of standardized inspection category databases, retrieving normalized coordinates and decomposing them into traceability data coordinates and inspection data coordinates.

[0076] In this step, the number of layers of the hierarchical fitting function is determined based on the number of standardized test category databases. Each medical subject corresponds to a standardized test category database, that is, the number of layers of the hierarchical fitting function is determined according to the number of medical subjects, thereby ensuring that each medical subject can be assigned a traceability fitting function and a data fitting function. Before fitting, the X coordinate and Y coordinate of the normalized coordinate are extracted as the traceability data coordinates, where the X coordinate records the batch of inspection, and the Y coordinate records the number of the corresponding medical inspection category. According to the number of the medical inspection category, the corresponding medical subject and the inspection item to be performed can be determined, and the X coordinate and Z coordinate of the normalized coordinate are extracted as the inspection data coordinates, where the Z coordinate records the normalized inspection data of the batch inspection, and the value is between 0-1.

[0077] S402 , performing function fitting based on the traceability data coordinates and the inspection data coordinates, respectively, to obtain a set of traceability fitting functions and data fitting functions.

[0078] S403, construct the traceability fitting difference coordinates and the inspection data difference coordinates according to the fitting results and the normalized coordinates, repeat the fitting multiple times, and obtain multiple groups of traceability fitting functions and data fitting functions. Each standardized inspection category database is assigned a traceability fitting function and a data fitting function to form an inspection data fitting function group, which is stored.

[0079] In this step, function fitting is performed based on the traceability data coordinates and the inspection data coordinates. Taking the traceability data coordinates as an example, the corresponding traceability data coordinates are retrieved in turn according to the inspection batch, and data fitting tools such as matlab data processing software are used for fitting to obtain the traceability fitting function corresponding to the traceability data coordinates. When fitting, the corresponding fitting accuracy is set. In the process of multiple fitting, if there are 5 medical subjects, 5 fitting accuracy are set and used in five fitting processes respectively. The fitting accuracy gradually increases. When fitting is performed for the first time, the lowest fitting accuracy is used. After the fitting is completed, the first set of traceability fitting functions is obtained. Subsequently, the fitting error of each fitting point is calculated according to the traceability data coordinates and the traceability fitting function. The inspection batch is imported into the traceability fitting function, and the calculated value is subtracted from the Y coordinate in the traceability data coordinates. Mark, get the difference, and use the batch as the horizontal coordinate and the difference as the vertical coordinate to construct a new round of traceability data coordinates, then use the second fitting accuracy to fit the new round of traceability data coordinates to get the first set of traceability fitting functions, repeat this step to get five sets of traceability fitting functions, similarly, five sets of data fitting functions will be obtained, and each medical subject will be assigned a traceability fitting function and data fitting function. The medical subject will determine whether to disclose the assigned traceability fitting function and data fitting function based on its own data needs. If disclosed, then when the medical sample inspection data needs to be extracted later, there is no need to separately seek the consent of the medical subject. If not disclosed, then the consent of the medical subject will be required later, and the correspondence between the unique code of the medical sample and each medical subject will be recorded in the sample code sharing database.

[0080] When it is necessary to extract medical sample inspection data, the unique code of the medical sample is queried according to the sample code sharing database, and the traceability fitting function and data fitting function are requested from the corresponding medical entity according to the unique code. After obtaining all the traceability fitting functions and data fitting functions, all the obtained traceability fitting functions are superimposed, and all the data fitting functions are superimposed to obtain the traceability fitting composite function and the data fitting composite function. According to the number of inspection batches of the medical sample, the inspection batch numbers are imported in sequence, and the Y coordinate of the normalized coordinate can be output through the traceability fitting composite function, and the Z coordinate of the normalized coordinate can be output through the data fitting function. The medical inspection category performed for the batch inspection is determined according to the value of the Y coordinate, and the corresponding standardized inspection category database is queried according to the Z coordinate to obtain the corresponding inspection result.

[0081] like Figure 5 FIG. 1 is a full-process tracking and management system for medical test samples provided by an embodiment of the present invention, the system comprising:

[0082] The shared database construction module 100 is used to construct a sample code shared database, which is used to store the unique codes of medical samples and basic sample information.

[0083] In this system, the shared database construction module 100 constructs a sample code sharing database. During the flow of medical test samples, they will pass through multiple different medical entities (medical units) and undergo different tests in different medical entities to produce different results. The data between different medical entities are not interoperable. A sample code sharing database is constructed between medical entities. When a medical test sample involves multiple medical entities, the unique code of the medical test sample is imported into the sample code sharing database. The unique code is bound to the QR code of the medical test sample. The sample basic information is used to record relevant information of the medical test sample, such as the source of the sample, the collection method, the storage method, the content information of the sample, etc.

[0084] The test category database construction module 200 is used to obtain medical test category data and construct a standardized test category database based on the medical test category data. The standardized test category database is used to store the numbers of different test categories and the normalized parameter ranges of the corresponding test categories.

[0085] In this system, the test category database construction module 200 obtains medical test category data. Each medical subject has its own internal medical test category, such as different test items. Each test item corresponds to a medical test category. For example, the blood test department includes the red blood cell count test and the hemoglobin count test in the blood. Each test corresponds to a corresponding detection range, such as the platelet count 0-1000*10 9 / L, each inspection item is a medical inspection category, and the inspection range of each medical inspection category is normalized, and the inspection ranges of all medical inspection categories are normalized to the same range interval, such as 0-1, that is, the results of each medical inspection category are represented by a number between 0 and 1 to achieve normalization. In the standardized inspection category database, the name, number, initial inspection range and normalized inspection range of each medical inspection category are stored. The numbers of the medical inspection categories in the standardized inspection category database corresponding to different medical subjects are not repeated and are numbered continuously.

[0086] The data normalization module 300 is used to obtain medical sample test data, normalize the medical sample test data based on a standardized test category database, and construct normalized coordinates.

[0087] In this system, the data normalization module 300 obtains medical sample inspection data, which is the inspection results of the medical inspection sample conducted in different medical subjects. For example, three tests are conducted on medical subject A and two tests are conducted on medical subject B. That is, the medical sample inspection data includes the inspection results conducted on all medical subjects. The medical sample inspection data is divided. Since different medical subjects have different standards for the same inspection items, the same inspection items corresponding to different medical subjects are regarded as two independent inspection items. When performing normalization processing, the corresponding standardized inspection category database is queried according to the medical subject corresponding to the medical sample inspection data, and the relevant information of the corresponding medical inspection category is retrieved therefrom. The normalization processing of the medical sample inspection data is completed according to the standardized inspection category database. According to the normalization processing results, normalized coordinates are constructed, and the batch of inspection, inspection category number and corresponding normalized data are recorded in the normalized coordinates.

[0088] The traceability function construction module 400 is used to determine the number of layers of the hierarchical fitting function according to the number of standardized inspection category databases, perform hierarchical fitting based on the number of layers, obtain multiple inspection data fitting function groups, and store them. Each inspection data fitting function group contains a traceability fitting function and a data fitting function.

[0089] In this system, the traceability function construction module 400 determines the number of layers of the hierarchical fitting function according to the number of standardized test category databases. The number of standardized test category databases is the number of layers of the hierarchical fitting function. The traceability fitting function and the data fitting function are constructed for the medical sample test data. The traceability fitting function is used to determine the flow path of the medical sample, that is, path tracing, and the data fitting function is used to restore the test results of each test, that is, result tracing. When fitting, the normalized coordinates are split into two parts, one part is used to fit the traceability fitting function, and the other part is used to fit the data fitting function. When fitting, multiple fittings are performed according to the set accuracy, and each fitting obtains a set of fitting functions, which are the traceability fitting function and the data fitting function. After a single fitting, the fitting error is calculated, and fitting is performed again according to the fitting error to obtain a new round of traceability fitting function and data fitting function. According to the number of layers of the layered fitting function, the corresponding number of fittings is performed to obtain the corresponding number of traceability fitting functions and data fitting functions. A traceability fitting function and a data fitting function are assigned to each medical subject, and the test data fitting function group is obtained by combining them. Each medical subject stores the test data fitting function group separately.

[0090] like Figure 6 As shown, as a preferred embodiment of the present invention, the inspection category database construction module 300 includes:

[0091] The category statistics unit 301 is used to obtain medical examination category data, perform statistics on the medical examination categories, and determine their total number.

[0092] In this module, the category statistics unit 301 obtains medical examination category data. For each medical subject, it obtains all medical examination category data of the medical subject. Each medical examination category is an examination item, and the total number of medical examination categories included in all medical subjects is counted.

[0093] The number generating unit 302 is used to set a number for each medical examination category. The numbers of the medical examination categories are generated continuously.

[0094] In this module, the numbering unit 302 sets a number for each medical examination category. When numbering, continuous numbering is performed. Specifically, natural numbers are used as numbers to randomly sort the medical examination categories. After sorting, each medical examination category is assigned a number in sequence.

[0095] The standard normalization unit 303 is used to obtain the standard parameter range of each medical examination category, normalize the standard parameter range to obtain normalized parameters, store the normalized parameters, and obtain a standardized examination category database.

[0096] In this module, the standard normalization unit 303 obtains the standard parameter range of each medical examination category. The standard parameter range is the maximum detection range of the examination item. The maximum detection range not only includes the normal reference range, but also includes the abnormal range, that is, the lower limit and upper limit of the examination item. Through normalization processing, it is integrated into the same range, such as between 0-1. The original maximum detection range and the corresponding normalization parameters of each medical examination category are stored to construct a standardized examination category database.

[0097] like Figure 7 As shown, as a preferred embodiment of the present invention, the data normalization module 300 includes:

[0098] The parameter extraction unit 301 is used to obtain medical sample test data, query the standardized test category database, and extract normalized parameters corresponding to the medical sample test data.

[0099] The normalization processing unit 302 is used to process the medical sample test data according to the normalization parameters, complete the normalization processing, convert it into the range corresponding to the normalization parameters, and obtain normalized test data.

[0100] In this module, the parameter extraction unit 301 obtains medical sample test data, which at least includes the test items that have been performed and the corresponding test result data. According to the different test items performed on the medical sample, the corresponding standardized test category database is retrieved, and each test result is converted into normalized test data according to the maximum detection range and corresponding normalization parameters recorded in the standardized test category database, that is, it is also converted into the 0-1 range.

[0101] The normalized coordinate generation unit 303 is used to record the batch and medical examination category number of each normalized test data and generate normalized coordinates. The normalized coordinates include three sets of coordinate values, namely batch coordinates, examination category number coordinates and normalized data coordinates.

[0102] In this module, the normalized coordinate generation unit 303 records the batch number and medical test category number of each normalized test data. The normalized coordinates include X-coordinate, Y-coordinate and Z-coordinate, where the X-coordinate is used to record the batch of the current test item. For example, for a medical sample, the first test is the first batch, batch 1, and the nth test is n. The Y-coordinate is used to record the test category number coordinate, the number of the medical test category corresponding to the nth test, and the Z-coordinate is used to record the normalized test data of the test.

[0103] like Figure 8 As shown, as a preferred embodiment of the present invention, the traceability function construction module 400 includes:

[0104] The hierarchical layer number counting unit 401 is used to count the number of layers of the hierarchical fitting function according to the number of the standardized inspection category database, and retrieve the normalized coordinates to decompose them into traceability data coordinates and inspection data coordinates.

[0105] In this module, the hierarchical layer number statistics unit 401 determines the number of layers of the hierarchical fitting function according to the number of standardized test category databases. Each medical subject corresponds to a standardized test category database, that is, the number of layers of the hierarchical fitting function is determined according to the number of medical subjects, thereby ensuring that each medical subject can be assigned a traceability fitting function and a data fitting function. Before fitting, the X coordinate and Y coordinate of the normalized coordinate are extracted as the traceability data coordinates, where the X coordinate records the batch of inspection and the Y coordinate records the number of the corresponding medical inspection category. According to the number of the medical inspection category, the corresponding medical subject and the inspection item to be performed can be determined, and the X coordinate and Z coordinate of the normalized coordinate are extracted as the inspection data coordinates, where the Z coordinate records the normalized inspection data of the batch inspection, and the value is between 0 and 1.

[0106] The basic fitting unit 402 is used to perform function fitting based on the traceability data coordinates and the inspection data coordinates, respectively, to obtain a set of traceability fitting functions and data fitting functions.

[0107] The repeated fitting unit 403 is used to construct the traceability fitting difference coordinates and the inspection data difference coordinates based on the fitting results and the normalized coordinates, and repeat the fitting multiple times to obtain multiple groups of traceability fitting functions and data fitting functions. Each standardized inspection category database is assigned a traceability fitting function and a data fitting function to form an inspection data fitting function group, which is stored.

[0108] In this module, the basic fitting unit 402 performs function fitting based on the traceability data coordinates and the inspection data coordinates. Taking the traceability data coordinates as an example, the corresponding traceability data coordinates are retrieved in sequence according to the inspection batch, and the data fitting tools, such as matlab data processing software, are used for fitting to obtain the traceability fitting function corresponding to the traceability data coordinates. When fitting, the corresponding fitting accuracy is set. In multiple fitting processes, if there are 5 medical subjects, 5 fitting accuracy are set and used in five fitting processes respectively. The fitting accuracy gradually increases. When fitting is performed for the first time, the lowest fitting accuracy is used. After the fitting is completed, the first set of traceability fitting functions is obtained. Subsequently, the fitting error of each fitting point is calculated according to the traceability data coordinates and the traceability fitting function. The inspection batch is imported into the traceability fitting function, and the calculated value is subtracted from the traceability data coordinates. The Y coordinate in the label is obtained, and the batch is used as the horizontal coordinate and the difference is used as the vertical coordinate to construct a new round of traceability data coordinates. Subsequently, the second fitting accuracy is used to fit the new round of traceability data coordinates to obtain the first set of traceability fitting functions. Repeat this step to obtain five sets of traceability fitting functions. Similarly, five sets of data fitting functions are also obtained. A traceability fitting function and a data fitting function are assigned to each medical subject. The medical subject determines whether to disclose the assigned traceability fitting function and data fitting function based on its own data needs. If disclosed, there is no need to separately seek the consent of the medical subject when the medical sample inspection data needs to be extracted later. If not disclosed, the consent of the medical subject will be required later, and the correspondence between the unique code of the medical sample and each medical subject will be recorded in the sample code sharing database.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A full-process tracking and management method for medical test samples, characterized in that: The method comprises: Building a sample code sharing database, wherein the sample code sharing database is used to store unique codes of medical samples and basic information of samples; Acquire medical examination category data, and build a standardized examination category database based on the medical examination category data, wherein the standardized examination category database is used to store numbers of different examination categories and normalized parameter ranges of corresponding examination categories; Obtain medical sample test data, normalize the medical sample test data based on the standardized test category database, and construct normalized coordinates; The number of layers of the hierarchical fitting function is determined according to the number of standardized inspection category databases, and hierarchical fitting is performed based on the number of layers to obtain multiple inspection data fitting function groups, which are stored. Each inspection data fitting function group contains a traceability fitting function and a data fitting function.

2. The full-process tracking and management method for medical test samples according to claim 1, characterized in that: The step of obtaining medical examination category data and building a standardized examination category database based on the medical examination category data includes: Obtain medical examination category data, conduct statistics on medical examination categories, and determine their total number; Set a number for each medical examination category, and the numbers of medical examination categories are generated continuously; Obtain the standard parameter range of each medical examination category, normalize the standard parameter range to obtain normalized parameters, store the normalized parameters, and obtain a standardized examination category database.

3. The full-process tracking and management method for medical test samples according to claim 2, characterized in that: The steps of obtaining medical sample test data, normalizing the medical sample test data based on a standardized test category database, and constructing normalized coordinates specifically include: Obtain medical sample test data, query the standardized test category database, and extract the normalization parameters corresponding to the medical sample test data; Processing the medical sample test data according to the normalization parameters, completing the normalization process, converting it into a range corresponding to the normalization parameters, and obtaining normalized test data; Record the batch number and medical test category number of each normalized test data, and generate normalized coordinates. The normalized coordinates contain three sets of coordinate values, namely batch coordinates, test category number coordinates, and normalized data coordinates.

4. The full-process tracking and management method for medical test samples according to claim 1, characterized in that: The step of determining the number of layers of the hierarchical fitting function according to the number of standardized test category databases, performing hierarchical fitting based on the number of layers, obtaining multiple test data fitting function groups, and storing the groups includes: Statistics: Determine the number of layers of the hierarchical fitting function according to the number of standardized inspection category databases, retrieve the normalized coordinates and decompose them into traceability data coordinates and inspection data coordinates; Function fitting is performed based on the coordinates of the traceability data and the coordinates of the inspection data to obtain a set of traceability fitting functions and data fitting functions respectively; According to the fitting results and normalized coordinates, the traceability fitting difference coordinates and the inspection data difference coordinates are constructed, and the fitting is repeated multiple times to obtain multiple sets of traceability fitting functions and data fitting functions. Each standardized inspection category database is assigned a traceability fitting function and a data fitting function to form an inspection data fitting function group, which is stored.

5. The full-process tracking and management method for medical test samples according to claim 1, characterized in that: The medical sample is provided with a QR code, which is bound to the unique code of the medical sample.

6. A full-process tracking and management system for medical test samples, characterized in that: The system comprises: A shared database construction module is used to construct a sample code shared database, wherein the sample code shared database is used to store the unique code of the medical sample and the basic information of the sample; A test category database construction module is used to obtain medical test category data and construct a standardized test category database based on the medical test category data. The standardized test category database is used to store the numbers of different test categories and the normalized parameter ranges of the corresponding test categories; The data normalization module is used to obtain medical sample test data, normalize the medical sample test data based on the standardized test category database, and construct normalized coordinates; The traceability function construction module is used to determine the number of layers of the hierarchical fitting function according to the number of standardized inspection category databases, perform hierarchical fitting based on the number of layers, obtain multiple inspection data fitting function groups, and store them. Each inspection data fitting function group contains a traceability fitting function and a data fitting function.

7. The full-process tracking and management system for medical test samples according to claim 6, characterized in that: The inspection category database construction module includes: Category statistics unit, used to obtain medical examination category data, count the medical examination categories, and determine their total number; A number generation unit is used to set a number for each medical examination category. The numbers of the medical examination categories are generated continuously. The standard normalization unit is used to obtain the standard parameter range of each medical examination category, normalize the standard parameter range to obtain normalized parameters, store the normalized parameters, and obtain a standardized examination category database.

8. The full-process tracking and management system for medical test samples according to claim 7, characterized in that: The data normalization module includes: A parameter extraction unit is used to obtain medical sample test data, query the standardized test category database, and extract normalized parameters corresponding to the medical sample test data; A normalization processing unit is used to process the medical sample test data according to the normalization parameters, complete the normalization processing, convert it into a range corresponding to the normalization parameters, and obtain normalized test data; The normalized coordinate generation unit is used to record the batch number and medical examination category number of each normalized test data and generate normalized coordinates. The normalized coordinates contain three sets of coordinate values, namely batch coordinates, examination category number coordinates and normalized data coordinates.

9. The full-process tracking and management system for medical test samples according to claim 6, characterized in that: The traceability function building module includes: A hierarchical layer number statistics unit is used to determine the number of layers of the hierarchical fitting function according to the number of standardized inspection category databases, and to retrieve the normalized coordinates and decompose them into traceability data coordinates and inspection data coordinates; A basic fitting unit is used to perform function fitting based on the traceability data coordinates and the inspection data coordinates, respectively, to obtain a set of traceability fitting functions and data fitting functions; The repeated fitting unit is used to construct the traceability fitting difference coordinates and the inspection data difference coordinates based on the fitting results and the normalized coordinates. The fitting is repeated multiple times to obtain multiple sets of traceability fitting functions and data fitting functions. Each standardized inspection category database is assigned a traceability fitting function and a data fitting function to form an inspection data fitting function group, which is stored.

10. The full-process tracking and management system for medical test samples according to claim 6, characterized in that: The medical sample is provided with a QR code, which is bound to the unique code of the medical sample.

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