Medical data traceability method and device, storage medium, and terminal equipment

Abstract

This disclosure relates to the field of computer technology, specifically to a method and apparatus, storage medium, and terminal device for medical data tracing. The medical data tracing method includes: acquiring information from several data tables; constructing a basic directed graph based on the table names and feature fields in the data table information; wherein the data table information corresponds to several data tables to be processed in a target database; performing data manipulation language logic parsing on the target database to determine the logical relationships between different feature fields; connecting different basic directed graphs based on the logical relationships to construct a target directed graph corresponding to the target database, so as to perform data tracing based on the target directed graph. This solution facilitates rapid investigation of data sources, enables automatic tracing of problematic data, and improves data tracing efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, specifically to a method and apparatus for tracing medical data, a storage medium, and a terminal device. Background Technology

[0002] A data lake generally refers to a repository or system that stores data in its raw format, storing data as is without prior structuring. A data lake can store structured, semi-structured, unstructured, and binary data. A data warehouse generally refers to a collection of data stores of a certain size, typically storing data from different sources and integrating source data to provide a unified view; these resources can include transaction systems, application log files, relational databases, etc.

[0003] Data lakes or data warehouses often contain massive amounts of data. Due to the diverse sources of this data and its management by different people, it is often difficult to identify the complete upstream and downstream relationships. In practice, when anomalies or problems occur in the generated data, manual analysis of SQL (Structured Query Language) is often required for tracing the source, resulting in significant time and labor costs.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a method, apparatus, storage medium, and terminal device for medical data tracing, thereby overcoming, to at least a certain extent, the limitations and defects of related technologies, enabling rapid investigation of data sources, automatic tracing of problematic data, and improving data tracing efficiency.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to a first aspect of this disclosure, a method for tracing medical data is provided, comprising:

[0008] Obtain information from several data tables, and construct a basic directed graph based on the table names and feature fields in the data table information; wherein, the data table information is the data table information corresponding to several data tables to be processed in the target database;

[0009] The target database is parsed using a data manipulation language logic to determine the logical relationships between different feature fields.

[0010] Based on the logical relationship, different basic directed graphs are connected to construct the target directed graph corresponding to the target database, so as to perform data tracing based on the target directed graph.

[0011] In one exemplary embodiment of this disclosure, the step of constructing a basic directed graph based on the table name and feature fields in the data table information includes:

[0012] The data table information of the data table to be processed is parsed to obtain the corresponding table name and feature fields;

[0013] Establish a first direction from the table name to the feature field based on the association between the table name and the feature field;

[0014] Based on the table name, feature fields, and the first direction, a basic directed graph corresponding to the data table to be processed is constructed; wherein, the table name corresponds to at least one of the feature fields.

[0015] In one exemplary embodiment of this disclosure, the step of performing data manipulation language logic parsing on the target database to determine the logical relationships between different feature fields includes:

[0016] The data manipulation language is analyzed to determine the logical relationships between different feature fields, and the source feature field, associated feature field, and second direction from the source feature field to the associated feature field are determined based on the logical relationships.

[0017] In one exemplary embodiment of this disclosure, connecting different underlying directed graphs based on the logical relationship to construct a target directed graph corresponding to the target database includes:

[0018] Based on the table name and feature fields corresponding to each of the basic directed graphs, and by connecting different feature fields in the second direction from the source feature field to the associated feature field, the target directed graph is constructed.

[0019] In one exemplary embodiment of this disclosure, the data tracing based on the target directed graph includes:

[0020] Obtain a data tracing task; wherein, the data tracing task includes a target field;

[0021] The target field is matched with the feature fields in the target directed graph, and the matching result is configured as the starting node of the target field in the target directed graph;

[0022] Based on the starting node, query the target directed graph in the reverse direction to determine the source node corresponding to the target field;

[0023] The tracing path is determined based on the starting node, the source node, and the intermediate path between the starting node and the source node.

[0024] In one exemplary embodiment of this disclosure, the method further includes:

[0025] Obtain a data analysis task, wherein the data analysis task includes the name of the table to be analyzed and / or the field to be analyzed;

[0026] The table name and / or field to be analyzed are matched with the target directed graph to determine the position of the table name and / or field to be analyzed in the target directed graph;

[0027] The source node corresponding to the field to be analyzed is identified by utilizing the directed relationship between feature fields;

[0028] Nodes to be processed are selected based on the source nodes, and preset operations are performed on the nodes to be processed.

[0029] In one exemplary embodiment of this disclosure, when determining the position of the table name to be analyzed and / or the field to be analyzed in the target directed graph, the method further includes:

[0030] In the target directed graph, the matching results and source nodes are marked for filtering the nodes to be processed based on the marking results; wherein the nodes to be processed include unmarked table names.

[0031] According to a second aspect of this disclosure, a medical data traceability device is provided, comprising:

[0032] The basic directed graph construction module is used to obtain information from several data tables and construct a basic directed graph based on the table names and feature fields in the data table information; wherein, the data table information is the data table information corresponding to several data tables to be processed in the target database;

[0033] The field logic relationship parsing module is used to perform data manipulation language logic parsing on the target database to determine the logical relationships between different feature fields.

[0034] The directed graph construction module is used to connect different basic directed graphs based on the logical relationship and construct a target directed graph corresponding to the target database, so as to perform data tracing based on the target directed graph.

[0035] According to a third aspect of this disclosure, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described medical data tracing method.

[0036] According to a fourth aspect of this disclosure, an electronic device is provided, comprising:

[0037] Processor; and

[0038] Memory for storing the executable instructions of the processor;

[0039] The processor is configured to execute the aforementioned medical data tracing method by executing the executable instructions.

[0040] In one embodiment of the medical data tracing method disclosed herein, a basic directed graph is constructed using table names and feature fields from data table information. Different basic directed graphs are then connected according to the logical relationships between different feature fields, and logical connection directions are configured between different feature fields to construct a complete target directed graph. This target directed graph allows for the storage of data table information in graph form. Furthermore, by configuring different feature fields and directed relationships between table names and feature fields in the target directed graph, reverse lookup can be performed during data tracing using the directed graph until the corresponding root node is found, enabling rapid identification of problem points.

[0041] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0043] Figure 1 The illustration shows a schematic diagram of a medical data tracing method according to an exemplary embodiment of the present disclosure;

[0044] Figure 2 The schematic diagram illustrates a system architecture in an exemplary embodiment of the present disclosure;

[0045] Figure 3 This illustration schematically depicts a directed graph in an exemplary embodiment of the present disclosure;

[0046] Figure 4 This illustration schematically depicts another medical data tracing method according to an exemplary embodiment of the present disclosure;

[0047] Figure 5 This illustration schematically shows a method for processing a medical data analysis task in an exemplary embodiment of the present disclosure;

[0048] Figure 6 This schematic diagram illustrates the composition of a medical data tracing device according to an exemplary embodiment of the present disclosure;

[0049] Figure 7 This schematic diagram illustrates the composition of a terminal device according to an exemplary embodiment of the present disclosure;

[0050] Figure 8 The schematic diagram illustrates the composition of a storage medium in an exemplary embodiment of the present disclosure. Detailed Implementation

[0051] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0052] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0053] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0054] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily need to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0055] In the medical field, the databases used to store medical data are often large and complex, with numerous data sources and complex relationships between data points, making it difficult to analyze the connections between them. When problems arise with medical data after it has been produced, a significant amount of time is required to locate the original data causing the problem.

[0056] To address the technical problems existing in the prior art, this example implementation first provides a medical data tracing method, which facilitates rapid tracing of problematic data. (Reference) Figure 1 As shown, the above-mentioned medical data traceability method may include the following steps:

[0057] Step S11: Obtain information from several data tables, and construct a basic directed graph based on the table names and feature fields in the data table information; wherein, the data table information is the data table information corresponding to several data tables to be processed in the target database;

[0058] Step S12: Perform data manipulation language logic parsing on the target database to determine the logical relationships between different feature fields;

[0059] Step S13: Connect the different basic directed graphs based on the logical relationship and construct the target directed graph corresponding to the target database, so as to trace the data source according to the target directed graph.

[0060] The medical data tracing method provided in this example implementation constructs a basic directed graph using table names and feature fields contained in the data table information. Then, it connects different basic directed graphs based on the logical relationships between different feature fields and configures logical connection directions between different feature fields, thereby constructing a complete target directed graph. This target directed graph allows for the storage of data table information in graph form. Furthermore, by configuring different feature fields and directed relationships between table names and feature fields in the target directed graph, data tracing can be performed using the directed graph. A reverse lookup can be performed until the corresponding root node is found, enabling rapid identification of problem points. This facilitates quick troubleshooting of problematic data sources, achieving automatic tracing of problematic data and improving data tracing efficiency.

[0061] The following will describe in more detail each step of the medical data tracing method in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0062] In this example implementation, refer to Figure 2 As shown, a system architecture is provided that can be used to execute and implement the aforementioned medical data traceability method. (Reference) Figure 2 As shown, the system architecture may include a terminal device 201, a network 202, and a server 203. The terminal device 201 may be one or more of a smartphone, tablet, desktop computer, or portable computer; the network 202 serves as the medium for providing a communication link between the terminal device and the server. The network 202 may include various connection types, such as wired communication links, wireless communication links, etc. It should be understood that... Figure 2The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be used. For example, server 202 could be a server cluster consisting of multiple servers providing database services, or it could be a cloud server, and so on. Specifically, the aforementioned medical data tracing method can be executed by the server side, or by a collaboration between the terminal devices and the server side. For instance, based on the above system architecture, a user can submit a data tracing request to the server side through terminal device 201; server 203 responds to the data tracing request, analyzes and performs calculations on the directed graph corresponding to the target database, and feeds back the data tracing results to terminal device 201.

[0063] In the aforementioned medical data traceability methods, a directed graph can be constructed first. For example, this can be created on the server side, and the task of constructing a directed graph for the target data warehouse can be executed. Specifically:

[0064] In step S11, several data table information is obtained, and a basic directed graph is constructed based on the table names and feature fields in the data table information; wherein, the data table information is the data table information corresponding to several data tables to be processed in the target database.

[0065] In this exemplary embodiment, step S11 described above may specifically include:

[0066] Step S111: Parse the data table information of the data table to be processed to obtain the corresponding table name and feature fields;

[0067] Step S112: Establish a first direction from the table name to the feature field based on the association between the table name and the feature field;

[0068] Step S113: Construct a basic directed graph corresponding to the data table to be processed based on the table name, feature fields, and the first direction; wherein the table name corresponds to at least one of the feature fields.

[0069] Specifically, a data warehouse or data lake can include multiple data tables from different sources. Data tables in the data warehouse can be used as tables to be processed. Text recognition can be performed on the metadata of each table to be processed to obtain the corresponding table information, extracting the table name and feature fields. For a single table to be processed, it can include the table name and one or more corresponding feature fields. For example, the first table to be processed is named Table 1, and its corresponding feature fields include fields 1.1, 1.2, 1.3, and 1.4; the second table to be processed is named Table 2, and its corresponding feature fields include fields 2.1, 2.2, and 2.3; and so on.

[0070] For each data table to be processed, the direction from the table name to the feature field can be configured as the first direction. Based on this first direction, table name, and feature field, a basic directed graph corresponding to each data table can be constructed as a sub-directed graph. For example, refer to... Figure 3 The diagram shows the basic directed graph constructed between Table 1 and fields 1.1, 1.2, 1.3, and 1.4. The first direction can be used to describe the source direction between the table name and the feature fields.

[0071] In step S12, the target database is parsed using a data manipulation language logic to determine the logical relationships between different feature fields.

[0072] In this example implementation, Data Manipulation Language (DML) is a programming language used for database operations, performing access operations on objects and data within the database. The primary function of DML is data access; therefore, its syntax is mainly focused on reading and writing to the database. By parsing the Data Manipulation Language (DML), the logical relationships between different feature fields in the target database can be determined, and based on these logical relationships, the source feature field, associated feature field, and the second direction from the source feature field to the associated feature field can be identified.

[0073] For example, refer to Figure 3 As shown, field 2.1 is obtained by processing field 1.2. Therefore, field 1.2 serves as the source feature field of field 2.1, and field 2.1 serves as the associated feature field, establishing a second direction between field 1.2 and field 2.1. This second direction can be used to describe the source direction between different fields.

[0074] In step S13, different basic directed graphs are connected based on the logical relationship to construct a target directed graph corresponding to the target database, so as to perform data tracing based on the target directed graph.

[0075] In this example implementation, the target directed graph is constructed based on the table name and feature fields corresponding to each of the basic directed graphs, and by connecting different feature fields in a second direction from the source feature fields to the associated feature fields.

[0076] Specifically, after determining the underlying directed graph corresponding to each proxy data table, and determining the source relationship between each feature field between different underlying directed graphs, the target directed graph can be constructed based on this logical relationship.

[0077] For example, refer to Figure 3As shown, field 2.2 is derived from fields 1.3 and 1.2, and field 2.1 is derived from field 1.2. Based on this directed logical relationship, the first and second data tables to be processed can be associated. Of course, the feature fields corresponding to table 1 and table 2 can also be connected in a directed manner with other feature fields.

[0078] For example, taking medical data, specifically patient profiles (PP) in a production system, the intermediate table "Personal Basic Information All" has a "Home Address" field. This field has multiple upstream data sources from external systems, such as immigration data and hospital databases. Downstream applications for this field are also numerous, such as the "Address" field in the "Patient Basic Information Strategy" table used for front-end system display, and the "Geographic Location" field used to calculate the geographical coordinates of nearby hospitals.

[0079] In this example implementation, based on the constructed directed graph, a source tracing task for the target field can be performed. Specifically, refer to... Figure 4 As shown, the above method for data tracing based on the target directed graph includes:

[0080] Step S41, obtain the data tracing task; wherein, the data tracing task includes the target field;

[0081] Step S42: Match the target field with the feature fields in the target directed graph, and configure the matching result as the starting node of the target field in the target directed graph;

[0082] Step S43: Based on the starting node, query the target directed graph in the reverse direction to determine the source node corresponding to the target field;

[0083] Step S44: Determine the tracing path based on the starting node, the source node, and the intermediate path between the starting node and the source node.

[0084] Specifically, for data tracing tasks in directed graphs, these tasks can be created when errors occur in production data. Users can initiate data tracing tasks from their terminal devices to the server, including the currently identified error fields as target fields to be traced. These target fields can be one or more. When a data tracing task includes multiple target fields, the server can create separate tracing subtasks based on each target field and process each subtask individually.

[0085] When performing data tracing tasks, you can first determine the node containing the target field in the directed graph; this node can be a table name node or a feature field node. After determining the node corresponding to the target field, start from that node and search upstream in the opposite direction of the arrows in the directed graph until the corresponding root node is found, thus determining the complete tracing path. For example, refer to... Figure 3 As shown in the directed graph, if the value of a field dependent on field 2.1 is incorrect, the data can be traced back to field 2.1 by following the arrow direction, and then further traced to field 1.2. Since field 1.2 does not reference any other fields, it can be determined that it is the original source of the data, thus achieving rapid data tracing. In actual production, most data problems are caused by outliers in the original data. Traditional analysis methods require manual, layer-by-layer analysis to find the source of the data and then confirm it. This solution, however, can trace the source using a directed graph, directly finding the corresponding original data field, significantly reducing the time required to locate the problem.

[0086] In this example implementation, refer to Figure 5 As shown, the above method may further include:

[0087] Step S51: Obtain a data analysis task, wherein the data analysis task includes the name of the table to be analyzed and / or the field to be analyzed;

[0088] Step S52: Match the table name and / or field to be analyzed with the target directed graph to determine the position of the table name and / or field to be analyzed in the target directed graph;

[0089] Step S53: Identify the source node corresponding to the field to be analyzed by utilizing the directed relationship between the feature fields;

[0090] Step S54: Filter the nodes to be processed based on the source nodes, and perform preset operations on the nodes to be processed.

[0091] The directed relationship between feature fields refers to the relationship between two adjacent feature fields determined by the directed graph of adjacent feature fields in the target directed graph. For example, if feature field 1 points to feature field 2, then the directed relationship between feature field 1 and feature field 2 is that feature field 1 points to feature field 2, and the source node of feature field 2 is feature field 1.

[0092] Specifically, users can create data analysis tasks on their terminal devices; for example, creating a redundant data analysis task and sending it to the server. The task request can include table names and fields; these could be table names and fields retrieved from other business systems. For the server, it can first match the table name and fields to be analyzed against a directed graph to determine the matching results. When determining the matching results, they can be marked on the directed graph. Then, the server can use the arrows in the directed graph to perform a reverse query until the source is identified. Simultaneously, all nodes along this path can be marked until all potentially marked nodes are identified. Unmarked nodes in the directed graph can be used as nodes to be filtered; these nodes can be considered redundant nodes and deleted. Furthermore, if all fields corresponding to a certain table name are redundant, then that table is also a redundant data table. (Reference) Figure 3 As shown, a directed graph records the impact of data changes; the marked nodes indicate that they are directly used by the business, or that they are needed to calculate the data used by the business. For example, field 1.2 pointing to 2.1 and 2.2 means that when the value of field 1.2 changes (including adding a record, deleting a record, or modifying an existing record), the values ​​of fields 2.1 and 2.2 may change. When calculating redundancy, it is necessary to determine whether a table is used directly or indirectly.

[0093] The medical data tracing method provided in the exemplary embodiments of this disclosure constructs a directed graph. When performing data tracing or data analysis, it can quickly determine the root node corresponding to a certain node by reverse querying according to the data association direction in the directed graph. This can greatly reduce the manpower and time costs when investigating data problems and realize the automated processing of data queries.

[0094] Further reference Figure 6 As shown, this example implementation also provides a medical data traceability device 60, including: a basic directed graph construction module 601, a field logical relationship parsing module 602, and a directed graph construction module 603. Among them,

[0095] The basic directed graph construction module 601 can be used to obtain information from several data tables and construct a basic directed graph based on the table names and feature fields in the data table information; wherein, the data table information is the data table information corresponding to several data tables to be processed in the target database.

[0096] The field logic relationship parsing module 602 can be used to perform data manipulation language logic parsing on the target database to determine the logical relationship between different feature fields.

[0097] The directed graph construction module 603 can be used to connect different basic directed graphs based on the logical relationship and construct a target directed graph corresponding to the target database, so as to perform data tracing based on the target directed graph.

[0098] In some exemplary embodiments, the basic directed graph construction module 601 can be used to parse the data table information of the data table to be processed to obtain the corresponding table name and feature fields; establish a first direction from the table name to the feature fields based on the association between the table name and the feature fields; and construct a basic directed graph corresponding to the data table to be processed based on the table name, feature fields, and the first direction; wherein the table name corresponds to at least one of the feature fields.

[0099] In some exemplary embodiments, the field logical relationship parsing module 602 can be used to analyze the data manipulation language to determine the logical relationship between different feature fields, and determine the source feature field, associated feature field, and a second direction from the source feature field to the associated feature field based on the logical relationship.

[0100] In some exemplary embodiments, the directed graph construction module 603 can be used to construct the target directed graph based on the table name and feature fields corresponding to each of the basic directed graphs, and by connecting different feature fields in a second direction from the source feature fields to the associated feature fields.

[0101] In some exemplary embodiments, the apparatus further includes a data tracing task processing module.

[0102] The data tracing task processing module can be used to obtain a data tracing task; wherein, the data tracing task includes a target field; the target field is matched with a feature field in the target directed graph, and the matching result is configured as the starting node of the target field in the target directed graph; the target directed graph is queried in the reverse direction based on the starting node to determine the source node corresponding to the target field; and a tracing path is determined based on the starting node, the source node, and the intermediate path between the starting node and the source node.

[0103] In some exemplary embodiments, the apparatus further includes a data analysis task processing module.

[0104] The data analysis task processing module can be used to acquire data analysis tasks, wherein the data analysis tasks include a table name to be analyzed and / or a field to be analyzed; match the table name to be analyzed and / or the field to be analyzed with the target directed graph to determine the position of the table name to be analyzed and / or the field to be analyzed in the target directed graph; identify the source node corresponding to the field to be analyzed using the directed relationship between feature fields; filter the nodes to be processed based on the source nodes, and perform preset operations on the nodes to be processed.

[0105] In some exemplary embodiments, the apparatus further includes a tagging processing module.

[0106] The marking processing module can be used to mark the matching results and source nodes in the target directed graph when determining the position of the table name to be analyzed and / or the field to be analyzed in the target directed graph, so as to filter the nodes to be processed according to the marking results; wherein the nodes to be processed include unmarked table names.

[0107] The specific details of each module in the aforementioned data analysis device have been described in detail in the corresponding medical data traceability method, so they will not be repeated here.

[0108] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0109] In an exemplary embodiment of this disclosure, a computer system capable of implementing the above-described method is also provided.

[0110] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely hardware implementations, entirely software implementations (including firmware, microcode, etc.), or implementations combining hardware and software aspects, collectively referred to herein as “circuits,” “modules,” or “systems.”

[0111] The following reference Figure 7 To describe a terminal device 700 according to this embodiment of the present invention. Figure 7 The composition of the terminal device 700 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0112] like Figure 7As shown, the components of the terminal device 700 may include, but are not limited to: at least one processing unit 710, at least one storage unit 720, and a bus 730 connecting different system components (including storage unit 720 and processing unit 710).

[0113] The storage unit stores program code that can be executed by the processing unit 710, causing the processing unit 710 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 710 can perform actions such as... Figure 1 The steps are shown in the figure.

[0114] Storage unit 720 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 7201 and / or a cache memory unit 7202, and may further include a read-only memory unit (ROM) 7203.

[0115] The storage unit 720 may also include a program / utility 7204 having a set (at least one) program module 7205, such program module 7205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0116] Bus 730 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0117] The terminal can also communicate with one or more external devices 300 (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable user interaction with the terminal, and / or any device that enables the terminal to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 750. Furthermore, the terminal can communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 760. As shown, network adapter 760 communicates with other modules of the terminal via bus 730. Processing unit 710 is connected to display unit 740 via bus 730. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the terminal, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0118] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0119] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0120] refer to Figure 8 As shown, a program product 800 for implementing the above-described method according to an embodiment of the present invention is described. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0121] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0122] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0123] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0124] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0125] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0126] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0127] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for tracing medical data, characterized in that, include: Obtain information from several data tables, wherein the data table information is the information of several data tables to be processed in the target database; The data table information of the data table to be processed is parsed to obtain the corresponding table name and feature fields; a first direction from the table name to the feature fields is established based on the relationship between the table name and the feature fields; a basic directed graph corresponding to the data table to be processed is constructed based on the table name, feature fields, and the first direction; wherein, the table name corresponds to at least one of the feature fields; The target database is subjected to data manipulation language logic parsing to determine the logical relationship between different feature fields, including: analyzing the data manipulation language to determine the logical relationship between different feature fields, and determining the source feature field, associated feature field, and a second direction from the source feature field to the associated feature field based on the logical relationship; Based on the logical relationship, different basic directed graphs are connected to construct the target directed graph corresponding to the target database, so as to perform data tracing based on the target directed graph; The construction of the target directed graph corresponding to the target database includes: constructing the target directed graph by connecting different feature fields in a second direction from the source feature field to the associated feature field based on the table name and feature field corresponding to each of the basic directed graphs; The data tracing based on the target directed graph includes: Obtain a data tracing task; wherein, the data tracing task includes a target field; The target field is matched with the feature fields in the target directed graph, and the matching result is configured as the starting node of the target field in the target directed graph; Based on the starting node, query the target directed graph in the reverse direction to determine the source node corresponding to the target field; The tracing path is determined based on the starting node, the source node, and the intermediate path between the starting node and the source node.

2. The medical data traceability method according to claim 1, characterized in that, The method further includes: Obtain a data analysis task, wherein the data analysis task includes the name of the table to be analyzed and / or the field to be analyzed; The table name and / or field to be analyzed are matched with the target directed graph to determine the position of the table name and / or field to be analyzed in the target directed graph; The source node corresponding to the field to be analyzed is identified by utilizing the directed relationship between feature fields; Nodes to be processed are selected based on the source nodes, and preset operations are performed on the nodes to be processed.

3. The medical data traceability method according to claim 2, characterized in that, When determining the position of the table name to be analyzed and / or the field to be analyzed in the target directed graph, the method further includes: In the target directed graph, the matching results and source nodes are marked for filtering the nodes to be processed based on the marking results; wherein the nodes to be processed include unmarked table names.

4. A medical data traceability device, characterized in that, include: The basic directed graph construction module is used to obtain information about several data tables, which are the data table information corresponding to several data tables to be processed in the target database. The data table information of the data table to be processed is parsed to obtain the corresponding table name and feature fields; a first direction from the table name to the feature fields is established based on the relationship between the table name and the feature fields; a basic directed graph corresponding to the data table to be processed is constructed based on the table name, feature fields, and the first direction; wherein, the table name corresponds to at least one of the feature fields; The field logic relationship parsing module is used to perform data manipulation language logic parsing on the target database to determine the logical relationship between different feature fields, including: analyzing the data manipulation language to determine the logical relationship between different feature fields, and determining the source feature field, associated feature field, and a second direction from the source feature field to the associated feature field based on the logical relationship; A directed graph construction module is used to connect different basic directed graphs based on the logical relationship and construct a target directed graph corresponding to the target database, so as to perform data tracing based on the target directed graph; The construction of the target directed graph corresponding to the target database includes: constructing the target directed graph by connecting different feature fields in a second direction from the source feature field to the associated feature field based on the table name and feature field corresponding to each of the basic directed graphs; The data tracing based on the target directed graph includes: Obtain a data tracing task; wherein, the data tracing task includes a target field; The target field is matched with the feature fields in the target directed graph, and the matching result is configured as the starting node of the target field in the target directed graph; Based on the starting node, query the target directed graph in the reverse direction to determine the source node corresponding to the target field; The tracing path is determined based on the starting node, the source node, and the intermediate path between the starting node and the source node.

5. A storage medium having a computer program stored thereon, the program being executed by a processor to implement the medical data tracing method according to any one of claims 1 to 3.

6. A terminal device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the medical data tracing method of any one of claims 1 to 3 by executing the executable instructions.

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