A data asset integration method, device, equipment and medium in the health field

Abstract

The present invention relates to a method, device, equipment and medium for integrating data assets in the field of health, and belongs to the field of data assets. The method includes collecting data assets in the field of health and entering metadata in the field of health; constructing a knowledge graph in the field of health based on the metadata in the field of health; generating an automatic identification carrier based on the knowledge graph in the field of health, and realizing the integration of data assets in the field of health based on the automatic identification carrier. The present invention realizes the integration of data assets in the field of health by constructing a knowledge graph and generating an automatic identification carrier.

Description

Technical Field

[0001] The present invention belongs to the technical field of data assets, and specifically relates to a data asset integration method, device, equipment and medium in the health field. Background Art

[0002] The purpose of data asset registration is to clarify the complex ownership of data, which involves the right to hold data resources, the right to process and use data, the right to operate data products, etc. In the process of promoting the implementation of digital transformation of enterprises, the realization of data asset registration is a major test for the ability of managers and technical experts. Data asset registration in the medical industry has always been a hot topic for research and application. At present, the medical industry still uses traditional methods to realize data asset registration, without fully considering the needs of data assetization. It is equivalent to that the product only has the name and specifications, without cataloging, barcodes, or QR codes. This inefficient method needs to be improved urgently. Summary of the invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a data asset integration method, device, equipment and medium in the health field.

[0004] The purpose of the present invention can be achieved through the following technical solutions: A data asset integration method in the health field, the implementation of which includes the following steps: Collect health data assets and enter health metadata; Constructing a health field knowledge graph based on the health field metadata; An automatic identification carrier is generated based on the health field knowledge graph, and data asset integration in the health field is achieved based on the automatic identification carrier.

[0005] Preferably, the collecting of health-related data assets and entering of health-related metadata includes: Read the health field asset information statistics table and store the information in the form of the health field data assets; Develop unified metadata standards; The health-related metadata is extracted from the health-related data assets based on the metadata standard and stored in a centralized metadata management system.

[0006] Preferably, the metadata standard needs to ensure that the health-domain metadata of the health-domain data assets are comparable and consistent.

[0007] Preferably, the constructing of a health field knowledge graph based on the health field metadata includes: Obtaining a health domain ontology through the health domain metadata; Defining data attributes and object attributes of the health domain ontology; By mapping, the data attributes and object attributes of the health domain ontology are mapped to the health domain knowledge graph.

[0008] Preferably, obtaining the health domain ontology through the health domain metadata includes: Extracting the core metadata of the health field metadata through a shared domain framework of the health field and performing element expansion; By describing the relationship between the core metadata of the health field, a network structure is formed, which is the health field ontology; Preferably, the generating of the automatic identification carrier based on the health field knowledge graph includes: Preset classification rules, extract the core metadata of the health field belonging to the same category, and generate a unified basic automatic identification carrier; The health field knowledge graph is embedded into the basic automatic identification carrier through a steganographic algorithm to form the automatic identification carrier.

[0009] Preferably, the forming of the automatic identification carrier comprises: The health field knowledge graph is embedded in the basic automatic recognition carrier to obtain an initial automatic recognition carrier, and a pixel point (i, j) in the initial automatic recognition carrier is randomly selected to calculate the horizontal deviation and vertical deviation. The calculation formula is: , where A(i,j) is the horizontal deviation at (i,j), B(i,j) is the vertical deviation at (i,j), r(i,j-1) is the pixel value of the adjacent pixel to the left of (i,j), r(i,j+1) is the pixel value of the adjacent pixel to the right of (i,j), r(i-1,j) is the pixel value of the adjacent pixel below (i,j), and r(i+1,j) is the pixel value of the adjacent pixel above (i,j); The horizontal weight and the vertical weight are calculated based on the horizontal deviation and the vertical deviation. The calculation formula is: , where W A (i,j) is the horizontal weight at (i,j), W B (i,j) is the vertical weight at (i,j); The metadata loss value is calculated based on the horizontal weight and the vertical weight, and the calculation formula is: , where T(i,j) is the metadata loss value at (i,j), and the sum of the metadata loss values ​​of each pixel in the initial automatic identification carrier is the total metadata loss value; The automatic identification carrier is obtained by adjusting the initial automatic identification carrier to minimize the total metadata loss value.

[0010] A data asset integration device in the health field, used to execute the data asset integration method described above, including a metadata collection module, a knowledge graph construction module, and a carrier generation module; The metadata collection module is used to collect health field data assets and enter health field metadata; The knowledge graph construction module is used to construct a health field knowledge graph based on the health field metadata; The carrier generation module is used to generate an automatic identification carrier based on the health field knowledge graph, and realize the integration of data assets in the health field based on the automatic identification carrier.

[0011] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned data asset integration method when executing the program.

[0012] A storage medium containing computer executable instructions, which are used to perform the data asset integration method described above when executed by a computer processor.

[0013] The beneficial effects of the present invention are: (1) By generating automatic identification carriers, data assets in the health field can be integrated, making assets identifiable and registrable, enabling asset evaluation, entry into tables and transactions, making it easier for non-proprietary departments to identify and count assets, thus completing the registration of data assets.

[0014] (2) By embedding the knowledge graph in the health field into a universal basic automatic identification carrier, the information of the same type of data assets can be clearly seen at a glance, making it easier for readers to classify and identify them.

[0015] (3) The initial automatic identification carrier is adjusted in a way that minimizes the metadata loss value, and the automatic identification carrier is finally obtained. The process is more precise, and the obtained automatic identification carrier has higher accuracy, is easier to identify, and is less likely to deviate. At the same time, it ensures that the embedding process does not lose data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 The present invention is a flowchart of the steps of a method for integrating data assets in the health field. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0019] The working principle and use process of the present invention: See also Figure 1 , an integrated approach to data assets in the health sector, including: S1: Collect health data assets and enter health metadata. The health data assets are data elements included in the financial statements of the medical and health field and recorded as inventory or intangible assets; S2: construct a health field knowledge graph based on the health field metadata; S3: Generate an automatic identification carrier based on the health field knowledge graph, the automatic identification carrier includes a QR code, a barcode, etc., and realize the integration of data assets in the health field based on the automatic identification carrier.

[0020] In this embodiment, collecting health data assets and entering health metadata can be implemented through the following steps: S101: Read the health field asset information statistics table and store the information in the form of health field data assets; S102: Formulate a unified metadata standard to ensure that the health field metadata of the health field data assets are comparable and consistent; S103: Extract the health field metadata from the health field data assets based on the metadata standard, and store it in a centralized metadata management system. The health field metadata includes but is not limited to data type, data length, data definition, and access rights.

[0021] In this embodiment, building a health field knowledge graph based on the health field metadata can be implemented by the following steps: S201: Obtaining a health domain ontology through the health domain metadata, where the health domain ontology can be regarded as metadata of the health domain metadata; S202: defining data attributes and object attributes of the health domain ontology; S203: By mapping, with the help of knowledge graph software such as Apache Jena and TDA, the data attributes and object attributes of the health domain ontology are mapped to the health domain knowledge graph.

[0022] In this embodiment, the health domain ontology is obtained through the health domain metadata, which can be implemented by the following steps: S201-1: extracting the core metadata of the health field metadata through the shared domain framework of the health field and performing element expansion; S201-2: By describing the relationship between the core metadata of the health field, a network structure is formed, which is the health field ontology. For example, the coronary angiography machine is the center of the network structure, which is stored in the cardiology department. The cardiology department has a correlation relationship with other departments and a subordinate relationship with the hospital. Multiple branches can be extended. The properties of the coronary angiography machine itself, such as price, manager, frequency of use, etc., can also extend multiple branches, and finally form an overall network structure of the health field; In this embodiment, the automatic identification carrier is generated based on the health field knowledge graph, which can be implemented by the following steps: S301: Preset classification rules, extract the core metadata of the health field belonging to the same category, and generate a unified basic automatic identification carrier. For example, if the coronary angiography machine belongs to the cardiology department, all the core metadata of the health field of the cardiology department are collected and assigned a unified basic QR code. The QR code at this time is not a complete QR code; S302: Embed the health field knowledge graph into the basic automatic identification carrier through a steganographic algorithm to form the automatic identification carrier.

[0023] In this embodiment, the health field knowledge graph is embedded into the basic automatic identification carrier through a steganographic algorithm to form the automatic identification carrier, which can be implemented by the following steps: S302-1: embed the health field knowledge graph into the basic automatic recognition carrier to obtain an initial automatic recognition carrier, randomly select a pixel point (i, j) in the initial automatic recognition carrier and calculate the horizontal deviation and vertical deviation. The calculation formula is: , where A(i,j) is the horizontal deviation at (i,j), B(i,j) is the vertical deviation at (i,j), r(i,j-1) is the pixel value of the adjacent pixel to the left of (i,j), r(i,j+1) is the pixel value of the adjacent pixel to the right of (i,j), r(i-1,j) is the pixel value of the adjacent pixel below (i,j), and r(i+1,j) is the pixel value of the adjacent pixel above (i,j); S302-2: Calculate the horizontal weight and the vertical weight based on the horizontal deviation and the vertical deviation, and the calculation formula is: , where W A (i,j) is the horizontal weight at (i,j), W B (i,j) is the vertical weight at (i,j); S302-3: Calculate the metadata loss value based on the horizontal weight and the vertical weight, and the calculation formula is: , where T(i,j) is the metadata loss value at (i,j), and the sum of the metadata loss values ​​of each pixel in the initial automatic identification carrier is the total metadata loss value; S302-4: The initial automatic identification carrier is adjusted to obtain the automatic identification carrier by minimizing the total metadata loss value.

[0024] A data asset integration device in the health field, including a metadata collection module, a knowledge graph construction module, and a carrier generation module; The metadata collection module is used to collect health field data assets and enter health field metadata. The health field data assets are data elements included in the financial statements of the medical and health field and recorded as inventory or intangible assets; The knowledge graph construction module is used to construct a health field knowledge graph based on the health field metadata; The carrier generation module is used to generate an automatic identification carrier based on the health field knowledge graph. The automatic identification carrier includes a QR code, a barcode, etc., and the data asset integration in the health field is realized based on the automatic identification carrier.

[0025] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0026] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0027] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0028] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A data asset integration method in the health field, characterized in that: The implementation of the data asset integration method includes the following steps: Collect health data assets and enter health metadata; Constructing a health field knowledge graph based on the health field metadata; Generate an automatic identification carrier based on the health field knowledge graph, and realize the integration of data assets in the health field based on the automatic identification carrier; The automatic identification carrier is generated based on the health field knowledge graph by presetting classification rules, extracting the core metadata of the health field belonging to the same category, and generating a unified basic automatic identification carrier; The health field knowledge graph is embedded into the basic automatic identification carrier through a steganographic algorithm to form the automatic identification carrier.

2. The data asset integration method according to claim 1, characterized in that: The collecting of health data assets and entering of health metadata includes: Read the health field asset information statistics table and store the information in the form of the health field data assets; Develop unified metadata standards; The health-related metadata is extracted from the health-related data assets based on the metadata standard and stored in a centralized metadata management system.

3. The data asset integration method according to claim 2, characterized in that: The metadata standard needs to ensure that the health field metadata of the health field data assets are comparable and consistent.

4. The data asset integration method according to claim 1, characterized in that: The constructing of a health field knowledge graph based on the health field metadata includes: Obtaining a health domain ontology through the health domain metadata; Defining data attributes and object attributes of the health domain ontology; By mapping, the data attributes and object attributes of the health domain ontology are mapped to the health domain knowledge graph.

5. The data asset integration method according to claim 4, characterized in that: The obtaining of the health domain ontology through the health domain metadata includes: Extracting the core metadata of the health field metadata through a shared domain framework of the health field and performing element expansion; By describing the relationship between the core metadata of the health field, a network structure is formed, which is the health field ontology.

6. The data asset integration method according to claim 1, characterized in that: The forming of the automatic identification carrier comprises: The health field knowledge graph is embedded in the basic automatic recognition carrier to obtain an initial automatic recognition carrier, and a pixel point (i, j) in the initial automatic recognition carrier is randomly selected to calculate the horizontal deviation and vertical deviation. The calculation formula is: , where A(i,j) is the horizontal deviation at (i,j), B(i,j) is the vertical deviation at (i,j), r(i,j-1) is the pixel value of the adjacent pixel to the left of (i,j), r(i,j+1) is the pixel value of the adjacent pixel to the right of (i,j), r(i-1,j) is the pixel value of the adjacent pixel below (i,j), and r(i+1,j) is the pixel value of the adjacent pixel above (i,j); The horizontal weight and the vertical weight are calculated based on the horizontal deviation and the vertical deviation. The calculation formula is: , where W A (i,j) is the horizontal weight at (i,j), W B (i,j) is the vertical weight at (i,j); The metadata loss value is calculated based on the horizontal weight and the vertical weight, and the calculation formula is: , where T(i,j) is the metadata loss value at (i,j), and the sum of the metadata loss values ​​of each pixel in the initial automatic identification carrier is the total metadata loss value; The automatic identification carrier is obtained by adjusting the initial automatic identification carrier to minimize the total metadata loss value.

7. A data asset integration device in the health field, characterized in that: The device is applied to the data asset integration method as described in any one of claims 1 to 6, including a metadata collection module, a knowledge graph construction module, and a carrier generation module; The metadata collection module is used to collect health field data assets and enter health field metadata; The knowledge graph construction module is used to construct a health field knowledge graph based on the health field metadata; The carrier generation module is used to generate an automatic identification carrier based on the health field knowledge graph, and realize the integration of data assets in the health field based on the automatic identification carrier; the automatic identification carrier generated based on the health field knowledge graph is: presetting classification rules, extracting the core metadata of the health field belonging to the same category, and generating a unified basic automatic identification carrier; embedding the health field knowledge graph into the basic automatic identification carrier through a steganographic algorithm to form the automatic identification carrier.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the data asset integration method as described in any one of claims 1-6 is implemented.

9. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the data asset integration method as described in any one of claims 1-6 when executed by a computer processor.

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