CN115269701A

CN115269701A - Structured data generation method and device, storage medium and electronic equipment

Info

Publication number: CN115269701A
Application number: CN202210901620.7A
Authority: CN
Inventors: 赵雪婷; 刘水清
Original assignee: Nanjing Yiduyun Medical Technology Co ltd
Current assignee: Yidu Cloud Beijing Technology Co Ltd
Priority date: 2019-08-12
Filing date: 2019-08-12
Publication date: 2022-11-01
Also published as: CN110442633B; CN110442633A

Abstract

The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for generating structured data, a storage medium, and an electronic device, where the method includes: acquiring preliminary structured data to be processed; and acquiring a combinational logic, and calculating and recombining the preliminary structured data to be processed according to the combinational logic to acquire final structured data. According to the technical scheme, the preliminary structured data to be processed can be automatically calculated and recombined through the combinational logic, the process of carrying out statistical analysis on the preliminary structured data to be processed through manual work is avoided, the data processing efficiency is further improved, and the possibility of errors is reduced.

Description

Structured data generation method and device, storage medium and electronic equipment

The application is a divisional application of a Chinese patent application with an application number of 201910740189.0 and an application name of 'structured data generation method and device, storage medium and electronic equipment' filed in 12/08/2019.

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for generating structured data, a storage medium, and an electronic device.

Background

With the development of information technology in the fields of internet, internet of things, mobile computers and the like, more and more different types of data are produced in large quantities. However, these mass produced data often require further processing to convert the data to data that is directly available to the user.

At present, in order to convert a large amount of produced data into data directly available to a user, statistical analysis is often required to be performed on the structured information of each data source extracted by a machine manually, so that the directly available data can be obtained. For example, in the medical field, after a machine extracts structured data from sources such as a medical history, an examination report, and a pathological examination report of a certain patient, the structured data needs to be analyzed and counted manually, so as to obtain a final examination result of the patient directly available to medical staff.

However, in the above method, since the machine can only extract the structured data from various sources, the process of converting the structured data into the data directly usable by the user still needs to be performed manually, which results in low efficiency of data processing and is prone to errors.

It is noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure and therefore may include information that does not constitute prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

The present disclosure aims to provide a method and an apparatus for generating structured data, a storage medium, and an electronic device, so as to overcome, at least to some extent, the problems of low data processing efficiency and easy error caused by the fact that data conversion completely depends on human labor.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to a first aspect of the present disclosure, there is provided a structured data generation method, including:

acquiring preliminary structured data to be processed; and acquiring a combinational logic, and calculating and recombining the preliminary structured data to be processed according to the combinational logic to acquire final structured data.

In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the preliminary structured data to be processed includes at least one pair of key-value pair data, which includes key data and value data corresponding to each other;

the obtaining of the combinational logic and the calculation and recombination of the preliminary structured data to be processed according to the combinational logic to obtain the final structured data includes:

grouping key value pair data in the preliminary structured data to be processed according to the key data;

acquiring a combinational logic corresponding to each group, and calculating all the value data in the group according to the combinational logic to acquire final value data;

and combining the key data corresponding to each group and the corresponding final value data into final key value pair data to obtain final structured data.

In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the obtaining the combinational logic corresponding to each group includes:

reading the data type of the value data in each packet;

calling corresponding preset combinational logic according to the data type;

and setting the preset combination logic as the combination logic corresponding to each group.

In an exemplary embodiment of the present disclosure, the data types include boolean, column-type, and date-type based on the foregoing scheme.

In an exemplary embodiment of the disclosure, based on the foregoing scheme, before the obtaining the combinational logic, the method further includes:

in response to user input of logical data, combinatorial logic is configured based on the logical data.

In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the method further includes:

and structuring all text data corresponding to the target object to obtain preliminary structured data to be processed.

In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the structuring all text data corresponding to a target object to obtain preliminary structured data to be processed includes:

acquiring all text data corresponding to a target object;

calling a corresponding preset strategy according to the source of the text data;

and structuring the text data respectively according to a preset strategy to obtain preliminary structured data to be processed.

According to a second aspect of the present disclosure, there is provided a structured data generation apparatus comprising:

the data acquisition module is used for acquiring preliminary structured data to be processed; and the calculation and recombination module is used for acquiring the combinational logic and calculating and recombining the preliminary structured data to be processed according to the combinational logic to acquire final structured data.

In an exemplary embodiment of the present disclosure, based on the foregoing solution, the apparatus further includes:

a logic configuration module to configure the combinational logic based on the logic data in response to user input of the logic data.

and the data processing module is used for structuring all the text data corresponding to the target object to obtain preliminary structured data to be processed.

According to a third aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the structured data generation method as described in the first aspect of the embodiments above.

According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:

a processor; and

a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the structured data generation method as described in the first aspect of the embodiments above.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

according to the structured data generation method provided by the embodiment of the disclosure, the corresponding final structured data can be obtained by calculating and recombining the preliminary structured data to be processed according to the combinational logic. In the process, the preliminary structural data to be processed can be automatically calculated and recombined through the combinational logic, the process of carrying out statistical analysis on the preliminary structural data to be processed through manual work is avoided, the data processing efficiency is further improved, and the possibility of errors is reduced.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:

FIG. 1 schematically illustrates a flow chart of a structured data generation method in an exemplary embodiment of the present disclosure;

FIG. 2 is a flow chart schematically illustrating a method for obtaining combinational logic and performing computational reorganization on the preliminary structured data to be processed according to the combinational logic to obtain final structured data in an exemplary embodiment of the present disclosure;

fig. 3 schematically illustrates a flowchart of a method of obtaining corresponding combinational logic of each group in an exemplary embodiment of the present disclosure;

fig. 4 schematically illustrates a flowchart of a method for structuring all text data corresponding to a target object to obtain preliminary structured data to be processed in an exemplary embodiment of the present disclosure;

FIG. 5 is a schematic diagram illustrating components of a structured data generation apparatus in an exemplary embodiment of the present disclosure;

FIG. 6 is a schematic diagram illustrating the components of another structured data generation apparatus in an exemplary embodiment of the present disclosure;

FIG. 7 is a schematic diagram illustrating the components of another structured data generation apparatus in an exemplary embodiment of the present disclosure;

FIG. 8 schematically illustrates a structural diagram of a computer system suitable for use with an electronic device that implements an exemplary embodiment of the present disclosure;

fig. 9 schematically illustrates a schematic diagram of a computer-readable storage medium, according to some embodiments of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example 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.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

In the present exemplary embodiment, a structured data generation method is first provided, which can be applied to a process of data conversion. The execution subject of the structured data generation method can be terminal equipment capable of data processing, such as a mobile phone, a computer and the like. Referring to fig. 1, the above-mentioned structured data generation method may include the following steps:

s110, acquiring preliminary structured data to be processed;

and S120, acquiring a combinational logic, and calculating and recombining the preliminary structured data to be processed according to the combinational logic to acquire final structured data.

According to the structured data generation method provided in the present exemplary embodiment, the corresponding final structured data can be obtained by performing calculation and recombination on the preliminary structured data to be processed according to the combinational logic. In the process, the preliminary structural data to be processed can be automatically calculated and recombined through the combinational logic, the process of carrying out statistical analysis on the preliminary structural data to be processed through manual work is avoided, the data processing efficiency is further improved, and the possibility of errors is reduced.

Hereinafter, each step of the structured data generation method in the present exemplary embodiment will be described in more detail with reference to the drawings and the embodiments.

And step S110, acquiring preliminary structured data to be processed.

In an example embodiment of the present disclosure, the to-be-processed preliminary structured data includes to-be-processed preliminary structured data corresponding to at least one text data, and may also include to-be-processed preliminary structured data corresponding to a plurality of text data. The preliminary structured data to be processed may be data existing in pairs, or may be data existing in other forms, and the disclosure is not limited thereto.

And step S120, acquiring a combinational logic, and calculating and recombining the preliminary structured data to be processed according to the combinational logic to acquire final structured data.

In an example embodiment of the present disclosure, prior to the obtaining the combinatorial logic, the method further comprises: in response to user input of logical data, combinatorial logic is configured based on the logical data.

In an example embodiment of the present disclosure, a user may be allowed to customize logical data according to special requirements of the preliminary structured data to be processed, and configure the logical data into combinational logic, so that the preliminary structured data to be processed may be computationally reorganized according to the requirements of the user to obtain final structured data required by the user. The preliminary structured data to be processed can be calculated and recombined according to the requirements of the user by allowing the user to define the combinational logic, so that the final structured data which is more suitable for the requirements of the user can be obtained.

In an example embodiment of the present disclosure, the preliminary structured data to be processed may include at least one pair of key-value pair data. Wherein the key-value pair data includes key data and value data corresponding to each other. For example, the preliminary structured data to be processed of the medical class may include text data of a bone scan examination report and structured data to be processed corresponding to text data of a pathology examination report, and specifically may include "bone scan — whether to transfer: is "and" pathology-whether metastasis: yes ", where" bone scan-whether metastasis "and" pathology-whether metastasis "are key data, and" yes "is value data; as another example, the credit-like pending preliminary structured data may be "xx banks-whether to pay on time: yes, where "xx bank-whether to pay on time" is key data and "yes" is value data.

In an example embodiment of the present disclosure, when the to-be-processed preliminary structured data specifically includes at least one pair of key-value pair data, the obtaining a combinational logic, and performing calculation and recombination on the to-be-processed preliminary structured data according to the combinational logic to obtain final structured data may include, as shown in fig. 2, the following steps S210 to S230:

and step S210, grouping the key value pair data in the preliminary structured data to be processed according to the key data.

In an example embodiment of the present disclosure, the key-value pair information may be grouped according to all key data in the preliminary structured data to be processed, and the key-value pair data with the tangible identical structure field may be divided into the same group to facilitate further calculation processing. For example, when the preliminary structured data to be processed includes the key value pair data "bone scan — whether to transfer" from the bone scan detection report: yes ", the key-value pair data obtained from the pathology examination report" pathology-metastasis: is "and" pathology-metastasis site: liver ", bone scan-transfer can be" according to the key data "transfer or not: is "and" pathology-whether metastasis: is "two pairs of key-value pair data are grouped, dividing" pathology-metastasis site: liver "is divided into another group. By classifying the key value pair data according to the key data, the key value pair data of the same name structure field can be divided into the same group, and then the key value pair data of different groups can be conveniently and respectively processed.

Step S220, acquiring the combination logic corresponding to each group, and calculating all the value data in the group according to the combination logic to acquire final value data.

In an example embodiment of the present disclosure, the calculation basis of the value data may be different in different groups, and thus, the combinational logic corresponding to each group needs to be obtained separately. The corresponding combinational logic may be obtained according to the data type of the key value pair data in each group, and specifically, as shown in fig. 3, the following steps S310 to S330 may be included:

in step S310, the data type of the value data in each of the packets is read.

Step S320, calling corresponding preset combinational logic according to the data type;

step S330, setting the preset combinational logic as a combinational logic corresponding to each of the groups.

In an example embodiment of the present disclosure, a corresponding preset combination logic may be called according to a data type of value data in the data of the key value in the group, and the preset combination logic may be set as the combination logic corresponding to the group. The data types may include boolean, column-type, and date-type, which respectively represent that the value data in the key-value pair data in the current grouping is boolean, column-type, date-type, and the like. The corresponding combinational logic is set according to the data types of the median data in different groups, so that the selected combinational logic can be matched with the data types of the value data, more accurate and directly available final value data can be obtained, and the problem of mismatching caused by the fact that the same combinational logic is selected for all key value data is avoided.

In an example embodiment of the present disclosure, since data types of the value data are different, the preset combination logic corresponds to the difference. For example, the key-value pair data in the packet is "bone scan — whether to branch: is "and" pathology-whether metastasis: if yes, the preset combination logic may be set to "when any one of the boolean data is yes, the final value data is yes"; for another example, when the value data is list-type data, the preset combinational logic may be set to "take a union of a plurality of value data lists as final value data"; for another example, when the value data is date type data, the preset combination logic may be set to "take a date closest to a certain specified date as final value data". Since the types of the different value data and the requirements for the final value data are different, the preset combination logic generally includes some common and general combination logics, which can be determined according to the types of the value data and the requirements for the final value data, and the disclosure is not particularly limited thereto.

Step S230, combining the key data corresponding to each group and the corresponding final value data into a final key value pair data to obtain final structured data.

In an example embodiment of the present disclosure, the key data and the final value data corresponding to each group are combined to obtain final key-value pair data, and the final key-value pair data corresponding to all groups constitute data as final structured data. For example, if the final value data obtained by "whether to branch" of the key data is "yes", the final key-value pair data is "whether to branch: is ". The final value data is obtained by calculating the value data in the packet, so that the process of integrating the value data corresponding to the same key data into the final value data is realized, the key data and the final value data are combined into the final key value data, the data required by a user can be directly provided for the user, the process of manually inquiring, analyzing and counting by the user is avoided, and the data processing efficiency is improved.

In an example embodiment of the present disclosure, to obtain preliminary structured data to be processed, the method further comprises: and structuring all text data corresponding to the target object to obtain preliminary structured data to be processed. For example, in the medical field, in order to learn about the examination condition of a target patient, a user may structure all text data corresponding to a target object, obtain preliminary structured data to be processed, and further process the preliminary structured data to obtain final structured data.

In an example embodiment of the present disclosure, the structuring all text data corresponding to the target object to obtain preliminary structured data to be processed, as shown in fig. 4, includes the following steps S410 to S430:

step S410, acquiring all text data corresponding to the target object.

In an example embodiment of the present disclosure, the target object may be set according to a requirement of a user. For example, in the medical field, in order to know the medical information of patient a, the user needs to acquire all the text data corresponding to patient a, which may include examination reports corresponding to various medical examinations.

Step S420, a corresponding preset policy is called according to the source of the text data.

In an example embodiment of the present disclosure, since natural language habits and text structures used by text data from different sources are different, strategies for extracting structured data from different text data are also different. Correspondingly, in order to structure the text data according to the natural language habits and the text structures used by the text data, a preset strategy needs to be preset, and then the corresponding preset strategy is called according to the source of the text data.

And step S430, structuring the text data respectively according to a preset strategy to obtain preliminary structured data to be processed.

In an example embodiment of the present disclosure, the text data is structured according to a preset policy determined by a data source of the text data, and preliminary structured data to be processed may be obtained. For example, the textual data from the bone scan examination report is "post-esophageal cancer resection, radioactive concentration at the left sacral joint, considering metastasis", which is structured to obtain preliminary structured data to be processed "bone scan-whether metastasis is present: is "and" bone scan-metastasis site: bone ".

The following explains implementation details of the technical solution of the embodiment of the present disclosure by taking the structured data generation of the patient a in the medical field as an example:

1. text data corresponding to all examination reports of patient A

The corresponding text data of the pathological examination report are as follows: the esophageal resection specimen, esophageal ulcer type low-differentiation squamous cell carcinoma, and the liver tissue which is detected additionally has cancer cells, which are in line with metastasis. "

The text data corresponding to the bone scanning examination report is as follows: "following esophageal cancer resection, the left sacral joint is radioactively concentrated, taking into account metastasis. "

The corresponding text data of the skull CT examination report is as follows: after the esophageal cancer resection, no metastasis and recurrence are observed. "

2. All text data corresponding to the patient A are structured to obtain preliminary structured data to be processed

The structured data obtained by structuring the text data corresponding to the pathological examination report is as follows: pathology-whether or not metastasis: is as follows; pathology-metastatic site: liver.

The structured data obtained by structuring the text data corresponding to the bone scanning examination report is as follows: bone scan-whether metastasis: is that; bone scan-metastasis site: bone.

The structured data obtained by structuring the text data corresponding to the skull CT examination report is as follows: cranial CT-whether metastasis: if not; cranial CT-metastatic site: and (4) NA.

3. Grouping key value pair data in all preliminary structured data to be processed according to key data

The key-value pair data whose key data includes "whether to branch" includes: pathology-whether metastasis is present: is as follows; bone scan-whether metastasis: is that; cranial CT-whether metastasis: and no.

The key-value pair data whose key data includes "transfer site" includes: pathology-metastatic site: liver; bone scan-metastasis site: a bone; cranial CT-site of metastasis: none.

4. Obtaining the combinational logic and calculating the final value data according to the combinational logic

Invoking combinational logic according to the data type of the data median data of the key values in the packet as follows:

the grouping logic corresponding to the grouping of which the key data includes "whether to transfer" is: for a plurality of boolean data, any one is "yes", the end result is "yes".

The grouping of key data including "transfer sites" corresponds to the combinational logic of: for multiple list type data, the union of multiple lists is taken as the final result.

The final value data calculated from the combinational logic is:

the packet whose key data includes "transition or not" corresponds to the final value data as: "is".

The final value data corresponding to the grouping in which the key data includes "transition site" is: "liver, bone".

5. Combining the key data corresponding to each group and the corresponding final value data into a final key value pair data to obtain final structured data

The final key-value pair data is: "transfer or not: is "and" transfer site: liver, bone ".

The final structured data is: whether to transfer: is that; transfer site: liver, bone.

After text data corresponding to the examination report of the patient A is converted into preliminary structured data, key-value pair data are grouped according to key data in the preliminary structured data, value data in the groups are calculated to obtain final value data, and finally the key data corresponding to the groups and the final value data are combined to obtain the final key-value pair data. The process that the final key value pair data is obtained by manually carrying out statistical analysis on the inquired text data is avoided through the generation mode of the structured data, the data processing efficiency is improved, and the possibility of human errors is reduced.

It is noted that the above-mentioned figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

In addition, in the exemplary embodiment of the disclosure, a structured data generation device is also provided. Referring to fig. 5, the structured data generating apparatus 500 includes: a data acquisition module 510 and a computational reorganization module 520.

The data obtaining module 510 may be configured to obtain preliminary structured data to be processed;

the computation reorganization module 520 may be configured to obtain a combination logic, and perform computation reorganization on the preliminary structured data to be processed according to the combination logic to obtain final structured data.

In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the computation reorganization module 520 may be configured to group key-value pair data in the preliminary structured data to be processed according to the key data; acquiring a combination logic corresponding to each group, and calculating all the value data in the groups according to the combination logic to acquire final value data; and combining the key data corresponding to each group and the corresponding final value data into final key value pair data to obtain final structured data.

In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the calculation reorganization module 520 may be configured to read a data type of value data in each of the packets; and calling corresponding preset combinational logic according to the data types and setting the preset combinational logic as the combinational logic corresponding to each group.

In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the computation reassembly module 520 may be further configured to obtain the logic data input by the user, and set the logic data as the combinational logic corresponding to the group.

In an exemplary embodiment of the present disclosure, based on the foregoing solution, as shown in fig. 6, the structured data generating apparatus 500 further includes: a logic configuration module 530 may be used to configure the combinational logic based on the logic data in response to user input of the logic data.

In an exemplary embodiment of the present disclosure, based on the foregoing solution, as shown in fig. 7, the structured data generating apparatus 500 further includes: the data processing module 540 may be configured to structure all text data corresponding to the target object to obtain preliminary structured data to be processed.

In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the data processing module 540 may be configured to obtain all text data corresponding to a target object; calling a corresponding preset strategy according to the source of the text data; and structuring the text data respectively according to a preset strategy to obtain preliminary structured data to be processed.

For details which are not disclosed in the embodiment of the apparatus of the present disclosure, please refer to the embodiment of the above-mentioned structured data generation method of the present disclosure for the details which are not disclosed in the embodiment of the apparatus of the present disclosure.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above structured data generation method is also provided.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 800 according to such an embodiment of the disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.

As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one memory unit 820, a bus 830 connecting different system components (including the memory unit 820 and the processing unit 810), and a display unit 840.

Wherein the storage unit stores program code that can be executed by the processing unit 810, such that the processing unit 810 performs the steps according to various exemplary embodiments of the present disclosure described in the above section "exemplary method" of this specification. For example, the processing unit 810 may perform step S110 as shown in fig. 1: acquiring preliminary structured data to be processed; s120: and acquiring a combinational logic, and calculating and recombining the preliminary structured data to be processed according to the combinational logic to acquire final structured data.

As another example, the electronic device may implement the steps shown in fig. 2 to 4.

The storage unit 820 may include readable media in the form of volatile storage units, such as a random access storage unit (RAM) 821 and/or a cache storage unit 822, and may further include a read only storage unit (ROM) 823.

Storage unit 820 may also include a program/utility 824 having a set (at least one) of program modules 825, such program modules 825 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 830 may be any of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 800 may also communicate with one or more external devices 870 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with any device (e.g., router, modem, etc.) that enables the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 860. As shown, the network adapter 860 communicates with the other modules of the electronic device 800 via the bus 830. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the present disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.

Referring to fig. 9, a program product 900 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not so limited, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The 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 electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).

Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.

Claims

1. A method for structured data generation, comprising:

acquiring preliminary structured data to be processed; the preliminary structured data to be processed comprises at least one pair of key-value pair data; the key-value pair data includes key data and value data corresponding to each other;

acquiring a combination logic corresponding to each group, and calculating all the value data in the groups according to the combination logic to acquire final value data;

2. The method according to claim 1, wherein the grouping key-value pair data in the preliminary structured data to be processed according to the key data comprises:

the key-value pair data with the same structure field are divided into the same group.

3. The method of claim 1, wherein obtaining the combinational logic corresponding to each group comprises:

reading the data type of the value data in each of the packets;

calling corresponding preset combinational logic according to the data type;

4. The method of claim 3, wherein the data types include Boolean type, column phenotype, and date type.

5. The method of claim 1, wherein prior to said obtaining combinatorial logic, the method further comprises:

6. The method of claim 1, further comprising:

and structuring all text data corresponding to the target object to obtain preliminary structured data to be processed, wherein the text data comprises examination reports corresponding to various medical examinations.

7. The method according to claim 6, wherein the structuring all text data corresponding to the target object to obtain preliminary structured data to be processed comprises:

acquiring all text data corresponding to a target object;

8. A structured data generation apparatus, comprising:

the data acquisition module is used for acquiring preliminary structured data to be processed; the preliminary structured data to be processed comprises at least one pair of key-value pair data; the key-value pair data includes key data and value data corresponding to each other;

the grouping module is used for grouping the key value pair data in the preliminary structured data to be processed according to the key data;

the calculation module is used for acquiring the combinational logic corresponding to each group and calculating all the value data in the group according to the combinational logic to acquire final value data;

and the combination module is used for combining the key data corresponding to each group and the corresponding final value data into a final key value pair data to obtain final structured data.

9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the structured data generation method according to any one of claims 1 to 7.

10. An electronic device, comprising:

a processor; and

memory storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the structured data generation method of any one of claims 1 to 7.