CN117976116A - An intelligent analysis method for accelerated recovery after surgery data - Google Patents

Abstract

The invention provides an intelligent analysis method for accelerating rehabilitation surgical data, which comprises the following steps: s100, establishing a resident rehabilitation data database, and acquiring rehabilitation data analysis requirement sentences of a user; s200, carrying out SQL analysis on the rehabilitation data analysis requirement statement of the user to obtain a word assisting, an implicit query condition and a display query condition in the rehabilitation data analysis requirement statement of the user, and generating an SQL statement according to the word assisting, the implicit query condition and the display query condition; s300, executing recovery data query analysis of the user according to the SQL statement. The invention can realize convenient data filling and verification, can cover most projects which need to be filled by doctors through the basic database, and can remind the doctors through the verification of the upper limit and the lower limit. The invention can realize automatic analysis, and doctors can select items to be analyzed independently through the software page, thereby achieving the purpose of rapidness and convenience.

Description

An intelligent analysis method for accelerated recovery after surgery data

Technical Field

The present invention relates to the technical field of design of intelligent analysis schemes for accelerated recovery surgery data, and in particular to an intelligent analysis method for accelerated recovery surgery data.

Background technique

As a cutting-edge concept in surgical surgery, ERAS has received great attention from the world. In recent years, many domestic hospitals have also begun to explore ERAS and accumulated certain data. Only by conducting scientific evaluation and research based on the existing data, and classifying and comparing the data, can better conclusions be drawn, and ultimately promote the development and improvement of ERAS.

Existing technologies can complete data analysis to a certain extent, but most of them are limited to a single table and cannot achieve analysis in multiple dimensions.

Therefore, the existing technology needs to be further developed.

Summary of the invention

The purpose of the present invention is to overcome the above technical deficiencies and provide an intelligent analysis method for accelerated recovery after surgery data to solve the problems existing in the prior art.

In order to achieve the above technical objectives, the present invention provides an intelligent analysis method for ERAS data, the method comprising:

S100, establishing a rehabilitation data database for inpatients and obtaining a user's rehabilitation data analysis requirement statement;

S200, performing SQL parsing on the user's rehabilitation data analysis demand statement, obtaining the auxiliary words, implicit query conditions and explicit query conditions in the user's rehabilitation data analysis demand statement, and generating SQL statements according to the auxiliary words, implicit query conditions and explicit query conditions;

S300: Execute user's rehabilitation data query and analysis according to SQL statements.

Specifically, the establishment of the inpatient rehabilitation data database includes:

The rehabilitation data generated by the inpatients during their hospitalization is acquired by using the information acquisition terminal installed in the hospital, and the rehabilitation data generated by the inpatients during their hospitalization is transmitted to the inpatient rehabilitation data database.

Specifically, the method of injecting the data generated by the inpatient during hospitalization into the standard database through various systems includes at least one of the following:

Database direct connection method; webService method; restFul method.

Specifically, the S100 further includes:

Create a dictionary of terms and generate an ERAS filling template.

Specifically, the step of creating a dictionary of terms and generating an ERAS filling template includes:

A set of terms required for ERAS research is created, wherein the terms required for ERAS research include term names, data types, and upper and lower limits of data. The set of terms required for ERAS research is used for selection according to needs when a user generates an ERAS filling template.

Specifically, the S100 further includes:

Obtain a set of entries selected by the user according to the user's own analysis needs when generating the ERAS filling template, generate an ERAS filling template according to the set of entries selected by the user according to the user's own analysis needs when generating the ERAS filling template, and generate an electronic form according to the ERAS filling template, obtain the name information of the required inpatient input by the user, and retrieve the rehabilitation data of the corresponding inpatient in the rehabilitation data database according to the name information of the required inpatient, and the upper and lower limits of the rehabilitation data of each entry, judge whether the rehabilitation data of the entry is normal according to the upper and lower limits of the data corresponding to the entry, and output a prompt signal to prompt the corresponding inpatient whether the rehabilitation data of the entry is normal according to the judgment result, establish an ERAS database and store the electronic form in the ERAS database.

Specifically, the S100 includes:

If the upper and lower limits of the data corresponding to the entry are within the upper and lower limits of the data corresponding to the entry, a prompt signal is output to remind the corresponding inpatient that the rehabilitation data of the entry is normal.

Specifically, the S100 includes:

If the upper and lower limits of the data corresponding to the entry are not within the upper and lower limits of the data corresponding to the entry, a prompt signal is outputted to remind the corresponding inpatient that the rehabilitation data of the entry is abnormal.

Specifically, the S100 includes:

A data analysis element database was established, and indexes were created for each ERAS data item in the electronic form of the ERAS database. The index contained the field names and table names corresponding to each ERAS data item for later query.

Specifically, the S100 includes:

Generate a particle database, enter the verbs, particles and meaningless words in the natural language into the particle database and define them, and use them to perform SQL analysis on the user's rehabilitation data analysis requirement statements.

Beneficial effects:

1. The present invention provides a set of methods for data collection, screening and analysis. By classifying, labeling, screening and generating data models, it ultimately provides researchers with a complete set of data comparison and analysis conclusions. Researchers can obtain the results autonomously and intuitively through graphics.

2. The present invention completes data collection by integrating the information acquisition terminals in various hospitals, thereby reducing the amount of filling in by hospital staff and alleviating their burden.

3. The present invention provides a liberalized template and verification: through the entry dictionary, the hospital administrator can freely set the filling items and configure the filling requirements, making data filling more flexible.

4. The present invention provides a highly intelligent analysis system: hospital staff can independently complete analysis and research through a piece of text or by selecting corresponding analysis items, which greatly improves the analysis speed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of an intelligent analysis method for enhanced recovery after surgery data provided in a specific embodiment of the present invention;

FIG. 2 is a structural diagram of an intelligent analysis system for accelerated recovery after surgery data provided in a specific embodiment of the present invention.

Detailed ways

In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings of the present invention. Based on the embodiments in this application, other similar embodiments obtained by ordinary technicians in this field without making creative work should all fall within the scope of protection of this application. In addition, the directional words mentioned in the following embodiments, such as "up", "down", "left", "right", etc., are only reference to the directions of the accompanying drawings. Therefore, the directional words used are used to illustrate rather than limit the invention.

The present invention will be further described below in conjunction with the accompanying drawings and preferred embodiments.

Referring to FIG. 1 , the present invention provides an intelligent analysis method for ERAS data, comprising:

S100: Establish a rehabilitation data database for inpatients and obtain a user's rehabilitation data analysis requirement statement.

It should be noted that the function of S100 is to create a standard database and inject the data generated during the patient's hospitalization into the standard database through various systems. The system is connected to the database directly or by webService or restFul.

S200, performing SQL parsing on the user's rehabilitation data analysis demand statement to obtain the auxiliary words, implicit query conditions and explicit query conditions in the user's rehabilitation data analysis demand statement, and generating SQL statements according to the auxiliary words, implicit query conditions and explicit query conditions.

S300: Execute user's rehabilitation data query and analysis according to SQL statements.

Specifically, the establishment of the inpatient rehabilitation data database includes:

The rehabilitation data generated by the inpatients during their hospitalization is acquired by using the information acquisition terminal installed in the hospital, and the rehabilitation data generated by the inpatients during their hospitalization is transmitted to the inpatient rehabilitation data database.

Specifically, the method of injecting the data generated by the inpatient during hospitalization into the standard database through various systems includes at least one of the following:

Database direct connection method; webService method; restFul method.

Specifically, the S100 further includes:

Create a dictionary of terms and generate an ERAS filling template.

Specifically, the step of creating a dictionary of terms and generating an ERAS filling template includes:

A set of terms required for ERAS research is created, wherein the terms required for ERAS research include term names, data types, and upper and lower limits of data. The set of terms required for ERAS research is used for selection according to needs when a user generates an ERAS filling template.

It should be noted here that the above steps are used to create an entry dictionary. This step is used by hospital administrators to create the required entries for ERAS. The entry contains the name, data type, and upper and lower limits of the data, which are used for selection when generating the ERAS filling template.

Specifically, the S100 further includes:

Obtain a set of entries selected by the user according to the user's own analysis needs when generating the ERAS filling template, generate an ERAS filling template according to the set of entries selected by the user according to the user's own analysis needs when generating the ERAS filling template, and generate an electronic form according to the ERAS filling template, obtain the name information of the required inpatient input by the user, and retrieve the rehabilitation data of the corresponding inpatient in the rehabilitation data database according to the name information of the required inpatient, and the upper and lower limits of the rehabilitation data of each entry, judge whether the rehabilitation data of the entry is normal according to the upper and lower limits of the data corresponding to the entry, and output a prompt signal to prompt the corresponding inpatient whether the rehabilitation data of the entry is normal according to the judgment result, establish an ERAS database and store the electronic form in the ERAS database.

Specifically, the S100 includes:

If the upper and lower limits of the data corresponding to the entry are within the upper and lower limits of the data corresponding to the entry, a prompt signal is output to remind the corresponding inpatient that the rehabilitation data of the entry is normal.

Specifically, the S100 further includes:

If the upper and lower limits of the data corresponding to the entry are not within the upper and lower limits of the data corresponding to the entry, a prompt signal is outputted to remind the corresponding inpatient that the rehabilitation data of the entry is abnormal.

It should be noted that the above steps are used to create a data filling template. The hospital administrator selects the terms required for ERAS research in the dictionary. After the selection is completed, the system will automatically generate a filling template and a form for doctors and nurses to fill in. The doctor selects the patient in the hospitalization list and enters the data filling page. He can click Import to automatically import the data that meets the corresponding terms in the standard database into the form. The rationality is judged by the upper and lower limits of the form, and the doctor is reminded. The data that cannot be imported will be filled in by the doctor and saved and put into the ERAS database.

It is understandable that hospital staff can complete the analysis in two ways: First: Enter a paragraph of text. Hospital staff can enter a paragraph of analysis requirements, and the system automatically parses it into a SQL statement. For example, the doctor writes the field words: Analyze the ratio of male and female patients in the Department of Breast Disease every day from July 3, 2020 to August 20, 2020. First, the system will look for auxiliary words. In this sentence, the auxiliary words are "for", "to", "daily", "of", "and", "proportion", and the meaningless words are "patient". Second, get the explicit query conditions. The explicit query condition in the sentence is "operation time". Third, get the implicit query conditions: The first condition is "Breast Disease Department". By querying the data analysis database, the following search conditions are obtained [Data: RXBKHL-Breast Disease Department (General Surgery Ward 9) (Nursing)] [Field Name: Ward Name] [Table Name: Maindata], the second condition is "Male", and the following search conditions are obtained [Data: Male] [Field Name: Gender] [Table Name: Maindata], the third condition is "Female", which is similar to the second condition, and then the SQL statement is obtained according to the meaning of the auxiliary words and the search conditions. Finally, the search conditions are determined as follows: surgery time: July 3, 2020 to August 20, 2020, ward name: RXBKHL-Breast Disease Department (General Surgery Ward 9) (Nursing), gender: male and female, comparison condition: gender, graph: bar chart. At the same time, if the hospital staff feels that it is inaccurate, they can choose the second method, select the analyzed terms, graphic style, search conditions, etc. on the data analysis page, and click Finish. The system automatically generates the splicing sql and displays it on the front-end page.

Specifically, the S100 includes:

A data analysis element database was established, and indexes were created for each ERAS data item in the electronic form of the ERAS database. The index contained the field names and table names corresponding to each ERAS data item for later query.

It should be noted here that the above steps are used to establish a data analysis element database and index the ERAS data. The index contains the field names and table names corresponding to the data for later queries.

Specifically, the S100 includes:

Generate a particle database, enter the verbs, particles and meaningless words in the natural language into the particle database and define them, and use them to perform SQL analysis on the user's rehabilitation data analysis requirement statements.

It should be noted here that the above steps are used to generate a particle database to fill in verbs, particles and meaningless words in natural language, such as "的", "至", "到" and other words, which are stored and defined to help understand their meaning in sentences and train them.

The following is an example of the process of the intelligent analysis method for enhanced recovery after surgery data of the present invention:

1. Create a standard database and inject the data generated during the patient's hospitalization into the standard database through various systems. The system can be connected directly to the database, webService, restFul, etc.

2. Create an entry dictionary. This step is used by hospital administrators to create the required entries for ERAS. The entry contains the name, data type, and upper and lower limits of the data, which are used for selection when generating the ERAS filling template.

3. Create a data filling template. The hospital administrator selects the entries required for ERAS research in the dictionary. After the selection is completed, the system will automatically generate a filling template and a form for doctors and nurses to fill in.

4. The doctor selects the patient in the hospitalization list and enters the data filling page. He can click Import to automatically import the data that meets the corresponding entries in the standard database into the form. The doctor will make a rationality judgment based on the upper and lower limits of the form and remind the doctor. If the data cannot be imported, the doctor will fill it in and save it and put it into the ERAS database.

5. Establish a data analysis element database and index the ERAS data. The index contains the field name and table name corresponding to the data for later query.

6. Generate a particle database to fill in the verbs, particles and meaningless words in natural language, such as "的", "至", "到" and other words, and define them to help understand their meaning in sentences and train them.

7. Create a data analysis template. Hospital staff can complete the analysis in two ways: First: Enter a paragraph of text. Hospital staff can enter a paragraph of analysis requirements, and the system automatically parses it into a SQL statement. For example, a doctor writes a field sentence: Analyze the ratio of male and female patients in the Department of Breast Diseases every day from July 3, 2020 to August 20, 2020. First, the system will look for auxiliary words. In this sentence, auxiliary words include "for", "to", "daily", "of", "and", "proportion", and meaningless words include "patient". Second, get the explicit query condition. The explicit query condition in the sentence is "operation time". Third, get the implicit query condition: The first condition is "Breast Disease Department". By querying the data analysis database, the following search conditions are obtained [Data: RXBKHL-Breast Disease Department (General Surgery Ward 9) (Nursing)] [Field Name: Ward Name] [Table Name: Maindata], the second condition is "Male", and the following search conditions are obtained [Data: Male] [Field Name: Gender] [Table Name: Maindata], the third condition is "Female", which is similar to the second condition, and then the SQL statement is obtained according to the meaning of the auxiliary word and the search condition. Finally, the search conditions are determined as follows: surgery time: July 3, 2020 to August 20, 2020, ward name: RXBKHL-Breast Disease Department (General Surgery Ward 9) (Nursing), gender: male and female, comparison condition: gender, graph: bar chart. At the same time, if the hospital staff feels that it is inaccurate, they can choose the second method, select the analyzed terms, graphic style, search conditions, etc. on the data analysis page, and click Finish. The system automatically generates the splicing sql and displays it on the front-end page.

It can be understood that the present invention provides a set of methods for data collection, screening and analysis. By classifying, labeling, screening and generating data models, it ultimately provides researchers with a complete set of data comparison and analysis conclusions, and researchers can obtain the results independently and intuitively through graphics.

It should be noted here that the present invention completes data collection by integrating the information acquisition terminals in various hospitals, thereby reducing the amount of filling in by hospital staff and alleviating their burden.

It can be understood that the present invention provides a liberalized template and verification: through the entry dictionary, the hospital administrator can freely set the filling items and configure the filling requirements, making data filling more flexible.

It should be noted here that the present invention provides a highly intelligent analysis system: hospital staff can independently complete analysis and research through a piece of text or by selecting corresponding analysis items, which greatly improves the analysis speed.

It is understandable that the present invention standardizes the data of various systems of the hospital through a standard database and stores it in a basic database for calling. The present invention has selectable templates, and hospital administrators can configure the filling template by selecting a configured entry dictionary. The present invention can realize convenient data filling and verification. Through the basic database, it can cover most of the items that doctors need to fill in, and through the verification of upper and lower limits, doctors can be reminded. The present invention can realize automated analysis, and doctors can independently select the items to be analyzed through the software page, achieving the purpose of fast and convenient.

Please refer to FIG. 2 . The present invention provides another embodiment. This embodiment provides an intelligent analysis system for ERAS data. The intelligent analysis system for ERAS data includes:

An acquisition module 100 is used to acquire a user's rehabilitation data analysis requirement statement;

The control module 200 is used to establish a rehabilitation data database for inpatients; or to perform SQL parsing on a user's rehabilitation data analysis requirement statement to obtain auxiliary words, implicit query conditions and explicit query conditions in the user's rehabilitation data analysis requirement statement, and generate SQL statements based on the auxiliary words, implicit query conditions and explicit query conditions; or to execute a user's rehabilitation data query analysis based on the SQL statement.

It can be understood that the present invention provides a set of methods for data collection, screening and analysis. By classifying, labeling, screening and generating data models, it ultimately provides researchers with a complete set of data comparison and analysis conclusions, and researchers can obtain the results independently and intuitively through graphics.

It should be noted here that the present invention completes data collection by integrating the information acquisition terminals in various hospitals, thereby reducing the amount of filling in by hospital staff and alleviating their burden.

It can be understood that the present invention provides a liberalized template and verification: through the entry dictionary, the hospital administrator can freely set the filling items and configure the filling requirements, making data filling more flexible.

It should be noted here that the present invention provides a highly intelligent analysis system: hospital staff can independently complete analysis and research through a piece of text or by selecting corresponding analysis items, which greatly improves the analysis speed.

It is understandable that the present invention standardizes the data of various systems of the hospital through a standard database and stores it in a basic database for calling. The present invention has selectable templates, and hospital administrators can configure the filling template by selecting a configured entry dictionary. The present invention can realize convenient data filling and verification. Through the basic database, it can cover most of the items that doctors need to fill in, and through the verification of upper and lower limits, doctors can be reminded. The present invention can realize automated analysis, and doctors can independently select the items to be analyzed through the software page, achieving the purpose of fast and convenient.

In a preferred embodiment, the present application further provides an electronic device, the electronic device comprising:

A memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for intelligent analysis of accelerated recovery surgery data is implemented. The computer device can be broadly a server, a terminal, or any other electronic device with necessary computing and/or processing capabilities. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected via a system bus. The processor of the computer device can be used to provide necessary computing, processing and/or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, the steps of the method of the present invention are performed.

The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed and executed in a distributed manner by one or more computer devices or processors. A single method step/operation, or two or more method steps/operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps/operations may be performed by one or more computer devices or processors, and one or more other method steps/operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step/operation, or perform two or more method steps/operations.

It will be appreciated by those skilled in the art that the method steps of the present invention may be performed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program may be stored in a non-temporary computer-readable storage medium, which causes the steps of the present invention to be performed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database, or other media herein may include non-volatile and/or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

It can be understood that the present invention provides a set of methods for data collection, screening and analysis. By classifying, labeling, screening and generating data models, it ultimately provides researchers with a complete set of data comparison and analysis conclusions, and researchers can obtain the results independently and intuitively through graphics.

It should be noted here that the present invention completes data collection by integrating the information acquisition terminals in various hospitals, thereby reducing the amount of filling in by hospital staff and alleviating their burden.

It can be understood that the present invention provides a liberalized template and verification: through the entry dictionary, the hospital administrator can freely set the filling items and configure the filling requirements, making data filling more flexible.

It should be noted here that the present invention provides a highly intelligent analysis system: hospital staff can independently complete analysis and research through a piece of text or by selecting corresponding analysis items, which greatly improves the analysis speed.

It is understandable that the present invention standardizes the data of various systems of the hospital through a standard database and stores it in a basic database for calling. The present invention has selectable templates, and hospital administrators can configure the filling template by selecting a configured entry dictionary. The present invention can realize convenient data filling and verification. Through the basic database, it can cover most of the items that doctors need to fill in, and through the verification of upper and lower limits, doctors can be reminded. The present invention can realize automated analysis, and doctors can independently select the items to be analyzed through the software page, achieving the purpose of fast and convenient.

The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

The specific implementation of the present invention described above does not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. An intelligent analysis method for accelerating rehabilitation surgical data, which is characterized by comprising the following steps:

s100, establishing a resident rehabilitation data database, and acquiring rehabilitation data analysis requirement sentences of a user;

S200, carrying out SQL analysis on the rehabilitation data analysis requirement statement of the user to obtain a word assisting, an implicit query condition and a display query condition in the rehabilitation data analysis requirement statement of the user, and generating an SQL statement according to the word assisting, the implicit query condition and the display query condition;

S300, executing recovery data query analysis of the user according to the SQL statement.

2. The method for intelligent analysis of accelerated recovery surgical data of claim 1, wherein said creating a resident recovery data database comprises:

And acquiring rehabilitation data generated during inpatient hospitalization by using an information acquisition terminal arranged in the hospital, and transmitting the rehabilitation data generated during inpatient hospitalization to an inpatient rehabilitation data database.

3. The method of intelligent analysis of accelerated rehabilitation surgical data according to claim 2, wherein the means for injecting data generated during hospitalization of the resident person into the standard database through the respective systems comprises at least one of:

a direct database butt joint mode; webService mode; restFul mode.

4. The accelerated recovery surgical data intelligent analysis method of claim 1, wherein S100 further comprises:

Creating a dictionary of terms and generating an erat filling template.

5. The method of intelligent analysis of accelerated rehabilitation surgical data of claim 4 wherein creating a dictionary of terms and generating an era filling template comprises:

Creating a set of terms required for the ERAS study, wherein the terms required for the ERAS study comprise term names, data types and upper and lower data limits, and the set of terms required for the ERAS study is used for selecting according to requirements when a user generates an ERAS filling template.

6. The accelerated recovery surgical data intelligent analysis method of claim 5, wherein S100 further comprises:

Acquiring a set of terms selected according to analysis requirements of a user when the user generates an ERAS filling template, generating the ERAS filling template according to the set of terms selected according to analysis requirements of the user when the user generates the ERAS filling template, generating an electronic form according to the ERAS filling template, acquiring name information of required inpatients input by the user, retrieving recovery data of corresponding inpatients according to the name information of the required inpatients, and judging whether the recovery data of each term is normal according to the upper limit and the lower limit of the data corresponding to the term, and outputting a prompt signal related to whether the recovery data of the term prompting the corresponding inpatients is normal according to a judging result, building an ERAS database and storing the electronic form in the ERAS database.

7. The accelerated recovery surgical data intelligent analysis method of claim 6, wherein S100 comprises:

If the upper and lower limits of the data corresponding to the vocabulary entry are within the upper and lower limits of the data corresponding to the vocabulary entry, outputting a prompting signal related to prompting the normal rehabilitation data of the vocabulary entry of the corresponding resident.

8. The accelerated recovery surgical data intelligent analysis method of claim 6, wherein S100 comprises:

If the upper and lower limits of the data corresponding to the vocabulary entry are not within the upper and lower limits of the data corresponding to the vocabulary entry, a prompting signal related to abnormal rehabilitation data of the vocabulary entry prompting the corresponding resident is output.

9. The accelerated recovery surgical data intelligent analysis method of claim 6, wherein S100 comprises:

establishing a data analysis element database, and establishing an index for each ERAS data in an electronic form in the ERAS database, wherein the index contains field names and table names corresponding to each ERAS data and is used for later inquiry.

10. The accelerated rehabilitation surgical data intelligent analysis method of claim 1, wherein S100 comprises:

Generating a word-assisting database, inputting verbs, word-assisting and nonsensical words in natural language into the word-assisting database and defining the verbs, the word-assisting and the nonsensical words, and performing SQL analysis on rehabilitation data analysis requirement sentences of the user.