CN117034230A - Data verification method, device, equipment and storage medium thereof - Google Patents
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Abstract
The embodiment of the application belongs to the technical fields of artificial intelligence and digital medical treatment, and relates to a data verification method, a device, equipment and a storage medium thereof, wherein the method, the device and the storage medium are applied to a process of converting Chinese business rules into code logic in a digital medical treatment business system and comprise the steps of obtaining newly input business rules; extracting key words of the newly input business rules one by one through a rule configuration model, explaining the extracted key words into machine code sentences, and integrating the newly input business rules into complete program code sentences; and loading a target verification rule through a rule loading layer, and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule. The traditional business rule configuration mode is changed through the rule configuration model, keywords are automatically extracted, machine code sentences are generated, program code sentences and rule verification are arranged, and the rule configuration readability is improved, and automatic maintenance and updating are facilitated.
Description
Technical Field
The application relates to the technical fields of artificial intelligence and digital medical treatment, which is applied to the process of converting Chinese business rules into code logic in a digital medical business system, in particular to a data verification method, a data verification device, data verification equipment and a storage medium thereof.
Background
Along with the development of the computer industry, the traditional medical industry business is gradually transformed to the digital medical business, and particularly on medical cloud services crossing multi-party online medical platforms, as the platforms related to the medical cloud services and offline cooperative medical organization are more numerous, in the online configuration process aiming at the business rule configuration of the medical cloud services, the business personnel always put forward rules, and the developer configures the rules to realize logic.
Because all the business rules are required to be pre-prepared by a business person, then the developer is informed to develop, and the developer receives the business rules and analyzes the business rules into code logic. In the process, communication between service personnel and developers is difficult to generate deviation, so that a code realization scene is not covered, the readability of rule configuration is poor, the rule configuration depends on the code development of the developers, the maintainability of the rule code is poor, the coupling between the service personnel and the developers is heavy, and in the iterative development process, the maintenance and updating of the existing service rules are difficult due to the rule configuration change of the service personnel and the developers.
Disclosure of Invention
The embodiment of the application aims to provide a data verification method, a device, equipment and a storage medium thereof, which are used for solving the problems of poor rule configuration readability and difficult rule code maintenance and updating caused by the fact that a traditional business rule configuration mode is used in the prior art.
In order to solve the above technical problems, the embodiment of the present application provides a data verification method, which adopts the following technical scheme:
a data verification method, comprising the steps of:
acquiring a newly input business rule;
the newly input business rules are sent to a preset rule configuration model one by one in units of number, wherein the rule configuration engine comprises a keyword extraction layer, a keyword interpretation layer, a rule arrangement layer and a rule loading layer;
extracting keywords one by one from the newly input business rules through the keyword extraction layer to obtain keywords in each target business rule;
interpreting keywords in each label business rule as machine code statements based on the keyword interpretation layer;
arranging machine code sentences corresponding to each target business rule through a compiler in the rule arranging layer to acquire program code sentences corresponding to each target business rule;
Integrating program code sentences corresponding to each standard business rule according to preset program execution logic and a linker in the rule arrangement layer, and acquiring integrated complete program code sentences corresponding to the newly input business rules;
and loading a target verification rule through the rule loading layer, and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule.
Further, before executing the step of transmitting the newly input business rule to a preset rule configuration model piece by piece in units of number, the method further includes:
starting an initialized rule configuration model;
acquiring a full traffic rule from a target rule base;
each business rule in the full business rules is sent to the initialized rule configuration model one by one, and machine learning training is carried out on the initialized rule configuration model;
and setting a rule configuration model with training completed as the preset rule configuration model.
Further, the step of performing machine learning training on the initialized rule configuration model specifically includes:
training the keyword extraction layer according to the full traffic rule;
Training the keyword interpretation layer based on a preset object field reference table;
training the verification processing process of the rule loading layer according to the total verification rules in a preset verification rule base.
Further, the step of training the keyword extraction layer according to the full traffic rule specifically includes:
acquiring a preset keyword reference table;
carrying out semantic analysis on each business rule in the total business rules to obtain a semantic analysis result corresponding to each business rule;
screening out keywords hit by a semantic analysis result corresponding to each business rule in the total business rules from the keyword reference table, wherein the keywords comprise nouns and verbs;
constructing a first knowledge graph based on each business rule in the total business rules and the keywords hit by each business rule;
deploying the first knowledge graph to the keyword extraction layer to complete training of the keyword extraction layer;
the step of training the keyword interpretation layer based on the preset object field reference table specifically comprises the following steps:
screening class objects and behavior objects corresponding to keywords in each business rule hit in the total business rule according to the object field reference table, wherein the object field reference table comprises class objects and behavior objects, the class objects correspond to nouns in the keywords, and the behavior objects correspond to verbs in the keywords;
Generating a machine code sentence based on a class object and a behavior object corresponding to a keyword hit by each business rule in the total business rule, wherein the behavior object refers to an operation instruction directly executed by a computer, and the class object comprises a main body object for executing the operation instruction and storage address information of the operation instruction;
constructing an association relation between each business rule in the full business rules and a corresponding machine code statement;
constructing a second knowledge graph according to the association relation between each business rule in the full business rules and the corresponding machine code statement;
and deploying the second knowledge graph to the keyword interpretation layer to complete training of the keyword interpretation layer.
Further, before executing the step of training the verification process of the rule loading layer according to the total verification rule in the preset verification rule base, the method further includes:
arranging machine code sentences corresponding to each business rule in the full business rules through a compiler in the rule arrangement layer to obtain program code sentences corresponding to each business rule in the full business rules;
Integrating program code sentences corresponding to each business rule in the total business rules according to preset program execution logic and a linker in the rule arrangement layer, and acquiring integrated complete program code sentences corresponding to the total business rules after integration;
the step of training the verification processing process of the rule loading layer according to the total verification rules in the preset verification rule base specifically comprises the following steps:
acquiring a full-quantity verification rule in the verification rule base;
each check rule in the full-volume check rule is called one by one to check the complete program code statement corresponding to the full-volume business rule, and a check result is obtained;
according to the verification result, identifying whether each verification rule passes verification, and if the verification rule which does not pass verification exists, sending a request for adjusting the complete program code statement corresponding to the full traffic rule to the target monitoring terminal;
and after the complete program code statement corresponding to the full-quantity business rule is adjusted, re-executing the verification step until each verification rule passes, recording and acquiring a specific adjustment mode when the complete program code statement corresponding to the full-quantity business rule is adjusted each time and verification rules which do not pass each time are adjusted in the whole verification process, and completing training of the rule loading layer verification processing process.
Further, the step of extracting the keywords from the newly input business rules one by one through the keyword extraction layer to obtain keywords in each target business rule specifically includes:
judging whether the first knowledge graph can identify keywords hit by each target business rule or not;
if the first knowledge graph can identify the keywords hit by each target business rule, directly acquiring the keywords corresponding to the target business rules according to the first knowledge graph;
if a target business rule of a keyword cannot be identified by the first knowledge graph exists, carrying out semantic analysis on the target business rule, obtaining a semantic analysis result corresponding to the target business rule, screening keywords hit by the semantic analysis result of the target business rule from the keyword reference table, and updating the first knowledge graph based on the corresponding relation between the target business rule and the keywords;
the step of interpreting the keywords in each label business rule as machine code sentences based on the keyword interpretation layer specifically comprises the following steps:
judging whether the second knowledge graph can identify a machine code statement corresponding to each target business rule;
If the second knowledge graph can identify the machine code statement corresponding to each target business rule, directly acquiring the machine code statement corresponding to the target business rule according to the second knowledge graph;
if the second knowledge graph cannot identify the target business rule of the machine code sentence, screening out a class object and a behavior object corresponding to the keyword in the target business rule according to the object field reference table, generating the machine code sentence based on the class object and the behavior object corresponding to the keyword in the target business rule, and updating the second knowledge graph based on the association relation between the target business rule and the corresponding machine code sentence.
Further, the step of loading a target verification rule through the rule loading layer and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule specifically includes:
transmitting the complete program code statement to a rule loading layer after training is completed;
automatically checking the complete program code statement corresponding to the newly input business rule according to the rule loading layer after training;
Judging whether a verification failure condition exists or not;
if the verification fails, acquiring a verification rule when the verification fails, and identifying whether the verification rule has a recorded specific adjustment mode or not;
if the recorded specific adjustment mode exists, adjusting the complete program code statement corresponding to the newly input business rule according to the recorded specific adjustment mode, and automatically checking the adjusted complete program code statement again;
if the recorded specific adjustment mode does not exist, a request for adjusting the complete program code statement corresponding to the newly input business rule is sent to the target monitoring end, after the complete program code statement corresponding to the newly input business rule is adjusted, automatic verification is carried out again, and the specific adjustment mode is recorded;
and (3) completing automatic verification until no verification failure exists.
In order to solve the above technical problems, the embodiment of the present application further provides a data verification device, which adopts the following technical scheme:
a data verification apparatus, comprising:
the business rule acquisition module is used for acquiring newly input business rules;
The business rule sending module is used for sending the newly input business rules to a preset rule configuration model one by one in units of number, wherein the rule configuration engine comprises a keyword extraction layer, a keyword interpretation layer, a rule arrangement layer and a rule loading layer;
the keyword extraction module is used for extracting keywords one by one from the newly input business rules through the keyword extraction layer to obtain keywords in each target business rule;
a keyword interpretation module, configured to interpret, based on the keyword interpretation layer, keywords in each label business rule as machine code statements, where the machine code statements refer to coding statements composed of 0 and 1;
the machine code arrangement module is used for arranging machine code sentences corresponding to each target business rule through a compiler in the rule arrangement layer to obtain program code sentences corresponding to each target business rule, wherein the program code sentences refer to code sentences which can be identified and executed by a target business system;
the program code integration module is used for integrating the program code statement corresponding to each standard business rule according to the preset program execution logic and the linker in the rule arrangement layer, and acquiring the complete program code statement corresponding to the newly input business rule after integration;
And the automatic verification module is used for loading a target verification rule through the rule loading layer and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule.
In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:
a computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the data verification method described above.
In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:
a computer readable storage medium having stored thereon computer readable instructions which when executed by a processor perform the steps of a data verification method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
according to the data verification method, the newly input business rule is obtained; the newly input business rules are sent to a preset rule configuration model one by one in units of number; extracting keywords one by one from the newly input business rules through a keyword extraction layer; interpreting the extracted keywords as machine code statements based on a keyword interpretation layer; integrating the newly input business rules into complete program code sentences through a rule arrangement layer; and loading a target verification rule through a rule loading layer, and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule. The traditional business rule configuration mode is changed through the rule configuration model, automatic keyword extraction, machine code statement generation, program code statement arrangement and rule verification are facilitated, rule configuration readability is improved, automatic maintenance and updating are facilitated, automatic keyword extraction, machine code statement generation, program code statement arrangement and rule verification of medical cloud service of the medical platform on a multi-party line are improved, and rule configuration readability, automatic maintenance and updating of the medical platform on the multi-party line are facilitated.
Drawings
In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a data verification method according to the present application;
FIG. 3 is a flow chart of one particular embodiment of machine learning training of the initialized rule configuration model according to a data verification method of an embodiment of the present application;
FIG. 4 is a flow chart of one embodiment of step 301 shown in FIG. 3;
FIG. 5 is a flow chart of one embodiment of step 302 shown in FIG. 3;
FIG. 6 is a flow chart of one embodiment of step 303 shown in FIG. 3;
FIG. 7 is a schematic diagram illustrating the structure of one embodiment of a data verification device in accordance with the present application;
FIG. 8 is a schematic diagram of a specific embodiment of the business rule model training module in the data verification device of the present application;
FIG. 9 is a schematic diagram of an embodiment of a computer device in accordance with the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the data verification method provided by the embodiment of the present application is generally executed by a server, and accordingly, the data verification device is generally disposed in the server.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of a data verification method according to the present application is shown. The data verification method comprises the following steps:
step 201, a newly input business rule is obtained.
In this embodiment, the business rule refers to descriptive text content defining and constraining a business, and according to different classification standards, the business rule may be classified into a constraint rule, a behavior rule, a calculation rule and a deduction rule.
Each business rule only defines one judgment and operation, and each business rule at least comprises a condition part and an execution part, wherein the condition part corresponds to defined judgment content, and the execution part corresponds to defined operation content.
Specifically, the newly input business rule may be a business rule applied to a Medical cloud service, where the Medical cloud (Medical group) refers to creating a Medical health service cloud platform by using "cloud computing" in combination with a Medical technology on the basis of new technologies such as cloud computing, mobile technology, multimedia, 4G communication, big data, internet of things, and the like, so as to realize sharing of Medical resources and expansion of Medical scope. The application and combination of the cloud computing technology can improve the efficiency of medical institutions and facilitate residents to seek medical attention. For example, appointment registration, electronic medical records, medical insurance and the like are products of combination of cloud computing and medical fields, and the medical cloud technology has the advantages of data security, information sharing, dynamic expansion and overall layout.
Step 202, the newly input business rules are sent to a preset rule configuration model one by one in units of number, wherein the rule configuration engine comprises a keyword extraction layer, a keyword interpretation layer, a rule arrangement layer and a rule loading layer.
In this embodiment, before executing the step of sending the newly input business rule to a preset rule configuration model one by one in units of number, the method further includes: starting an initialized rule configuration model; acquiring a full traffic rule from a target rule base; each business rule in the full business rules is sent to the initialized rule configuration model one by one, and machine learning training is carried out on the initialized rule configuration model; and setting a rule configuration model with training completed as the preset rule configuration model.
With continued reference to fig. 3, fig. 3 is a flowchart of a specific embodiment of machine learning training the initialized rule configuration model according to a data verification method according to an embodiment of the present application, including:
step 301, training the keyword extraction layer according to the full traffic rule;
specifically, training the keyword extraction layer according to the full traffic rule by combining a natural language understanding technology, wherein the natural language understanding technology can be an NLP natural language understanding technology;
with continued reference to fig. 4, fig. 4 is a flow chart of one embodiment of step 301 shown in fig. 3, comprising:
step 401, acquiring a preset keyword reference table;
step 402, performing semantic analysis on each business rule in the full business rules to obtain a semantic analysis result corresponding to each business rule;
specifically, semantic analysis is carried out on each business rule in the full-volume business rules through the natural language understanding technology, and a semantic analysis result corresponding to each business rule is obtained;
step 403, screening out keywords hit by a semantic analysis result corresponding to each business rule in the total business rules from the keyword reference table, wherein the keywords comprise nouns and verbs;
Step 404, constructing a first knowledge graph based on each business rule in the full business rules and the keywords hit by each business rule;
and step 405, deploying the first knowledge graph to the keyword extraction layer to complete training of the keyword extraction layer.
By training the keyword extraction layer, a first knowledge graph is obtained, the business rules on the medical cloud business are taken as examples continuously, and when a subsequent business person inputs a new business rule by training the first knowledge graph, whether the corresponding keyword can be directly obtained is judged according to the first knowledge graph, and the keyword extraction is automatically carried out, so that the processing speed of the rule configuration model is further improved.
Step 302, training the keyword interpretation layer based on a preset object field reference table;
with continued reference to fig. 5, fig. 5 is a flow chart of one embodiment of step 302 shown in fig. 3, comprising:
step 501, screening out class objects and behavior objects corresponding to keywords in each business rule hit in the total business rules according to the object field reference table;
in this embodiment, the object field reference table includes a class object and a behavior object, where the class object corresponds to a noun in the keyword, and the behavior object corresponds to a verb in the keyword.
Step 502, generating a machine code sentence based on class objects and behavior objects corresponding to the keywords hit by each business rule in the full business rule;
in this embodiment, the behavior object refers to an operation instruction directly executed by a computer, and the class object includes a main object for executing the operation instruction, and storage address information of the operation instruction.
Specifically, the machine code statement refers to a code statement that can be recognized by a machine before compiling, for example, taking the C language as an example, that is, a code statement in the C file.
Step 503, constructing an association relationship between each business rule in the full business rules and the corresponding machine code statement;
step 504, constructing a second knowledge graph according to the association relation between each business rule in the full business rules and the corresponding machine code statement;
and 505, deploying the second knowledge graph to the keyword interpretation layer to complete training of the keyword interpretation layer.
By training the keyword interpretation layer, a second knowledge graph is obtained, the service rules on the medical cloud service are taken as examples continuously, and when a subsequent service person inputs a new service rule by training the second knowledge graph, whether a corresponding machine code sentence can be directly obtained or not is judged according to the second knowledge graph, machine code sentence conversion is automatically carried out, and the processing speed of the rule configuration model is further improved.
Step 303, training the verification processing process of the rule loading layer according to the total verification rules in the preset verification rule base.
In this embodiment, before executing the step of training the verification process of the rule loading layer according to the total verification rule in the preset verification rule base, the method further includes: arranging machine code sentences corresponding to each business rule in the full business rules through a compiler in the rule arrangement layer to obtain program code sentences corresponding to each business rule in the full business rules; integrating the program code statement corresponding to each business rule in the total business rule according to the preset program execution logic and the linker in the rule arrangement layer, and obtaining the integrated complete program code statement corresponding to the total business rule.
Specifically, the program code statement refers to a code statement that can be executed by a computer after compiling, for example, taking the C language as an example, that is, a code statement in an exe file.
With continued reference to fig. 6, fig. 6 is a flow chart of one embodiment of step 303 shown in fig. 3, comprising:
step 601, acquiring a full-scale verification rule in the verification rule base;
Step 602, each check rule in the full-volume check rules is called one by one to check the complete program code statement corresponding to the full-volume business rule, and a check result is obtained;
step 603, identifying whether each check rule passes the check according to the check result, and if the check rule which does not pass the check rule exists, sending a request for adjusting the complete program code statement corresponding to the full traffic rule to the target monitoring terminal;
and step 604, after the complete program code statement corresponding to the full-volume business rule is adjusted, re-executing the verification step until each verification rule passes, recording and obtaining a specific adjustment mode when the complete program code statement corresponding to the full-volume business rule is adjusted each time and verification rules which do not pass each time are adjusted in the whole verification process, and completing training of the rule loading layer verification processing process.
By training the rule loading layer, the service rules on the medical cloud service are taken as examples continuously, so that rule verification can be automatically performed when a subsequent service person inputs new service rules, and the processing speed of the rule configuration model is further improved.
And 203, extracting keywords from the newly input business rules one by one through the keyword extraction layer to obtain keywords in each target business rule.
In this embodiment, the keyword extraction layer extracts the target business rules one by one to obtain keywords in each target business rule, where the keywords in each target business rule include key information fields in medical texts, the medical texts include medical electronic records (Electronic Healthcare Record), and the electronic records include a series of electronic records with values of saving and checking, such as medical records, electrocardiographs, and medical images, and may also be medical data, such as personal health records, prescriptions, and inspection reports
Specifically, the step of extracting keywords from the newly input business rules one by one through the keyword extraction layer to obtain keywords in each target business rule includes: judging whether the first knowledge graph can identify keywords hit by each target business rule or not; if the first knowledge graph can identify the keywords hit by each target business rule, directly acquiring the keywords corresponding to the target business rules according to the first knowledge graph; if the target business rule of the keyword cannot be identified by the first knowledge graph exists, semantic analysis is carried out on the target business rule through the natural voice understanding technology, a semantic analysis result corresponding to the target business rule is obtained, keywords hit by the semantic analysis result of the target business rule are screened out from the keyword reference table, and the first knowledge graph is updated based on the corresponding relation between the target business rule and the keywords.
By identifying, judging and updating the first knowledge graph, each time a new input business rule is acquired, not only the new input business rule is identified, but also the first knowledge graph is updated according to the identification condition, so that the applicability of the rule configuration model is further improved, and the business performance is improved.
Step 204, interpreting the keywords in each label business rule into machine code sentences based on the keyword interpretation layer, wherein the machine code sentences refer to coding sentences formed by 0 and 1.
In this embodiment, the step of interpreting, based on the keyword interpretation layer, the keywords in each label business rule as machine code sentences specifically includes: judging whether the second knowledge graph can identify a machine code statement corresponding to each target business rule; if the second knowledge graph can identify the machine code statement corresponding to each target business rule, directly acquiring the machine code statement corresponding to the target business rule according to the second knowledge graph; if the second knowledge graph cannot identify the target business rule of the machine code sentence, screening out a class object and a behavior object corresponding to the keyword in the target business rule according to the object field reference table, generating the machine code sentence based on the class object and the behavior object corresponding to the keyword in the target business rule, and updating the second knowledge graph based on the association relation between the target business rule and the corresponding machine code sentence.
And through identification judgment and updating of the second knowledge graph, each time a new input business rule is acquired, not only is the new input business rule identified, but also the second knowledge graph is updated according to the identification condition, so that the applicability of the rule configuration model is further improved, and the business performance is improved.
And step 205, arranging machine code sentences corresponding to each target business rule by a compiler in the rule arrangement layer to obtain program code sentences corresponding to each target business rule, wherein the program code sentences refer to code sentences which can be identified and executed by a target business system.
And step 206, integrating the program code sentences corresponding to each standard business rule according to the preset program execution logic and the linker in the rule arrangement layer, and obtaining the integrated complete program code sentences corresponding to the newly input business rules.
And step 207, loading a target verification rule through the rule loading layer, and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule.
In this embodiment, the step of loading a target verification rule through the rule loading layer and automatically verifying a complete program code statement corresponding to the newly input service rule according to the target verification rule specifically includes: transmitting the complete program code statement to a rule loading layer after training is completed; automatically checking the complete program code statement corresponding to the newly input business rule according to the rule loading layer after training; judging whether a verification failure condition exists or not; if the verification fails, acquiring a verification rule when the verification fails, and identifying whether the verification rule has a recorded specific adjustment mode or not; if the recorded specific adjustment mode exists, adjusting the complete program code statement corresponding to the newly input business rule according to the recorded specific adjustment mode, and automatically checking the adjusted complete program code statement again; if the recorded specific adjustment mode does not exist, a request for adjusting the complete program code statement corresponding to the newly input business rule is sent to the target monitoring end, after the complete program code statement corresponding to the newly input business rule is adjusted, automatic verification is carried out again, and the specific adjustment mode is recorded; and (3) completing automatic verification until no verification failure exists.
The rule check layer is perfected through identification judgment and updating, so that each time a newly input business rule is acquired, not only is the newly input business rule identified, but also the rule check layer is updated according to identification conditions, the applicability of the rule configuration model is further perfected, and the business performance is improved.
The application obtains the newly input business rule; the newly input business rules are sent to a preset rule configuration model one by one in units of number; extracting keywords one by one from the newly input business rules through a keyword extraction layer; interpreting the extracted keywords as machine code statements based on a keyword interpretation layer; integrating the newly input business rules into complete program code sentences through a rule arrangement layer; and loading a target verification rule through a rule loading layer, and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule. The traditional business rule configuration mode is changed through the rule configuration model, keywords are automatically extracted, machine code sentences are generated, program code sentences and rule verification are arranged, and the rule configuration readability is improved, and automatic maintenance and updating are facilitated. In addition, by training and updating the keyword extraction layer, the keyword interpretation layer and the rule loading layer of the rule configuration model in advance, the processing speed and the applicability of the rule configuration model are further improved, the business performance of the rule configuration model is improved, the automatic extraction of keywords, the generation of machine code sentences, the arrangement of program code sentences and the rule verification of medical cloud services of the medical platform on a multi-party line are improved, and the rule configuration readability, the automatic maintenance and the update of the medical platform on the multi-party line are improved.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
In the embodiment of the application, the traditional business rule configuration mode is changed by training the rule configuration model, the keywords are automatically extracted, the machine code sentences are generated, the program code sentences and the rule verification are arranged, the rule configuration readability is improved, the automatic maintenance and updating are convenient, the processing speed and the applicability of the rule configuration model are further improved, and the business performance is improved.
With further reference to fig. 7, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a data verification apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 7, the data verification apparatus 700 according to the present embodiment includes: business rule acquisition module 701, business rule transmission module 702, keyword extraction module 703, keyword interpretation module 704, machine code orchestration module 705, program code integration module 706, and automatic verification module 707.
Wherein:
a business rule obtaining module 701, configured to obtain a newly input business rule;
the business rule sending module 702 is configured to send the newly input business rules to a preset rule configuration model one by one in units of number, where the rule configuration engine includes a keyword extraction layer, a keyword interpretation layer, a rule arrangement layer, and a rule loading layer;
the keyword extraction module 703 is configured to extract, by using the keyword extraction layer, keywords in the newly input business rule one by one, and obtain keywords in each target business rule;
a keyword interpretation module 704, configured to interpret, based on the keyword interpretation layer, keywords in each label business rule as machine code statements, where the machine code statements refer to code statements composed of 0 and 1;
The machine code arranging module 705 is configured to arrange, by using a compiler in the rule arranging layer, machine code statements corresponding to each target service rule, and obtain a program code statement corresponding to each target service rule, where the program code statement refers to a code statement that can be identified and executed by a target service system;
a program code integration module 706, configured to integrate program code statements corresponding to each standard business rule according to preset program execution logic and a linker in the rule arrangement layer, and obtain integrated complete program code statements corresponding to the newly input business rule;
and the automatic verification module 707 is configured to load a target verification rule through the rule loading layer, and automatically verify a complete program code statement corresponding to the newly input service rule according to the target verification rule.
With continued reference to fig. 8, in some embodiments of the present application, the data verification apparatus 700 further includes: the business rule model training module 708, the business rule model training module 708 includes a keyword extraction layer training sub-module 801, a keyword interpretation layer training sub-module 802, and a rule loading layer verification training sub-module 803, wherein:
The keyword extraction layer training sub-module 801 is configured to train the keyword extraction layer according to the full traffic rules in combination with a natural language understanding technology, specifically, obtain a preset keyword reference table, perform semantic analysis on each traffic rule in the full traffic rules through the natural language understanding technology, obtain a semantic analysis result corresponding to each traffic rule, screen out keywords hit by the semantic analysis result corresponding to each traffic rule in the full traffic rules from the keyword reference table, construct a first knowledge graph based on each traffic rule in the full traffic rules and the keywords hit by each traffic rule, and deploy the first knowledge graph to the keyword extraction layer to complete training of the keyword extraction layer.
The keyword interpretation layer training sub-module 802 is configured to train the keyword interpretation layer based on a preset object field reference table, specifically, screen out a class object and a behavior object corresponding to a keyword in each business rule of the full business rule according to the object field reference table, generate machine code sentences based on a class object and a behavior object corresponding to a keyword in each business rule of the full business rule, construct an association relationship between each business rule of the full business rule and a corresponding machine code sentence thereof, construct a second knowledge graph according to an association relationship between each business rule of the full business rule and a corresponding machine code sentence thereof, and deploy the second knowledge graph to the keyword interpretation layer to complete training of the keyword interpretation layer.
The rule loading layer verification training submodule 803 is configured to train a verification processing procedure of the rule loading layer according to a full-scale verification rule in a preset verification rule base, specifically, acquire the full-scale verification rule in the verification rule base, call each verification rule in the full-scale verification rule one by one to verify a complete program code statement corresponding to the full-scale service rule, acquire a verification result, identify whether each verification rule passes verification according to the verification result, if the verification rule does not pass verification, send a request for adjusting the complete program code statement corresponding to the full-scale service rule to the target monitoring end, re-execute a verification step after adjusting the complete program code statement corresponding to the full-scale service rule until each verification rule passes, record and acquire a specific adjustment mode when adjusting the complete program code statement corresponding to the full-scale service rule each time and a verification rule which does not pass each time in the whole verification process, and train the rule loading layer verification processing procedure.
The application obtains the newly input business rule; the newly input business rules are sent to a preset rule configuration model one by one in units of number; extracting keywords one by one from the newly input business rules through a keyword extraction layer; interpreting the extracted keywords as machine code statements based on a keyword interpretation layer; integrating the newly input business rules into complete program code sentences through a rule arrangement layer; and loading a target verification rule through a rule loading layer, and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule. The traditional business rule configuration mode is changed through the rule configuration model, keywords are automatically extracted, machine code sentences are generated, program code sentences and rule verification are arranged, and the rule configuration readability is improved, and automatic maintenance and updating are facilitated. In addition, by training and updating the keyword extraction layer, the keyword interpretation layer and the rule loading layer of the rule configuration model in advance, the processing speed and the applicability of the rule configuration model are further improved, the business performance of the rule configuration model is improved, the automatic extraction of keywords, the generation of machine code sentences, the arrangement of program code sentences and the rule verification of medical cloud services of the medical platform on a multi-party line are improved, and the rule configuration readability, the automatic maintenance and the update of the medical platform on the multi-party line are improved.
Those skilled in the art will appreciate that implementing all or part of the above described embodiment methods may be accomplished by computer readable instructions, stored on a computer readable storage medium, that the program when executed may comprise the steps of embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 9, fig. 9 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 9 comprises a memory 9a, a processor 9b, a network interface 9c communicatively connected to each other via a system bus. It should be noted that only a computer device 9 having components 9a-9c is shown in the figures, but it should be understood that not all of the illustrated components need be implemented, and that more or fewer components may alternatively be implemented. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 9a includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 9a may be an internal storage unit of the computer device 9, such as a hard disk or a memory of the computer device 9. In other embodiments, the memory 9a may also be an external storage device of the computer device 9, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 9. Of course, the memory 9a may also comprise both an internal memory unit of the computer device 9 and an external memory device. In this embodiment, the memory 9a is typically used to store an operating system and various application software installed on the computer device 9, such as computer readable instructions of a data verification method. Further, the memory 9a may be used to temporarily store various types of data that have been output or are to be output.
The processor 9b may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 9b is typically used to control the overall operation of the computer device 9. In this embodiment, the processor 9b is configured to execute computer readable instructions stored in the memory 9a or process data, such as computer readable instructions for executing the data verification method.
The network interface 9c may comprise a wireless network interface or a wired network interface, which network interface 9c is typically used for establishing a communication connection between the computer device 9 and other electronic devices.
The computer equipment provided by the embodiment belongs to the technical field of artificial intelligence and digital medical treatment, and is applied to the process of converting Chinese business rules into code logic in a digital medical business system. The application obtains the newly input business rule; the newly input business rules are sent to a preset rule configuration model one by one in units of number; extracting keywords one by one from the newly input business rules through a keyword extraction layer; interpreting the extracted keywords as machine code statements based on a keyword interpretation layer; integrating the newly input business rules into complete program code sentences through a rule arrangement layer; and loading a target verification rule through a rule loading layer, and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule. The traditional business rule configuration mode is changed through the rule configuration model, keywords are automatically extracted, machine code sentences are generated, program code sentences and rule verification are arranged, and the rule configuration readability is improved, and automatic maintenance and updating are facilitated. In addition, the keyword extraction layer, the keyword interpretation layer and the rule loading layer of the rule configuration model are trained and updated in advance, so that the processing speed and applicability of the rule configuration model are further improved, and the service performance of the rule configuration model is improved.
The present application also provides another embodiment, namely, a computer readable storage medium storing computer readable instructions executable by a processor to cause the processor to perform the steps of the data verification method as described above.
The computer readable storage medium provided by the embodiment belongs to the technical field of artificial intelligence and digital medical treatment, and is applied to the process of converting Chinese business rules into code logic in a digital medical business system. The application obtains the newly input business rule; the newly input business rules are sent to a preset rule configuration model one by one in units of number; extracting keywords one by one from the newly input business rules through a keyword extraction layer; interpreting the extracted keywords as machine code statements based on a keyword interpretation layer; integrating the newly input business rules into complete program code sentences through a rule arrangement layer; and loading a target verification rule through a rule loading layer, and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule. The traditional business rule configuration mode is changed through the rule configuration model, keywords are automatically extracted, machine code sentences are generated, program code sentences and rule verification are arranged, and the rule configuration readability is improved, and automatic maintenance and updating are facilitated. In addition, the keyword extraction layer, the keyword interpretation layer and the rule loading layer of the rule configuration model are trained and updated in advance, so that the processing speed and applicability of the rule configuration model are further improved, and the service performance of the rule configuration model is improved.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (10)
1. A data verification method, comprising the steps of:
acquiring a newly input business rule;
the newly input business rules are sent to a preset rule configuration model one by one in units of number, wherein the rule configuration engine comprises a keyword extraction layer, a keyword interpretation layer, a rule arrangement layer and a rule loading layer;
extracting keywords one by one from the newly input business rules through the keyword extraction layer to obtain keywords in each target business rule;
interpreting keywords in each label business rule as machine code statements based on the keyword interpretation layer;
arranging machine code sentences corresponding to each target business rule through a compiler in the rule arranging layer to acquire program code sentences corresponding to each target business rule;
integrating program code sentences corresponding to each standard business rule according to preset program execution logic and a linker in the rule arrangement layer, and acquiring integrated complete program code sentences corresponding to the newly input business rules;
and loading a target verification rule through the rule loading layer, and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule.
2. The data verification method according to claim 1, wherein before the step of transmitting the newly inputted business rule piece by piece to a preset rule configuration model in units of pieces is performed, the method further comprises:
starting an initialized rule configuration model;
acquiring a full traffic rule from a target rule base;
each business rule in the full business rules is sent to the initialized rule configuration model one by one, and machine learning training is carried out on the initialized rule configuration model;
and setting a rule configuration model with training completed as the preset rule configuration model.
3. The method for verifying data according to claim 2, wherein the step of performing machine learning training on the initialized rule configuration model specifically comprises:
training the keyword extraction layer according to the full traffic rule;
training the keyword interpretation layer based on a preset object field reference table;
training the verification processing process of the rule loading layer according to the total verification rules in a preset verification rule base.
4. The data verification method according to claim 3, wherein the step of training the keyword extraction layer according to the full traffic rule specifically includes:
Acquiring a preset keyword reference table;
carrying out semantic analysis on each business rule in the total business rules to obtain a semantic analysis result corresponding to each business rule;
screening out keywords hit by a semantic analysis result corresponding to each business rule in the total business rules from the keyword reference table, wherein the keywords comprise nouns and verbs;
constructing a first knowledge graph based on each business rule in the total business rules and the keywords hit by each business rule;
deploying the first knowledge graph to the keyword extraction layer to complete training of the keyword extraction layer;
the step of training the keyword interpretation layer based on the preset object field reference table specifically comprises the following steps:
screening class objects and behavior objects corresponding to keywords in each business rule hit in the total business rule according to the object field reference table, wherein the object field reference table comprises class objects and behavior objects, the class objects correspond to nouns in the keywords, and the behavior objects correspond to verbs in the keywords;
generating a machine code sentence based on a class object and a behavior object corresponding to a keyword hit by each business rule in the total business rule, wherein the behavior object refers to an operation instruction directly executed by a computer, and the class object comprises a main body object for executing the operation instruction and storage address information of the operation instruction;
Constructing an association relation between each business rule in the full business rules and a corresponding machine code statement;
constructing a second knowledge graph according to the association relation between each business rule in the full business rules and the corresponding machine code statement;
and deploying the second knowledge graph to the keyword interpretation layer to complete training of the keyword interpretation layer.
5. A data verification method according to claim 3, wherein, before the step of training the verification process of the rule loading layer according to the total number of verification rules in the preset verification rule base, the method further comprises:
arranging machine code sentences corresponding to each business rule in the full business rules through a compiler in the rule arrangement layer to obtain program code sentences corresponding to each business rule in the full business rules;
integrating program code sentences corresponding to each business rule in the total business rules according to preset program execution logic and a linker in the rule arrangement layer, and acquiring integrated complete program code sentences corresponding to the total business rules after integration;
the step of training the verification processing process of the rule loading layer according to the total verification rules in the preset verification rule base specifically comprises the following steps:
Acquiring a full-quantity verification rule in the verification rule base;
each check rule in the full-volume check rule is called one by one to check the complete program code statement corresponding to the full-volume business rule, and a check result is obtained;
according to the verification result, identifying whether each verification rule passes verification, and if the verification rule which does not pass verification exists, sending a request for adjusting the complete program code statement corresponding to the full traffic rule to the target monitoring terminal;
and after the complete program code statement corresponding to the full-quantity business rule is adjusted, re-executing the verification step until each verification rule passes, recording and acquiring a specific adjustment mode when the complete program code statement corresponding to the full-quantity business rule is adjusted each time and verification rules which do not pass each time are adjusted in the whole verification process, and completing training of the rule loading layer verification processing process.
6. The data verification method according to claim 4, wherein the step of extracting keywords from the newly input business rules one by one through the keyword extraction layer to obtain keywords in each target business rule specifically comprises:
Judging whether the first knowledge graph can identify keywords hit by each target business rule or not;
if the first knowledge graph can identify the keywords hit by each target business rule, directly acquiring the keywords corresponding to the target business rules according to the first knowledge graph;
if a target business rule of a keyword cannot be identified by the first knowledge graph exists, carrying out semantic analysis on the target business rule, obtaining a semantic analysis result corresponding to the target business rule, screening keywords hit by the semantic analysis result of the target business rule from the keyword reference table, and updating the first knowledge graph based on the corresponding relation between the target business rule and the keywords;
the step of interpreting the keywords in each label business rule as machine code sentences based on the keyword interpretation layer specifically comprises the following steps:
judging whether the second knowledge graph can identify a machine code statement corresponding to each target business rule;
if the second knowledge graph can identify the machine code statement corresponding to each target business rule, directly acquiring the machine code statement corresponding to the target business rule according to the second knowledge graph;
If the second knowledge graph cannot identify the target business rule of the machine code sentence, screening out a class object and a behavior object corresponding to the keyword in the target business rule according to the object field reference table, generating the machine code sentence based on the class object and the behavior object corresponding to the keyword in the target business rule, and updating the second knowledge graph based on the association relation between the target business rule and the corresponding machine code sentence.
7. The data verification method according to claim 5, wherein the step of loading a target verification rule through the rule loading layer and automatically verifying a complete program code statement corresponding to the newly input business rule according to the target verification rule comprises the following steps:
transmitting the complete program code statement to a rule loading layer after training is completed;
automatically checking the complete program code statement corresponding to the newly input business rule according to the rule loading layer after training;
judging whether a verification failure condition exists or not;
if the verification fails, acquiring a verification rule when the verification fails, and identifying whether the verification rule has a recorded specific adjustment mode or not;
If the recorded specific adjustment mode exists, adjusting the complete program code statement corresponding to the newly input business rule according to the recorded specific adjustment mode, and automatically checking the adjusted complete program code statement again;
if the recorded specific adjustment mode does not exist, a request for adjusting the complete program code statement corresponding to the newly input business rule is sent to the target monitoring end, after the complete program code statement corresponding to the newly input business rule is adjusted, automatic verification is carried out again, and the specific adjustment mode is recorded;
and (3) completing automatic verification until no verification failure exists.
8. A data verification apparatus, comprising:
the business rule acquisition module is used for acquiring newly input business rules;
the business rule sending module is used for sending the newly input business rules to a preset rule configuration model one by one in units of number, wherein the rule configuration engine comprises a keyword extraction layer, a keyword interpretation layer, a rule arrangement layer and a rule loading layer;
the keyword extraction module is used for extracting keywords one by one from the newly input business rules through the keyword extraction layer to obtain keywords in each target business rule;
The keyword interpretation module is used for interpreting keywords in each label business rule into machine code sentences based on the keyword interpretation layer;
the machine code arrangement module is used for arranging machine code sentences corresponding to each target business rule through a compiler in the rule arrangement layer to obtain program code sentences corresponding to each target business rule;
the program code integration module is used for integrating the program code statement corresponding to each standard business rule according to the preset program execution logic and the linker in the rule arrangement layer, and acquiring the complete program code statement corresponding to the newly input business rule after integration;
and the automatic verification module is used for loading a target verification rule through the rule loading layer and automatically verifying the complete program code statement corresponding to the newly input business rule according to the target verification rule.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the data verification method of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the data verification method according to any of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117938405A (en) * | 2024-03-21 | 2024-04-26 | 北京火山引擎科技有限公司 | CDN service arrangement method, device, equipment and storage medium in multi-cloud environment |
| CN117938405B (en) * | 2024-03-21 | 2024-05-31 | 北京火山引擎科技有限公司 | CDN service arrangement method, device, equipment and storage medium in multi-cloud environment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117938405A (en) * | 2024-03-21 | 2024-04-26 | 北京火山引擎科技有限公司 | CDN service arrangement method, device, equipment and storage medium in multi-cloud environment |
| CN117938405B (en) * | 2024-03-21 | 2024-05-31 | 北京火山引擎科技有限公司 | CDN service arrangement method, device, equipment and storage medium in multi-cloud environment |
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