CN113221570A - Processing method, device, equipment and storage medium based on-line inquiry information - Google Patents
Processing method, device, equipment and storage medium based on-line inquiry information Download PDFInfo
- Publication number
- CN113221570A CN113221570A CN202110598866.7A CN202110598866A CN113221570A CN 113221570 A CN113221570 A CN 113221570A CN 202110598866 A CN202110598866 A CN 202110598866A CN 113221570 A CN113221570 A CN 113221570A
- Authority
- CN
- China
- Prior art keywords
- information
- user
- user demand
- entity
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 87
- 238000000034 method Methods 0.000 claims abstract description 62
- 230000008569 process Effects 0.000 claims abstract description 45
- 230000000007 visual effect Effects 0.000 claims abstract description 36
- 230000002776 aggregation Effects 0.000 claims description 34
- 238000004220 aggregation Methods 0.000 claims description 34
- 238000004458 analytical method Methods 0.000 claims description 32
- 238000000605 extraction Methods 0.000 claims description 24
- 101150060512 SPATA6 gene Proteins 0.000 claims description 19
- 230000003340 mental effect Effects 0.000 claims description 13
- 230000011218 segmentation Effects 0.000 claims description 12
- 238000012550 audit Methods 0.000 claims description 11
- 238000001914 filtration Methods 0.000 claims description 11
- 238000013507 mapping Methods 0.000 claims description 9
- 238000013461 design Methods 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 5
- 230000004931 aggregating effect Effects 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 238000005457 optimization Methods 0.000 description 9
- 238000004422 calculation algorithm Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000012800 visualization Methods 0.000 description 6
- 238000010276 construction Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 230000001149 cognitive effect Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000012795 verification Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002688 persistence Effects 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G06F40/295—Named entity recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/284—Lexical analysis, e.g. tokenisation or collocates
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Finance (AREA)
- General Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Accounting & Taxation (AREA)
- Data Mining & Analysis (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Biomedical Technology (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Public Health (AREA)
- Computational Linguistics (AREA)
- Medical Informatics (AREA)
- Artificial Intelligence (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Pathology (AREA)
- Game Theory and Decision Science (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Software Systems (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to the technical field of artificial intelligence, is applied to the field of intelligent medical treatment, and provides a processing method, a device, equipment and a storage medium based on-line inquiry information, which are used for improving the efficiency of business process processing based on the on-line inquiry information. The processing method based on the on-line inquiry information comprises the following steps: predicting user requirements of user consultation information based on-line inquiry to obtain target user requirement information; acquiring a user demand entity and a user demand field object of target user demand information; retrieving the medical service relation topological graph through a domain model, a user demand entity and a user demand domain object to obtain target medical service information; acquiring an execution request of a visual form based on target medical service information; and calling a service execution system through the domain model to execute the service flow corresponding to the execution request. In addition, the invention also relates to a block chain technology, and the user consultation information based on online inquiry can be stored in the block chain.
Description
Technical Field
The invention relates to the field of intelligent decision making of artificial intelligence, in particular to a processing method, a device, equipment and a storage medium based on-line inquiry information.
Background
With the development of internet technology and computer technology, an online inquiry system is used, and business execution functions of doctor interface reservation, medical information feedback based on consultation of doctor, medical data audit and the like are realized through the online inquiry system. Currently, for service flow execution based on online inquiry, a service execution system generally acquires user consultation information of the online inquiry, searches a database according to the user consultation information to obtain corresponding service operation information, and calls a preset interface to execute service operation corresponding to the service operation information.
However, the accuracy of analysis of the user's consultation information is low, the modules of the business execution system have high coupling, the business execution system is limited by the structure of the database, and the business logic of the business execution system is poor in editability, which results in inefficient business process based on the on-line inquiry information.
Disclosure of Invention
The invention provides a processing method, a device, equipment and a storage medium based on-line inquiry information, which are used for improving the efficiency of business process processing based on the on-line inquiry information.
The invention provides a processing method based on-line inquiry information, which comprises the following steps:
acquiring user consultation information based on-line inquiry, and predicting the user consultation information based on user requirements to obtain target user requirement information;
carrying out entity identification and value object analysis on the target user demand information to obtain a user demand entity and a user demand field object;
retrieving a preset medical service relation topological graph based on the user demand entity and the user demand field object through a preset field model to obtain target medical service information;
generating a visual form of the target medical service information, sending the visual form to a user side, and receiving an execution request based on the visual form sent by the user side;
and calling a service execution system corresponding to the execution request through the domain model, and executing a service process corresponding to the execution request based on a preset application program logic.
Optionally, in a first implementation manner of the first aspect of the present invention, the obtaining user consultation information based on online inquiry, and predicting the user consultation information based on user requirements to obtain target user requirement information includes:
acquiring question-answer information based on-line inquiry, calling a preset processing model, and performing feature extraction based on user session information on the question-answer information based on the on-line inquiry to obtain user consultation information;
and acquiring a user portrait corresponding to the user consultation information, and performing prediction based on a user expected field and information matching based on the field on the user consultation information through a preset mental model and the user portrait to obtain target user demand information.
Optionally, in a second implementation manner of the first aspect of the present invention, the retrieving, by a preset domain model, a preset medical service relationship topological graph based on the user requirement entity and the user requirement domain object to obtain target medical service information includes:
performing directed graph topological sequence generation processing on a preset medical service relation topological graph through a preset domain model to obtain a target directed graph topological sequence set, and performing sequence hash value generation processing on the user demand entity and the user demand domain object to obtain a target sequence hash value;
and retrieving the target directed graph topology sequence set through the target sequence hash value to obtain target medical service information.
Optionally, in a third implementation manner of the first aspect of the present invention, the performing entity identification and value object analysis on the target user requirement information to obtain a user requirement entity and a user requirement field object includes:
performing word segmentation processing on the target user demand information based on a medical field dictionary to obtain target user demand word segmentation;
performing part-of-speech merging and filtering and entity recognition on the target user demand participle to obtain a user demand entity;
and carrying out object mapping on the user demand entity through a preset value object of the medical field to obtain a user demand field object.
Optionally, in a fourth implementation manner of the first aspect of the present invention, before the obtaining user consultation information based on online inquiry, and predicting the user consultation information based on user requirements to obtain target user requirement information, the method further includes:
acquiring historical user demand information and medical service data, and performing aggregation processing and association processing on the historical user demand information and the medical service data to obtain aggregation root data;
and creating a medical service relation topological graph and a field model through a preset field driving design strategy and the aggregation root data.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the acquiring historical user demand information and medical service data, and performing aggregation processing and association processing on the historical user demand information and the medical service data to obtain aggregation root data includes:
acquiring historical user demand information and medical service data, creating a corresponding relation between the historical user demand information and the medical service data, and determining the historical user demand information and the medical service data which create the corresponding relation as to-be-processed data;
sequentially carrying out domain classification, context recognition and context relation extraction on the data to be processed to obtain domain context information;
respectively performing entity extraction and value object generation on the domain context information to obtain a domain service entity and an entity service value object;
and grouping and combining the domain service entities and the entity service value objects and analyzing the association relationship to obtain aggregation root data.
Optionally, in a sixth implementation manner of the first aspect of the present invention, after the calling, by the domain model, a service execution system corresponding to the execution request and executing, based on a preset application logic, a service flow corresponding to the execution request, the method further includes:
and reading log information executed by a business process, acquiring audit data based on the business process, and optimizing the mental model, the domain model and the business execution system through the log information and the audit data.
The second aspect of the present invention provides a processing apparatus based on online inquiry information, including:
the prediction module is used for acquiring user consultation information based on-line inquiry, and predicting the user consultation information based on user requirements to obtain target user requirement information;
the identification analysis module is used for carrying out entity identification and value object analysis on the target user demand information to obtain a user demand entity and a user demand field object;
the retrieval module is used for retrieving a preset medical service relation topological graph based on the user demand entity and the user demand field object through a preset field model to obtain target medical service information;
the generation and transmission module is used for generating a visual form of the target medical service information, transmitting the visual form to a user side and receiving an execution request based on the visual form and transmitted by the user side;
and the calling execution module is used for calling a service execution system corresponding to the execution request through the domain model and executing a service process corresponding to the execution request based on a preset application program logic.
Optionally, in a first implementation manner of the second aspect of the present invention, the prediction module is specifically configured to:
acquiring question-answer information based on-line inquiry, calling a preset processing model, and performing feature extraction based on user session information on the question-answer information based on the on-line inquiry to obtain user consultation information;
and acquiring a user portrait corresponding to the user consultation information, and performing prediction based on a user expected field and information matching based on the field on the user consultation information through a preset mental model and the user portrait to obtain target user demand information.
Optionally, in a second implementation manner of the second aspect of the present invention, the retrieving module is specifically configured to:
performing directed graph topological sequence generation processing on a preset medical service relation topological graph through a preset domain model to obtain a target directed graph topological sequence set, and performing sequence hash value generation processing on the user demand entity and the user demand domain object to obtain a target sequence hash value;
and retrieving the target directed graph topology sequence set through the target sequence hash value to obtain target medical service information.
Optionally, in a third implementation manner of the second aspect of the present invention, the identification analysis module is specifically configured to:
performing word segmentation processing on the target user demand information based on a medical field dictionary to obtain target user demand word segmentation;
performing part-of-speech merging and filtering and entity recognition on the target user demand participle to obtain a user demand entity;
and carrying out object mapping on the user demand entity through a preset value object of the medical field to obtain a user demand field object.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the processing apparatus based on online inquiry information further includes:
the aggregation association module is used for acquiring historical user demand information and medical service data, and performing aggregation processing and association processing on the historical user demand information and the medical service data to obtain aggregation root data;
and the creating module is used for creating a medical service relation topological graph and a field model through a preset field drive design strategy and the aggregation root data.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the aggregation association module is specifically configured to:
acquiring historical user demand information and medical service data, creating a corresponding relation between the historical user demand information and the medical service data, and determining the historical user demand information and the medical service data which create the corresponding relation as to-be-processed data;
sequentially carrying out domain classification, context recognition and context relation extraction on the data to be processed to obtain domain context information;
respectively performing entity extraction and value object generation on the domain context information to obtain a domain service entity and an entity service value object;
and grouping and combining the domain service entities and the entity service value objects and analyzing the association relationship to obtain aggregation root data.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the processing apparatus based on online inquiry information further includes:
and the reading optimization module is used for reading log information executed by the business process, acquiring audit data based on the business process, and optimizing the mental model, the field model and the business execution system through the log information and the audit data.
A third aspect of the present invention provides a processing device based on online inquiry information, comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the online inquiry information-based processing device to perform the above-described online inquiry information-based processing method.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein instructions, which, when run on a computer, cause the computer to execute the above-described processing method based on-line inquiry information.
According to the technical scheme provided by the invention, user consultation information based on-line inquiry is obtained, and prediction based on user requirements is carried out on the user consultation information to obtain target user requirement information; carrying out entity identification and value object analysis on target user demand information to obtain a user demand entity and a user demand field object; retrieving a preset medical service relation topological graph based on a user demand entity and a user demand field object through a preset field model to obtain target medical service information; generating a visual form of the target medical service information, sending the visual form to a user side, and receiving an execution request based on the visual form sent by the user side; and calling a service execution system corresponding to the execution request through the domain model, and executing a service process corresponding to the execution request based on preset application program logic. According to the embodiment of the invention, the accuracy of analysis of the user consultation information is improved, the decoupling among the modules of the business execution system is realized, the editability of the business logic of the business execution system is improved, the execution flexibility of the business execution system is improved, and the efficiency of business process processing based on the on-line inquiry information is improved.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a processing method based on-line inquiry information in an embodiment of the present invention;
FIG. 2 is a schematic diagram of another embodiment of a processing method based on-line inquiry information according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a processing device based on-line inquiry information according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of another embodiment of a processing device based on-line inquiry information according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an embodiment of a processing device based on online inquiry information in the embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a processing method, a device, equipment and a storage medium based on-line inquiry information, which improve the efficiency of business process processing based on the on-line inquiry information.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a specific flow of the embodiment of the present invention is described below, and referring to fig. 1, an embodiment of the processing method based on the online inquiry information in the embodiment of the present invention includes:
101. and acquiring user consultation information based on-line inquiry, and predicting the user consultation information based on user requirements to obtain target user requirement information.
It is to be understood that the executing subject of the present invention may be a processing device based on-line inquiry information, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
After obtaining user authorization, a server calls a preset question-answering robot, sends and receives question-answering information based on-line inquiry to a user side establishing connection to obtain the question-answering information based on-line inquiry, the question-answering information based on-line inquiry comprises a robot information label and a user information label, the question-answering information based on-line inquiry is detected based on safety, integrity, readability and data format, if the detection is passed, the question-answering information based on-line inquiry is classified according to the user information label to obtain user consultation information based on-line inquiry, if the detection is not passed, a detection result is returned, and the question-answering information based on-line inquiry is obtained again. Wherein, the user consultation information comprises consultation information about medical information and/or consultation information about medical business process information. The target user requirement information comprises medical data and medical service execution requirement data, wherein the medical data can be disease data, medical data and service data related to the medical data, and the medical service execution requirement data can be appointment of a doctor end and service execution information related to the medical service (such as purchase operation flow of medical insurance, corresponding page interface call and the like).
The server calls preset predictive processing models to perform feature extraction on the user consultation information to obtain consultation feature information, classifies the consultation feature information based on preset cognitive domain types to obtain classified consultation feature information, performs classification type extraction on the classified consultation feature information through a preset label extraction algorithm to obtain consultation domain types, and the predictive processing models are used for describing, explaining and predicting the consultation domain of the user consultation information; the server determines user consultation information and consultation field types as to-be-retrieved comparison information, retrieves a pre-established field knowledge base through the to-be-retrieved comparison information to obtain initial demand information, the initial demand information comprises medical field service demand data corresponding to the to-be-retrieved comparison information, the initial demand information further comprises similarity between the medical field service demand data and the to-be-retrieved comparison information, the field knowledge base comprises medical field service demand data based on a user cognitive strategy, the server judges whether the similarity is larger than a preset threshold value, if yes, the corresponding initial demand information is determined as target user demand information, and if not, execution is stopped.
102. And carrying out entity identification and value object analysis on the target user demand information to obtain a user demand entity and a user demand field object.
The server carries out word segmentation processing, word combination processing and part-of-speech filtering on the target user demand information in sequence to obtain initial word segmentation; the method comprises the steps of obtaining a target medical field corresponding to target user demand information, calling a medical field dictionary corresponding to the target medical field, verifying initial participles, determining the initial participles as target participles if verification is passed, and performing participle processing, word merging processing and part-of-speech filtering based on the medical field dictionary on the initial participles if verification is not passed to obtain the target participles.
The server sequentially performs entity word boundary identification and entity type identification on the target word segmentation based on preset medical field entity information to obtain a user demand entity; the method comprises the steps that a server obtains measurement (or description information), invariance information and value equality of a user demand entity, and value object mapping processing is carried out on the user demand entity on the basis of the measurement (or description information), the invariance information and the value equality through a preset Object Relational Mapping (ORM) persistence mechanism to obtain a user demand field object.
103. And retrieving the preset medical service relation topological graph based on the user demand entity and the user demand field object through a preset field model to obtain target medical service information.
The server calls a pre-established domain model after obtaining a user demand entity and a user demand domain object, generates a hash value of the user demand entity and the user demand domain object, retrieves a preset medical service relation topological graph through the hash value to obtain initial medical service information corresponding to the user demand entity and the user demand domain object, and determines the initial medical service information and the service execution system as target medical service information through the preset domain model based on the corresponding relation between a pre-established service execution system and a service architecture which is a service architecture corresponding to the medical service relation topological graph, acquires the service execution system corresponding to the initial medical service information, wherein the medical service relation topological graph comprises medical service information corresponding to each medical field and the relation between the medical service information, the medical service information comprises service information in the medical field and service operation process node information corresponding to the service information.
104. And generating a visual form of the target medical service information, sending the visual form to the user side, and receiving an execution request based on the visual form sent by the user side.
The method comprises the steps that a server obtains configuration information of a visual form, a preset visual form generating engine is called, the visual form of target medical service information is generated based on the configuration information, the visual form is sent to a user side, the user side receives the visual form, renders the visual form to a preset visual editing interface, the user selects the target medical service information needing to be operated or searched through the visual editing interface, the user side generates an execution request based on the visual form according to the target medical service information needing to be operated or searched and clicked by the user on the visual editing interface, the execution request based on the visual form is sent to the server, and the server receives the execution request based on the visual form.
105. And calling a service execution system corresponding to the execution request through the domain model, and executing a service process corresponding to the execution request based on preset application program logic.
When receiving an execution request based on a visual form, a server sequentially carries out security detection and analysis on the execution request based on the visual form to obtain request key information, obtains a service execution system corresponding to the request key information through a domain model, calls a corresponding service execution script through the service execution system based on preset application program logic, executes a service process corresponding to the execution request, and improves the accuracy and efficiency of service execution. The specific execution process for calling the corresponding service execution script may be: and retrieving a script module set stored in a preset database through a service execution system based on preset application program logic to obtain a plurality of script modules, and splicing the script modules according to the application program logic to obtain a service execution script.
According to the embodiment of the invention, the accuracy of analysis of the user consultation information is improved, the decoupling among the modules of the business execution system is realized, the editability of the business logic of the business execution system is improved, the execution flexibility of the business execution system is improved, and the efficiency of business process processing based on the on-line inquiry information is improved. This scheme can be applied to in the wisdom medical field to promote the construction in wisdom city.
Referring to fig. 2, another embodiment of the processing method based on the online inquiry information according to the embodiment of the present invention includes:
201. acquiring historical user demand information and medical service data, and performing aggregation processing and association processing on the historical user demand information and the medical service data to obtain aggregation root data.
The server extracts historical user demand information and medical service data corresponding to each medical field from a preset database; the method comprises the steps of establishing a corresponding relation between historical user demand information and medical service data, determining the historical user demand information and the medical service data with the corresponding relation as data to be processed, performing context extraction on the data to be processed to obtain field context information, performing entity extraction and value object generation on the field context information to obtain a field service entity and an entity service value object, calling a preset aggregation algorithm, grouping and combining the field service entities to obtain first aggregated data, performing incidence relation analysis on the entity service value object to obtain second aggregated data, and determining the first aggregated data and the second aggregated data as aggregation root data.
Specifically, the server acquires historical user demand information and medical service data, creates a corresponding relation between the historical user demand information and the medical service data, and determines the historical user demand information and the medical service data with the corresponding relation as to-be-processed data; sequentially carrying out domain classification, context identification and context relation extraction on data to be processed to obtain domain context information; respectively performing entity extraction and value object generation on the domain context information to obtain a domain service entity and an entity service value object; and grouping and combining the domain business entities and the entity business value objects and analyzing the association relationship to obtain the aggregation root data.
The method comprises the steps that a server creates a corresponding relation between historical user demand information and medical service data, the historical user demand information and the medical service data which create the corresponding relation are determined as data to be processed, field classification is conducted on the data to be processed through a preset classification model and a preset field label to obtain field classification data, the field classification data are subjected to context-based identification, classification and labeling through preset context division configuration information to obtain context information, entity relation combined extraction is conducted on the context information to obtain field context information; performing entity identification and entity extraction, entity relationship identification and entity relationship extraction on the domain context information through a preset entity extraction model to obtain a domain business entity; acquiring object configuration information of the domain context information, calling a preset object generation interface, and creating a value object of the domain context information based on the object configuration information to obtain an entity service value object, wherein the object configuration information comprises the value, the attribute and the type of the object; calling a preset aggregation algorithm, carrying out classification and merging normalization processing based on similarity on the domain business entities to obtain first aggregation data, carrying out incidence relation analysis on the entity business value objects to obtain second aggregation data, and determining the first aggregation data and the second aggregation data as aggregation root data.
202. And creating a medical service relation topological graph and a field model through a preset field-driven design strategy and aggregation root data.
The method comprises the steps that a server carries out node matching on aggregated root data through a preset topological relation automatic generation algorithm and establishes a topological relation between a polygon and an arc section based on the node matching to obtain a medical service relation topological graph, and a field model is established based on the medical service relation topological graph through a preset field driving design strategy, wherein the field driving design strategy comprises a strategy of concurrent enabling and enabling time periods of a service starting interface corresponding to the medical service relation topological graph, a strategy of cache updating and database updating, a configuration strategy of a service disabling interface, and a strategy of corresponding mapping a service execution system and a service architecture corresponding to the medical service relation topological graph.
203. And acquiring user consultation information based on-line inquiry, and predicting the user consultation information based on user requirements to obtain target user requirement information.
Specifically, the server acquires question-answer information based on-line inquiry, calls a preset processing model, and performs feature extraction based on user session information on the question-answer information based on the on-line inquiry to obtain user consultation information; and acquiring a user portrait corresponding to the user consultation information, and performing prediction based on a user expected field and information matching based on the field on the user consultation information through a preset mental model and the user portrait to obtain target user demand information.
The server receives the on-line inquiry-based question-answer information sent by the user side, performs data cleaning-and-data conversion-based data preprocessing on the on-line inquiry-based question-answer information to obtain initial question-answer information, performs data type identification on the initial question-answer information to obtain a target data type, wherein the target data type comprises data types of texts, voices and images, and the on-line inquiry-based question-answer information comprises a robot information tag and a user information tag. The server classifies the initial question and answer information according to the user information labels to obtain initial consultation information, retrieves a preset processing model tree according to the target data type to obtain a processing model corresponding to the target data type, calls the processing model to identify and convert texts, voices or images of the initial consultation information to obtain text information, and performs multi-level convolution processing, up-sampling processing and batch processing on the text information to obtain the user consultation information.
The server obtains a user portrait corresponding to the user consultation information, retrieves the user portrait through the user consultation information to obtain a user interest field, retrieves a preset field knowledge base according to the user interest field to obtain first requirement information, the user portrait comprises a medical field of interest of the user consultation, and the field knowledge base comprises service requirement data of each medical field based on a user cognitive strategy. Classifying the user consultation information based on the preset medical consultation field type based on user cognition through a preset mental model to obtain a user expectation field, matching field information corresponding to the user expectation field from a preset field knowledge base to obtain second demand information, and performing duplication elimination and fusion on the first demand information and the second demand information to obtain target user demand information.
204. And carrying out entity identification and value object analysis on the target user demand information to obtain a user demand entity and a user demand field object.
Specifically, the server carries out word segmentation processing on the target user demand information based on a medical field dictionary to obtain target user demand segmented words; performing part-of-speech merging and filtering and entity recognition on the target user demand participle to obtain a user demand entity; and carrying out object mapping on the user demand entity through a preset value object of the medical field to obtain a user demand field object.
The server calls a medical field dictionary corresponding to the target user demand information, word segmentation processing is carried out on the target user demand information through the medical field dictionary to obtain initial user demand segmented words, the initial user demand segmented words are combined according to a preset medical field segmented word combination strategy to obtain target user demand segmented words, part of speech filtering is carried out on the target user demand segmented words according to a preset medical field segmented word filtering strategy to obtain filtering segmented words, a preset entity recognition model is called, and entity matching is carried out on the filtering segmented words based on a preset medical field entity map to obtain user demand entities. The server obtains value object configuration information, the value object configuration information comprises values, attributes, types, measurement information (or description information), invariance information and value equality of the medical field object and the user demand entity object, and the user demand entity is mapped into a preset value object of the medical field through a preset object relation mapping mechanism and the value object configuration information, so that the user demand field object is obtained.
205. And retrieving the preset medical service relation topological graph based on the user demand entity and the user demand field object through a preset field model to obtain target medical service information.
Specifically, the server performs directed graph topology sequence generation processing on a preset medical service relation topological graph through a preset domain model to obtain a target directed graph topology sequence set, and performs sequence hash value generation processing on a user demand entity and a user demand domain object to obtain a target sequence hash value; and retrieving the target directed graph topology sequence set through the target sequence hash value to obtain target medical service information.
The method comprises the steps that a server obtains a target vertex in a preset medical service relation topological graph, the target vertex is a vertex without predecessor, a topological sorting algorithm and a Dijkstra algorithm of a preset directed graph are called, based on the target vertex, sorting and shortest path calculation are carried out on the preset medical service relation topological graph, a target directed graph topological sequence set is obtained, a sequence hash value of a user demand entity and a user demand field object is generated, and the target directed graph topological sequence set is matched through the sequence hash value, so that target medical service information is obtained.
206. And generating a visual form of the target medical service information, sending the visual form to the user side, and receiving an execution request based on the visual form sent by the user side.
207. And calling a service execution system corresponding to the execution request through the domain model, and executing a service process corresponding to the execution request based on preset application program logic.
The process of steps 206-207 is similar to the process of steps 104-105, and will not be described herein again.
208. And reading the log information executed by the business process, acquiring audit data based on the business process, and optimizing the mind model, the field model and the business execution system through the log information and the audit data.
And the server monitors the execution state of the business process, reads the log information generated in the execution process of the business process when the execution state of the business process is monitored to be finished, and performs abnormal value analysis and execution time analysis on the log information to obtain log analysis data. The method comprises the steps that a server sends log information and log analysis data executed by a business process to an auditing end, the number of the auditing ends comprises one or more than one, the log information and the log analysis data are manually audited through the auditing end, the auditing data input by manual auditing are sent to the server, the server receives the auditing data, retrieves a preset and established optimization strategy tree through the log information, the log analysis data and the auditing data to obtain a target optimization strategy, and optimizes an intellectual model, a field model and a business execution system according to the target optimization strategy, wherein the target optimization strategy comprises a structural optimization scheme and an establishment optimization scheme of the model, and an execution node and an execution script optimization scheme corresponding to the business execution system. The accuracy of the mental model, the domain model and the business execution system is improved, and therefore the efficiency of business process processing based on-line inquiry information is improved.
According to the embodiment of the invention, the accuracy of analysis of the user consultation information is improved, the decoupling between the modules of the business execution system is realized, the editability of the business logic of the business execution system is improved, the execution flexibility of the business execution system is improved, and the accuracy of the mental model, the field model and the business execution system is improved, so that the efficiency of business process processing based on the on-line inquiry information is improved. This scheme can be applied to in the wisdom medical field to promote the construction in wisdom city.
In the above description of the processing method based on the online inquiry information in the embodiment of the present invention, referring to fig. 3, a processing device based on the online inquiry information in the embodiment of the present invention is described below, and an embodiment of the processing device based on the online inquiry information in the embodiment of the present invention includes:
the prediction module 301 is configured to obtain user consultation information based on online inquiry, and predict the user consultation information based on user requirements to obtain target user requirement information;
the identification analysis module 302 is used for performing entity identification and value object analysis on the target user demand information to obtain a user demand entity and a user demand field object;
the retrieval module 303 is configured to retrieve a preset medical service relationship topological graph based on a user demand entity and a user demand field object through a preset field model to obtain target medical service information;
the generation and transmission module 304 is configured to generate a visualization form of the target medical service information, transmit the visualization form to the user side, and receive an execution request based on the visualization form, which is transmitted by the user side;
and the calling execution module 305 is configured to call the service execution system corresponding to the execution request through the domain model, and execute the service flow corresponding to the execution request based on the preset application logic.
The function implementation of each module in the processing device based on the online inquiry information corresponds to each step in the processing method embodiment based on the online inquiry information, and the function and implementation process are not described in detail herein.
According to the embodiment of the invention, the accuracy of analysis of the user consultation information is improved, the decoupling among the modules of the business execution system is realized, the editability of the business logic of the business execution system is improved, the execution flexibility of the business execution system is improved, and the efficiency of business process processing based on the on-line inquiry information is improved. This scheme can be applied to in the wisdom medical field to promote the construction in wisdom city.
Referring to fig. 4, another embodiment of the processing device based on the online inquiry information according to the embodiment of the present invention includes:
the aggregation association module 307 is configured to acquire historical user demand information and medical service data, and perform aggregation processing and association processing on the historical user demand information and the medical service data to obtain aggregation root data;
the creating module 308 is configured to create a medical service relational topology diagram and a domain model through a preset domain-driven design strategy and aggregation root data;
the prediction module 301 is configured to obtain user consultation information based on online inquiry, and predict the user consultation information based on user requirements to obtain target user requirement information;
the identification analysis module 302 is used for performing entity identification and value object analysis on the target user demand information to obtain a user demand entity and a user demand field object;
the retrieval module 303 is configured to retrieve a preset medical service relationship topological graph based on a user demand entity and a user demand field object through a preset field model to obtain target medical service information;
the generation and transmission module 304 is configured to generate a visualization form of the target medical service information, transmit the visualization form to the user side, and receive an execution request based on the visualization form, which is transmitted by the user side;
the calling execution module 305 is configured to call a service execution system corresponding to the execution request through the domain model, and execute a service flow corresponding to the execution request based on a preset application program logic;
and the reading optimization module 306 is configured to read log information executed by the business process, acquire audit data based on the business process, and optimize the mental model, the domain model and the business execution system through the log information and the audit data.
Optionally, the prediction module 301 may be further specifically configured to:
acquiring question-answer information based on-line inquiry, calling a preset processing model, and performing feature extraction based on user session information on the question-answer information based on the on-line inquiry to obtain user consultation information;
and acquiring a user portrait corresponding to the user consultation information, and performing prediction based on a user expected field and information matching based on the field on the user consultation information through a preset mental model and the user portrait to obtain target user demand information.
Optionally, the retrieving module 303 may be further specifically configured to:
performing directed graph topological sequence generation processing on a preset medical service relation topological graph through a preset domain model to obtain a target directed graph topological sequence set, and performing sequence hash value generation processing on a user demand entity and a user demand domain object to obtain a target sequence hash value;
and retrieving the target directed graph topology sequence set through the target sequence hash value to obtain target medical service information.
Optionally, the recognition analysis module 302 may be further specifically configured to:
performing word segmentation processing on the target user demand information based on a medical field dictionary to obtain target user demand segmented words;
performing part-of-speech merging and filtering and entity recognition on the target user demand participle to obtain a user demand entity;
and carrying out object mapping on the user demand entity through a preset value object of the medical field to obtain a user demand field object.
Optionally, the aggregation association module 307 may be further specifically configured to:
acquiring historical user demand information and medical service data, creating a corresponding relation between the historical user demand information and the medical service data, and determining the historical user demand information and the medical service data which create the corresponding relation as to-be-processed data;
sequentially carrying out domain classification, context identification and context relation extraction on data to be processed to obtain domain context information;
respectively performing entity extraction and value object generation on the domain context information to obtain a domain service entity and an entity service value object;
and grouping and combining the domain business entities and the entity business value objects and analyzing the association relationship to obtain the aggregation root data.
The function implementation of each module and each unit in the processing device based on the online inquiry information corresponds to each step in the processing method embodiment based on the online inquiry information, and the function and implementation process are not described in detail herein.
According to the embodiment of the invention, the accuracy of analysis of the user consultation information is improved, the decoupling between the modules of the business execution system is realized, the editability of the business logic of the business execution system is improved, the execution flexibility of the business execution system is improved, and the accuracy of the mental model, the field model and the business execution system is improved, so that the efficiency of business process processing based on the on-line inquiry information is improved. This scheme can be applied to in the wisdom medical field to promote the construction in wisdom city.
Fig. 3 and 4 above describe the processing device based on online inquiry information in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the processing device based on online inquiry information in the embodiment of the present invention is described in detail from the perspective of hardware processing.
Fig. 5 is a schematic structural diagram of an online inquiry information-based processing device according to an embodiment of the present invention, where the online inquiry information-based processing device 500 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instructions operating on the processing device 500 based on the on-line interrogation information. Still further, the processor 510 may be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on the processing device 500 based on the online interrogation information.
The online interrogation information-based processing device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and the like. Those skilled in the art will appreciate that the configuration of the online interrogation information-based processing device shown in fig. 5 does not constitute a limitation of the online interrogation information-based processing device, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
The present application further provides a processing device based on online inquiry information, comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line; the at least one processor invokes the instructions in the memory to cause the online inquiry information-based processing device to perform the steps in the online inquiry information-based processing method described above.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and which may also be a volatile computer-readable storage medium, having stored thereon instructions, which, when executed on a computer, cause the computer to perform the steps of the processing method based on-line interrogation information.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A processing method based on-line inquiry information is characterized by comprising the following steps:
acquiring user consultation information based on-line inquiry, and predicting the user consultation information based on user requirements to obtain target user requirement information;
carrying out entity identification and value object analysis on the target user demand information to obtain a user demand entity and a user demand field object;
retrieving a preset medical service relation topological graph based on the user demand entity and the user demand field object through a preset field model to obtain target medical service information;
generating a visual form of the target medical service information, sending the visual form to a user side, and receiving an execution request based on the visual form sent by the user side;
and calling a service execution system corresponding to the execution request through the domain model, and executing a service process corresponding to the execution request based on a preset application program logic.
2. The processing method based on the on-line inquiry information as claimed in claim 1, wherein the obtaining of the user consultation information based on the on-line inquiry and the prediction of the user consultation information based on the user requirement to obtain the target user requirement information comprises:
acquiring question-answer information based on-line inquiry, calling a preset processing model, and performing feature extraction based on user session information on the question-answer information based on the on-line inquiry to obtain user consultation information;
and acquiring a user portrait corresponding to the user consultation information, and performing prediction based on a user expected field and information matching based on the field on the user consultation information through a preset mental model and the user portrait to obtain target user demand information.
3. The processing method based on the on-line inquiry information as claimed in claim 1, wherein the retrieving the preset medical service relationship topological graph based on the user requirement entity and the user requirement domain object through the preset domain model to obtain the target medical service information comprises:
performing directed graph topological sequence generation processing on a preset medical service relation topological graph through a preset domain model to obtain a target directed graph topological sequence set, and performing sequence hash value generation processing on the user demand entity and the user demand domain object to obtain a target sequence hash value;
and retrieving the target directed graph topology sequence set through the target sequence hash value to obtain target medical service information.
4. The processing method based on the on-line inquiry information as claimed in claim 1, wherein the performing entity recognition and value object analysis on the target user requirement information to obtain the user requirement entity and the user requirement field object comprises:
performing word segmentation processing on the target user demand information based on a medical field dictionary to obtain target user demand word segmentation;
performing part-of-speech merging and filtering and entity recognition on the target user demand participle to obtain a user demand entity;
and carrying out object mapping on the user demand entity through a preset value object of the medical field to obtain a user demand field object.
5. The processing method based on-line inquiry information as claimed in claim 1, wherein before obtaining the user consultation information based on-line inquiry and predicting the user consultation information based on user requirements, the method further comprises:
acquiring historical user demand information and medical service data, and performing aggregation processing and association processing on the historical user demand information and the medical service data to obtain aggregation root data;
and creating a medical service relation topological graph and a field model through a preset field driving design strategy and the aggregation root data.
6. The processing method based on the on-line inquiry information as claimed in claim 5, wherein the acquiring of the historical user demand information and the medical service data, the aggregating and associating of the historical user demand information and the medical service data to obtain the aggregated root data comprises:
acquiring historical user demand information and medical service data, creating a corresponding relation between the historical user demand information and the medical service data, and determining the historical user demand information and the medical service data which create the corresponding relation as to-be-processed data;
sequentially carrying out domain classification, context recognition and context relation extraction on the data to be processed to obtain domain context information;
respectively performing entity extraction and value object generation on the domain context information to obtain a domain service entity and an entity service value object;
and grouping and combining the domain service entities and the entity service value objects and analyzing the association relationship to obtain aggregation root data.
7. The processing method based on the on-line inquiry information as claimed in any one of claims 1 to 6, wherein said invoking a business execution system corresponding to the execution request by the domain model, based on a preset application logic, further comprises, after executing a business process corresponding to the execution request:
and reading log information executed by a business process, acquiring audit data based on the business process, and optimizing the mental model, the domain model and the business execution system through the log information and the audit data.
8. An online inquiry information-based processing apparatus, comprising:
the prediction module is used for acquiring user consultation information based on-line inquiry, and predicting the user consultation information based on user requirements to obtain target user requirement information;
the identification analysis module is used for carrying out entity identification and value object analysis on the target user demand information to obtain a user demand entity and a user demand field object;
the retrieval module is used for retrieving a preset medical service relation topological graph based on the user demand entity and the user demand field object through a preset field model to obtain target medical service information;
the generation and transmission module is used for generating a visual form of the target medical service information, transmitting the visual form to a user side and receiving an execution request based on the visual form and transmitted by the user side;
and the calling execution module is used for calling a service execution system corresponding to the execution request through the domain model and executing a service process corresponding to the execution request based on a preset application program logic.
9. An online inquiry information-based processing apparatus, characterized in that the online inquiry information-based processing apparatus comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the online interrogation information-based processing device to perform the online interrogation information-based processing method of any of claims 1-7.
10. A computer-readable storage medium having instructions stored thereon, wherein the instructions, when executed by a processor, implement the method for processing based on-line inquiry information according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110598866.7A CN113221570B (en) | 2021-05-31 | Processing method, device, equipment and storage medium based on online inquiry information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110598866.7A CN113221570B (en) | 2021-05-31 | Processing method, device, equipment and storage medium based on online inquiry information |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113221570A true CN113221570A (en) | 2021-08-06 |
| CN113221570B CN113221570B (en) | 2024-07-26 |
Family
ID=
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112035567A (en) * | 2020-08-21 | 2020-12-04 | 腾讯科技（深圳）有限公司 | Data processing method and device and computer readable storage medium |
| CN115438995A (en) * | 2022-09-21 | 2022-12-06 | 青岛酷特智能股份有限公司 | Service processing method and equipment for garment customization enterprise based on knowledge graph |
| CN116301731A (en) * | 2023-02-17 | 2023-06-23 | 武汉天恒信息技术有限公司 | Demand analysis method, device and storage device based on natural language |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110504028A (en) * | 2019-08-22 | 2019-11-26 | 上海软中信息***咨询有限公司 | A kind of disease way of inquisition, device, system, computer equipment and storage medium |
| CN111477345A (en) * | 2019-01-23 | 2020-07-31 | 四川君德利远程医疗科技有限公司 | Intelligent telemedicine system |
| CN112035642A (en) * | 2020-08-31 | 2020-12-04 | 康键信息技术（深圳）有限公司 | Customer service matching method, device, equipment and storage medium |
| CN112035636A (en) * | 2020-08-28 | 2020-12-04 | 康键信息技术（深圳）有限公司 | Question-answer management method, device, equipment and storage medium of medical inquiry system |
| CN112035611A (en) * | 2020-08-28 | 2020-12-04 | 康键信息技术（深圳）有限公司 | Target user recommendation method and device, computer equipment and storage medium |
| CN112181397A (en) * | 2020-09-30 | 2021-01-05 | 平安科技（深圳）有限公司 | Flow visualization monitoring method and device, computer equipment and storage medium |
| CN112395883A (en) * | 2021-01-19 | 2021-02-23 | 阿里健康科技(杭州)有限公司 | Inquiry processing method, inquiry data processing method and device |
| CN112509682A (en) * | 2020-12-15 | 2021-03-16 | 康键信息技术（深圳）有限公司 | Text recognition-based inquiry method, device, equipment and storage medium |
| CN112700888A (en) * | 2020-12-31 | 2021-04-23 | 北大医疗信息技术有限公司 | Method and system for generating inquiry flow and readable storage medium |
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111477345A (en) * | 2019-01-23 | 2020-07-31 | 四川君德利远程医疗科技有限公司 | Intelligent telemedicine system |
| CN110504028A (en) * | 2019-08-22 | 2019-11-26 | 上海软中信息***咨询有限公司 | A kind of disease way of inquisition, device, system, computer equipment and storage medium |
| CN112035636A (en) * | 2020-08-28 | 2020-12-04 | 康键信息技术（深圳）有限公司 | Question-answer management method, device, equipment and storage medium of medical inquiry system |
| CN112035611A (en) * | 2020-08-28 | 2020-12-04 | 康键信息技术（深圳）有限公司 | Target user recommendation method and device, computer equipment and storage medium |
| CN112035642A (en) * | 2020-08-31 | 2020-12-04 | 康键信息技术（深圳）有限公司 | Customer service matching method, device, equipment and storage medium |
| CN112181397A (en) * | 2020-09-30 | 2021-01-05 | 平安科技（深圳）有限公司 | Flow visualization monitoring method and device, computer equipment and storage medium |
| CN112509682A (en) * | 2020-12-15 | 2021-03-16 | 康键信息技术（深圳）有限公司 | Text recognition-based inquiry method, device, equipment and storage medium |
| CN112700888A (en) * | 2020-12-31 | 2021-04-23 | 北大医疗信息技术有限公司 | Method and system for generating inquiry flow and readable storage medium |
| CN112395883A (en) * | 2021-01-19 | 2021-02-23 | 阿里健康科技(杭州)有限公司 | Inquiry processing method, inquiry data processing method and device |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112035567A (en) * | 2020-08-21 | 2020-12-04 | 腾讯科技（深圳）有限公司 | Data processing method and device and computer readable storage medium |
| CN115438995A (en) * | 2022-09-21 | 2022-12-06 | 青岛酷特智能股份有限公司 | Service processing method and equipment for garment customization enterprise based on knowledge graph |
| CN115438995B (en) * | 2022-09-21 | 2023-10-10 | 青岛酷特智能股份有限公司 | Business processing method and equipment for clothing customization enterprise based on knowledge graph |
| CN116301731A (en) * | 2023-02-17 | 2023-06-23 | 武汉天恒信息技术有限公司 | Demand analysis method, device and storage device based on natural language |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110765275B (en) | Search method, search device, computer equipment and storage medium | |
| US7930242B2 (en) | Methods and systems for multi-credit reporting agency data modeling | |
| AU2020236989B2 (en) | Handling categorical field values in machine learning applications | |
| CN111767716B (en) | Method and device for determining enterprise multi-level industry information and computer equipment | |
| WO2012080077A1 (en) | Cleansing a database system to improve data quality | |
| WO2022068160A1 (en) | Artificial intelligence-based critical illness inquiry data identification method and apparatus, device, and medium | |
| CN112685374B (en) | Log classification method and device and electronic equipment | |
| CN116956026A (en) | Training method and system for network asset identification model | |
| CN114372082B (en) | Data query method and device based on artificial intelligence, electronic equipment and medium | |
| CN114491079A (en) | Knowledge graph construction and query method, device, equipment and medium | |
| CN113806492A (en) | Record generation method, device and equipment based on semantic recognition and storage medium | |
| CN111783425B (en) | Intention identification method based on syntactic analysis model and related device | |
| CN116741358A (en) | Inquiry registration recommendation method, inquiry registration recommendation device, inquiry registration recommendation equipment and storage medium | |
| CN115146653B (en) | Dialogue scenario construction method, device, equipment and storage medium | |
| CN113706207B (en) | Order success rate analysis method, device, equipment and medium based on semantic analysis | |
| CN115147020A (en) | Decoration data processing method, device, equipment and storage medium | |
| CN115017187A (en) | Data query method, device, equipment and storage medium | |
| CN113221570A (en) | Processing method, device, equipment and storage medium based on-line inquiry information | |
| CN113221570B (en) | Processing method, device, equipment and storage medium based on online inquiry information | |
| CN113888265A (en) | Product recommendation method, device, equipment and computer-readable storage medium | |
| CN114708073A (en) | Intelligent detection method and device for surrounding mark and serial mark, electronic equipment and storage medium | |
| CN114064893A (en) | Abnormal data auditing method, device, equipment and storage medium | |
| CN113611427A (en) | User portrait generation method, device, equipment and storage medium | |
| CN112818215A (en) | Product data processing method, device, equipment and storage medium | |
| CN113434760B (en) | Construction method recommendation method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |