CN111190964B - Data docking method, device, equipment and storage medium - Google Patents
Data docking method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN111190964B CN111190964B CN201911401627.7A CN201911401627A CN111190964B CN 111190964 B CN111190964 B CN 111190964B CN 201911401627 A CN201911401627 A CN 201911401627A CN 111190964 B CN111190964 B CN 111190964B
- Authority
- CN
- China
- Prior art keywords
- data
- new
- service
- preset
- model
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Finance (AREA)
- Data Mining & Analysis (AREA)
- Accounting & Taxation (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- General Business, Economics & Management (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention relates to the technical field of big data, and discloses a data docking method, a device, equipment and a storage medium, which are used for extracting service data of a service target according to a data acquisition instruction, a preset dimension model and a preset dimension subdivision model, improving the efficiency of docking the service data of the target service and solving the problem of difficult data docking. The method comprises the following steps: acquiring a data acquisition request instruction; judging the data type of the service data of the target service based on the data acquisition request instruction; if the service data of the target service belongs to the historical data, extracting the historical service data according to the data acquisition request instruction; if the service data of the target service belongs to the new data, obtaining new service data according to the new data, the second preset dimension model and the second preset dimension subdivision model, and extracting the new service data; and transmitting the new service data or the historical service data to a transmission interface matched with the URL address of the preset uniform resource locator.
Description
Technical Field
The present invention relates to the field of big data technologies, and in particular, to a data docking method, apparatus, device, and storage medium.
Background
At present, the big data concept is widely popularized in the society, and data accumulation and data service are applied to various industries. For example, in the daily business of company a, it is often necessary to interface data with company B, so company a needs to acquire information data from company B and then interface the information data into company B. It follows that how to interface data is an important item for big data services.
However, as the data or data forms of the butt joint between companies are different, protocols and systems for the butt joint of the data to be accessed are complex, and the problems of difficult butt joint and low butt joint efficiency are caused.
Disclosure of Invention
The invention provides a data docking method, a device, equipment and a storage medium, which are used for directly extracting and constructing historical service data of a first preset dimension model and a first preset dimension subdivision model according to a data acquisition request instruction, or inputting the data into a second preset dimension model and a second preset dimension subdivision model for data splitting and recombination to obtain new service data, and sending the historical service data or the new service data to a target system, so that the efficiency of docking the service data of a target service is improved, and the problem of difficult data docking is solved.
A first aspect of an embodiment of the present invention provides a data interfacing method, including: acquiring a data acquisition request instruction, wherein the data acquisition request instruction is used for acquiring service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service; judging the data type of the business data of the target business based on the data acquisition request instruction, wherein the data type comprises historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space; if the service data of the target service belong to the historical data, extracting the historical service data according to the data acquisition request instruction, wherein the historical service data are historical data required for establishing a first preset dimension model and a first preset dimension subdivision model; if the service data of the target service belongs to the new data, obtaining new service data according to the new data, a second preset dimension model and a second preset dimension subdivision model, and extracting the new service data; and transmitting the new service data or the historical service data to a transmission interface matched with a preset Uniform Resource Locator (URL) address.
Optionally, in a first implementation manner of the first aspect of the embodiment of the present invention, if the service data of the target service belongs to the historical data, extracting the historical service data according to the data acquisition request instruction, where the historical service data is historical data required for establishing a first preset dimension model and a first preset dimension subdivision model, and the historical data includes: if the service data of the target service belong to the historical data, determining a screening condition, a first preset dimension model and a first preset dimension subdivision model according to the data acquisition request instruction; and extracting historical service data meeting the screening condition from the first preset dimension model and the first preset dimension subdivision model.
Optionally, in a second implementation manner of the first aspect of the embodiment of the present invention, if the service data of the target service belongs to the historical data, determining, according to the data acquisition request instruction, a screening condition, a first preset dimension model, and a first preset dimension subdivision model includes: if the service data of the target service belongs to the historical data, determining a screening condition according to the data acquisition request instruction; determining a first preset dimension model in preset dimension models according to the screening conditions; and determining a first preset dimension subdivision model in preset dimension subdivision models according to the screening conditions and the first preset dimension model.
Optionally, in a third implementation manner of the first aspect of the embodiment of the present invention, if the service data of the target service belongs to the new data, obtaining new service data according to the new data, the second preset dimension model, and the second preset dimension subdivision model, and extracting the new service data includes: if the service data of the target service belongs to the new data, screening the new data according to the screening condition to obtain new standard data; calculating the similarity of the new standard data and a plurality of preset dimension models, and determining a second preset dimension model and a second preset dimension subdivision model according to the similarity; and recombining the new standard data according to the second preset dimension model and the second preset dimension subdivision model to obtain new service data and extracting the new service data.
Optionally, in a fourth implementation manner of the first aspect of the embodiment of the present invention, the recombining the new standard data according to the second preset dimension model and the second preset dimension subdivision model to obtain new service data and extracting the new service data includes: processing the new standard data through a preset calculation engine to obtain new dimension granularity data and new dimension subdivision granularity data, wherein the new dimension granularity data and the new dimension subdivision granularity data are refined data of the new standard data; inputting the new dimension granularity data and the new dimension subdivision granularity data into a first list and other lists of a preset fact data table respectively to obtain a new fact data table; mapping the new fact data table to a preset attribute data table to obtain a new attribute data table; and extracting new service data according to the new attribute data table.
Optionally, in a fifth implementation manner of the first aspect of the embodiment of the present invention, before the obtaining a data acquisition request instruction, where the data acquisition request instruction is used to obtain service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service, the data docking method further includes: acquiring historical data; establishing a plurality of preset dimension models and preset dimension subdivision models according to the historical data; judging whether historical data for establishing the preset dimension model and the preset dimension subdivision model are changed or not; and if the historical data is changed, adjusting the preset dimension model and the preset dimension subdivision model according to the changed historical data.
Optionally, in a sixth implementation manner of the first aspect of the embodiment of the present invention, after obtaining new service data and extracting the new service data according to the new data, the second preset dimension model and the second preset dimension subdivision model if the service data of the target service belongs to the new data, and before transmitting the new service data or the historical service data to a transmission interface matched with a preset uniform resource locator URL address, the data docking method further includes: and writing a URL address in a preset page to obtain a preset URL address, and storing the URL address into a database, wherein the URL address is the address of a target system.
A second aspect of an embodiment of the present invention provides a data interfacing apparatus, including: the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a data acquisition request instruction, the data acquisition request instruction is used for acquiring service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service; a judging unit, configured to judge, based on the data acquisition request instruction, a data type to which service data of the target service belongs, where the data type includes historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space; the first extraction unit is used for extracting the historical service data according to the data acquisition request instruction if the service data of the target service belong to the historical data, wherein the historical service data are historical data required for establishing a first preset dimension model and a first preset dimension subdivision model; a second extraction unit, configured to, if the service data of the target service belongs to the new data, obtain new service data according to the new data, a second preset dimension model, and a second preset dimension subdivision model, and extract the new service data; and the transmission unit is used for transmitting the new service data or the historical service data to a transmission interface matched with a preset Uniform Resource Locator (URL) address.
Optionally, in a first implementation manner of the second aspect of the embodiment of the present invention, the first extracting unit specifically includes: the determining module is used for determining a screening condition, a first preset dimension model and a first preset dimension subdivision model according to the data acquisition request instruction if the service data of the target service belongs to the historical data; and the first extraction module is used for extracting historical service data meeting the screening condition from the first preset dimension model and the first preset dimension subdivision model.
Optionally, in a second implementation manner of the second aspect of the embodiment of the present invention, the determining module is specifically configured to: if the service data of the target service belongs to the historical data, determining a screening condition according to the data acquisition request instruction; determining a first preset dimension model in preset dimension models according to the screening conditions; and determining a first preset dimension subdivision model in preset dimension subdivision models according to the screening conditions and the first preset dimension model.
Optionally, in a third implementation manner of the second aspect of the embodiment of the present invention, the second extraction unit specifically includes: the screening module is used for screening the new data according to the screening condition to obtain new standard data if the service data of the target service belongs to the new data; the calculation module is used for calculating the similarity between the new standard data and the plurality of preset dimension models and determining a second preset dimension model and a second preset dimension subdivision model according to the similarity; and the recombination module is used for recombining the new standard data according to the second preset dimension model and the second preset dimension subdivision model to obtain new service data and extracting the new service data.
Optionally, in a fourth implementation manner of the second aspect of the embodiment of the present invention, the reassembly module is specifically configured to: processing the new standard data through a preset calculation engine to obtain new dimension granularity data and new dimension subdivision granularity data, wherein the new dimension granularity data and the new dimension subdivision granularity data are refinement data of the new standard data; inputting the new dimension granularity data and the new dimension subdivision granularity data into a first list and other lists of a preset fact data table respectively to obtain a new fact data table; mapping the new fact data table to a preset attribute data table to obtain a new attribute data table; and extracting new service data according to the new attribute data table.
Optionally, in a fifth implementation manner of the second aspect of the embodiment of the present invention, the data interfacing apparatus is further configured to: acquiring historical data; establishing a plurality of preset dimension models and preset dimension subdivision models according to the historical data; judging whether historical data for establishing the preset dimension model and the preset dimension subdivision model are changed or not; and if the historical data is changed, adjusting the preset dimension model and the preset dimension subdivision model according to the changed historical data.
Optionally, in a sixth implementation manner of the second aspect of the embodiment of the present invention, the data interfacing apparatus is further configured to: and writing a URL address in a preset page to obtain a preset URL address, and storing the URL address into a database, wherein the URL address is the address of a target system.
A third aspect of an embodiment of the present invention provides a data docking device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the data docking method according to any one of the foregoing embodiments.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, having stored therein instructions, which, when executed on a computer, cause the computer to perform the method of the first aspect.
According to the technical scheme, the embodiment of the invention has the following advantages:
the invention provides a data docking method, a data docking device, data docking equipment and a storage medium, wherein a data acquisition request instruction is obtained and used for obtaining service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service; judging the data type of the service data of the target service based on the data acquisition request instruction, wherein the data type comprises historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space; if the service data of the target service belong to historical data, extracting the historical service data according to a data acquisition request instruction, wherein the historical service data is historical data required for establishing a first preset dimension model and a first preset dimension subdivision model; if the service data of the target service belongs to the new data, obtaining new service data according to the new data, the second preset dimension model and the second preset dimension subdivision model, and extracting the new service data; and transmitting the new service data or the historical service data to a transmission interface matched with the URL address of the preset uniform resource locator. The embodiment of the invention extracts the service data of the service target according to the data acquisition instruction, the preset dimension model and the preset dimension subdivision model and sends the service data to the target system, thereby improving the efficiency of butt joint of the service data of the target service and solving the problem of difficult data butt joint.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a data docking method according to the present invention;
FIG. 2 is a schematic diagram of another embodiment of the data docking method of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a data docking device according to the present invention;
FIG. 4 is a schematic diagram of another embodiment of the data docking device of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a data docking device in the present invention.
Detailed Description
The invention provides a data docking method, which is used for directly extracting and constructing historical service data of a first preset dimension model and a first preset dimension subdivision model according to a data acquisition request instruction, or inputting the data into a second preset dimension model and a second preset dimension subdivision model for data splitting and recombination to obtain new service data, and sending the historical service data or the new service data to a target system, so that the efficiency of docking the service data of a target service is improved, and the problem of difficult data docking is solved.
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
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 implemented in other sequences than those 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.
Referring to fig. 1, an embodiment of a data interfacing method according to an embodiment of the present invention includes:
101. and acquiring a data acquisition request instruction, wherein the data acquisition request instruction is used for acquiring service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service.
The server acquires a data acquisition request instruction, wherein the data acquisition request instruction is used for acquiring service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service.
The data acquisition request instruction may be a data acquisition request instruction sent by each organization in each province and city, the types of services to be acquired by each organization in each province and city are different, and the server needs to send corresponding service data to each organization in each province and city, that is, each target system, according to different data acquisition request instructions.
For example, the Shenzhen transportation bureau sends a data acquisition request instruction for acquiring the vehicle insurance, and the server extracts the service data of the vehicle insurance according to the data acquisition request instruction of the vehicle insurance and sends the service data of the vehicle insurance to the target system corresponding to the Shenzhen transportation bureau; and if the Shenzhen industry association sends a data acquisition request instruction for acquiring the business insurance, the server extracts the business data of the business insurance according to the data request instruction of the business insurance and sends the business data of the business insurance to a target system corresponding to the Shenzhen industry association.
102. And judging the data type of the service data of the target service based on the data acquisition request instruction, wherein the data type comprises historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space.
The server judges the data type of the service data of the target service based on the data acquisition request instruction, wherein the data type comprises historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space.
Since the service data of the target service can be extracted from the historical data or the new data, the server needs to determine whether the data type of the service data of the target service is the historical data or the new data, and the different data types of the service data of the target service represent different extraction modes of the server. In this embodiment, the historical data is policy data stored in the database, and the new data is new policy data and is stored in the flash memory space.
It should be noted that the storage locations of the historical data and the new data are different, the historical data is stored in the database, the new data is stored in the flash memory space, and the server determines whether the data type of the service data of the target service is the historical data or the new data according to the storage locations of the historical data and the new data. When the new data preset in the flash memory space is transmitted to the target system once, the storage location of the new data becomes a database, and the new data becomes history data at this time.
103. If the service data of the target service belong to historical data, extracting the historical service data according to the data acquisition request instruction, wherein the historical service data are historical data required for establishing a first preset dimension model and a first preset dimension subdivision model.
If the service data of the target service belong to historical data, the server extracts the historical service data according to the data acquisition request instruction, wherein the historical service data are historical data required for building a first preset dimension model and a first preset dimension subdivision model.
The server judges whether the business data of the target business belongs to new data or historical data, if the business data of the target business belongs to the historical data, the server needs to extract the business data of the target business from the historical data, the server extracts corresponding historical data according to a data acquisition request instruction to serve as historical business data, and the historical business data is needed when a first preset dimension model is built and a first preset dimension subdivision model is built.
104. And if the service data of the target service belong to the new data, obtaining the new service data according to the new data, the second preset dimension model and the second preset dimension subdivision model, and extracting the new service data.
And if the service data of the target service belongs to the new data, the server obtains the new service data according to the new data, the second preset dimension model and the second preset dimension subdivision model and extracts the new service data.
The server judges whether the service data of the target service belongs to new data or historical data, if the service data of the target service belongs to the new data, the server needs to extract the new service data according to the new data, and because the data form or the data type of the new data and the like do not accord with the data form or the data type of the service data of the target service, the server needs to input the new data into the second preset dimension model and the second preset dimension subdivision model which are matched with each other, the new data are split and recombined, and the new service data are obtained.
105. And transmitting the new service data or the historical service data to a transmission interface matched with the URL address of the preset uniform resource locator.
And the server transmits the new service data or the historical service data to a transmission interface matched with the URL address of the preset uniform resource locator.
Each target system has an independent URL address, each URL address has a corresponding transmission interface, and the server uploads the extracted new service data or historical service data to the transmission interface matched with the URL address to complete data docking with the target system.
The embodiment of the invention extracts the business data of the business target according to the data acquisition instruction, the preset dimension model and the preset dimension subdivision model and sends the business data to the target system, thereby improving the efficiency of butt joint of the business data of the target business and solving the problem of difficult data butt joint.
Referring to fig. 2, another embodiment of the data interfacing method according to the embodiment of the present invention includes:
201. and acquiring a data acquisition request instruction, wherein the data acquisition request instruction is used for acquiring service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service.
The server acquires a data acquisition request instruction, wherein the data acquisition request instruction is used for acquiring service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service.
The data acquisition request instruction may be a data acquisition request instruction sent by each organization in each province and city, the types of services to be acquired by each organization in each province and city are different, and the server needs to send corresponding service data to each organization in each province and city, that is, each target system, according to different data acquisition request instructions.
For example, the Shenzhen transportation bureau sends a data acquisition request instruction for acquiring the vehicle insurance, and the server extracts the service data of the vehicle insurance according to the data acquisition request instruction of the vehicle insurance and sends the service data of the vehicle insurance to the target system corresponding to the Shenzhen transportation bureau; and if the Shenzhen industry association sends a data acquisition request instruction for acquiring the business insurance, the server extracts the business data of the business insurance according to the data request instruction of the business insurance and sends the business data of the business insurance to a target system corresponding to the Shenzhen industry association.
202. And judging the data type of the service data of the target service based on the data acquisition request instruction, wherein the data type comprises historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space.
The server judges the data type of the service data of the target service based on the data acquisition request instruction, wherein the data type comprises historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space.
Since the service data of the target service can be extracted from the historical data or the new data, the server needs to determine whether the data type of the service data of the target service is the historical data or the new data, and the different data types of the service data of the target service represent different extraction modes of the server. In this embodiment, the historical data is policy data stored in the database, and the new data is new policy data and stored in the flash memory space.
It should be noted that the storage location of the historical data is different from the storage location of the new data, the historical data is stored in the database, the new data is stored in the flash memory space, and the server determines whether the data type of the service data of the target service is the historical data or the new data according to the storage location of the historical data and the new data. When the new data preset in the flash memory space is transmitted to the target system once, the storage location of the new data becomes a database, and the new data becomes history data at this time.
203. And if the service data of the target service belongs to the historical data, determining a screening condition, a first preset dimension model and a first preset dimension subdivision model according to the data acquisition request instruction.
And if the service data of the target service belongs to the historical data, the server determines a screening condition, a first preset dimension model and a first preset dimension subdivision model according to the data acquisition request instruction.
And judging whether the service data of the target service belongs to the new data or the historical data through the server, if the service data of the target service belongs to the historical data, indicating that the server needs to extract the service data of the target service from the historical data, and determining a screening condition, a first latitude model and a first latitude subdivision model which are matched with the data acquisition request instruction by the server.
For example, the data acquisition request command is a policy of vehicle insurance for 20 years old or older for the insurance policy, the server first determines the screening conditions of the vehicle insurance for 20 years old or older, and selects a first preset latitude model and a first preset latitude subdivision model from a plurality of preset latitude models and preset latitude subdivision models according to the screening conditions to prepare for extracting historical business data.
Specifically, if the service data of the target service belongs to historical data, the server determines a screening condition according to the data acquisition request instruction; the server determines a first preset dimension model in the preset dimension models according to the screening conditions; and the server determines a first preset dimension subdivision model in the preset dimension subdivision models according to the screening conditions and the first preset dimension model.
For ease of understanding, the following description is made in conjunction with an application scenario:
assuming that the data acquisition request instruction is a policy of insurance for vehicles over 20 years old, the server determines the screening condition over 20 years old and vehicle insurance according to the data acquisition request instruction, and extracts the service data of the target service according to the screening condition over 20 years old and vehicle insurance. The server determines first preset dimension models such as an insurance type model, an insurer model and a vehicle model according to the screening conditions of the vehicle insurance. Since the screening condition is 20 years old or older, the server determines a first preset dimension subdivision model such as 20 years old, 21 years old and 22 years old from preset dimension subdivision models corresponding to the first dimension model. And finally, the server extracts the service data of the target service through the first preset dimension models and the first preset dimension subdivision models.
204. And extracting historical service data meeting the screening condition from the first preset dimension model and the first preset dimension subdivision model.
The server determines a screening condition, a first preset dimension model and a first preset dimension subdivision model according to the data acquisition request instruction.
After the server determines the screening condition, the first preset latitude model and the first preset latitude subdivision model according to the data acquisition request instruction, the server directly extracts historical data for establishing the first preset latitude model and the first preset latitude subdivision model as historical business data.
205. And if the service data of the target service belongs to the new data, screening the new data according to the screening condition to obtain new standard data.
And if the service data of the target service belongs to the new data, the server screens the new data according to the screening condition to obtain new standard data.
The server judges whether the service data of the target service belongs to new data or historical data, if the service data of the target service belongs to the new data, the server needs to extract the new service data according to the new data, the server firstly determines a screening condition according to a data acquisition request instruction, and then screens the new data according to the screening condition to obtain new standard data.
For example, if the data collection request command is an insurance policy for insurance for a vehicle aged 20 or older, the server performs screening among a plurality of new data according to the vehicle aged 20 or older and the screening conditions for the vehicle insurance, and obtains new standard data for the insurance policy for the vehicle aged 20 or older.
206. And calculating the similarity between the new standard data and the plurality of preset dimension models, and determining a second preset dimension model and a second preset dimension subdivision model according to the similarity.
And the server calculates the similarity between the new standard data and the plurality of preset dimension models, and determines a second preset dimension model and a second preset dimension subdivision model according to the similarity.
The server firstly obtains a vector of new standard data and a vector of a preset dimension model according to a preset vector model, secondly calculates the similarity between the vector of the new standard data and the vector of the preset dimension model according to an Euclidean distance formula and a similarity formula, then searches the preset dimension model with the maximum similarity to obtain the preset dimension model with the maximum similarity, namely a second preset dimension model, and the server obtains a corresponding second preset dimension subdivision model according to the second preset dimension model. And finally, the server inputs the new standard data into the second preset latitude model and the second preset latitude subdivision model for data recombination to obtain new service data and extract the new service data.
The Euclidean distance formula is as follows:
in the formula (d) xy To match the distance, x i As vectors of new standard data, y i Is a vector of the preset dimension model.
The similarity formula is specifically shown below:
in the formula, S xy The similarity between the vector of the new standard data and the vector of the preset dimension model can be seen from a formula, the smaller the matching distance is, the greater the similarity is, and the higher the similarity is, the more the new standard data is matched with the preset dimension model is.
For ease of understanding, the following description is made in conjunction with an application scenario:
assuming that the data acquisition request instruction is a policy for insurance of the vehicle with the age of 20 or more, the server performs screening in the plurality of new policy data according to the age of 20 or more and the screening condition of the vehicle insurance to obtain new standard policy data, namely new standard data, of the policy for insurance of the vehicle with the age of 20 or more. The server quantizes the new standard policy data by adopting a preset vector model to obtain a vector of the new standard data, and quantizes the preset latitude model by adopting the preset vector model to obtain a vector of the preset dimension model. The similarity between the new standard data vector and the preset dimension model vector is calculated by using the euclidean distance formula and the similarity formula, for example, the similarity between the new standard data vector and the insurance model, the vehicle information model, the insurance type model and the insurance family model is (0.8.8.0.8.0.2), so that the second preset dimension model is determined to be the insurance model, the vehicle information model and the insurance type model, the corresponding second preset dimension subdivision model is determined according to the insurance model, the vehicle information model and the insurance type model, and the second preset dimension subdivision model can be a preset dimension subdivision model such as a name, a telephone and an address corresponding to an insurance person.
207. And recombining the new standard data according to the second preset dimension model and the second preset dimension subdivision model to obtain new service data and extracting the new service data.
And the server recombines the new standard data according to the second preset dimension model and the second preset dimension subdivision model to obtain new service data and extracts the new service data.
Specifically, the server processes the new standard data through a preset calculation engine to obtain new dimension granularity data and new dimension subdivision granularity data, wherein the new dimension granularity data and the new dimension subdivision granularity data are refinement data of the new standard data; the server respectively inputs the new dimension granularity data and the new dimension subdivision granularity data into a first list and other lists of a preset fact data table to obtain a new fact data table; the server maps the new fact data table to a preset attribute data table to obtain a new attribute data table; and the server extracts new service data according to the new attribute data table.
It should be noted that, in this embodiment, the preset calculation engine is a Spark calculation engine, the Spark calculation engine is a fast and general calculation engine specially designed for large-scale data processing, and different dimension granularity data and dimension subdivision granularity data can be obtained by the Spark calculation engine. The preset fact data table may contain business sales data, such as data generated by insurance companies, which typically contains a large number of rows. The preset fact data table should not contain descriptive information nor any data other than the numeric measurement field and the associated index field that associates a fact with a corresponding entry in the preset attribute data table. The preset attribute data table may be viewed as a window for a user to analyze data, and includes properties of the fact data records in the preset fact data table, some properties providing descriptive information, some properties specifying how to summarize the data of the fact data table to provide useful information to the analyst, and the preset attribute data table includes a hierarchy of properties that help summarize the data.
For ease of understanding, the following description is made in conjunction with specific scenarios:
the server splits the new vehicle policy data by adopting a Spark calculation engine to obtain new dimension granularity data such as an insurer, an insurance category, vehicle information and the like, can also obtain new dimension subdivision granularity data such as name, address, telephone and the like matched with the new dimension granularity data of the insurer, new dimension subdivision granularity data such as protection time limit, policy price and the like matched with the insurance category, and new dimension subdivision granularity data such as a vehicle brand, a license plate number and the like matched with the vehicle information, and inputs the new dimension granularity data and the new dimension subdivision granularity data into a preset fact data table. Inputting new dimension granularity data such as insurers, insurance types and vehicle information into a first list of a preset fact data table in the form of fact data (digital data); and inputting the new dimension subdivision granularity data such as names, addresses and telephones and the like matched with the dimension granularity information of the insurers, the new dimension subdivision granularity data such as protection time limit and policy price and the like matched with the insurance types, and the new dimension subdivision granularity data such as vehicle brands and license plate numbers and the like matched with the vehicle information into other lists of the preset fact data table in the form of fact data so as to obtain a new fact data table. The server maps the new fact data table into a preset attribute data table, namely, the fact data is converted into data (attribute data) with descriptive information to obtain a new attribute data table, and the server extracts attribute data corresponding to the new dimension granularity data and the new dimension subdivision granularity data, namely, new service data.
208. And transmitting the new service data or the historical service data to a transmission interface matched with the URL address of the preset uniform resource locator.
And the server transmits the new service data or the historical service data to a transmission interface matched with the URL address of the preset uniform resource locator.
Each target system is provided with an independent URL address, each URL address is provided with a corresponding transmission interface, and the server uploads the extracted new service data or historical service data to the transmission interface matched with the URL address to complete data docking with the target system.
Optionally, before 201, the data interfacing method further includes: the server acquires historical data; the server establishes a plurality of preset dimension models and preset dimension subdivision models according to historical data; the server judges whether historical data for establishing a preset dimension model and establishing a preset dimension subdivision model are changed or not; and if the historical data is changed, the server adjusts the preset dimension model and the preset dimension subdivision model according to the changed historical data.
The server obtains the historical data, and splits the historical data by adopting a Spark calculation engine to obtain different dimension granularity data and dimension subdivision granularity data. For example, the policy data is processed into new dimension granularity data, the new dimension granularity data can be data of an applicant, a vehicle owner, insurance types and the like, and the policy data is divided into new dimension subdivision granularity data, and the new dimension subdivision granularity data comprises data of names, addresses, telephones and the like. Setting the dimension granularity data in the first column of a preset fact data table, setting the dimension subdivision granularity data in other columns of the preset fact data table, and mapping the preset fact data table into a preset attribute data table. And the server performs model training according to the preset attribute data table to obtain a preset dimension model and a preset dimension subdivision model. When the historical data are changed, the server resets the updated data into the preset fact data table to obtain an updated preset fact data table, maps the updated preset fact data table into an updated preset attribute data table, and adjusts the preset dimension model and the preset dimension subdivision model according to the updated preset attribute data table.
Optionally, after 207 and before 208, the data interfacing method further includes: and the server writes the URL address in the preset page to obtain a preset URL address, and stores the URL address in the database, wherein the URL address is the address of the target system.
For example, the Shenzhen transportation bureau initiates a data acquisition request instruction, the server acquires the data acquisition request instruction and writes a URL address of the Shenzhen transportation bureau in a page, so that the service data of the target service required by the Shenzhen transportation bureau is transmitted to the transmission interface corresponding to the URL address.
The embodiment of the invention extracts the business data of the business target according to the data acquisition instruction, the preset dimension model and the preset dimension subdivision model and sends the business data to the target system, thereby improving the efficiency of butt joint of the business data of the target business and solving the problem of difficult data butt joint.
With reference to fig. 3, the data docking method in the embodiment of the present invention is described above, and a data docking apparatus in the embodiment of the present invention is described below, where an embodiment of the data docking apparatus in the embodiment of the present invention includes:
an obtaining unit 301, configured to obtain a data acquisition request instruction, where the data acquisition request instruction is used to obtain service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service;
a determining unit 302, configured to determine, based on the data acquisition request instruction, a data type to which service data of the target service belongs, where the data type includes historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space;
a first extracting unit 303, configured to extract historical service data according to a data acquisition request instruction if the service data of the target service belongs to historical data, where the historical service data is historical data required for building a first preset dimension model and building a first preset dimension subdivision model;
a second extracting unit 304, configured to, if the service data of the target service belongs to the new data, obtain new service data according to the new data, the second preset dimension model, and the second preset dimension subdivision model, and extract the new service data;
a transmitting unit 305, configured to transmit the new service data or the historical service data to a transmission interface matched with the preset uniform resource locator URL address.
The embodiment of the invention extracts the service data of the service target according to the data acquisition instruction, the preset dimension model and the preset dimension subdivision model and sends the service data to the target system, thereby improving the efficiency of butt joint of the service data of the target service and solving the problem of difficult data butt joint.
Referring to fig. 4, another embodiment of the data interfacing apparatus according to the embodiment of the present invention includes:
an obtaining unit 301, configured to obtain a data acquisition request instruction, where the data acquisition request instruction is used to obtain service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service;
a determining unit 302, configured to determine, based on the data acquisition request instruction, a data type to which the service data of the target service belongs, where the data type includes historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space;
a first extracting unit 303, configured to extract historical service data according to the data acquisition request instruction if the service data of the target service belongs to the historical data, where the historical service data is historical data required for establishing a first preset dimension model and a first preset dimension subdivision model;
a second extracting unit 304, configured to, if the service data of the target service belongs to the new data, obtain new service data according to the new data, the second preset dimension model, and the second preset dimension subdivision model, and extract the new service data;
a transmitting unit 305, configured to transmit the new service data or the historical service data to a transmission interface matched with the preset uniform resource locator URL address.
Optionally, the first extracting unit 303 specifically includes:
the determining module 3031 is configured to determine a screening condition, a first preset dimension model and a first preset dimension subdivision model according to the data acquisition request instruction if the service data of the target service belongs to the historical data;
the first extraction module 3032 is configured to extract historical service data meeting the screening condition from the first preset dimension model and the first preset dimension subdivision model.
Optionally, the determining module 3031 is specifically configured to:
if the service data of the target service belong to historical data, determining a screening condition according to the data acquisition request instruction;
determining a first preset dimension model in the preset dimension models according to the screening conditions;
and determining a first preset dimension subdivision model in the preset dimension subdivision models according to the screening conditions and the first preset dimension model.
Optionally, the second extracting unit 304 specifically includes:
a screening module 3041, configured to, if the service data of the target service belongs to new data, screen the new data according to the screening condition to obtain new standard data;
a calculating module 3042, configured to calculate similarities between the new standard data and the multiple preset dimension models, and determine a second preset dimension model and a second preset dimension subdivision model according to the similarities;
the restructuring module 3043 is configured to restructure the new standard data according to the second preset dimension model and the second preset dimension subdivision model to obtain new service data and extract the new service data.
Optionally, the restructuring module 3043 is specifically configured to:
processing the new standard data through a preset calculation engine to obtain new dimension granularity data and new dimension subdivision granularity data, wherein the new dimension granularity data and the new dimension subdivision granularity data are refinement data of the new standard data;
inputting the new dimension granularity data and the new dimension subdivision granularity data into a first list and other lists of a preset fact data table respectively to obtain a new fact data table;
mapping the new fact data table to a preset attribute data table to obtain a new attribute data table;
and extracting new service data according to the new attribute data table.
Optionally, the data interfacing device is further configured to:
acquiring historical data;
establishing a plurality of preset dimension models and preset dimension subdivision models according to historical data;
judging whether historical data for establishing a preset dimension model and establishing a preset dimension subdivision model are changed or not;
and if the historical data is changed, adjusting the preset dimension model and the preset dimension subdivision model according to the changed historical data.
Optionally, the data interfacing apparatus is further configured to:
and writing a URL address in the preset page to obtain a preset URL address, and storing the URL address into a database, wherein the URL address is the address of a target system.
The embodiment of the invention extracts the business data of the business target according to the data acquisition instruction, the preset dimension model and the preset dimension subdivision model and sends the business data to the target system, thereby improving the efficiency of butt joint of the business data of the target business and solving the problem of difficult data butt joint.
Fig. 3 to fig. 4 describe the data docking apparatus in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the data docking device in the embodiment of the present invention is described in detail from the perspective of hardware processing.
The following describes each component of the data docking device in detail with reference to fig. 5:
fig. 5 is a schematic structural diagram of a data interfacing device according to an embodiment of the present invention, where the data interfacing device 500 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 501 (e.g., one or more processors) and a memory 509, and one or more storage media 508 (e.g., one or more mass storage devices) storing an application 507 or data 506. Memory 509 and storage medium 508 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 508 may include one or more modules (not shown), each of which may include a series of instruction operations for a check-in management device. Still further, processor 501 may be configured to communicate with storage medium 508 to execute a series of instruction operations in storage medium 508 on data docking device 500.
The following describes each component of the data docking device in detail with reference to fig. 5:
the processor 501 is a control center of the data docking apparatus, and can perform processing according to a data docking method. The processor 501 connects the various parts of the whole data docking device by using various interfaces and lines, and extracts the service data of the service target by using the preset dimension model and the preset dimension subdivision model by running or executing the software program and/or module stored in the memory 509 and calling the data stored in the memory 509, thereby improving the efficiency of docking the service data of the target service and solving the problem of difficult data docking. The storage medium 508 and the memory 509 are carriers for storing data, in the embodiment of the present invention, the storage medium 508 may be an internal memory with a small storage capacity but a high speed, and the memory 509 may be an external memory with a large storage capacity but a low storage speed.
The memory 509 may be used to store software programs and modules, and the processor 501 executes various functional applications and data processing of the data docking device 500 by operating the software programs and modules stored in the memory 509. The memory 509 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 use of the data docking apparatus, and the like. Further, the memory 509 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. The data interfacing program and the received data stream provided in the embodiments of the present invention are stored in a memory, and when needed, the processor 501 calls from the memory 509.
The procedures or functions according to the embodiments of the invention are brought about in whole or in part when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, optical fiber, twisted pair) or wirelessly (e.g., infrared, wireless, microwave, etc.). Computer-readable storage media can be any available media that a computer can store or a data storage device, such as a server, data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., compact disk), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
It can be clearly understood by those skilled in the art that, for convenience and simplicity 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.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is only a logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
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 should 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 method of data interfacing, comprising:
acquiring a data acquisition request instruction, wherein the data acquisition request instruction is used for acquiring service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service;
judging the data type of the business data of the target business based on the data acquisition request instruction, wherein the data type comprises historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space;
if the service data of the target service belong to the historical data, extracting the historical service data according to the data acquisition request instruction, wherein the historical service data are historical data required for establishing a first preset dimension model and a first preset dimension subdivision model;
if the service data of the target service belongs to the new data, obtaining new service data according to the new data, a second preset dimension model and a second preset dimension subdivision model, and extracting the new service data;
and transmitting the new service data or the historical service data to a transmission interface matched with a preset Uniform Resource Locator (URL) address.
2. The data docking method according to claim 1, wherein if the service data of the target service belongs to the historical data, extracting the historical service data according to the data acquisition request instruction, where the historical service data is historical data required for establishing a first preset dimension model and a first preset dimension subdivision model, and the historical data comprises:
if the service data of the target service belongs to the historical data, determining a screening condition, a first preset dimension model and a first preset dimension subdivision model according to the data acquisition request instruction;
and extracting historical service data meeting the screening condition from the first preset dimension model and the first preset dimension subdivision model.
3. The data docking method according to claim 2, wherein if the service data of the target service belongs to the historical data, determining a screening condition, a first preset dimension model, and a first preset dimension subdivision model according to the data acquisition request instruction includes:
if the service data of the target service belongs to the historical data, determining a screening condition according to the data acquisition request instruction;
determining a first preset dimension model in preset dimension models according to the screening conditions;
and determining a first preset dimension subdivision model in preset dimension subdivision models according to the screening conditions and the first preset dimension model.
4. The data docking method according to claim 1, wherein if the service data of the target service belongs to the new data, obtaining new service data according to the new data, a second preset dimension model, and a second preset dimension subdivision model, and extracting the new service data comprises:
if the service data of the target service belong to the new data, screening the new data according to screening conditions to obtain new standard data;
calculating the similarity of the new standard data and a plurality of preset dimension models, and determining a second preset dimension model and a second preset dimension subdivision model according to the similarity;
and recombining the new standard data according to the second preset dimension model and the second preset dimension subdivision model to obtain new service data and extracting the new service data.
5. The data docking method according to claim 4, wherein the recombining the new standard data according to the second preset dimension model and the second preset dimension subdivision model to obtain new service data and extract the new service data comprises:
processing the new standard data through a preset calculation engine to obtain new dimension granularity data and new dimension subdivision granularity data, wherein the new dimension granularity data and the new dimension subdivision granularity data are refinement data of the new standard data;
inputting the new dimension granularity data and the new dimension subdivision granularity data into a first list and other lists of a preset fact data table respectively to obtain a new fact data table;
mapping the new fact data table to a preset attribute data table to obtain a new attribute data table;
and extracting new service data according to the new attribute data table.
6. The data docking method according to claim 1, wherein before the data acquisition request command is used to acquire service data of a target service required by a target system, and different data acquisition request commands correspond to service data of different target services, the data docking method further comprises:
acquiring historical data;
establishing a plurality of preset dimension models and preset dimension subdivision models according to the historical data;
judging whether historical data for establishing the preset dimension model and the preset dimension subdivision model are changed or not;
and if the historical data is changed, adjusting the preset dimension model and the preset dimension subdivision model according to the changed historical data.
7. The data docking method according to any one of claims 1 to 6, wherein after obtaining new service data and extracting the new service data according to the new data, a second preset dimension model and a second preset dimension subdivision model if the service data of the target service belongs to the new data, before transmitting the new service data or the historical service data to a transmission interface matching a preset Uniform Resource Locator (URL) address, the data docking method further comprises:
and writing a URL address in a preset page to obtain a preset URL address, and storing the URL address in a database, wherein the URL address is the address of a target system.
8. A data interfacing apparatus, comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a data acquisition request instruction, the data acquisition request instruction is used for acquiring service data of a target service required by a target system, and different data acquisition request instructions correspond to different service data of the target service;
a judging unit, configured to judge, based on the data acquisition request instruction, a data type to which service data of the target service belongs, where the data type includes historical data and new data, the historical data is data preset in a database, and the new data is data preset in a flash memory space;
the first extraction unit is used for extracting the historical service data according to the data acquisition request instruction if the service data of the target service belongs to the historical data, wherein the historical service data is the historical data required for establishing a first preset dimension model and a first preset dimension subdivision model;
a second extraction unit, configured to, if the service data of the target service belongs to the new data, obtain new service data according to the new data, a second preset dimension model, and a second preset dimension subdivision model, and extract the new service data;
and the transmission unit is used for transmitting the new service data or the historical service data to a transmission interface matched with a preset Uniform Resource Locator (URL) address.
9. A data docking device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the data docking method as claimed in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the steps of the data interfacing method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911401627.7A CN111190964B (en) | 2019-12-31 | 2019-12-31 | Data docking method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911401627.7A CN111190964B (en) | 2019-12-31 | 2019-12-31 | Data docking method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111190964A CN111190964A (en) | 2020-05-22 |
| CN111190964B true CN111190964B (en) | 2022-11-08 |
Family
ID=70707876
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911401627.7A Active CN111190964B (en) | 2019-12-31 | 2019-12-31 | Data docking method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111190964B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112671845B (en) * | 2020-12-11 | 2023-12-29 | 国汽(北京)智能网联汽车研究院有限公司 | Data processing method and device, electronic equipment, storage medium and cloud system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105354376A (en) * | 2015-10-28 | 2016-02-24 | 上海同筑信息科技有限公司 | Method and system for interaction between BIM based multi-dimensional data and OA database |
| CN109753525A (en) * | 2018-12-28 | 2019-05-14 | 国云科技股份有限公司 | It is a kind of based on enterprise query system that can be properties and its implementation |
| CN110032591A (en) * | 2018-12-28 | 2019-07-19 | 国网浙江省电力有限公司信息通信分公司 | A kind of assets big data intelligent analysis method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7564458B2 (en) * | 2004-12-15 | 2009-07-21 | Microsoft Corporation | System and method for interactively linking data to shapes in a diagram |
| US11327475B2 (en) * | 2016-05-09 | 2022-05-10 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for intelligent collection and analysis of vehicle data |
-
2019
- 2019-12-31 CN CN201911401627.7A patent/CN111190964B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105354376A (en) * | 2015-10-28 | 2016-02-24 | 上海同筑信息科技有限公司 | Method and system for interaction between BIM based multi-dimensional data and OA database |
| CN109753525A (en) * | 2018-12-28 | 2019-05-14 | 国云科技股份有限公司 | It is a kind of based on enterprise query system that can be properties and its implementation |
| CN110032591A (en) * | 2018-12-28 | 2019-07-19 | 国网浙江省电力有限公司信息通信分公司 | A kind of assets big data intelligent analysis method |
Non-Patent Citations (1)
| Title |
|---|
| 基于SOA架构的化工企业办公系统的设计与实现;马丁;《中国优秀硕士学位论文全文数据库 信息科技辑》;20140315;第I138-386页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111190964A (en) | 2020-05-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107798038B (en) | Data response method and data response equipment | |
| CN110083789B (en) | Applet page acquisition method, server, client and electronic equipment | |
| US20180285596A1 (en) | System and method for managing sensitive data | |
| CN109788031B (en) | Service data acquisition method and device, computer equipment and storage medium | |
| CN111046237B (en) | User behavior data processing method and device, electronic equipment and readable medium | |
| CN109299157B (en) | Data export method and device for distributed big single table | |
| CN113836131A (en) | Big data cleaning method and device, computer equipment and storage medium | |
| CN109740129B (en) | Report generation method, device and equipment based on blockchain and readable storage medium | |
| CN107665229B (en) | Information searching method, device and equipment | |
| CN110297995B (en) | Method and device for collecting information | |
| CN110086894B (en) | Personnel association information mining method, communication recommendation method and related device | |
| CN109871354B (en) | File processing method and device | |
| CN112800197A (en) | Method and device for determining target fault information | |
| CN115659375A (en) | Data processing method, data processing device, storage medium and electronic equipment | |
| CN111241137B (en) | Data processing method, device, electronic equipment and storage medium | |
| CN111190964B (en) | Data docking method, device, equipment and storage medium | |
| CN111159213A (en) | Data query method, device, system and storage medium | |
| CN111159131A (en) | Performance optimization method, device, equipment and computer readable storage medium | |
| CN112835863A (en) | Processing method and processing device of operation log | |
| CN109271543B (en) | Thumbnail display method and device, terminal and computer-readable storage medium | |
| CN111045983A (en) | Nuclear power station electronic file management method and device, terminal equipment and medium | |
| CN115858905A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN110941711A (en) | Electronic search report acquisition method and apparatus, storage medium, and electronic apparatus | |
| CN115186164A (en) | Search request control method and device, equipment, medium and product thereof | |
| US9984136B1 (en) | System, method, and program product for lightweight data federation |
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 | ||
| GR01 | Patent grant |