CN115759014A - Dynamic intelligent analysis method and system and electronic equipment - Google Patents
Dynamic intelligent analysis method and system and electronic equipment Download PDFInfo
- Publication number
- CN115759014A CN115759014A CN202211463893.4A CN202211463893A CN115759014A CN 115759014 A CN115759014 A CN 115759014A CN 202211463893 A CN202211463893 A CN 202211463893A CN 115759014 A CN115759014 A CN 115759014A
- Authority
- CN
- China
- Prior art keywords
- data
- report list
- analysis
- user
- pool
- 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.)
- Pending
Links
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to a dynamic intelligent analysis method, a system and electronic equipment, wherein the method comprises the steps of obtaining an analysis report list and user information, wherein the user information comprises user use data and user requirements, and the analysis report list comprises an index pool, a dimension pool, a method pool and a relation data model; according to the user requirements and the analysis report list, determining the matching degree of each index, dimension and method in the user requirements and the analysis report list; and determining a recommended report list according to the matching degree and the user use data. The invention solves the problem of low efficiency of manually constructing the business analysis table by the business intelligent analysis tool.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a dynamic intelligent analysis method, system, and electronic device.
Background
At present, the traditional BI (Business Intelligence) tool, namely a Business intelligent analysis tool, often needs to manually establish various dimensions and statistical indexes, and needs to manually construct a plurality of built-in analysis charts in advance. However, due to flexibility and diversity of business requirements, indexes and statistical dimensions required by customers are often more variable, and the traditional manual construction mode cannot cover the requirements of users and is lower in efficiency.
The above prior art solutions have the following drawbacks: the business intelligent analysis tool has the problem of low efficiency in manually constructing the business analysis table.
Disclosure of Invention
In order to solve the problem that the efficiency of manually constructing a business analysis table is low in a business intelligent analysis tool, the application aims to provide a dynamic intelligent analysis method.
The above object of the present application is achieved by the following technical solutions:
a method of dynamic intelligent analysis, comprising:
the method comprises the steps of obtaining an analysis report list and user information, wherein the user information comprises user use data and user requirements, and the analysis report list comprises an index pool, a dimension pool, a method pool and a relation data model;
according to the user requirement and the analysis report list, determining the matching degree of each index, dimension and method in the user requirement and the analysis report list;
and determining a recommended report list according to the matching degree and the user use data.
By adopting the technical scheme, firstly, an analysis report list is acquired, then user information is acquired, the user information comprises user requirements and user use data, the matching degree of each index, dimension and method in the analysis report list and the user requirements is acquired according to the user requirements and the analysis report list, and analysis reports in the analysis report list are sequenced through the matching degree and the user use data to form a recommended report list. The coverage of the constructed analysis report list on the user requirements can be enlarged by acquiring the analysis report list, and the recommended report list formed by the corresponding analysis report can be directly recommended to the user according to the user requirements, so that the time for manually constructing the business analysis report is reduced, and the efficiency for constructing the business analysis report is improved.
The application may be further configured in a preferred example to: before the user requirement is obtained, the method includes:
acquiring user input data;
and determining the user requirements according to the user input data and the keyword extraction rule.
The present application may be further configured in a preferred example to: the acquiring of the analysis report list comprises the following steps:
acquiring basic data;
acquiring index data, dimension data, method data and relation data according to the basic data;
the relationship data comprises a correspondence between the metric data, the dimension data, and the method data;
constructing an index pool according to the index data;
constructing a dimension pool according to the dimension data;
constructing a method pool according to the method data;
constructing a relational data model according to the relational data;
and determining the analysis report list according to the index pool, the dimension pool, the method pool and the relational data model.
The present application may be further configured in a preferred example to: the acquiring of the basic data comprises: the basic data is obtained in real time through dynamic scanning or self-defining of a database.
The present application may be further configured in a preferred example to: the determining the matching degree of each index, dimension and method in the user requirement and the analysis report list according to the user requirement and the analysis report list comprises the following steps: determining demand data according to synonym analysis rules and the user demands;
and determining the matching degree of each index, dimension and method in the demand data and the analysis report list according to the demand data and the analysis report list.
The present application may be further configured in a preferred example to: determining a recommended report list according to the matching degree and the user use data, wherein the method comprises the following steps:
according to the matching degree, sorting the analysis reports corresponding to the indexes, dimensions and methods in the analysis report list in a descending order; and determining a recommended report list according to the sequencing and the user use data.
The present application may be further configured in a preferred example to: the method further comprises the following steps: outputting a target report list according to the recommended report list and the list output rule, wherein the step of outputting the target report list comprises the following steps:
selecting the first five analysis reports to form a first target report list according to the recommended report list;
outputting the first target report list;
if the user feedback information is not acquired within a preset time period;
sequentially selecting five analysis reports to form a second target report list according to the recommended report list;
and outputting the second target report list.
The second purpose of the application is to provide a dynamic intelligent analysis system.
The second application object of the present application is achieved by the following technical scheme:
a dynamic intelligent analysis system, comprising:
the data acquisition module is used for acquiring basic data and user information, wherein the user information comprises user use data and user requirements; the list construction module is used for determining an analysis report list according to the basic data, wherein the analysis report list comprises an index pool, a dimension pool, a method pool and a relation data model;
the matching calculation module is used for determining the matching degree of each index, dimension and method in the user requirement and the analysis report list according to the user requirement and the analysis report list;
the list determining module is used for determining a recommended report list according to the user use data and the matching degree;
and the list recommending module is used for outputting a target report list according to the recommended report list and the list output rule.
The third purpose of the present application is to provide an electronic device.
The third objective of the present application is achieved by the following technical solutions:
an electronic device comprising a memory and a processor, said memory having stored thereon a computer program that can be loaded by the processor and that performs the above-mentioned dynamic intelligent analysis method.
The fourth purpose of the present application is to provide a computer storage medium capable of storing a corresponding program.
The fourth application purpose of the present application is achieved by the following technical scheme:
a computer readable storage medium storing a computer program that can be loaded by a processor and executed to perform any of the above-described methods of dynamic intelligent analysis.
In summary, the present application includes at least one of the following beneficial technical effects:
1. and obtaining user requirements and user use data through the obtained analysis report list, obtaining the matching degree of the user requirements and each analysis report in the analysis report list according to the user requirements and the analysis report list, and sequencing the analysis reports through the matching degree and the user use data to form a recommended report list. The coverage of the analysis report form on the user requirement can be enlarged by obtaining the basic data to construct the analysis report form list, and meanwhile, the recommended report form list formed by the corresponding analysis report forms can be directly recommended to the user according to the user requirement, so that the time for manually constructing the business analysis report form is reduced, and the efficiency for constructing the business analysis report form is improved.
Drawings
FIG. 1 is a schematic flow diagram of a dynamic intelligent analysis method provided herein.
Fig. 2 is a schematic structural diagram of a dynamic intelligent analysis system provided in the present application.
Fig. 3 is a schematic structural diagram of an electronic device provided in the present application.
In the figure, 200, a dynamic intelligent analysis system; 201. a data acquisition module; 202. a manifest construction module; 203. a matching calculation module; 204. a manifest determination module; 205. a manifest recommendation module; 301. a CPU; 302. a ROM; 303. a RAM; 304. an I/O interface; 305. an input section; 306. an output section; 307. a storage section; 308. a communication section; 309. a driver; 310. a removable media.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.
The embodiments of the present application will be described in further detail with reference to the drawings.
The embodiment of the application provides a dynamic intelligent analysis method, and the main flow of the method is described as follows.
As shown in fig. 1:
step S101: basic data and user information are acquired.
Specifically, the basic data is obtained through dynamic scanning of the custom database, meanwhile, the basic data is updated through dynamic scanning, the data obtained through dynamic scanning is compared with the basic data, if new data exists, the basic data is updated, and if new data does not exist, the basic data is not updated. By using dynamic scanning to obtain the basic data, the basic data can be ensured to meet the business requirements of users to a certain extent. The user information includes user usage data and user requirements. The method includes that a user needs to manually input user input data, the user input data can be a session or a plurality of phrases, after the user input data is obtained, the user input data is processed by using a keyword extraction rule, namely keywords or keywords related to requirements are extracted, the keywords or keywords form the user requirements, in the embodiment, the keyword extraction rule comprises a named entity identification technology, and the named entity identification technology can identify entity type, time type and digital type data, and content of name, organization name, place name, time, date, currency and percentage in text to be processed, namely the user input data. The dynamic scanning refers to scanning of a custom database, the custom database is manually updated through workers according to business changes, basic data can be updated in real time through dynamic scanning of the basic database, coverage of the basic data on user requirements can be enlarged, and user experience is improved.
Step S102: and constructing an analysis report list according to the basic data.
Specifically, the basic data includes index data, dimension data, method data and relationship data, and the dimension data includes fields of year, month, time, region, city, organization, each service classification, and the like; the index data comprises attributes such as unique identification, money amount, quantity and the like; the method data comprises contents such as same ratio, ring ratio, average, sum, median and the like; the relationship data includes a correspondence between the index data, the dimension data, and the method data. Before basic data are obtained, firstly, test data are obtained, a machine learning model is trained and learned through the test data, a training model capable of identifying index data, dimension data, method data and relationship data can be obtained, the basic data obtained through dynamic scanning can be classified through the training model, the basic data are divided into the index data, the dimension data, the method data and the relationship data, all the index data are combined to form an index pool, all the dimension data are combined to form a dimension pool, all the method data are combined to form the method pool, and all the relationship data are combined to form a relationship data model. In this embodiment, first, relevant fields such as a table name, a table remark, a field name, a field type, a field remark field, and the like are extracted from the custom database, where the table name and the table remark generally represent what service, for example, a sales table, and the field name and the field type represent a data type of the field, for example, if the field type corresponding to the field name is a date, the field should be a time class dimension. The table names are related in the same kind by a preset synonym analysis rule, and for example, related words such as "sales table", "sales _ list", "t _ xiaoshou", "sales amount" and the like are extracted from related fields such as table names and table notes and are related.
And obtaining an analysis report list containing a plurality of analysis reports through the index pool, the dimension pool, the method pool and the relational data model. By constructing the index pool, the dimension pool, the method pool and the relation data model, a data base is provided for subsequent matching degree calculation, and meanwhile, data in the analysis report is classified according to the index, the dimension and the method, so that the user requirements can be better matched. If the data of the analysis report is not classified, all the data in the analysis report needs to be matched when the matching degree is calculated subsequently, the calculated matching degree is falsely high due to the high similarity of the data content of the analysis report, and the data is actually not similar to the analysis report type required by the user. After the analysis report is classified, index data, dimension data and method data required by a user can be matched with the data of the corresponding type in the analysis report list, so that the accuracy of the matching degree of the analysis report can be improved.
Step S103: and determining the matching degree of each index, dimension and method in the user requirement and analysis report list according to the user requirement and the analysis report list.
Specifically, firstly, user requirements are processed, namely the user requirements are expanded through a synonym analysis rule, requirement data containing the user requirements and similar requirements of the user requirements are obtained, the requirement data are sequentially matched with data in an index pool, a dimension pool and a method pool, and the matching rate of each data is obtained. The synonym analysis rule comprises the steps of refining data in the index pool, the dimension pool and the method pool to construct a synonym frame, wherein the steps comprise the step of carrying out generalized expansion on a field name, a word2vec model constructed based on English semantics and a word2vec model constructed based on Chinese semantics, and the step of binding and storing the generalized field name and the similar probability with an original field name. By taking an example of service classification in the dimension pool, for example, the service classification is "sale", according to a word2vec model constructed based on Chinese semantics, the close probability of "sell" and "sell" is 0.98, the close probability of "sell" and "sell" is 0.93, english of sale is sale can be obtained through "sell", then according to the word2vec model constructed based on english semantics, the close probability of "sell" and "sell" is 1, the pinyin of "sell" and "sae" obtained through "sell" is "xiaoshou", and the close probability of "sell" and "xiaoshou" is 1. The similar probability of the two adjacent field names is the association probability of the two field names, and the product of the two non-adjacent field names is the product of a plurality of similar probabilities spaced in the two field names, for example, the similar probability of the two adjacent field names is 1 for "sale" and "sale", and the similar probability of the two adjacent field names is 0.88 for "sale" and "market", and the similar probability of the two adjacent field names is 1 × 0.88 for "sale" and "market".
Step S104: and determining a recommended report list according to the matching degree and the user use data.
Specifically, when the matching degree is not null, that is, a user demand exists, the matching rate of the analysis report in each analysis report list is determined according to the matching degree, and for each analysis report, the matching rate of the analysis report is equal to the product of the matching degrees of the index data, the dimension data and the method data corresponding to the analysis report. And sequencing a plurality of analysis reports in the analysis report list in a descending order according to the matching rate of the analysis reports, and if the matching rate of the analysis reports is the same, sequencing the analysis reports with the same matching rate in a random sequencing mode. And the analysis report list after finishing sequencing is a recommended report list.
And when the matching degree is null, namely the user does not input the user requirement, acquiring user use data corresponding to the user, wherein the user use data comprises the number of times of an analysis report browsed by the user and index data, dimension data and method data corresponding to the analysis report. And calculating the matching rate of the analysis report by using the data by the user. The specific calculation formula is as follows:
wherein c (x, y, z) represents index x, dimension y, matching rate of the analysis report formed by method z, p (x, y, z) represents index x, dimension y, frequency of occurrence of the analysis report formed by method z in the user use data, p (x) represents frequency of occurrence of index x in the user use data, p (y) represents frequency of occurrence of dimension y in the user use data, and p (z) represents frequency of occurrence of method z in the user use data.
The method further comprises the steps of expanding the indexes, the dimensions and the methods through synonym analysis rules according to the indexes, the dimensions and the methods obtained through data use by a user, obtaining the corresponding matching degrees of the indexes, the dimensions and the methods obtained through data use by the user and the indexes, the dimensions and the methods obtained after expansion, and calculating the matching rate of the analysis report in each analysis report list according to the matching degrees, wherein for each analysis report, the matching rate of the analysis report is equal to the product of the matching degrees of the index data, the dimension data and the method data corresponding to the analysis report.
And after the matching rate of each analysis report is calculated, sequencing a plurality of analysis reports in the analysis report list in a descending order according to the matching rate of the analysis reports, and sequencing the analysis reports with the same matching rate in a random sequencing mode if the matching rate of the analysis reports is the same. And the analysis report list after finishing sequencing is a recommended report list.
Step S105: and outputting the target report list according to the recommended report list and the list output rule.
Specifically, according to the sorting result of the recommended report list, the first five analysis reports are selected to form a first target report list, the first target report list is output, user feedback information is obtained after the first target report list is output, if the feedback information of the user is not obtained within a preset time period, it is indicated that no analysis report required by the user exists in the first target report list, the sixth analysis report to the tenth analysis report in the recommended report list is selected to form a second target report list, the second target report list is output, and the above processes are repeated until the user feedback information is obtained within a preset time period or all the analysis reports in the recommended report list are output.
An embodiment of the present application provides a dynamic intelligent analysis system 200, and referring to fig. 2, the dynamic intelligent analysis system 200 includes:
a data obtaining module 201, configured to obtain basic data and user information, where the user information includes user usage data and user requirements;
the list building module 202 is configured to determine an analysis report list according to the basic data, where the analysis report list includes an index pool, a dimension pool, a method pool, and a relational data model;
the matching calculation module 203 is used for determining the matching degree of each index, dimension and method in the user requirement and the analysis report list according to the user requirement and the analysis report list;
the list determining module 204 is used for determining a recommended report list according to the user use data and the matching degree;
and the list recommending module 205 is configured to output a target report list according to the recommended report list and the list output rule.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.
The embodiment of the application discloses an electronic device. Referring to fig. 3, the electronic device includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage section 307 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus. An input/output (I/O) interface 304 is also connected to the bus.
The following components are connected to the I/O interface 304: an input section 305 including a keyboard, a mouse, and the like; an output section 306 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 307 including a hard disk and the like; and a communication section 308 including a network interface card such as a LAN card, a modem, or the like. The communication section 308 performs communication processing via a network such as the internet. Drivers 309 are also connected to the I/O interface 304 as needed. A removable medium 310 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 309 as necessary, so that a computer program read out therefrom is mounted into the storage section 307 as necessary.
In particular, according to embodiments of the present application, the process described above with reference to the flowchart fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication section 308, and/or installed from the removable medium 310. The above-described functions defined in the apparatus of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the application referred to in the present application is not limited to the embodiments with a particular combination of the above-mentioned features, but also encompasses other embodiments with any combination of the above-mentioned features or their equivalents without departing from the spirit of the application. For example, the above features and the technical features (but not limited to) having similar functions in the present application are mutually replaced to form the technical solution.
Claims (10)
1. A method for dynamic intelligent analysis, comprising:
the method comprises the steps of obtaining an analysis report list and user information, wherein the user information comprises user use data and user requirements, and the analysis report list comprises an index pool, a dimension pool, a method pool and a relation data model;
according to the user requirement and the analysis report list, determining the matching degree of each index, dimension and method in the user requirement and the analysis report list;
and determining a recommended report list according to the matching degree and the user use data.
2. The dynamic intelligent analysis method of claim 1, wherein the obtaining of the user requirement comprises:
acquiring user input data;
and determining the user requirements according to the user input data and the keyword extraction rule.
3. The dynamic intelligent analysis method according to claim 1, wherein the obtaining of the analysis report list comprises:
acquiring basic data;
acquiring index data, dimension data, method data and relation data according to the basic data;
the relationship data comprises a correspondence between the metric data, the dimension data, and the method data;
constructing an index pool according to the index data;
constructing a dimension pool according to the dimension data;
constructing a method pool according to the method data;
constructing a relational data model according to the relational data;
and determining the analysis report list according to the index pool, the dimension pool, the method pool and the relational data model.
4. The dynamic intelligent analysis method of claim 3, wherein the obtaining the base data comprises: the basic data is obtained in real time through dynamic scanning or self-defining of a database.
5. The dynamic intelligent analysis method according to claim 1, wherein the determining the matching degree of each index, dimension, and method in the user requirement and the analysis report list according to the user requirement and the analysis report list comprises:
determining demand data according to synonym analysis rules and the user demands;
and determining the matching degree of each index, dimension and method in the demand data and the analysis report list according to the demand data and the analysis report list.
6. The dynamic intelligent analysis method of claim 1, wherein determining a recommended report list according to the matching degree and the user usage data comprises:
according to the matching degree, sorting the analysis reports corresponding to the indexes, dimensions and methods in the analysis report list in a descending order;
and determining a recommended report list according to the sequencing and the user use data.
7. The dynamic intelligent analysis method of claim 1, further comprising: outputting a target report list according to the recommended report list and the list output rule, wherein the step of outputting the target report list comprises the following steps:
selecting the first five analysis reports to form a first target report list according to the recommended report list;
outputting the first target report list;
if the user feedback information is not acquired within a preset time period;
sequentially selecting five analysis reports to form a second target report list according to the recommended report list;
and outputting the second target report list.
8. A dynamic intelligent analysis system, comprising:
the data acquisition module (201) is used for acquiring basic data and user information, wherein the user information comprises user use data and user requirements;
the list building module (202) is used for determining an analysis report list according to the basic data, wherein the analysis report list comprises an index pool, a dimension pool, a method pool and a relation data model;
the matching calculation module (203) is used for determining the matching degree of each index, dimension and method in the user requirement and the analysis report list according to the user requirement and the analysis report list;
the list determining module (204) is used for determining a recommended report list according to the user use data and the matching degree;
and the list recommending module (205) is used for outputting a target report list according to the recommended report list and the list output rule.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that executes the method according to any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which can be loaded by a processor and which executes the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211463893.4A CN115759014A (en) | 2022-11-22 | 2022-11-22 | Dynamic intelligent analysis method and system and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211463893.4A CN115759014A (en) | 2022-11-22 | 2022-11-22 | Dynamic intelligent analysis method and system and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115759014A true CN115759014A (en) | 2023-03-07 |
Family
ID=85334615
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211463893.4A Pending CN115759014A (en) | 2022-11-22 | 2022-11-22 | Dynamic intelligent analysis method and system and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115759014A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117150348A (en) * | 2023-10-30 | 2023-12-01 | 宁德时代新能源科技股份有限公司 | Battery external damage data processing method, system, electronic equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844319A (en) * | 2016-12-21 | 2017-06-13 | 泰康保险集团股份有限公司 | Report form generation method and device |
| CN109271411A (en) * | 2018-09-28 | 2019-01-25 | 中国平安财产保险股份有限公司 | Report form generation method, device, computer equipment and storage medium |
| CN109614599A (en) * | 2018-10-23 | 2019-04-12 | 平安科技（深圳）有限公司 | Report form generation method, device, computer equipment and storage medium |
| CN111078784A (en) * | 2019-11-25 | 2020-04-28 | 苏宁云计算有限公司 | Report automatic recommendation system, method and computer system |
| CN111881224A (en) * | 2020-08-06 | 2020-11-03 | 广东省信息工程有限公司 | Multidimensional data analysis method and system |
| CN112632122A (en) * | 2020-12-18 | 2021-04-09 | 平安普惠企业管理有限公司 | Report retrieval method, device, equipment and storage medium based on multiple indexes |
| CN113157984A (en) * | 2021-04-21 | 2021-07-23 | 上海传英信息技术有限公司 | Processing method, terminal device and storage medium |
-
2022
- 2022-11-22 CN CN202211463893.4A patent/CN115759014A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844319A (en) * | 2016-12-21 | 2017-06-13 | 泰康保险集团股份有限公司 | Report form generation method and device |
| CN109271411A (en) * | 2018-09-28 | 2019-01-25 | 中国平安财产保险股份有限公司 | Report form generation method, device, computer equipment and storage medium |
| CN109614599A (en) * | 2018-10-23 | 2019-04-12 | 平安科技（深圳）有限公司 | Report form generation method, device, computer equipment and storage medium |
| CN111078784A (en) * | 2019-11-25 | 2020-04-28 | 苏宁云计算有限公司 | Report automatic recommendation system, method and computer system |
| CN111881224A (en) * | 2020-08-06 | 2020-11-03 | 广东省信息工程有限公司 | Multidimensional data analysis method and system |
| CN112632122A (en) * | 2020-12-18 | 2021-04-09 | 平安普惠企业管理有限公司 | Report retrieval method, device, equipment and storage medium based on multiple indexes |
| CN113157984A (en) * | 2021-04-21 | 2021-07-23 | 上海传英信息技术有限公司 | Processing method, terminal device and storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117150348A (en) * | 2023-10-30 | 2023-12-01 | 宁德时代新能源科技股份有限公司 | Battery external damage data processing method, system, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20060106755A1 (en) | Tracking usage of data elements in electronic business communications | |
| CN112256762B (en) | Enterprise portrait method, system, equipment and medium based on industrial map | |
| CN109598517B (en) | Commodity clearance processing, object processing and category prediction method and device thereof | |
| CN111523678A (en) | Service processing method, device, equipment and storage medium | |
| CN111680165B (en) | Information matching method and device, readable storage medium and electronic equipment | |
| CN116127020A (en) | Method for training generated large language model and searching method based on model | |
| CN109740642A (en) | Invoice category recognition methods, device, electronic equipment and readable storage medium storing program for executing | |
| CN113093958A (en) | Data processing method and device and server | |
| CN113742492A (en) | Insurance scheme generation method and device, electronic equipment and storage medium | |
| CN115239214B (en) | Enterprise evaluation processing method and device and electronic equipment | |
| CN113254542A (en) | Data visualization processing method and device and electronic equipment | |
| CN110362594A (en) | A kind of information processing method and system | |
| CN114549241A (en) | Contract examination method, device, system and computer readable storage medium | |
| CN114266443A (en) | Data evaluation method and device, electronic equipment and storage medium | |
| CN113641794A (en) | Resume text evaluation method and device and server | |
| CN117743543A (en) | Sentence generation method and device based on large language model and electronic equipment | |
| CN113434755A (en) | Page generation method and device, electronic equipment and storage medium | |
| CN115759014A (en) | Dynamic intelligent analysis method and system and electronic equipment | |
| CN113392920B (en) | Method, apparatus, device, medium, and program product for generating cheating prediction model | |
| CN111179055A (en) | Credit limit adjusting method and device and electronic equipment | |
| CN113327132A (en) | Multimedia recommendation method, device, equipment and storage medium | |
| CN117407726A (en) | Intelligent service data matching method, system and storage medium | |
| CN114036921A (en) | Policy information matching method and device | |
| CN116933130A (en) | Enterprise industry classification method, system, equipment and medium based on big data | |
| CN115936758A (en) | Intelligent customer-extending method based on big data and related device |
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 |