CN116739527A - Efficiency information generation method and device and electronic equipment - Google Patents

Efficiency information generation method and device and electronic equipment Download PDF

Info

Publication number
CN116739527A
CN116739527A CN202310805322.2A CN202310805322A CN116739527A CN 116739527 A CN116739527 A CN 116739527A CN 202310805322 A CN202310805322 A CN 202310805322A CN 116739527 A CN116739527 A CN 116739527A
Authority
CN
China
Prior art keywords
information
index
target
index parameters
parameters
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
Application number
CN202310805322.2A
Other languages
Chinese (zh)
Inventor
王彦迥
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Industrial and Commercial Bank of China Ltd ICBC
Original Assignee
Industrial and Commercial Bank of China Ltd ICBC
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Industrial and Commercial Bank of China Ltd ICBC filed Critical Industrial and Commercial Bank of China Ltd ICBC
Priority to CN202310805322.2A priority Critical patent/CN116739527A/en
Publication of CN116739527A publication Critical patent/CN116739527A/en
Pending legal-status Critical Current

Links

Classifications

    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/216—Parsing using statistical methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G06N5/025—Extracting rules from data
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis

Abstract

The invention discloses a method and a device for generating efficiency information and electronic equipment. Relates to the field of artificial intelligence, and the method comprises the following steps: acquiring a plurality of index parameters corresponding to a research and development document, wherein the index parameters are parameters obtained by associating the research and development document with an object behavior record according to an association algorithm; determining target index parameters for representing the index parameters according to the association relation among the index parameters; inputting the target index parameters into a target engine for calculation, and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters.

Description

Efficiency information generation method and device and electronic equipment
Technical Field
The invention relates to the field of artificial intelligence, in particular to a method and a device for generating efficiency information and electronic equipment.
Background
In the prior art, in order to monitor whether an event meets the standard, an efficiency insight platform is often utilized, so as to further implement measurement on a project, for example, devOps (a method of software development and operation and maintenance), and the like. The project measurement scene can comprise a plurality of projects or a single project measurement in a enterprise, the measurement and analysis report of different scenes are managed by the projects such as each project measurement, each iteration measurement and the like, and related indexes such as the delivery rate, the delivery period, the number of work items, the completion rate, the labor hour and the like are covered, but not limited to, so that the purposes of knowing the progress, the risk, the quality and the delivery efficiency of the projects are achieved. Further, the method can further comprise the step of statistically analyzing data such as event distribution, event overview, code distribution and the like of team members in a period of time, so that the measurement team members finish workload and work dynamics in a period of time, and finally summarize for analysis by a team administrator. However, monitoring is often performed by means of preset indicators and thresholds.
That is, the index is set in advance according to the experience of the technician, and an ideal index range is set for the index. However, the problems easily brought by adopting the traditional technical scheme are as follows: the index is single, the index threshold is fixed, the response speed is low, and the index lack of correlation, so that the efficiency information rule is single.
Aiming at the technical problem of single rule in the process of generating efficiency information in the related technology, no effective solution is proposed yet.
Accordingly, there is a need for improvements in the related art to overcome the drawbacks of the related art.
Disclosure of Invention
The embodiment of the invention provides a method and a device for generating efficiency information and electronic equipment, which at least solve the technical problem of single rule in the process of generating the efficiency information.
According to an aspect of an embodiment of the present invention, there is provided a method for generating efficiency information, including: acquiring a plurality of index parameters corresponding to a research and development document, wherein the index parameters are parameters obtained by associating the research and development document with an object behavior record according to an association algorithm; determining target index parameters for representing the index parameters according to the association relation among the index parameters; inputting the target index parameters into a target engine for calculation, and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters.
In an exemplary embodiment, performing association processing on a research and development document and an object behavior record to obtain a plurality of index parameters corresponding to the research and development document, where the method includes: extracting a first feature corresponding to the research and development document and a second feature corresponding to the object behavior record; calculating the similarity of the first feature and the second feature, and confirming that the research and development document is associated with the object behavior record under the condition that the similarity of the first feature and the second feature is larger than a threshold value; and obtaining a plurality of index parameters corresponding to the research and development document according to the association relation between the first characteristic and the second characteristic.
In an exemplary embodiment, extracting the first feature corresponding to the development document includes: scanning the research and development document according to a first preset period, and determining a high-frequency field with the occurrence frequency larger than a first threshold value in the research and development document; under the condition that a target field exists in the preset range of the high-frequency field, identifying that the target field and the high-frequency field have an association relation; determining association information corresponding to the research and development document according to the association relation, and generating a first feature based on the association information, wherein the association information comprises: high frequency field, target field, and correspondence between high frequency field and target field.
In an exemplary embodiment, extracting the second feature corresponding to the object behavior record includes: scanning the object behavior record according to a second preset period; and extracting target behavior information associated with index information in the object behavior record under the condition that the target behavior information is detected to exist, so as to generate a second characteristic according to the target behavior information, wherein the index information is used for adjusting index parameters.
In an exemplary embodiment, the method for generating efficiency information further includes: extracting index features in the associated information and determining index parameters corresponding to each index in the index features under the condition that the index features exist in the associated information; and calculating the index parameters through a preset algorithm to obtain parameter intervals of the index parameters, wherein the parameter intervals are used for determining the target index parameters.
In an exemplary embodiment, the method for generating efficiency information further includes: acquiring feedback information of a target object, which is sent based on efficiency information, under the condition that the object receives the efficiency information; and recording the index feedback in the behavior information when the index feedback for adjusting the index exists in the feedback information.
In an exemplary embodiment, the method for generating efficiency information further includes: under the condition that a query request sent by a target object is received, determining query information corresponding to the query request, wherein the query information at least comprises: index query information and standard rule query information; and positioning a response function at the target engine according to the query information, and controlling the response function to respond data according to parameters carried by the query information.
According to another aspect of the embodiment of the present invention, there is also provided a generating device for generating efficiency information, including:
the acquisition module is used for: the method comprises the steps of obtaining a plurality of index parameters corresponding to a research and development document, wherein the index parameters are parameters obtained by associating the research and development document with an object behavior record according to an association algorithm; the determining module is used for determining target index parameters for representing the index parameters according to the association relation among the index parameters; the training module is used for inputting the target index parameters into a target engine for calculation and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters.
According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program is configured to execute the above-described efficiency information generation method when run.
According to another aspect of an embodiment of the present application, there is also provided an electronic device including one or more processors; and a memory for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method for running the programs, wherein the programs are configured to perform the method for generating the efficiency information described above when run.
In the process, a plurality of index parameters corresponding to a research and development document are obtained, wherein the index parameters are obtained by associating the research and development document with an object behavior record according to an association algorithm; determining target index parameters for representing the index parameters according to the association relation among the index parameters; inputting the target index parameters into a target engine for calculation, and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: the standard index parameters and the target efficiency information corresponding to the standard index parameters can be seen from the result, and the technical problem of single rule in the efficiency information generation process in the prior art is solved by carrying out association processing on the research and development document and the object behavior record to obtain the efficiency information.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of an alternative method of generating efficiency information in accordance with an embodiment of the present application;
FIG. 2 is a flow chart of a method of generating efficiency information according to an embodiment of the present application;
FIG. 3 is a block diagram of an alternative efficiency information generating apparatus according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an information base unit of an alternative efficiency information generating apparatus according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a knowledge base unit of an alternative efficiency information generating apparatus, according to an embodiment of the application;
FIG. 6 is a schematic diagram of an efficiency insight index library of an alternative efficiency information generating apparatus according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a performance insight rule base of an alternative efficiency information generating apparatus according to an embodiment of the present application;
FIG. 8 is a schematic diagram of an efficiency insight engine library of an alternative efficiency information generating apparatus according to an embodiment of the present application;
FIG. 9 is a schematic diagram of a performance insight platform unit of an alternative efficiency information generating apparatus according to an embodiment of the present application;
fig. 10 is a schematic structural view of an alternative efficiency information generating apparatus according to an embodiment of the present application;
fig. 11 is a schematic diagram of an alternative electronic device according to an embodiment of the application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, 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.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for presentation, analyzed data, etc.) related to the present disclosure are information and data authorized by the user or sufficiently authorized by each party.
The application can be applied to various software products, control systems and program codes of clients (including but not limited to mobile clients, PCs and the like) of various financial institutions, and by taking the software products as an example for schematic description and scanning the program codes of the software products installed on the mobile clients, the stable running of the software programs for realizing business contents (including but not limited to business functions of transferring, financing, fund, paying, checking accounts, advertising, recommending and the like) of the financial institutions can be ensured.
For convenience of description, the following will describe some terms or terminology involved in the embodiments of the present application:
text mining: the process of extracting important patterns or knowledge of potential interest to the user from unstructured text information can be seen as an extension of data mining or knowledge discovery in databases. The text information is mined mainly based on mathematical statistics and computer linguistics, so that a computer can find out the rules of the occurrence of certain characters and the relation between the characters and the semantics and grammar. Text mining involves a number of discipline fields such as information retrieval, text analysis, information extraction, and the like.
Machine learning: is a data analysis technique, which enables a computer to execute the activities of people and animals: learning from experience. Machine learning algorithms use computational methods to "learn" information directly from the data, independent of a predetermined solution model. These algorithms may adaptively improve performance as the number of samples available for learning increases.
Research and development efficacy insight: the method is a method for forming an efficiency insight model, finding existing problems and assisting decision making by organizing various data generated in the research and development process.
Efficiency information: refers to information for monitoring projects, indicating completion progress, completion status. May include scoring or progress of an item or an index in an item, an individual.
The invention is further illustrated below in conjunction with the examples.
According to an embodiment of the present invention, there is provided an embodiment of a method of generating efficiency information, it being noted that the steps shown in the flowcharts of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that herein.
Fig. 1 is a flowchart of an alternative method for generating efficiency information according to an embodiment of the present invention, as shown in fig. 1, the method includes the steps of:
step S101: acquiring a plurality of index parameters corresponding to a research and development document, wherein the index parameters are parameters obtained by associating the research and development document with an object behavior record according to an association algorithm;
it should be noted that the research and development document is a document generated by a related technician in the process of researching and developing related projects, and includes various materials, specifications, documents and the like in the process of managing software research and development. The object behavior record may be an operation related to the project that the relevant technician generates during the development process.
Step S102: determining target index parameters for representing the index parameters according to the association relation among the index parameters;
step S103: inputting the target index parameters into a target engine for calculation, and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters.
The user information and data are information and data which are fully authorized. Specifically, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for presentation, analyzed data, etc.) referred to in the present application are both user-authorized or fully authorized information and data.
Through the steps, a plurality of index parameters corresponding to the research and development document are obtained, wherein the index parameters are obtained by associating the research and development document with the object behavior record according to an association algorithm; determining target index parameters for representing the index parameters according to the association relation among the index parameters; inputting the target index parameters into a target engine for calculation, and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters. The technical problem of single rule in the efficiency information generation process in the prior art is solved.
In an exemplary embodiment, performing association processing on a research and development document and an object behavior record to obtain a plurality of index parameters corresponding to the research and development document, where the method includes: extracting a first feature corresponding to the research and development document and a second feature corresponding to the object behavior record; calculating the similarity of the first feature and the second feature, and confirming that the research and development document is associated with the object behavior record under the condition that the similarity of the first feature and the second feature is larger than a threshold value; and obtaining a plurality of index parameters corresponding to the research and development document according to the association relation between the first characteristic and the second characteristic.
That is, in the case where the similarity of the development document and the behavior information is greater than the threshold value, the development document and the behavior information are considered to be related. It should be noted that, the research and development document and the behavior information both include various kinds of data and information, and the direct comparison may be not reasonable. Therefore, the features to be clearly compared, that is, the first features of the research and development document and the second features of the object behavior record are extracted for comparison, so that a more accurate comparison result can be obtained.
Optionally, the development document includes a text-mining material and a specification system document.
In an exemplary embodiment, extracting the first feature corresponding to the development document includes: scanning the research and development document according to a first preset period, and determining a high-frequency field with the occurrence frequency larger than a first threshold value in the research and development document; under the condition that a target field exists in the preset range of the high-frequency field, identifying that the target field and the high-frequency field have an association relation; determining association information corresponding to the research and development document according to the association relation, and generating a first feature based on the association information, wherein the association information comprises: high frequency field, target field, and correspondence between high frequency field and target field.
That is, the first feature is generated by determining fields, texts, words, etc. whose frequency of occurrence is greater than a threshold value, that is, high-frequency fields, in the development document, and recognizing texts associated with these fields, texts, words, etc. through an association relationship therebetween. In order to determine whether a field, a text and a word are associated with the text, determining whether a target field exists in a preset range of a high-frequency field, and under the condition that the target field exists in the preset range of the high-frequency field, confirming that an association relationship exists between the high-frequency field and the target field, and generating a first feature according to the association relationship.
Optionally, the method for determining the target field may further be: after the high-frequency word is identified, reading a similar field in a preset range of the high-frequency word, and determining that the similar field is a target field, namely determining that the similar field has an association relationship with the high-frequency field when the number of times of the similar field in the preset range is larger than a second threshold value.
In an exemplary embodiment, extracting the second feature corresponding to the object behavior record includes: scanning the object behavior record according to a second preset period; and extracting target behavior information associated with index information in the object behavior record under the condition that the target behavior information is detected to exist, so as to generate a second characteristic according to the target behavior information, wherein the index information is used for adjusting index parameters.
Alternatively, the object behavior record may include an object access log, object history operation information, and the like.
It should be noted that, the behavior recording of the scanning object may be performed according to a preset period, for example: scanning every 1 hour or every 1 day, etc., the invention is not limited in this regard. In the case that the index information for adjusting the index parameter exists in the user behavior information, the condition that the user can maintain the index is explained, and the condition that the index parameter at the moment is not in accordance with the expectations of the user is further explained. And further provides a basis for a plurality of standard index parameters. And extracting the target information to obtain the second characteristic.
In an exemplary embodiment, the method for generating efficiency information further includes: extracting index features in the associated information and determining index parameters corresponding to each index in the index features under the condition that the index features exist in the associated information; and calculating the index parameters through a preset algorithm to obtain parameter intervals of the index parameters, wherein the parameter intervals are used for determining the target index parameters.
In order to determine a plurality of index parameters, it is necessary to extract index features in the associated information. Optionally, the index feature may include a current parameter of the index, a historical adjustment frequency of the index, and a historical adjustment parameter of the index. Optionally, the parameter interval is obtained by calculating the current parameter of the index, the historical adjustment times of the index and the historical adjustment parameter of the index.
Alternatively, the operation may be that after the average value of the parameters after the multiple adjustment of the index is obtained, a first standard value is obtained, and the parameters within a preset range of the first standard value are set as the target index parameters or the parameter intervals of the target index parameters;
optionally, the above operation may also be to assign different weights to the current parameter of the index and the historical adjustment parameter of the index, respectively, then calculate a second standard value according to the different weights, and set the parameter within the preset range of the second standard value as the parameter interval of the target index parameter
In an exemplary embodiment, the method for generating efficiency information further includes: acquiring feedback information of a target object, which is sent based on efficiency information, under the condition that the object receives the efficiency information; and recording the index feedback in the behavior information when the index feedback for adjusting the index exists in the feedback information.
That is, when the user sends out index feedback for adjusting the index based on the efficiency information, the current adjustment is recorded in the behavior information, so that the effect of expanding the behavior information is achieved, and the behavior information is richer.
In an exemplary embodiment, the method for generating efficiency information further includes: under the condition that a query request sent by a target object is received, determining query information corresponding to the query request, wherein the query information at least comprises: index query information and standard rule query information; and positioning a response function at the target engine according to the query information, and controlling the response function to respond data according to parameters carried by the query information.
It should be noted that, in order to facilitate the subsequent maintenance and query of the target engine by the technician, the response to the query request issued by the target object may further include: index inquiry information and standard rule inquiry information, and controlling a response function to respond data according to parameters carried by the inquiry information.
It will be apparent that the embodiments described above are merely some, but not all, embodiments of the application. In order to better understand the response method of the above function, the following description will explain the above process with reference to the embodiments, but is not intended to limit the technical solution of the embodiments of the present application, specifically:
fig. 2 is a flowchart of a method for generating efficiency information according to an embodiment of the present application, as shown in fig. 2, including the following steps:
step 1: and loading the materials and the standard system documents to form a research and development document, and providing the research and development document to a knowledge base unit.
Step 2: through daily batch scanning of research and development documents, high-frequency words are extracted as keywords, the high-frequency word groups are extracted to serve as testing key points, knowledge related information is generated, and the knowledge related information is provided for an index library for use.
Step 3: and generating index related information by scanning knowledge information in batches every day, and providing the index related information for an insight rule base.
Step 4: the knowledge association information, the index information and the user behavior information are scanned in batches every day to generate rule related information, wherein the rule related information comprises combination relations among indexes, thresholds of the indexes and heat of the indexes, and a rule algorithm is automatically optimized through machine learning and is used for an efficiency insight engine.
Step 5: the efficiency insight engine obtains model results through daily batch scan calculations.
Step 6: and generating insight information comprising the same ratio, the ring ratio, the three-ratio three-view and the like through the model result.
It should be noted that the "same ratio" refers to a same ratio increase rate, that is, a period of the same period of the last year (or other preset period), that is, a period of the present period is compared with a period of the last year. The "ring ratio" refers to the ring ratio increase rate, that is, the comparison between the current statistical segment and the last statistical segment connected, that is, the comparison with the last adjacent period, for example: comparison of month n with month n-1. The term "three-over-three view" refers to a comparison between different data, such as: the data of a business entity is compared with market data, the data of a business entity is compared with sibling entities, and the data of a business entity is compared with the historical data of the business.
Step 7: and acquiring insight information every day to form an efficacy insight scene, wherein the efficacy insight scene comprises scenes of research and development quality, period, admission and the like, and the scenes are provided for users.
Step 8: and obtaining the user access log and the correlation relationship, and providing the user access log and the correlation relationship for a knowledge base.
And (5) repeating the steps 1 to 8 every day until the efficiency is improved.
In an alternative embodiment, the repetition period may be a preset period such as an hour, a week, or a real time period, which is not limited in this regard.
Through the steps, the research and development document is formed through the text mining material and the standard system document. And then generating knowledge related information by scanning the research and development documents and the user behavior information in batches every day, and providing the knowledge related information for an index library. The index library generates index related information by scanning knowledge information in batches every day, and provides the index related information for the insight rule library. The insight rule base generates rule-related information by scanning knowledge-related information, index information and user behavior information in batches every day, and provides the rule-related information for the efficiency insight engine through a machine learning automatic optimization rule algorithm. The efficiency insight engine obtains model results through daily batch scanning calculation, generates insight information, forms an efficiency insight scene, and is provided for a user to use, and meanwhile, access logs and correlation relations of the user are obtained and provided for a knowledge base to use. Thereby achieving the purpose of improving the research and development efficiency.
The following describes the scheme of the present application further with reference to fig. 3, and fig. 3 is a schematic block diagram of an apparatus for generating optional efficiency information according to an embodiment of the present application:
The system comprises an information base unit 1, a knowledge base unit 2, a performance insight index base unit 3, a performance insight rule base unit 4, a performance insight engine unit 5 and a performance insight platform unit 6. Specifically:
information base unit 1: and managing various materials, specifications and documents in the software research and development process, extracting keywords and related relations thereof through word segmentation technology in text mining, and providing research and development documents for a knowledge base unit.
Knowledge base unit 2: and forming a knowledge base through the research and development documents provided by the information base unit, and providing knowledge information for the efficiency insight index base unit to use.
Efficacy insight index library unit 3: and forming an efficiency insight index base through the knowledge information provided by the knowledge base unit, and providing index information for the efficiency insight rule base unit.
The efficacy insight rule base unit 4: and forming a performance insight rule base by using a machine learning method through the index information provided by the performance insight index base unit, and providing a plurality of index parameters for the performance insight engine unit to use.
The performance insight engine unit 5: and forming a performance insight engine unit through a plurality of index parameters provided by the performance insight rule base unit, and providing insight information for the performance insight platform unit.
Efficacy insight platform unit 6: and the insight information provided by the docking efficiency insight engine unit is provided for a user to use, and the user behavior information is provided for the insight rule unit to use.
Fig. 4 is a schematic diagram of an information base unit of an apparatus for generating alternative efficiency information according to an embodiment of the present application, and as shown in fig. 4, the information base unit 1 is composed of a material document module 21 and a specification system module 22 and a project document module 23:
the material document module 21: and scanning the archived documents of various materials, extracting words which occur frequently as keywords, and simultaneously extracting the relationship of the keywords to form a research and development document and providing the research and development document to a knowledge base unit.
Specification system module 22: and scanning various standard systems, extracting words which occur frequently as keywords, and extracting the relationship of the keywords to form a research and development document which is provided for a knowledge base unit.
Project document module 23: and scanning various project documents, extracting words which occur frequently as keywords, and extracting the relationship of the keywords to form research and development documents and providing the research and development documents to a knowledge base unit.
Fig. 5 is a schematic diagram of a knowledge base unit of an apparatus for generating alternative efficiency information according to an embodiment of the present application, and as shown in fig. 5, the knowledge base unit 2 is composed of a knowledge generation module 31, a knowledge maintenance module 32, and a knowledge query module 33:
Knowledge generation module 31: knowledge-related information is generated by scanning the development documents in batches daily.
Knowledge maintenance module 32: and adjusting the generated knowledge information to form knowledge base information.
Knowledge query module 33: the user may query knowledge information in the foreground.
Fig. 6 is a schematic diagram of an efficiency insight index library of an apparatus for generating alternative efficiency information according to an embodiment of the present application, and as shown in fig. 6, the efficiency insight index library 3 is composed of an index generating module 41, an index maintaining module 42, and an index querying module 43:
the index generation module 41: and generating index related information by scanning the knowledge information in batches every day.
Index maintenance module 42: and maintaining index basic information and logic information.
Index query module 43: the user can query the index information in the foreground.
Fig. 7 is a schematic diagram of an efficiency insight rule base of an alternative efficiency information generating apparatus according to an embodiment of the present application, and as shown in fig. 7, the efficiency insight rule base 4 is composed of a rule generating module 51, a rule maintaining module 52, and a rule querying module 53:
the rule generation module 51: rule-related information is generated by scanning knowledge-related information, index information, and user behavior information in batches daily, and rule algorithms are automatically optimized by machine learning.
Rule maintenance module 52: and adjusting a plurality of index parameters to form a performance insight rule base.
Rule query module 53: a user may query a plurality of index parameters in the foreground.
Fig. 8 is a schematic diagram of an efficiency insight engine library of an alternative efficiency information generating apparatus according to an embodiment of the present application, and as shown in fig. 8, the efficiency insight engine library 5 is composed of a batch calculation module 61 and an insight information generating module 62.
Batch calculation module 61: model results were obtained by daily batch scan calculations.
The insight information generation module 62: and generating insight information through the model result.
Fig. 9 is a schematic diagram of an efficiency insight platform unit of an alternative efficiency information generating apparatus according to an embodiment of the present application, and as shown in fig. 9, the efficiency insight platform unit 6 is composed of a rule generating module 71 and a rule maintaining module 72:
the efficiency insight information query module 71: and acquiring insight information every day to form an efficacy insight scene, and providing the insight information for a user.
The user behavior information acquisition module 72: and acquiring a user access log and a correlation relationship, and providing the user access log and the correlation relationship for an insight rule base.
Through the above modules, a performance insight asset accumulation is formed. It should be noted that, the knowledge base is an asset base of the performance insight formed by the basic data obtained through text mining and the common maintenance of performance insight experts, is a good deposit of related assets of the performance insight, and can be intelligently shared. Through the problems in the research and development process discovered by the users of each level through the efficiency insight scene, and timely adjustment and improvement are realized, so that the purposes of improving the efficiency and quality of the efficiency insight work are achieved. Through daily scanning of document specifications and user behavior information, corresponding insight scenes are supplemented through machine learning, changes of efficiency insights brought by changes of research and development requirements can be timely and accurately handled, problems existing in communication and timeliness are avoided, and handling capacity of efficiency insights is effectively improved.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method of the various embodiments of the present invention. The embodiment also provides a device for generating efficiency information, which is used for implementing the foregoing embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the devices described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also possible and contemplated.
The following describes the scheme of the present application further with reference to fig. 10, and fig. 10 is a schematic structural diagram of an apparatus for generating optional efficiency information according to an embodiment of the present application:
acquisition module 1002: the method comprises the steps of obtaining a plurality of index parameters corresponding to a research and development document, wherein the index parameters are parameters obtained by associating the research and development document with an object behavior record according to an association algorithm;
a determining module 1004, configured to determine a target index parameter for representing the plurality of index parameters according to an association relationship between the plurality of index parameters;
the training module 1006 is configured to input the target indicator parameter to a target engine for calculation, and generate first efficiency information of the target indicator parameter, where the target engine is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: standard index parameters and target efficiency information corresponding to the standard index parameters.
Through the device, a plurality of index parameters corresponding to the research and development document are obtained, wherein the index parameters are obtained by associating the research and development document with the object behavior record according to an association algorithm; determining target index parameters for representing the index parameters according to the association relation among the index parameters; inputting the target index parameters into a target engine for calculation, and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters. Solves the technical problem of single rule in the efficiency information generation process in the prior art
In an exemplary embodiment, the obtaining module 1002 is further configured to: extracting a first feature corresponding to the research and development document and a second feature corresponding to the object behavior record; calculating the similarity of the first feature and the second feature, and confirming that the research and development document is associated with the object behavior record under the condition that the similarity of the first feature and the second feature is larger than a threshold value; and obtaining a plurality of index parameters corresponding to the research and development document according to the association relation between the first characteristic and the second characteristic.
In an exemplary embodiment, the above apparatus further includes: the first extraction module is used for scanning the research and development document according to a first preset period and determining a high-frequency field with the occurrence frequency larger than a first threshold value in the research and development document; under the condition that a target field exists in the preset range of the high-frequency field, identifying the association relation between the target field and the high-frequency field; determining association information corresponding to the research and development document according to the association relation, and generating a first feature based on the association information, wherein the association information comprises: high frequency field, target field, and correspondence between high frequency field and target field.
In an exemplary embodiment, the above apparatus further includes: the second extraction module is used for scanning the object behavior record according to a second preset period; and extracting the target behavior record to generate a second characteristic according to the target behavior record when the target behavior record associated with index information exists in the target behavior record, wherein the index information is used for adjusting index parameters.
In an exemplary embodiment, the above apparatus further includes: the operation module is used for extracting index features in the associated information and determining index parameters corresponding to each index in the index features under the condition that the index features exist in the associated information; and calculating the index parameters through a preset algorithm to obtain parameter intervals of the index parameters, wherein the parameter intervals are used for determining the target index parameters.
In an exemplary embodiment, the above apparatus further includes: the feedback module is used for acquiring feedback information of the target object, which is sent based on the efficiency information, under the condition that the object receives the efficiency information; and recording the index feedback in the behavior information when the index feedback for adjusting the index exists in the feedback information.
In an exemplary embodiment, the above apparatus further includes: the query module is used for determining query information corresponding to a query request under the condition that the query request sent by a target object is received, wherein the query information at least comprises: index query information and standard rule query information; and positioning a response function at the target engine according to the query information, and controlling the response function to respond data according to parameters carried by the query information.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
An embodiment of the present invention provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements a method of extracting information.
The embodiment of the invention provides a processor which is used for running a program, wherein the program runs to execute the information extraction method.
As shown in fig. 11, an embodiment of the present invention provides an electronic device, where the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, the following steps are implemented: acquiring a plurality of index parameters corresponding to a research and development document, wherein the index parameters are parameters obtained by associating the research and development document with an object behavior record according to an association algorithm; determining target index parameters for representing the index parameters according to the association relation among the index parameters; inputting the target index parameters into a target engine for calculation, and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters.
Optionally, obtaining a plurality of index parameters corresponding to the development document, where the plurality of index parameters are parameters obtained by associating the development document with the object behavior record according to an association algorithm, and the method includes: extracting a first feature corresponding to the research and development document and a second feature corresponding to the object behavior record; calculating the similarity of the first feature and the second feature, and confirming that the research and development document is associated with the object behavior record under the condition that the similarity of the first feature and the second feature is larger than a threshold value; and obtaining a plurality of index parameters corresponding to the research and development document according to the association relation between the first characteristic and the second characteristic.
Optionally, extracting the first feature corresponding to the development document includes: scanning the research and development document according to a first preset period, and determining a high-frequency field with the occurrence frequency larger than a first threshold value in the research and development document; under the condition that a target field exists in the preset range of the high-frequency field, identifying that the target field and the high-frequency field have an association relation; determining association information corresponding to the research and development document according to the association relation, and generating a first feature based on the association information, wherein the association information comprises: high frequency field, target field, and correspondence between high frequency field and target field.
Optionally, extracting the second feature corresponding to the object behavior record includes: scanning the object behavior record according to a second preset period; and extracting the target behavior record to generate a second characteristic according to the target behavior record when the target behavior record associated with index information exists in the target behavior record, wherein the index information is used for adjusting index parameters.
Optionally, under the condition that index features exist in the associated information, extracting the index features in the associated information, and determining index parameters corresponding to each index in the index features; and calculating the index parameters through a preset algorithm to obtain parameter intervals of the index parameters, wherein the parameter intervals are used for determining the target index parameters.
Optionally, under the condition that the object receives the efficiency information, acquiring feedback information of the target object, which is sent out based on the efficiency information; and recording the index feedback in the behavior information when the index feedback for adjusting the index exists in the feedback information.
Optionally, under the condition that a query request sent by a target object is received, determining query information corresponding to the query request, where the query information at least includes: index query information and standard rule query information; and positioning a response function at the target engine according to the query information, and controlling the response function to respond data according to parameters carried by the query information.
The application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of: acquiring a plurality of index parameters corresponding to a research and development document, wherein the index parameters are parameters obtained by associating the research and development document with an object behavior record according to an association algorithm; determining target index parameters for representing the index parameters according to the association relation among the index parameters; inputting the target index parameters into a target engine for calculation, and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters.
Optionally, obtaining a plurality of index parameters corresponding to the development document, where the plurality of index parameters are parameters obtained by associating the development document with the object behavior record according to an association algorithm, and the method includes: extracting a first feature corresponding to the research and development document and a second feature corresponding to the object behavior record; calculating the similarity of the first feature and the second feature, and confirming that the research and development document is associated with the object behavior record under the condition that the similarity of the first feature and the second feature is larger than a threshold value; and obtaining a plurality of index parameters corresponding to the research and development document according to the association relation between the first characteristic and the second characteristic.
Optionally, extracting the first feature corresponding to the development document includes: scanning the research and development document according to a first preset period, and determining a high-frequency field with the occurrence frequency larger than a first threshold value in the research and development document; under the condition that a target field exists in the preset range of the high-frequency field, identifying that the target field and the high-frequency field have an association relation; determining association information corresponding to the research and development document according to the association relation, and generating a first feature based on the association information, wherein the association information comprises: high frequency field, target field, and correspondence between high frequency field and target field.
Optionally, extracting the second feature corresponding to the object behavior record includes: scanning the object behavior record according to a second preset period; and extracting the target behavior record to generate a second characteristic according to the target behavior record when the target behavior record associated with index information exists in the target behavior record, wherein the index information is used for adjusting index parameters.
Optionally, under the condition that index features exist in the associated information, extracting the index features in the associated information, and determining index parameters corresponding to each index in the index features; and calculating the index parameters through a preset algorithm to obtain parameter intervals of the index parameters, wherein the parameter intervals are used for determining the target index parameters.
Optionally, under the condition that the object receives the efficiency information, acquiring feedback information of the target object, which is sent out based on the efficiency information; and recording the index feedback in the behavior information when the index feedback for adjusting the index exists in the feedback information.
Optionally, under the condition that a query request sent by a target object is received, determining query information corresponding to the query request, where the query information at least includes: index query information and standard rule query information; and positioning a response function at the target engine according to the query information, and controlling the response function to respond data according to parameters carried by the query information.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims (10)

1. A method for generating efficiency information, comprising:
acquiring a plurality of index parameters corresponding to a research and development document, wherein the index parameters are parameters obtained by associating the research and development document with an object behavior record according to an association algorithm;
determining target index parameters for representing the index parameters according to the association relation among the index parameters;
inputting the target index parameters into a target engine for calculation, and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters.
2. The method of claim 1, wherein obtaining a plurality of index parameters corresponding to a development document, wherein the plurality of index parameters are parameters obtained by associating the development document with an object behavior record according to an association algorithm, and the method comprises:
extracting a first feature corresponding to the research and development document and a second feature corresponding to the object behavior record;
Calculating the similarity of the first feature and the second feature, and confirming that the research and development document is associated with the object behavior record under the condition that the similarity of the first feature and the second feature is larger than a threshold value;
and obtaining a plurality of index parameters corresponding to the research and development document according to the association relation between the first characteristic and the second characteristic.
3. The method of claim 2, wherein extracting the first feature corresponding to the development document comprises:
scanning the research and development document according to a first preset period, and determining a high-frequency field with the occurrence frequency larger than a first threshold value in the research and development document;
under the condition that a target field exists in the preset range of the high-frequency field, identifying the association relation between the target field and the high-frequency field;
determining association information corresponding to the research and development document according to the association relation, and generating a first feature based on the association information, wherein the association information comprises: high frequency field, target field, and correspondence between high frequency field and target field.
4. The method of claim 2, wherein extracting the second feature corresponding to the object behavior record comprises:
Scanning the object behavior record according to a second preset period;
and extracting the target behavior record to generate a second characteristic according to the target behavior record when the target behavior record associated with index information exists in the target behavior record, wherein the index information is used for adjusting index parameters.
5. A method according to claim 3, characterized in that the method further comprises:
extracting index features in the associated information and determining index parameters corresponding to each index in the index features under the condition that the index features exist in the associated information;
and calculating the index parameters through a preset algorithm to obtain parameter intervals of the index parameters, wherein the parameter intervals are used for determining the target index parameters.
6. The method according to claim 1, wherein the method further comprises:
acquiring feedback information of a target object, which is sent based on efficiency information, under the condition that the object receives the efficiency information;
and recording the index feedback in the behavior information when the index feedback for adjusting the index exists in the feedback information.
7. The method according to claim 1, wherein the method further comprises:
under the condition that a query request sent by a target object is received, determining query information corresponding to the query request, wherein the query information at least comprises: index query information and standard rule query information;
and positioning a response function at the target engine according to the query information, and controlling the response function to respond data according to parameters carried by the query information.
8. An efficiency information generating apparatus, comprising:
the acquisition module is used for: the method comprises the steps of obtaining a plurality of index parameters corresponding to a research and development document, wherein the index parameters are parameters obtained by associating the research and development document with an object behavior record according to an association algorithm;
the determining module is used for determining target index parameters for representing the index parameters according to the association relation among the index parameters;
the training module is used for inputting the target index parameters into a target engine for calculation and generating first efficiency information of the target index parameters, wherein the target engine is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises: standard index parameters and target efficiency information corresponding to the standard index parameters.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program, wherein the computer program is arranged to execute the method of generating efficiency information according to any of the claims 1 to 7 at run-time.
10. An electronic device, the electronic device comprising one or more processors; a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement a method for running a program, wherein the program is configured to perform the method of generating efficiency information of any one of claims 1 to 7 when run.
CN202310805322.2A 2023-06-30 2023-06-30 Efficiency information generation method and device and electronic equipment Pending CN116739527A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310805322.2A CN116739527A (en) 2023-06-30 2023-06-30 Efficiency information generation method and device and electronic equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310805322.2A CN116739527A (en) 2023-06-30 2023-06-30 Efficiency information generation method and device and electronic equipment

Publications (1)

Publication Number Publication Date
CN116739527A true CN116739527A (en) 2023-09-12

Family

ID=87911494

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310805322.2A Pending CN116739527A (en) 2023-06-30 2023-06-30 Efficiency information generation method and device and electronic equipment

Country Status (1)

Country Link
CN (1) CN116739527A (en)

Similar Documents

Publication Publication Date Title
US20230252314A1 (en) Predicting aggregate value of objects representing potential transactions based on potential transactions expected to be created
CN109063966B (en) Risk account identification method and device
US11372896B2 (en) Method and apparatus for grouping data records
US10671926B2 (en) Method and system for generating predictive models for scoring and prioritizing opportunities
CN110546621B (en) Garbage collection for data storage
CN110309234B (en) Knowledge graph-based customer warehouse-holding early warning method and device and storage medium
CN110046188A (en) Method for processing business and its system
CN106033455B (en) Method and equipment for processing user operation information
CN115564486A (en) Data pushing method, device, equipment and medium
US11538048B1 (en) Predictively identifying activity subscribers
CN113656699B (en) User feature vector determining method, related equipment and medium
US11436237B2 (en) Ranking datasets based on data attributes
CN113850077A (en) Topic identification method, device, server and medium based on artificial intelligence
CN110941952A (en) Method and device for perfecting audit analysis model
CN116739527A (en) Efficiency information generation method and device and electronic equipment
CN112784168B (en) Information push model training method and device, information push method and device
CN110019771B (en) Text processing method and device
CN112712270A (en) Information processing method, device, equipment and storage medium
CN112818235A (en) Violation user identification method and device based on associated features and computer equipment
CN116150420B (en) Evaluation method and system for picture task pushing result
Chong et al. Customer Churn Prediction of Telecom Company Using Machine Learning Algorithms
US11416685B2 (en) System and method for artificial intelligence driven document analysis, including automated reuse of predictive coding rules based on management and curation of datasets or models
US11893265B2 (en) Garbage collection for data storage
CN114049217A (en) Method and device for supervising annuity data and electronic equipment
Anderst-Kotsis et al. Evaluating Customer Satisfaction: Using Behavioral Analytics of Data-Intensive Software Systems

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