CN112182057A - Project matching method, device and equipment based on block chain and storage medium - Google Patents
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Abstract
The embodiment provides a project matching method, a project matching device and a project matching storage medium based on a block chain, which are used for collecting source data of projects, markets, personnel and investment institutions; preprocessing source data; the preprocessing data comprises training data, prediction data and investment institution data; inputting first market data, first project data, first person data and first grading data into a preset machine learning model to obtain a trained machine learning model; inputting second market data, second project data and second personnel data into the trained machine learning model to obtain corresponding second grading data; obtaining investment institution scoring data corresponding to the investment institution according to the investment institution data; and matching the project and the investment institution according to the second grading data and the grading data of the investment institution to obtain a matching result. And meanwhile, the analysis of three-party data (market, project and personnel) is carried out, and an accurate evaluation result is provided.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a block chain-based item matching method, a block chain-based item matching apparatus, a computer device, and a storage medium.
Background
After a government work report is written for the first time in 2015, college graduates are used as the core strength of a new creation project (called a double creation project for short), more and more attention is paid to the society, and various investment institutions shift the service gravity center of the college graduates to the new creation graduates (called double creation graduates for short).
However, at present, China still has a plurality of problems in the butt joint aspect of the due double-creation graduation and investment institutions; for example, at the beginning of the graduation, even if there are good double-created projects, it is difficult to find a suitable sponsor and to place the project on the floor for implementation. Many good double-created projects are that the project matching efficiency is low after the graduates run for one time and run for the east and the west, meet with investment institutions and are rejected;
secondly, when facing a wide variety of 'double-created' projects and due double-created graduates, it is difficult to make a careful assessment of each double-created project and personnel, because this will cost a lot of manpower and material resources, with a huge cost and poor results. Investment institutions often miss or misplace items, resulting in damage to companies;
if some investment institutions are on the name of investors, the copyist can steal the double-creative projects and ideas of the double-creative graduates, and the graduates can cause difficult right-keeping due to insufficient social reading and unfamiliar related legal knowledge and no credible proof of right-of-truth.
Disclosure of Invention
In view of the above problems, the present embodiments are proposed to provide a blockchain-based item matching method, a blockchain-based item matching apparatus, a computer device, and a storage medium that overcome or at least partially solve the above problems.
In order to solve the above problem, the embodiment discloses an item matching method based on a block chain, including:
collecting source data of projects, markets, personnel and investment institutions;
preprocessing the source data to obtain preprocessed data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data;
inputting the first market data, the first project data, the first person data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
inputting the second market data, the second project data and the second personnel data into the trained machine learning model to obtain corresponding second grading data;
acquiring investment institution scoring data corresponding to the investment institution according to the investment institution data;
and matching the projects and the investment institutions according to the second grading data and the investment institution grading data to obtain matching results.
Preferably, the inputting the first market data, the first project data, the first person data, and the first score data into a preset machine learning model to obtain a trained machine learning model includes:
inputting the first market data, the first personnel data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or inputting the first market data, the first project data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or inputting the first person data, the first item data and the first grading data into a preset machine learning model to obtain a trained machine learning model.
Preferably, the inputting the second market data, the second project data, and the second person data into the trained machine learning model to obtain corresponding second score data includes:
inputting the second market data and second personnel data into the trained machine learning model to obtain corresponding third grading data;
and/or inputting the second market data and the second item data into the trained machine learning model to obtain corresponding fourth grading data;
and/or inputting the second person data and the second item data into the trained machine learning model to obtain corresponding fifth grading data;
and performing cross comparison and matching on the third scoring data and/or the fourth scoring data and/or the fifth scoring data to obtain second scoring data.
Preferably, the inputting the first market data, the first project data, the first person data, and the first score data into a preset machine learning model to obtain a trained machine learning model includes:
generating a corresponding first market portrait, a corresponding first project portrait and a corresponding first person portrait according to the first market data, the first project data and the first person data;
and inputting the first market portrait, the first project portrait, the first person portrait and the first grading data which are subjected to data integration into a preset machine learning model to obtain a trained machine learning model.
Preferably, the method further comprises:
and storing the source data, the preprocessing data, the second grading data and the matching result as block data into a block chain.
Preferably, the method further comprises:
receiving a search request for the item;
and responding to the search request to display second scoring data of the item.
Preferably, the first market data comprises target market existing size, support policy, development trend;
the first project data comprises invested funds, return on investment level, market admission standard, required personnel number, personnel skills, required interpersonal relationship network and business upstream and downstream channels;
the first personnel data comprises learning scores, extracurricular activity scores, course information, teacher evaluation, practice unit evaluation, practice posts and practice scores;
the generating of the corresponding first market portrait, first project portrait and first person portrait according to the first market data, first project data and first person data respectively comprises:
generating a corresponding first market portrait according to the existing scale, supporting policies and development trends of the target market;
and/or generating a corresponding first project portrait according to the invested capital, the return on investment level, the market admission standard, the required personnel number, the personnel skills, the required interpersonal relationship network and the business upstream and downstream channels;
and/or generating a corresponding first person portrait according to the learning achievement, the out-of-class activity achievement, the course information, the teacher assessment, the practice unit assessment, the practice post and the practice achievement.
The embodiment also discloses an item matching device based on the block chain, which includes:
the source data acquisition module is used for acquiring source data of projects, markets, personnel and investment institutions;
the preprocessing data module is used for preprocessing the source data to obtain preprocessing data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data;
the training module is used for inputting the first market data, the first project data, the first personnel data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
the prediction module is used for inputting the second market data, the second project data and the second personnel data into the trained machine learning model to obtain corresponding second grading data;
the investment institution module is used for obtaining investment institution scoring data corresponding to the investment institution according to the investment institution data;
and the matching result obtaining module is used for matching the project and the investment institution according to the second grading data and the grading data of the investment institution to obtain a matching result.
The embodiment also discloses a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above item matching method based on the block chain when executing the computer program.
The present embodiment also discloses a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the above-mentioned blockchain-based item matching method.
The present embodiment includes the following advantages:
in this embodiment, source data of projects, markets, personnel, and investment institutions are collected; preprocessing the source data to obtain preprocessed data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data; inputting the first market data, the first project data, the first person data and the first grading data into a preset machine learning model to obtain a trained machine learning model; inputting the second market data, the second project data and the second personnel data into the trained machine learning model to obtain corresponding second grading data; acquiring investment institution scoring data corresponding to the investment institution according to the investment institution data; and matching the projects and the investment institutions according to the second grading data and the investment institution grading data to obtain matching results. Meanwhile, three-party data (market, project and personnel) are analyzed, cross comparison and matching are carried out, and because the original data resources are richer and more comprehensive and the analysis means are more three-dimensional and scientific, the evaluation result is more accurate and credible, and good reference information is provided for investment institutions; the problem that the communication efficiency between personnel and an investment institution is low is solved, the resources and information of the two parties are integrated, and the communication efficiency is improved;
drawings
In order to more clearly illustrate the technical solution of the present embodiment, the drawings needed to be used in the description of the embodiment will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts
FIG. 1 is a flowchart illustrating steps of an embodiment of a block chain-based item matching method according to the present embodiment;
FIG. 2 is a flowchart illustrating a trained machine learning model obtaining step according to the present embodiment;
fig. 3 is a schematic flowchart of a second scoring data obtaining step according to this embodiment;
FIG. 4 is a flowchart illustrating a trained machine learning model obtaining step according to the present embodiment;
fig. 5 is a flowchart illustrating an item matching method based on a blockchain according to the present embodiment;
FIG. 6 is a flow chart illustrating an image generating step according to the present embodiment;
FIG. 7 is a block diagram of an embodiment of an item matching apparatus based on a blockchain according to the present embodiment;
FIG. 8 is an internal block diagram of a computer device of an embodiment.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects solved by the present embodiment more clearly apparent, the present embodiment is further described in detail below with reference to the accompanying drawings and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a flowchart illustrating steps of an embodiment of an item matching method based on a block chain according to the present embodiment is shown, and specifically, the method may include the following steps:
step 101, collecting source data of projects, markets, personnel and investment institutions;
specifically, the present embodiment may be applied to a terminal, where the terminal may include but is not limited to various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the system operated by the terminal may include an android system, a Windows system, an IOS system, and may further include a Linux system, a Unix system, and the like.
In this embodiment, first, source data of a project, source data of a market, source data of a person, and source data of an investment institution may be collected; specifically, the project may include an innovation and entrepreneurship project, or may be a project of other students or social personnel; similarly, the person may refer to entrepreneur and other main persons in charge of the project, such as students (including school students, students waiting for a graduation) or social persons, and the embodiment is not limited thereto.
For the source of the source data, the various data can be respectively captured on the network in a web crawler mode; for example, such as domestic and foreign news websites, college journal database websites, college websites, data analysis websites, industry report white paper websites, various statistical report websites, government legal policies and support policies websites, and official websites of institutions; furthermore, data collection can be performed through some internal websites, such as a teaching administration management system, an online learning platform, a practice management system, a employment and recruitment system, a school and enterprise cooperation system, and the like, which is not limited in this embodiment.
Step 102, preprocessing the source data to obtain preprocessed data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data;
further applied to the embodiment, after various source data are acquired, preprocessing needs to be performed on the source data, wherein the preprocessing mode may include missing value processing, abnormal value processing, missing backfill processing, and data standardization processing; the present embodiment does not unduly limit the specific manner of performing the pretreatment.
Specifically, various source data are preprocessed to obtain various training data, prediction data and investment institution data; the training data comprises first market data, first project data, first personnel data and first grading data; the prediction data includes second market data, second project data, and second person data, where the training data is a training sample, and it should be noted that the first scoring data refers to scoring data obtained by analyzing and evaluating the market data, the project data, and the person data, and the first scoring data may be preset, and the training data is used to train the machine learning model to obtain a trained machine learning model. It is noted that the training data, predictive data, and investment institution data are all integrated data.
Furthermore, the prediction data refers to data to be input into the trained machine learning model for prediction, and it can be understood that the prediction data is input into the trained machine learning model to obtain output score data;
it should be noted that after the preprocessing operation is completed, various data may be stored in respective databases, for example, the first market data and the second market data are stored in the market databases; the first project data and the second project data are stored in a project database; the first personnel data and the second personnel data are stored in a personnel database; storing the first grading data into a grading database; this embodiment is not limited thereto.
Step 103, inputting the first market data, the first project data, the first person data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
specifically, in the embodiment, the first market data, the first project data, the first person data and the first score data are input into a preset machine learning model, and the machine learning model is trained; the preset machine learning model is trained by using the training samples, and parameters in the model are continuously adjusted to obtain the trained machine learning model.
For the type of the Machine learning model, a supervised Machine learning model may be included, specifically, the supervised learning model mainly includes a model for classification and Regression, for example, a Linear Classifier model (Linear Classifier), a Support Vector Machine (Support Vector Machine), a Naive Bayes model Classifier (negative Bayes Classifier), a K-nearest neighbor model (K-nearest neighbor), a Decision Tree model (Decision Tree), a Linear Regression model (Linear Regression), a Regression Tree model (Regression Tree), and the like may be included, which is not limited in this embodiment.
The model training method can be used for training in a mode of combining the first market data, the first project data and the first person data in pairs, for example, the first market data, the first project data and the corresponding first scoring data are input into a preset machine learning model to obtain different scoring results, and the scoring results are subjected to interactive matching.
Step 104, inputting the second market data, the second project data and the second personnel data into the trained machine learning model to obtain corresponding second grading data;
specifically applied to the embodiment, the prediction data is input to the trained machine learning model to obtain corresponding second scoring data; namely, second market data, second project data and second personnel data are input into the trained machine learning model, and corresponding second grading data are obtained.
Similarly, the second market data, the second project data and the second person data may be predicted in a pairwise combination manner to obtain different second scoring data.
105, obtaining investment institution scoring data corresponding to the investment institution according to the investment institution data;
furthermore, investment institution scoring data corresponding to the investment institution can be obtained according to the investment institution data; specifically, investment institution data may be input to the machine learning model, resulting in investment institution scoring data.
The machine learning model may be an unsupervised learning model, and further, the unsupervised learning model mainly includes a data clustering model (K-means), a data dimension reduction model (Principal Component Analysis), a Random forest model (Random trees), a self-coding model (Auto encoding), a Principal Component Analysis model (Principal components Analysis), and the like, which is not limited in this embodiment.
And 106, matching the projects and the investment institutions according to the second grading data and the investment institution grading data to obtain matching results.
Further, the project and the investment institution can be matched according to the second scoring data and the investment institution scoring data to obtain a matching result. The second grading data obtained by the source data of the project, the market and the personnel through the machine learning model is matched with the grading data of the investment institution to obtain the matching result of the project and the investment institution. By the aid of technologies and algorithms such as artificial intelligence, data mining and analysis, and the like, the entrepreneurship investment risk assessment report generated intelligently can provide credible investment reference for investment institutions.
In this embodiment, source data of projects, markets, personnel, and investment institutions are collected; preprocessing the source data to obtain preprocessed data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data; inputting the first market data, the first project data, the first person data and the first grading data into a preset machine learning model to obtain a trained machine learning model; inputting the second market data, the second project data and the second personnel data into the trained machine learning model to obtain corresponding second grading data; acquiring investment institution scoring data corresponding to the investment institution according to the investment institution data; and matching the projects and the investment institutions according to the second grading data and the investment institution grading data to obtain matching results. Meanwhile, three-party data (market, project and personnel) are analyzed, cross comparison and matching are carried out, and because the original data resources are richer and more comprehensive and the analysis means are more three-dimensional and scientific, the evaluation result is more accurate and credible, and good reference information is provided for investment institutions; the problem that the communication efficiency between personnel and an investment institution is low is solved, the resources and information of the two parties are integrated, and the communication efficiency is improved;
in one embodiment, referring to fig. 2, a flowchart of a step of obtaining a trained machine learning model of this embodiment is shown, where the step of inputting the first market data, the first project data, the first person data, and the first score data into a preset machine learning model to obtain the trained machine learning model includes the following sub-steps:
a substep S11, inputting the first market data, the first person data and the first score data into a preset machine learning model to obtain a trained machine learning model;
substep S12, inputting the first market data, the first project data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and a substep S13, inputting the first person data, the first item data and the first score data into a preset machine learning model to obtain a trained machine learning model.
Specifically, first market data, first personnel data and first grading data are input into a preset machine learning model respectively to obtain a trained machine learning model; and training the training data in a pairwise combination manner, so that the accuracy of the machine learning model is improved.
In another preferred embodiment, referring to fig. 3, a flowchart of a second scoring data obtaining step of this embodiment is shown, where the inputting the second market data, the second project data, and the second person data into the trained machine learning model to obtain corresponding second scoring data includes the following sub-steps:
substep S21, inputting the second market data and the second personnel data into the trained machine learning model to obtain corresponding third scoring data;
substep S22, inputting the second market data and the second item data into the trained machine learning model to obtain corresponding fourth score data;
substep S23, inputting the second person data and the second item data into the trained machine learning model to obtain corresponding fifth scoring data;
and a substep S24, performing cross comparison and matching on the third score data and/or the fourth score data and/or the fifth score data to obtain second score data.
Similarly, the prediction data can be combined pairwise and input into the trained machine learning model to obtain different scoring data, and the multiple different scoring data are subjected to cross comparison and matching to obtain second scoring data.
In a preferred embodiment, referring to fig. 4, a flowchart of a trained machine learning model obtaining step of this embodiment is shown, where the inputting the first market data, the first project data, the first person data, and the first score data into a preset machine learning model to obtain the trained machine learning model includes the following sub-steps:
a substep S31, generating a corresponding first market portrait, a first project portrait and a first person portrait respectively according to the first market data, the first project data and the first person data;
and a substep S32 of inputting the first market portrait, the first project portrait, the first person portrait and the first score data, which are integrated by data, into a preset machine learning model to obtain a trained machine learning model.
Specifically, when the machine learning model is trained, a pre-processed data representation is first generated, specifically, a market representation is generated based on market data, a project representation is generated based on project data, a person representation is generated based on the project data, and then the market representation, the project representation, the person representation, and the first score data are integrated and concatenated and input to a preset machine learning model to obtain the trained machine learning model.
Similarly, when performing prediction through the trained machine learning model, the operation of data integration concatenation is also required.
In a preferred embodiment, the method further comprises: and storing the source data, the preprocessing data, the second grading data and the matching result as block data into a block chain. The block chain technology is utilized to ensure that all system and user data are real and reliable, the mutual trust problem of personnel (including double-creation graduates) and investment institutions is solved, and disputes can be solved through information traceability.
In a preferred embodiment, referring to fig. 5, a flowchart of an item matching method based on a blockchain according to the present embodiment is shown, where the method further includes the following steps:
step 107, receiving a search request for the item;
step 108, responding to the search request, and displaying second scoring data of the item.
In practical application, a search request for the item can be received on the interface, and the second scoring data of the item is displayed in response to the search request; in particular, the main information of the person data can also be presented.
In a preferred embodiment, referring to fig. 6, a flow chart of a portrait generation step of the embodiment is shown, wherein the first market data includes the existing size of the target market, the policy supported, and the development trend; the first project data comprises invested funds, return on investment level, market admission standard, required personnel number, personnel skills, required interpersonal relationship network and business upstream and downstream channels; the first personnel data comprises learning scores, extracurricular activity scores, course information, teacher evaluation, practice unit evaluation, practice posts and practice scores; the method for generating a first market portrait, a first project portrait and a first person portrait respectively according to the first market data, the first project data and the first person data comprises the following substeps:
a substep S41, generating a corresponding first market portrait according to the current scale, supporting policy and development trend of the target market;
a substep S42, generating a corresponding first project portrait according to the invested capital, the return on investment level, the market access standard, the required personnel number, the personnel skill, the required interpersonal relationship network and the business upstream and downstream channels;
and a substep S43 of generating a corresponding first person portrait according to the learning achievement, the achievement of the extracurricular activities, the course information, the teacher evaluation, the practice unit evaluation, the practice post and the practice achievement.
The corresponding images can be generated by preprocessing data according to the generation method of the market images, the project images and the personnel images.
In order to allow readers to better understand the embodiment, a block chain-based trusted double-creation system is taken as a specific example for explanation, specifically, the system provides a block chain-based trusted double-creation cooperative platform for graduates and investment institutions, analyzes and evaluates double-creation graduates, double-creation projects and corresponding markets through technologies such as artificial intelligence, data mining and analysis, and the like, provides trusted investment risk evaluation information for the investment institutions, and simultaneously stores investment information of the qualification of each investment institution in a chain manner, and provides trusted reference for other double-creation personnel.
The system mainly comprises a data mining module, an information storage module, an intelligent analysis module, an event tracing module, a result feedback module and a user management module;
1. the functions of the data mining module include: double-creation graduate data mining, double-creation project data mining, related project market data mining and investment institution data mining;
sources of data include: the system comprises a educational administration system, an online learning platform, a practice management system, a employment and recruitment system, a school-enterprise cooperation system and the like, as well as domestic and foreign news, college academic journal databases, college websites, data analysis websites, industry report white papers, various statistical reports, government legal policies, support policies and the like;
2. the functions of the information storage module include:
2.1 storing the mined data information, comprising: double-creation graduate data, double-creation project data, related project market data and investment institution data;
2.2 store the results of the intelligent analysis, including: market portraits (the existing scale, development trend and potential of a target market of a project, related support policies of a country or a place and the like) and scores, project portraits (invested capital required by the project, investment return level, market admission standard, required personnel quantity and skills, required interpersonal relationship network, business upstream and downstream channels and the like) and scores, student portraits (learning ability, technical ability, social ability, executive ability, speciality, hobbies, temperament and the like) and scores, double-creation overall evaluation reports, double-creation investment institution portraits (qualification, credit, score and the like) and scores;
2.3 store communications and investment activity information that occurs within the system, including: chat information, investment contracts, occurrence time of related information, personal information of both trading parties and the like;
2.4, storing user information including graduate names, identity card numbers, graduate colleges, professions, resumes and the like, investment company names, legal persons, unique social credit codes, registered funds and the like;
3. the functions of the intelligent analysis module include: analyzing and evaluating the data information of the current market of the project, the information of the project and the data information of students to form a double-creation comprehensive analysis and evaluation report (including a double-creation total score, each single-item score, a risk level, an investment suggestion and the like); analyzing the investment institutions to form comprehensive scores of the investment institutions; intelligently recommending an investment institution to students according to double-creation information of double-creation graduates; recommending the items of the double creation type and graduates interested by the investment institutions to the corresponding investment institutions; the system simultaneously combines the data information of the current market, the project information and the student data information to carry out comprehensive analysis and evaluation:
in the aspect of market, analyzing the current scale, development trend and potential of the target market of the project, and related supporting policies of the country or the place and the like to form a market portrait;
in the aspect of projects, the invested funds, the return on investment level, the market admission standard, the required personnel quantity and skills, the required interpersonal relationship network, the business upstream and downstream channels and the like required by the projects are analyzed to form project portraits;
in the aspect of students, a student portrait comprising information of learning ability, technical ability, social ability, executive ability, speciality, hobbies, personality and the like of students is formed by analyzing all uplink information (including scores, extrafarial activities, course selection, teacher evaluation, practice unit evaluation, practice posts, scores and the like) of the students from entering universities, to practice and to graduation recorded by a teaching affairs management system, an online learning platform, a practice management system, a employment recruitment system, a school-enterprise cooperation system and the like;
4. the event tracing module has the functions of: performing uplink storage on user communication and investment activity information performed in the system through a block chain technology, and forming a data information chain which is not falsifiable and traceable based on time nodes and operation behaviors; the user can obtain the operation information of the two parties at each main time node in the past by checking the information chain, wherein the operation information comprises chat records, investment contracts and the like;
5. the functions of the result feedback module include: providing a double-created comprehensive analysis and evaluation report for a user; providing related search results including investment institution information, double-creation graduate information, double-creation project information, historical operation records, investment records and the like according to conditions input by a user;
6. the functions of the user management module include: user registration management, identity verification, rights management, membership management, and the like.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art should understand that the present embodiment is not limited by the order of acts described, as some steps may be performed in other orders or simultaneously according to the present embodiment. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the embodiments.
Referring to fig. 7, a block diagram of an embodiment of an item matching apparatus based on a block chain according to this embodiment is shown, which may specifically include the following modules:
a source data acquisition module 301, configured to acquire source data of projects, markets, personnel, and investment institutions;
a preprocessing data module 302, configured to perform preprocessing on the source data to obtain preprocessed data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data;
the training module 303 is configured to input the first market data, the first project data, the first person data, and the first score data into a preset machine learning model to obtain a trained machine learning model;
the prediction module 304 is configured to input the second market data, the second project data, and the second person data into the trained machine learning model to obtain corresponding second score data;
an investment institution module 305, configured to obtain investment institution scoring data corresponding to the investment institution according to the investment institution data;
and a matching result obtaining module 306, configured to match the project and the investment institution according to the second scoring data and the investment institution scoring data to obtain a matching result.
In one embodiment, the training module comprises:
the first training module is used for inputting the first market data, the first person data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or the second training module is used for inputting the first market data, the first project data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or the third training module is used for inputting the first person data, the first project data and the first grading data into a preset machine learning model to obtain the trained machine learning model.
In one embodiment, the prediction module comprises:
the first prediction submodule is used for inputting the second market data and the second personnel data into the trained machine learning model to obtain corresponding third grading data;
and/or the second prediction submodule is used for inputting the second market data and the second item data into the trained machine learning model to obtain corresponding fourth grading data;
and/or the third prediction submodule is used for inputting the second personnel data and the second item data into the trained machine learning model to obtain corresponding fifth grading data;
and the fourth prediction submodule is used for performing cross comparison and matching on the third scoring data and/or the fourth scoring data and/or the fifth scoring data to obtain second scoring data.
In one embodiment, the training module comprises:
the portrait generation submodule is used for respectively generating a corresponding first market portrait, a corresponding first project portrait and a corresponding first person portrait according to the first market data, the corresponding first project data and the corresponding first person data;
and the fourth training submodule is used for inputting the first market portrait, the first project portrait, the first person portrait and the first grading data which are subjected to data integration into a preset machine learning model to obtain the trained machine learning model.
In one embodiment, the apparatus further comprises:
and the storage module is used for storing the source data, the preprocessing data, the second grading data and the matching result as block data into a block chain.
In one embodiment, the apparatus further comprises:
a receiving module for receiving a search request for the item;
and the display module is used for responding to the search request and displaying the second scoring data of the item.
In one embodiment, the first market data includes target market existing size, support policy, development trend;
the first project data comprises invested funds, return on investment level, market admission standard, required personnel number, personnel skills, required interpersonal relationship network and business upstream and downstream channels;
the first personnel data comprises learning scores, extracurricular activity scores, course information, teacher evaluation, practice unit evaluation, practice posts and practice scores;
the portrait generation sub-module includes:
the market portrait generating unit is used for generating a corresponding first market portrait according to the existing scale, supporting policies and development trends of the target market;
and/or the project portrait generating unit is used for generating a corresponding first project portrait according to the invested capital, the return on investment level, the market access standard, the required personnel number, the personnel skill, the required interpersonal relationship network and the business upstream and downstream channel;
and/or the personnel portrait generating unit is used for generating a corresponding first personnel portrait according to the learning achievement, the achievement of the extracurricular activities, the course information, the teacher evaluation, the practice unit evaluation, the practice post and the practice achievement.
For specific definition of the block chain-based item matching device, reference may be made to the above definition of the block chain-based item matching method, which is not described herein again. The modules in the block chain-based item matching apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
The above provided project matching device based on the block chain can be used for executing the project matching method based on the block chain provided by any of the above embodiments, and has corresponding functions and advantages.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of operation maintenance. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
collecting source data of projects, markets, personnel and investment institutions;
preprocessing the source data to obtain preprocessed data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data;
inputting the first market data, the first project data, the first person data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
inputting the second market data, the second project data and the second personnel data into the trained machine learning model to obtain corresponding second grading data;
acquiring investment institution scoring data corresponding to the investment institution according to the investment institution data;
and matching the projects and the investment institutions according to the second grading data and the investment institution grading data to obtain matching results.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
inputting the first market data, the first personnel data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or inputting the first market data, the first project data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or inputting the first person data, the first item data and the first grading data into a preset machine learning model to obtain a trained machine learning model.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
inputting the second market data and second personnel data into the trained machine learning model to obtain corresponding third grading data;
and/or inputting the second market data and the second item data into the trained machine learning model to obtain corresponding fourth grading data;
and/or inputting the second person data and the second item data into the trained machine learning model to obtain corresponding fifth grading data;
and performing cross comparison and matching on the third scoring data and/or the fourth scoring data and/or the fifth scoring data to obtain second scoring data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
generating a corresponding first market portrait, a corresponding first project portrait and a corresponding first person portrait according to the first market data, the first project data and the first person data;
and inputting the first market portrait, the first project portrait, the first person portrait and the first grading data which are subjected to data integration into a preset machine learning model to obtain a trained machine learning model.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and storing the source data, the preprocessing data, the second grading data and the matching result as block data into a block chain.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
receiving a search request for the item;
and responding to the search request to display second scoring data of the item.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
generating a corresponding first market portrait according to the existing scale, supporting policies and development trends of the target market;
and/or generating a corresponding first project portrait according to the invested capital, the return on investment level, the market admission standard, the required personnel number, the personnel skills, the required interpersonal relationship network and the business upstream and downstream channels;
and/or generating a corresponding first person portrait according to the learning achievement, the out-of-class activity achievement, the course information, the teacher assessment, the practice unit assessment, the practice post and the practice achievement.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
collecting source data of projects, markets, personnel and investment institutions;
preprocessing the source data to obtain preprocessed data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data;
inputting the first market data, the first project data, the first person data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
inputting the second market data, the second project data and the second personnel data into the trained machine learning model to obtain corresponding second grading data;
acquiring investment institution scoring data corresponding to the investment institution according to the investment institution data;
and matching the projects and the investment institutions according to the second grading data and the investment institution grading data to obtain matching results.
In one embodiment, the computer program when executed by the processor implements the steps of:
inputting the first market data, the first personnel data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or inputting the first market data, the first project data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or inputting the first person data, the first item data and the first grading data into a preset machine learning model to obtain a trained machine learning model.
In one embodiment, the computer program when executed by the processor implements the steps of:
inputting the second market data and second personnel data into the trained machine learning model to obtain corresponding third grading data;
and/or inputting the second market data and the second item data into the trained machine learning model to obtain corresponding fourth grading data;
and/or inputting the second person data and the second item data into the trained machine learning model to obtain corresponding fifth grading data;
and performing cross comparison and matching on the third scoring data and/or the fourth scoring data and/or the fifth scoring data to obtain second scoring data.
In one embodiment, the computer program when executed by the processor implements the steps of:
generating a corresponding first market portrait, a corresponding first project portrait and a corresponding first person portrait according to the first market data, the first project data and the first person data;
and inputting the first market portrait, the first project portrait, the first person portrait and the first grading data which are subjected to data integration into a preset machine learning model to obtain a trained machine learning model.
In one embodiment, the computer program when executed by the processor implements the steps of:
and storing the source data, the preprocessing data, the second grading data and the matching result as block data into a block chain.
In one embodiment, the computer program when executed by the processor implements the steps of:
receiving a search request for the item;
and responding to the search request to display second scoring data of the item.
In one embodiment, the computer program when executed by the processor implements the steps of:
generating a corresponding first market portrait according to the existing scale, supporting policies and development trends of the target market;
and/or generating a corresponding first project portrait according to the invested capital, the return on investment level, the market admission standard, the required personnel number, the personnel skills, the required interpersonal relationship network and the business upstream and downstream channels;
and/or generating a corresponding first person portrait according to the learning achievement, the out-of-class activity achievement, the course information, the teacher assessment, the practice unit assessment, the practice post and the practice achievement.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present embodiments may be provided as methods, apparatus, or computer program products. Accordingly, the present embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present embodiments 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 embodiments are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to the present embodiments. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, 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 terminal 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 terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present embodiments have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the present embodiment.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal 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 terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The above detailed description is provided for an item matching method based on a blockchain, an item matching device based on a blockchain, a computer device and a storage medium, and a specific example is applied in the present disclosure to explain the principle and the implementation of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. An item matching method based on a block chain is characterized by comprising the following steps:
collecting source data of projects, markets, personnel and investment institutions;
preprocessing the source data to obtain preprocessed data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data;
inputting the first market data, the first project data, the first person data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
inputting the second market data, the second project data and the second personnel data into the trained machine learning model to obtain corresponding second grading data;
acquiring investment institution scoring data corresponding to the investment institution according to the investment institution data;
and matching the projects and the investment institutions according to the second grading data and the investment institution grading data to obtain matching results.
2. The method of claim 1, wherein inputting the first market data, the first project data, the first person data, and the first score data into a preset machine learning model to obtain a trained machine learning model comprises:
inputting the first market data, the first personnel data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or inputting the first market data, the first project data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
and/or inputting the first person data, the first item data and the first grading data into a preset machine learning model to obtain a trained machine learning model.
3. The method of claim 1 or 2, wherein inputting the second market data, the second project data, and the second person data into the trained machine learning model to obtain corresponding second scoring data comprises:
inputting the second market data and second personnel data into the trained machine learning model to obtain corresponding third grading data;
and/or inputting the second market data and the second item data into the trained machine learning model to obtain corresponding fourth grading data;
and/or inputting the second person data and the second item data into the trained machine learning model to obtain corresponding fifth grading data;
and performing cross comparison and matching on the third scoring data and/or the fourth scoring data and/or the fifth scoring data to obtain second scoring data.
4. The method of claim 1, wherein inputting the first market data, the first project data, the first person data, and the first score data into a preset machine learning model to obtain a trained machine learning model comprises:
generating a corresponding first market portrait, a corresponding first project portrait and a corresponding first person portrait according to the first market data, the first project data and the first person data;
and inputting the first market portrait, the first project portrait, the first person portrait and the first grading data which are subjected to data integration into a preset machine learning model to obtain a trained machine learning model.
5. The method of claim 1, 2 or 4, further comprising:
and storing the source data, the preprocessing data, the second grading data and the matching result as block data into a block chain.
6. The method of claim 1, further comprising:
receiving a search request for the item;
and responding to the search request to display second scoring data of the item.
7. The method of claim 1, wherein the first market data comprises target market existing size, supported policies, trends in development;
the first project data comprises invested funds, return on investment level, market admission standard, required personnel number, personnel skills, required interpersonal relationship network and business upstream and downstream channels;
the first personnel data comprises learning scores, extracurricular activity scores, course information, teacher evaluation, practice unit evaluation, practice posts and practice scores;
the generating of the corresponding first market portrait, first project portrait and first person portrait according to the first market data, first project data and first person data respectively comprises:
generating a corresponding first market portrait according to the existing scale, supporting policies and development trends of the target market;
and/or generating a corresponding first project portrait according to the invested capital, the return on investment level, the market admission standard, the required personnel number, the personnel skills, the required interpersonal relationship network and the business upstream and downstream channels;
and/or generating a corresponding first person portrait according to the learning achievement, the out-of-class activity achievement, the course information, the teacher assessment, the practice unit assessment, the practice post and the practice achievement.
8. An item matching apparatus based on a blockchain, comprising:
the source data acquisition module is used for acquiring source data of projects, markets, personnel and investment institutions;
the preprocessing data module is used for preprocessing the source data to obtain preprocessing data; wherein the preprocessing data comprises training data, prediction data and investment institution data; the training data comprises first market data, first project data, first person data and first scoring data; the forecast data comprises second market data, second project data and second personnel data;
the training module is used for inputting the first market data, the first project data, the first personnel data and the first grading data into a preset machine learning model to obtain a trained machine learning model;
the prediction module is used for inputting the second market data, the second project data and the second personnel data into the trained machine learning model to obtain corresponding second grading data;
the investment institution module is used for obtaining investment institution scoring data corresponding to the investment institution according to the investment institution data;
and the matching result obtaining module is used for matching the project and the investment institution according to the second grading data and the grading data of the investment institution to obtain a matching result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the blockchain based item matching method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the blockchain-based item matching method of any one of claims 1 to 7.
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