CN117874542A

CN117874542A - Big data-based result conversion supply and demand matching method, device, equipment and medium

Info

Publication number: CN117874542A
Application number: CN202410183293.5A
Authority: CN
Inventors: 李伟洪; 陈国昌; 刘沛鹏; 陈进才
Original assignee: Guangdong Institute Of Computing Technology Application
Current assignee: Guangdong Institute Of Computing Technology Application
Priority date: 2024-02-19
Filing date: 2024-02-19
Publication date: 2024-04-12
Anticipated expiration: 2044-02-19

Abstract

The utility model provides a result conversion supply and demand matching method, device, equipment and medium based on big data, this method is through the hierarchical information tree of supply and demand, seeks the target supply and demand node that matches with the resource demand information of demand user side, and the hierarchical information tree of supply and demand can reduce the matching range through the mode of hierarchical matching layer by layer, improves supply and demand matching efficiency and matching accuracy. And acquiring the information of the supply and demand party according to the association relation between the target supply and demand node and the supply and demand party information database, and reducing the matching data quantity, improving the matching accuracy and improving the matching efficiency by isolating the storage of the information of the supply and demand party from the data matching. The push priority of each target supplier is determined by evaluating the result conversion rate of the target supplier and calculating the supply and demand reliability of the target supplier, and the information of the supplier is sent to the demand user side, so that supply and demand matching is completed, accuracy and success rate of the supply and demand matching can be improved, and efficiency of the result conversion supply and demand matching is improved.

Description

Big data-based result conversion supply and demand matching method, device, equipment and medium

Technical Field

The application relates to the technical field of product conversion supply and demand matching, in particular to a method, a device, equipment and a medium for product conversion supply and demand matching based on big data.

Background

The method aims at solving the problems of high hidden cost, strong risk sensitivity, weak result receiving capability and the like of small and medium enterprises caused by asymmetric information, mismatching of supply and demand, uncertain price and the like of technological result conversion. Medium and small enterprises face the embarrassment that the enterprises do not have the capability of independently developing high-tech products; meanwhile, many achievements of scientific research remain only in the theoretical stage.

The current technological achievement transformation mainly performs supply and demand matching by means of centralized matching and the like. However, when matching is performed between the resource party and the demand party at present, the search is generally performed through a direct search mode, and the matching search mode leads to too many resource parties or demand parties, so that the matching range is too large, the search results are also mixed and disordered, the supply and demand matching results are poor, and the matching efficiency is low.

Therefore, how to solve the technical problem that the matching efficiency of the current achievement transformation supply and demand is low becomes urgent to be solved.

Disclosure of Invention

The application provides a big data-based achievement transformation supply and demand matching method, device, equipment and storage medium, and aims to improve data processing efficiency of a target database.

In a first aspect, the present application provides a big data-based achievement transformation supply and demand matching method, where the method includes:

acquiring resource demand information of a demand user terminal based on a resource demand request initiated by the demand user terminal;

searching for a supply-demand node matched with the resource demand information in a supply-demand hierarchical information tree based on the resource demand information, and determining at least one target supply-demand node;

acquiring the supply and demand information corresponding to the target supply and demand node in the supply and demand information database based on the association relation between the target supply and demand node and the supply and demand information database;

determining at least one target supplier based on the supplier information;

determining push priority of each target supplier based on the corresponding supply and demand credit and the corresponding result conversion rate of each target supplier;

and sending the information of each supply and demand party to the demand user end based on the push priority so as to complete supply and demand matching between the demand user end and the response supply and demand end.

In a second aspect, the present application further provides a big data based result conversion supply and demand matching device, where the big data based result conversion supply and demand matching device includes:

The resource demand information acquisition module is used for acquiring the resource demand information of the demand user terminal based on the resource demand request initiated by the demand user terminal;

the target supply and demand node determining module is used for searching supply and demand nodes matched with the resource demand information in a supply and demand hierarchical information tree based on the resource demand information and determining at least one target supply and demand node;

the supply and demand side information acquisition module is used for acquiring supply and demand side information corresponding to the target supply and demand node in the supply and demand side information database based on the association relation between the target supply and demand node and the supply and demand side information database;

a target supplier determining module, configured to determine at least one target supplier based on the supplier information;

the achievement conversion rate evaluation module is used for evaluating the achievement conversion rate of each target supplier on the resource demand request based on the resource demand information and the supply-demand resource information of the target supplier;

the pushing priority determining module is used for determining the pushing priority of each target supplier based on the corresponding supply and demand credibility of each target supplier and the achievement conversion rate;

and the supply and demand matching module is used for sending the supply and demand party information to the demand user end based on the push priority so as to complete supply and demand matching between the demand user end and the response supply and demand end.

In a third aspect, the present application further provides a computer device, the computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements the steps of the big data based outcome conversion supply and demand matching method as described above.

In a fourth aspect, the present application further provides a computer readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the big data based achievement transformation supply and demand matching method as described above.

The application provides a big data-based achievement transformation supply and demand matching method, device, equipment and storage medium, wherein the method comprises the steps of obtaining resource demand information of a demand user terminal based on a resource demand request initiated by the demand user terminal; searching for a supply-demand node matched with the resource demand information in a supply-demand hierarchical information tree based on the resource demand information, and determining at least one target supply-demand node; acquiring the supply and demand information corresponding to the target supply and demand node in the supply and demand information database based on the association relation between the target supply and demand node and the supply and demand information database; determining at least one target supplier based on the supplier information; determining push priority of each target supplier based on the corresponding supply and demand credit and the corresponding result conversion rate of each target supplier; and sending the information of each supply and demand party to the demand user end based on the push priority so as to complete supply and demand matching between the demand user end and the response supply and demand end. Through the mode, the target supply and demand nodes matched with the resource demand information of the demand user side are searched through the supply and demand hierarchical information tree, the matching range of the supply and demand hierarchical information tree can be reduced layer by layer through the hierarchical matching mode, and the supply and demand matching efficiency and the matching accuracy are improved. According to the association relation between the target supply and demand node and the supply and demand information database, the supply and demand information is acquired, and the storage of the supply and demand information and the data matching are isolated, so that the matching data quantity is reduced, the interference of redundant information is avoided, the matching range is reduced, the matching accuracy is improved, and the matching efficiency is improved. The result conversion rate of the target supply and demand party is evaluated, the supply and demand reliability of the target supply and demand party is calculated, the push priority of each target supply and demand party is further determined, and the information of the supply and demand party is sent to the demand user side, so that supply and demand matching is completed, and therefore, the accuracy and the success rate of supply and demand matching can be improved according to the push priority, and further the efficiency of supply and demand matching of the result conversion is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a diagram of a big data based effort-to-demand matching system provided in an embodiment of the present application;

fig. 2 is a schematic flow chart of a first embodiment of a big data-based achievement transformation supply and demand matching method provided in the present application;

FIG. 3 is a flowchart of a second embodiment of a big data based result conversion supply and demand matching method provided in the present application;

fig. 4 is a schematic flow chart of a third embodiment of a big data-based result conversion supply and demand matching method provided in the present application;

fig. 5 is a schematic structural diagram of a first embodiment of a big data-based achievement transformation supply and demand matching device provided in the present application;

fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present application.

The realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments, referring to the attached drawings.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.

Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.

As shown in fig. 1, fig. 1 is a big data-based result conversion supply and demand matching system provided in an embodiment of the present application, where the system includes a terminal and a cloud server, the terminal includes a demand terminal and a supply and demand terminal, and the terminal is in communication connection with the cloud server.

The terminal comprises electronic equipment such as a mobile phone, a tablet personal computer, a notebook computer, a desktop computer, a personal digital assistant, wearable equipment and the like.

The cloud server comprises a single server or a server cluster.

In an embodiment, the cloud server may be configured with a result-to-supply-and-demand matching platform, where the result-to-supply-and-demand matching platform is used for supply-and-demand matching and communication session between the demand terminal and the supply-and-demand terminal.

The big data-based result conversion supply and demand matching system will be described in detail below with respect to the big data-based result conversion supply and demand matching method provided by the embodiment of the present application.

Referring to fig. 2, fig. 2 is a flow chart of a first embodiment of a big data-based result conversion supply and demand matching method provided in the present application. The big data-based result conversion supply and demand matching method can be used in a server of a big data-based result conversion supply and demand matching system.

As shown in fig. 2, the big data based achievement transformation supply and demand matching method includes steps S101 to S106.

S101, acquiring resource demand information of a demand user terminal based on a resource demand request initiated by the demand user terminal;

In an embodiment, the demand client may log in to the product conversion supply and demand matching platform through a terminal device (such as a mobile phone, a computer, a notebook computer, etc.), issue a resource demand request through the product conversion supply and demand matching platform, and evaluate and analyze the resource demand request by the product conversion supply and demand matching platform, and extract the resource demand information therein.

In an embodiment, the resource requirement information may include data such as an industry field, a requirement device, a requirement opposite party qualification, a personnel number, a personnel professional level, etc., for example, a requirement of building resources, a requirement supply and demand party is a building industry field, further, the resource requirement information may be defined according to a construction object, for example, equipment resources required by bridge construction, equipment resources required by road construction and equipment resources required by house construction are different, the requirement of the personnel professional level is also different, and even a requirement may be proposed for an engineering project that the requirement supply and demand party has received.

In an embodiment, the resource requirement information can be extracted according to the requirement description text provided by the requirement user side, and the user can be guided to describe and supplement through the intelligent dialogue. For example, what the demand industry is, the next guiding dialogue is generated according to the industry field, such as building industry requires building qualification, equipment resources, fire-fighting level, etc., and intellectual property industry requires agent qualification, etc.

S102, searching a supply and demand node matched with the resource demand information in a supply and demand hierarchical information tree based on the resource demand information, and determining at least one target supply and demand node;

in an embodiment, the supply and demand hierarchical information tree is a tree structure information table created by classifying the supply and demand information according to the preset classification type, and the supply and demand information is disassembled and classified according to the preset classification type, so that the classification integration of the supply and demand information is realized, the supply and demand information classification is clearer and more accurate, and the supply and demand matching efficiency is improved.

In one embodiment, the resource demand information may be demand keywords extracted from the resource demand request, and the demand keywords may be hierarchically partitioned, such as industry type, industry-type downlink attributes, professional capabilities, and the like.

For example, when designing a workpiece, the resource demander needs to convert the workpiece drawing into a workpiece model or a workpiece finished product, and at this time, the resource demander needs to find a supply and demand party capable of processing the workpiece, that is, a production service industry, further, needs to have lathe equipment, further, requirements on processing precision of the lathe equipment, and the like.

For example, the resource demand request of the resource demander may further include limitation on the duration of supply and demand, the region where the supply and demand is located, etc., such as requiring that the supply and demand be completed within three days, the supply and demand be within a certain province/city, etc.

Further, extracting a demand keyword in the resource demand request based on a text matching algorithm; classifying the demand keywords based on the classification standard, and determining each level of keywords corresponding to the resource demand request; and searching corresponding supply and demand nodes in the supply and demand hierarchical information tree based on the keywords of each level until the supply and demand node at the bottommost layer in the supply and demand hierarchical information tree is searched, and taking the supply and demand node as the target supply and demand node.

In an embodiment, according to the text matching algorithm, a preset keyword is used according to the resource requirement field of the requirement user side, and the corresponding keyword is matched in the resource requirement description text provided by the requirement user side to serve as the resource requirement information.

In an embodiment, the matching range is reduced according to sequential matching of multiple layers of nodes, key information of the next node can be provided after each matching, and a user can perform the next matching by selecting a certain node or inputting a key word to perform the matching in the data of the next node, so that the matching precision is improved.

In an embodiment, the feedback information can be combined to judge the demand deviation of the user, and key information of the next node is intelligently screened and recommended so as to reduce the matching range and improve the matching precision and efficiency. For example, the same lathe work, the demand user side may be biased to lathe work of a large workpiece, may be biased to lathe work of a small workpiece, or the demand user side may be biased to a machining precision requirement, may be biased to a supply and demand party with lower machining cost and slightly lower machining precision requirement.

S103, acquiring supply and demand information corresponding to the target supply and demand node in the supply and demand information database based on the association relation between the target supply and demand node and the supply and demand information database;

in one embodiment, the provider information is classified and classified through the provider-to-demand hierarchical information tree, so that data definition and query matching efficiency are improved, in addition, the provider information is stored through the provider-to-demand information database, and the provider-to-demand nodes in the provider-to-demand hierarchical information tree are associated with the corresponding provider information in the provider-to-demand information database, so that the provider-to-demand information data can be quickly extracted.

For example, the provider-to-consumer information database may store provider-to-consumer information corresponding to the same node in the same address according to the classification method for the provider in the provider-to-consumer classification information tree, and associate the storage address with the corresponding provider-to-consumer node in the provider-to-consumer classification information tree, so that when a user queries the provider-to-consumer node, the provider-to-consumer information corresponding to the storage address may be retrieved from the provider-to-consumer information database by the storage address.

S104, determining at least one target supplier based on the supplier information;

in an embodiment, the information of the supply and demand party may include information such as a name, supply and demand resource information, contact information, and the like of the supply and demand party, and there may be a plurality of supply and demand parties capable of initially meeting the resource demand request of the demand party, that is, the same target supply and demand node may correspond to one or more supply and demand party information, so that at least one target supply and demand party may be determined to provide resources for the demand user side.

S105, evaluating the result conversion rate of each target supplier to the resource demand request based on the resource demand information and the supply-demand resource information of the target supplier;

in one embodiment, the supply and demand resource information may include available equipment resources, equipment configuration information, available personnel information (including but not limited to personnel number, expertise information, expertise level information, etc.), historical supply and demand project information (including project summaries, project execution schemes, project execution processes and results, etc.), and the like.

In an embodiment, the supply and demand capability of each target supply and demand party may be initially estimated according to the supply and demand resource information of each target supply and demand party, and then the achievement transformation capability of each target supply and demand party for the resource demand information may be estimated according to the resource demand information of the demand user side, for example, "whether each target supply and demand party can satisfy the realization of the resource demand information? "what is the result of the implementation? (e.g., meet basic requirements, achieve results extensible, etc.), "what conditions are lacking if not achieved? Is this condition easily met? "and the like. The outcome transformation capacity may be converted into a numerical representation, i.e., an outcome transformation rate.

S106, determining push priorities of the target suppliers and the target suppliers based on the supply and demand credibility corresponding to the target suppliers and the result conversion rate;

in an embodiment, when there are multiple target suppliers and demand suppliers, in order to improve the matching efficiency and quality of the matching, the supply and demand evaluation may be performed on the multiple target suppliers, for example, the resource strength, the supply and demand capability, the supply and demand efficiency, and the historical supply and demand evaluation, to evaluate the supply and demand reliability of the target suppliers. And sequencing the target suppliers according to the supply and demand confidence levels of the target suppliers, and pushing according to the sequencing result.

For example, for a target supplier with high supply and demand reliability, the push priority is increased, and for a target supplier with low supply and demand reliability, the push priority is decreased.

Further, historical supply and demand data of the target supply and demand party are obtained, wherein the historical supply and demand data comprises at least one supply and demand sub-data, and the supply and demand sub-data comprises supply and demand resources, supply and demand times, supply and demand quality, supply and demand duration and supply and demand reputation; based on a preset evaluation algorithm, evaluating each supply and demand sub-data in the historical supply and demand data of the target supply and demand party, and determining an evaluation value of each supply and demand sub-data; and carrying out weighted summation on the evaluation values of the supply and demand sub-data based on the evaluation weights corresponding to the supply and demand sub-data, and obtaining the supply and demand reliability of the target supply and demand party.

In one embodiment, historical supply and demand data of the target supply and demand party can be collected, including supply and demand times, supply and demand objects, supply and demand resources and the like, and the resource supply and demand capability of the supply and demand party can be evaluated.

In an embodiment, the time period of each supply and demand can be included to assist in evaluating the supply and demand efficiency of the target supply and demand party.

In one embodiment, the reputation of each supply and demand may be included, such as whether to respond to the resource demand request on time, whether to complete the conversion of the results of the resource demand request on time, and so on.

In an embodiment, each piece of supply and demand sub-data in the historical supply and demand data of the target supply and demand party is evaluated according to a preset evaluation algorithm, wherein the preset evaluation algorithm can be a preset evaluation standard, corresponding scores under different results of each piece of supply and demand sub-data are preset, for example, a resource demand request of a demand user end which is completed on time can be evaluated as 80-90 minutes, advanced completion and evaluation of the demand user end score higher than 90 minutes (percentage) can be evaluated as 90-100 minutes, and therefore each piece of supply and demand sub-data can be converted into an evaluation score.

In an embodiment, for different items of supply and demand sub-data, different evaluation weights may be allocated, for example, supply and demand resources may be allocated with an evaluation weight of 30%, supply and demand times may be allocated with an evaluation weight of 15%, supply and demand quality may be allocated with an evaluation weight of 35%, and supply and demand duration and supply and demand reputation may be respectively allocated with an evaluation weight of 10%. And then, carrying out weighted summation on the evaluation scores of the supply and demand sub-data according to the evaluation weights and the evaluation scores corresponding to the supply and demand sub-data, and taking the score result of the weighted summation as the supply and demand credibility.

In one embodiment, the weighted sum formula may be:

supply-demand reliability = supply-demand resource x estimation weight 1+ supply-demand number x estimation weight 2+ supply-demand quality

X assessment weight 3+ supply-demand duration x assessment weight 4+ supply-demand reputation x assessment weight 5

And S107, based on the push priority, sending the information of each supply and demand party to the demand user end so as to complete supply and demand matching of the demand user end and the response supply and demand end.

In an embodiment, according to the push priority corresponding to each target supplier, the supplier information corresponding to each target supplier is sequentially sent to the demand client, and the demand client selects. All target suppliers and demand parties can be sent to the demand user side together, all target suppliers and demand parties are arranged according to the push priority, and the demand user side selects.

In an embodiment, the demand client can view the supply and demand resources, the supply and demand capabilities, and the like of the supply and demand party through the information of the supply and demand party, and can provide a communication dialogue button, and the demand client can perform dialogue with the supply and demand party through the communication dialogue button so as to further perform supply and demand communication.

The embodiment provides a big data-based result conversion supply and demand matching method, which searches a target supply and demand node matched with resource demand information of a demand user side through a supply and demand hierarchical information tree, and the supply and demand hierarchical information tree can reduce a matching range layer by layer in a hierarchical matching mode, so that supply and demand matching efficiency and matching accuracy are improved. According to the association relation between the target supply and demand node and the supply and demand information database, the supply and demand information is acquired, and the storage of the supply and demand information and the data matching are isolated, so that the matching data quantity is reduced, the interference of redundant information is avoided, the matching range is reduced, the matching accuracy is improved, and the matching efficiency is improved. The result conversion rate of the target supply and demand party is evaluated, the supply and demand reliability of the target supply and demand party is calculated, the push priority of each target supply and demand party is further determined, and the information of the supply and demand party is sent to the demand user side, so that supply and demand matching is completed, and therefore, the accuracy and the success rate of supply and demand matching can be improved according to the push priority, and further the efficiency of supply and demand matching of the result conversion is improved.

Referring to fig. 3, fig. 3 is a flow chart of a second embodiment of the big data-based result conversion supply and demand matching method provided in the present application.

In this embodiment, based on the embodiment shown in fig. 2, after step S104, the method specifically further includes:

s201, forwarding the resource demand request to each target supplier and calculating feedback results of each target supplier on the resource demand request in a preset time period, and determining a response supplier;

in an embodiment, when determining the target supply and demand party, the resource demand request is forwarded to the target supply and demand party, and the target supply and demand party can evaluate whether the target supply and demand party can meet the requirement of the demand party according to the resource demand request, for example, whether the supply and demand can be completed within the period required by the demand client, whether the accuracy requirement required by the demand client can be met, and the like. And then selecting whether the system becomes a supply and demand party at the demand user side, and feeding back the supply and demand party to the result conversion supply and demand matching system.

In an embodiment, the result conversion supply and demand matching system may forward the resource demand request to all target suppliers that satisfy the resource demand request, and receive feedback information of each target supplier in a preset period of time.

In an embodiment, by setting a preset time period, target supply and demand parties of which parts cannot be fed back in time can be primarily screened out, so that supply and demand matching efficiency is improved, resource demand requests of demand clients are prevented from being delayed due to untimely feedback, and therefore achievement conversion supply and demand matching efficiency is ensured.

Further, when the target supply-demand party feeds back response information within the preset time period and the response information is the request for receiving the resource demand, the target supply-demand party is determined to be the response supply-demand party.

In an embodiment, if the target supplier feeds back the response information within the preset time period and indicates that the resource demand request of the demand client can be received, the target supplier is determined to be the response supplier.

In an embodiment, when the target supplier feeds back the response information in the preset time period and the response information is a request for rejecting the resource demand, the target supplier is screened out; and screening out the target supply and demand party when the target supply and demand party does not feed back the response information in the preset time period.

In an embodiment, if the target supplier and the target consumer fail to feed back the response information within the preset time period, the target supplier and the target consumer fail to meet the resource demand request of the demand consumer, and the target supplier is screened out. If the target supplier feeds back the response information within the preset time period, but the response information is a request for rejecting the resource demand, the target supplier is considered to be incapable of meeting the resource demand request of the demand user side, and the target supplier is screened out. Therefore, further screening of target suppliers and consumers is realized, suppliers and consumers which cannot respond in time are screened out, and the supply and demand matching efficiency is further improved.

S202, calculating the push priority of each response supplier based on the supply and demand confidence of each response supplier;

in an embodiment, the push priority of each response provider is determined according to the credit-demand credit of each response provider, wherein the higher the push priority of the response provider with high credit-demand credit, the lower the push priority of the response provider with low credit-demand credit.

And S203, based on the push priority, sending the information of the supply and demand parties corresponding to the response supply and demand parties to the demand user end so as to complete the supply and demand matching of the demand user end and the response supply and demand end.

In an embodiment, according to the push priorities corresponding to the responding suppliers, the supplier information is sequentially sent to the demand clients, and the demand clients select.

In an embodiment, the demand client can establish a call with the responding supplier and further communicate the demand resources and the supply and demand resources of the two parties, so as to improve the success rate and the efficiency of supply and demand matching between the demand client and the responding supplier.

Referring to fig. 4, fig. 4 is a flow chart of a third embodiment of the big data-based result conversion supply and demand matching method provided in the present application.

In this embodiment, based on the embodiment shown in fig. 2, before step S101, the method specifically further includes:

s301, storing the supplier information of the supplier and the demand based on the supplier information database;

in one embodiment, a database of provider information may be created in the server for storing provider information for the provider. The supplier may request registration resource information such as equipment, qualification, capability, project data done, etc.

In one embodiment, for each supplier and consumer, a list or folder of supplier and consumer information may be created based on the resource information provided by the supplier and consumer, and then stored in a supplier and consumer information database, so that the supplier and consumer information may be quickly queried and extracted based on the unique identifying information (e.g., account number, name, number, etc.) of the supplier and consumer.

S302, classifying and classifying the information of the supply and demand party based on a preset classification standard, and determining the classification type and classification level corresponding to the supply and demand party;

in an embodiment, the preset classification criteria may be specified according to a supply and demand type, a supply and demand resource, and the like, for example, the first level may be a supply and demand service form, the second level may be an industry field, the third level may be a more specific service type in each industry field, for example, the first level is a company, a research institute, a processing plant, and the like, the second level may be a service industry, a building industry, a computer industry, and the like, and the third level in the service industry may be a home service industry, a hotel service industry, and the like.

S303, creating the supply and demand nodes corresponding to the supply and demand parties in the supply and demand hierarchical information tree based on the classification type and the classification level;

in an embodiment, the supply-demand hierarchical information tree is created according to different classification types and classification levels of supply-demand parties, wherein each level corresponds to each layer in the supply-demand hierarchical information tree, and each classification type can be each supply-demand node in the corresponding level.

S304, establishing association between the supply and demand nodes and the supply and demand information, and determining the association relation between the supply and demand nodes and the supply and demand information.

In an embodiment, after the creation of the supply and demand nodes is completed, only the supply and demand nodes are required to be matched with the supply and demand information, and the supply and demand nodes corresponding to the supply and demand information are determined, so that the supply and demand information can be stored in the corresponding supply and demand nodes, or the storage addresses of the supply and demand information in the supply and demand information database are stored in the corresponding supply and demand nodes, and the association relation between the supply and demand nodes and the supply and demand information is completed.

Further, after storing the supply and demand information of the supply and demand party based on the supply and demand party information database, the method further includes: updating the supply and demand data of the supply and demand party based on a preset updating period; calculating the updated supply and demand reliability of the supply and demand party based on the updated supply and demand data; and updating the current supply and demand reliability of the supply and demand party based on the updated supply and demand reliability.

In an embodiment, the supply and demand data stored in the supply and demand information database may be updated according to a preset update period, for example, the supply and demand data is updated once every month, or the supply and demand data is updated once every time the supply and demand is completed by the supply and demand party.

In an embodiment, according to the updated supply and demand data, the supply and demand reliability of the supply and demand parties is calculated iteratively, so as to update the current supply and demand reliability of each supply and demand party. For example, when the resource demand request of the demand user side is not responded for a long time, the supply and demand reliability of the demand user side can be reduced. For example, the provider company logs off and can synchronously save or screen out the provider information.

Referring to fig. 5, fig. 5 is a schematic structural diagram of a first embodiment of a big data-based result-to-supply-and-demand matching device for executing the above-mentioned big data-based result-to-supply-and-demand matching method. The big data-based achievement conversion supply and demand matching device can be configured in a server.

As shown in fig. 5, the big data based result conversion supply and demand matching device 400 includes: a resource demand information acquisition module 401, a target supply and demand node determination module 402, a supply and demand information acquisition module 403, a target supply and demand determination module 404, a result conversion rate evaluation module 405, a push priority determination module 406, and a supply and demand matching module 407.

A resource requirement information obtaining module 401, configured to obtain resource requirement information of a requirement user terminal based on a resource requirement request initiated by the requirement user terminal;

a target supply and demand node determining module 402, configured to search for a supply and demand node matching the resource demand information in a supply and demand hierarchical information tree based on the resource demand information, and determine at least one target supply and demand node;

a supply-demand side information obtaining module 403, configured to obtain supply-demand side information corresponding to the target supply-demand node in the supply-demand side information database based on an association relationship between the target supply-demand node and a supply-demand side information database;

a target supplier determination module 404, configured to determine at least one target supplier based on the supplier information;

a result conversion rate evaluation module 405, configured to evaluate a result conversion rate of the resource demand request by each target supplier based on the resource demand information and the supply-demand resource information of the target supplier;

a push priority determining module 406, configured to determine a push priority of each target provider based on the supply-demand confidence corresponding to each target provider and the achievement conversion rate;

And a supply and demand matching module 407, configured to send each piece of supply and demand side information to the demand client based on the push priority, so as to complete supply and demand matching between the demand client and the response supply and demand side.

In one embodiment, the big data-based achievement transformation supply and demand matching device 400 further includes:

the response supplier determining unit is used for forwarding the resource demand request to each target supplier, counting feedback results of each target supplier on the resource demand request in a preset time period and determining a response supplier;

a priority calculating unit configured to calculate the push priority of each of the response suppliers based on the supply-demand confidence levels of each of the response suppliers;

and the supply and demand matching unit is used for sending the supply and demand information corresponding to each response supply and demand party to the demand user end based on the push priority so as to complete the supply and demand matching of the demand user end and the response supply and demand end.

In an embodiment, the response supplier determination unit includes:

and the response supply and demand party determining subunit is used for determining that the target supply and demand party is the response supply and demand party when the target supply and demand party feeds back response information within the preset time period and the response information is the resource demand request.

In an embodiment, the big data-based achievement transformation supply and demand matching device 400 further includes a supply and demand reliability obtaining module, including:

a historical supply and demand data acquisition unit, configured to acquire historical supply and demand data of the target supply and demand party, where the historical supply and demand data includes at least one supply and demand sub-data, and the supply and demand sub-data includes supply and demand resources, supply and demand times, supply and demand quality, supply and demand duration, and supply and demand reputation;

the evaluation value determining unit is used for evaluating each supply and demand sub-data in the historical supply and demand data of the target supply and demand party based on a preset evaluation algorithm and determining the evaluation value of each supply and demand sub-data;

and the supply and demand reliability obtaining unit is used for carrying out weighted summation on the evaluation scores of the supply and demand sub-data based on the evaluation weights corresponding to the supply and demand sub-data to obtain the supply and demand reliability of the target supply and demand party.

In an embodiment, the big data based achievement transformation supply and demand matching device 400 further includes a node association module, including:

an information storage unit configured to store supply-demand side information of a supply-demand side based on the supply-demand side information database;

The supply and demand side classifying unit is used for classifying and matching the supply and demand side information based on a preset classifying standard, and determining the classifying type and classifying level corresponding to the supply and demand side;

a supply-demand node creation unit configured to create the supply-demand node corresponding to the supply-demand party in the supply-demand hierarchical information tree based on the classification type and the classification level;

and the association establishing unit is used for establishing association between the supply and demand node and the supply and demand party information and determining the association relation between the supply and demand node and the supply and demand party information.

In an embodiment, the big data-based achievement transformation supply and demand matching device 400 further includes a data updating module, including:

a supply and demand data updating unit, configured to update supply and demand data of the supply and demand party based on a preset updating period;

an updated supply-demand reliability calculation unit, configured to calculate updated supply-demand reliability of the supply-demand party based on the updated supply-demand data;

and the supply and demand credit updating unit is used for updating the current supply and demand credit of the supply and demand party based on the updated supply and demand credit.

In an embodiment, the target supply and demand node determining module 402 includes:

The demand keyword extraction unit is used for extracting demand keywords in the resource demand request based on a text matching algorithm;

the hierarchical keyword determining unit is used for classifying the demand keywords based on the classification standard and determining each hierarchical keyword corresponding to the resource demand request;

and the target supply and demand node searching unit is used for searching corresponding supply and demand nodes in the supply and demand hierarchical information tree based on the keywords of each level until the supply and demand node at the bottommost layer in the supply and demand hierarchical information tree is searched and used as the target supply and demand node.

It should be noted that, for convenience and brevity of description, specific working processes of the above-described apparatus and each module may refer to corresponding processes in the foregoing embodiment of the big data based achievement transformation supply and demand matching method, which are not described herein again.

The apparatus provided by the above embodiments may be implemented in the form of a computer program which may be run on a computer device as shown in fig. 6.

Referring to fig. 6, fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device may be a server.

With reference to FIG. 6, the computer device includes a processor, memory, and a network interface connected by a system bus, where the memory may include a non-volatile storage medium and an internal memory.

The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions which, when executed, cause the processor to perform any of a number of big data based achievement transformation supply and demand matching methods.

The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by a processor, causes the processor to perform any of a number of big data based achievement conversion supply and demand matching methods.

The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

Wherein in one embodiment the processor is configured to run a computer program stored in the memory to implement the steps of:

Determining at least one target supplier based on the supplier information;

based on the resource demand information and the supply-demand resource information of the target suppliers, evaluating the result conversion rate of each target supplier on the resource demand request;

In one embodiment, the processor, after implementing the storing of the time series data based on the target storage address in the target database, is further configured to implement:

in one embodiment, the processor, after implementing the determining at least one target supplier based on the supplier information, is further configured to implement:

forwarding the resource demand request to each target supplier and calculating feedback results of each target supplier for the resource demand request in a preset time period, and determining a response supplier;

Calculating the push priority of each response supplier based on the supply and demand confidence of each response supplier;

and sending the information of the supply and demand parties corresponding to each response supply and demand party to the demand user end based on the push priority so as to complete the supply and demand matching of the demand user end and the response supply and demand end.

In one embodiment, when implementing the statistics of feedback results of each target supplier to the resource demand request in a preset period, the processor is configured to implement:

and when the target supply-demand party feeds back response information within the preset time period and the response information is the request for receiving the resource demand, determining that the target supply-demand party is the response supply-demand party.

In one embodiment, before implementing the determining the push priority of each target supplier based on the supply-demand confidence corresponding to each target supplier and the achievement conversion rate, the processor is further configured to implement:

acquiring historical supply and demand data of the target supply and demand party, wherein the historical supply and demand data comprises at least one supply and demand sub-data, and the supply and demand sub-data comprises supply and demand resources, supply and demand times, supply and demand quality, supply and demand duration and supply and demand reputation;

Based on a preset evaluation algorithm, evaluating each supply and demand sub-data in the historical supply and demand data of the target supply and demand party, and determining an evaluation value of each supply and demand sub-data;

and carrying out weighted summation on the evaluation values of the supply and demand sub-data based on the evaluation weights corresponding to the supply and demand sub-data, and obtaining the supply and demand reliability of the target supply and demand party.

In one embodiment, before implementing the resource demand request initiated by the demand-based client, the processor is further configured to, before obtaining the resource demand information of the demand-based client, implement:

storing the supplier information of the supplier based on the supplier information database;

classifying and classifying matching are carried out on the information of the supply and demand parties based on a preset classification standard, and classification types and classification grades corresponding to the supply and demand parties are determined;

creating the supply and demand nodes corresponding to the supply and demand parties in the supply and demand hierarchical information tree based on the classification type and the classification level;

and establishing association between the supply and demand node and the supply and demand information, and determining the association relation between the supply and demand node and the supply and demand information.

In one embodiment, after implementing the storing of the supplier information of the supplier based on the supplier information database, the processor is further configured to implement:

Updating the supply and demand data of the supply and demand party based on a preset updating period;

calculating the updated supply and demand reliability of the supply and demand party based on the updated supply and demand data;

and updating the current supply and demand reliability of the supply and demand party based on the updated supply and demand reliability.

In one embodiment, the processor is configured to, when implementing the searching for a supply-demand node matching the resource requirement information in a supply-demand hierarchical information tree based on the resource requirement information, determine at least one target supply-demand node, implement:

extracting a demand keyword in the resource demand request based on a text matching algorithm;

classifying the demand keywords based on the classification standard, and determining each level of keywords corresponding to the resource demand request;

and searching corresponding supply and demand nodes in the supply and demand hierarchical information tree based on the keywords of each level until the supply and demand node at the bottommost layer in the supply and demand hierarchical information tree is searched, and taking the supply and demand node as the target supply and demand node.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, the computer program comprises program instructions, and the processor executes the program instructions to realize any big data-based achievement conversion supply and demand matching method.

The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, which are provided on the computer device.

While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A big data based achievement transformation supply and demand matching method, the method comprising:

determining at least one target supplier based on the supplier information;

2. The big data based effort to demand matching method of claim 1, wherein after determining at least one target supplier based on the supplier information, further comprising:

3. The big data based result conversion supply and demand matching method according to claim 2, wherein the counting feedback results of each target supply and demand party for the resource demand request in a preset time period, and determining a response supply and demand party comprises:

4. The big data based result conversion supply and demand matching method according to claim 1, wherein before determining the push priority of each target supplier based on the supply and demand confidence corresponding to each target supplier and the result conversion rate, further comprising:

5. The big data based achievement transformation supply and demand matching method according to claim 1, wherein before the resource demand request initiated by the demand user side obtains the resource demand information of the demand user side, the method further comprises:

6. The big data based effort to demand matching method of claim 5, wherein after storing the vendor information of the vendor based on the vendor information database, further comprising:

7. The big data based effort to demand matching method of claim 1, wherein searching for a supply and demand node matching the resource demand information in a supply and demand hierarchical information tree based on the resource demand information, determining at least one target supply and demand node, comprises:

8. The big data-based achievement transformation supply and demand matching device is characterized by comprising:

9. A computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor performs the steps of the big data based outcome conversion supply-demand matching method according to any of claims 1 to 7.

10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the big data based outcome conversion supply-demand matching method according to any of claims 1 to 7.