CN114357308A - Manufacturing enterprise supply and demand docking method and device based on recommendation - Google Patents
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Abstract
The invention discloses a manufacturing enterprise supply and demand docking method and device based on recommendation, and relates to the technical field of internet; the method comprises the following steps: creating an industry community according to the priority level batches; a user registers a supply and demand platform, completes enterprise real-name authentication and then edits enterprise information; modeling the business field of an enterprise; perfecting a supply and demand model of an enterprise according to the behavior data of the enterprise user; matching supply and demand according to the supply and demand model of the enterprise; after the enterprise user receives the recommendation, continuing to pay attention to the recommendation conversion rate; correcting the supply and demand model according to the recommended conversion result and the recommended conversion rate, and further perfecting the supply and demand model of the enterprise; paying attention to the feedback of the user, and continuously optimizing the matching algorithm according to the feedback of the user; the invention can meet the optimal supply and demand of enterprises, and reduce the purchasing cost and the selling cost of the enterprises; providing a transaction management and monitoring platform to realize the transparentization of project progress; the enterprise is concentrated on improving the product quality, and the benign competition of the enterprise is promoted.
Description
Technical Field
The invention relates to the technical field of Internet, in particular to a manufacturing enterprise supply and demand docking method and device based on recommendation.
Background
In recent years, with the technological development of mobile internet, big data and artificial intelligence, recommendation technology has been widely applied to more and more internet products. It can be said that recommendation technology has achieved a great deal of development and successful results in the personal entertainment field, but in the enterprise field, there is no particularly successful application case for recommendation technology.
The current common recommendation system generally adopts the following modes:
1. and establishing a basic interest model for the user, such as acquiring the age, the gender, the place, a mobile phone APP list and a contact list of the user, and establishing a basic model for the user.
2. And improving the interest model of the user, for example, repeatedly determining the interested fields and contents of the user by selecting interest tags by the user, analyzing browsing history of the user, analyzing operation behaviors of the user and the like. For example, the system finds that the user often browses football-related content, i.e., "sports" will be written to the user interest model.
3. And matching the content to the user according to the user interest model to complete recommendation. For example, the system has written "sports" into the user's interest tag, and in addition to the football content, also recommends content belonging to the category of "sports", such as basketball-related content;
with the above recommendation system, there are the following problems:
1. the algorithm is oriented to personal interests and is not suitable for an organization type object formed by multiple persons, such as an enterprise;
2. manufacturing enterprises generally face two scenes of supply and demand in a supply chain, namely raw material purchasing and product selling, and cannot match with simple interest;
3. supply and demand docking is carried out, the dimensionality of factors needing to be considered is large, and except for the industry, the supply and purchase capacity, the price quotation, the region and the commodity quality of an enterprise influence a recommendation algorithm; how to provide a matching method for solving the problems, which can meet the requirements of enterprises on optimal supply and demand and reduce the purchasing and selling costs, becomes a technical problem to be solved by technical personnel in the field.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a manufacturing enterprise supply and demand docking method and device based on recommendation. The invention can meet the optimal supply and demand of enterprises, and reduce the purchasing cost and the selling cost of the enterprises; a transaction management and monitoring platform is provided, so that the transparency of project progress is realized, and the transaction execution requirements of a capability demand party and a supply party are facilitated; the enterprise is concentrated on improving the product quality, and the benign competition of the enterprise is promoted.
The purpose of the invention can be realized by the following technical scheme:
a manufacturing enterprise supply and demand docking method based on recommendation comprises the following steps:
the method comprises the following steps: creating an industry community according to the priority level batches;
step two: a user registers a supply and demand platform; after entering the platform, the enterprise real-name authentication is completed, and then enterprise information is edited;
step three: the method for modeling the business field of the enterprise specifically comprises the following steps: the recommendation system generates a business field model according to a formula from enterprise information edited by a user, and calculates scores of all dimensions of the business field model according to values and weights of all variables;
step four: perfecting a supply and demand model of an enterprise according to the behavior data of the enterprise user;
step five: supply and demand matching is carried out according to the supply and demand model of the enterprise, and the method specifically comprises the following steps:
the recommendation system recommends a related industry community to the enterprise based on a supply and demand model of the enterprise, and guides enterprise users to log in the industry community;
and after guiding the enterprise user to log in the industry community, analyzing and matching the supply and demand model of the enterprise with supply and demand models of other users of the community, and recommending the relevant supply and demand users of the community to the enterprise.
Further, creating an industry community according to the priority level batches specifically comprises:
acquiring an enterprise model of a user in a recommendation system, and mining potential upstream and downstream of all enterprises by analyzing the enterprise model, wherein the potential upstream and downstream are expressed as associated industry communities;
counting the occurrence times of the same industry community according to the industry community and marking the occurrence times as the number of the associated enterprises of the industry community;
and determining the priority of the corresponding industry community according to the number of the associated enterprises, and creating the industry community according to the priority batch.
Further, determining the priority of the corresponding industry community according to the number of the associated enterprises, specifically:
acquiring the number of associated enterprises of the industry community, and determining the number interval of the associated enterprises in which the number of the associated enterprises is positioned in the corresponding mapping relation table;
and acquiring corresponding priority according to the number interval of the associated enterprises.
Further, in the fourth step, the supply and demand model of the enterprise is improved according to the behavior data of the enterprise user, specifically:
preliminarily obtaining a supply and demand model of an enterprise according to the business field model and the scores of all dimensions of the business field model;
acquiring behavior data of enterprise users, wherein the behavior data comprises an industry community concerned by the enterprise users, users concerned by the enterprise users and information contents browsed by the enterprise users, and the weight value of each behavior is different;
and recalculating the scores of all dimensions of the supply and demand model according to the weight value of each behavior, and further perfecting the supply and demand model of the enterprise.
Further, the method further comprises: after the enterprise user receives the recommended community-related supply and demand users, the enterprise user continues to pay attention to the recommendation conversion rate, specifically:
after receiving the recommendation, the enterprise user continuously pays attention to the follow-up behaviors of the enterprise user, wherein the follow-up behaviors comprise whether to contact or not and whether to transact or not;
if the transaction is carried out later, the recommended conversion is considered to be successful, otherwise, the recommended conversion is considered to be failed;
and counting the times of successful recommended transformation and the times of failure recommended transformation, and calculating to obtain a recommended transformation rate, namely the ratio of the times of successful recommended transformation.
Further, the method further comprises: correcting the supply and demand model according to the recommended conversion result and the recommended conversion rate, and further perfecting the supply and demand model of the enterprise; wherein the recommended transformation result comprises a recommended transformation success and a recommended transformation failure.
Further, the method further comprises: and (4) paying attention to the feedback of the user, and continuously optimizing the matching algorithm according to the feedback of the user.
Further, the manufacturing enterprise supply and demand docking device based on recommendation comprises an information acquisition module, a community creation module, a login registration module, a model generation module, a supply and demand matching module, a controller and a supply and demand correction module;
the information acquisition module: the system comprises a community establishing module, a recommendation system and a user model acquisition module, wherein the community establishing module is used for acquiring an enterprise model of a user in the recommendation system and transmitting the acquired enterprise model to the community establishing module;
a community creation module: acquiring enterprise models of users in a recommendation system, analyzing and mining potential upstream and downstream of all enterprises, and creating an industry community according to priority levels in batches;
a login registration module: the system is used for registering a supply and demand platform by a user and completing enterprise real-name authentication; after a user enters a platform, enterprise real-name authentication is completed, and then enterprise information is edited;
a model generation module: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for acquiring enterprise information, generating a business field model from the enterprise information according to a formula, and calculating scores of all dimensions of the business field model according to values and weights of all variables;
the model generation module is also used for establishing and perfecting a supply and demand model of an enterprise according to the business field model and the behavior data of the enterprise user;
supply and demand matching module: the system is used for matching supply and demand according to the supply and demand model of the enterprise; the method specifically comprises the following steps:
based on a supply and demand model of an enterprise, a supply and demand matching module recommends a related industry community to the enterprise and guides enterprise users to log in the industry community;
after guiding enterprise users to log in the industry community, analyzing and matching the supply and demand model of the enterprise with supply and demand models of other users of the community, and recommending relevant supply and demand users of the community to the enterprise;
supply and demand correction module: the method is used for correcting the enterprise supply and demand model.
Further, the supply and demand correction module comprises the following specific correction steps:
after the enterprise user receives the recommended community-related supply and demand users, continuing to pay attention to the recommendation conversion rate;
correcting the supply and demand model according to the recommended conversion result and the recommended conversion rate, and further perfecting the supply and demand model of the enterprise;
and (4) paying attention to the feedback of the user, and continuously optimizing the matching algorithm according to the feedback of the user.
Compared with the prior art, the invention has the beneficial effects that: the method establishes the industry community according to the priority level batch, establishes and perfects the enterprise supply and demand model according to the enterprise information edited by the user and the behavior data of the enterprise user, can meet the optimal supply and demand of the enterprise, and reduces the purchasing cost and the selling cost of the enterprise; a transaction management and monitoring platform is provided, so that the transparency of project progress is realized, and the transaction execution requirements of the capacity requiring party and the supplying party are facilitated; after the enterprise user receives the recommendation, the recommendation system continuously pays attention to the follow-up behaviors of the enterprise user, further improves the enterprise supply and demand model according to the recommendation conversion result and the recommendation conversion rate, pays attention to the feedback of the user, and continuously optimizes the matching algorithm according to the feedback of the user, so that the enterprise is concentrated on improving the product quality, and the benign competition of the enterprise is promoted.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a recommendation-based manufacturing enterprise supply-demand docking method according to the present invention;
FIG. 2 is a block diagram of a system for a recommendation-based manufacturing enterprise supply and demand docking facility in accordance with the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a manufacturing enterprise supply and demand docking method based on recommendation includes the following steps:
the method comprises the following steps: establishing an industry community in batches; the method specifically comprises the following steps:
acquiring an enterprise model of a user in a recommendation system, and mining potential upstream and downstream of all enterprises by analyzing the enterprise model, wherein the potential upstream and downstream are expressed as associated industry communities;
counting the occurrence times of the same industry community according to the industry community and marking the occurrence times as the number of the associated enterprises of the industry community;
determining the priority of the corresponding industry community according to the number of the associated enterprises, and creating the industry community according to the priority batch;
the method comprises the following steps of determining the priority of a corresponding industry community according to the number of associated enterprises, specifically:
acquiring the number of associated enterprises of the industry community, and determining the number interval of the associated enterprises in which the number of the associated enterprises is positioned in the corresponding mapping relation table; acquiring corresponding priority according to the number interval of the associated enterprises;
wherein, before determining the priority of the corresponding industry community according to the number of the associated enterprises, the method further comprises the following steps:
the method comprises the steps of creating priorities of a plurality of industry communities in advance according to user requirements;
associating the associated enterprise quantity interval with the priority of the industry community to generate a mapping relation table of the associated enterprise quantity and the priority;
step two: the user registers the supply and demand platform and completes the real-name authentication of the enterprise; the method specifically comprises the following steps:
after a user enters a platform, enterprise real-name authentication is completed, and then enterprise information is edited; the enterprise information comprises the industry, the production products, the scale, the region and the like of the enterprise;
step three: the method for modeling the business field of the enterprise specifically comprises the following steps: the recommendation system generates a business field model according to a formula from enterprise information edited by a user, and calculates scores of all dimensions of the business field model according to values and weights of all variables;
step four: perfecting a supply and demand model of an enterprise according to behavior data of enterprise users, which specifically comprises the following steps:
preliminarily obtaining a supply and demand model of an enterprise according to the business field model and the scores of all dimensions of the business field model;
acquiring behavior data of enterprise users, wherein the behavior data comprises industry communities concerned by the enterprise users, users concerned by the enterprise users, information contents browsed by the enterprise users and the like;
the weight value of each behavior is different, and the scores of all dimensions of the supply and demand model are recalculated according to the weight value of each behavior, so that the supply and demand model of an enterprise is further improved;
step five: supply and demand matching is carried out according to the supply and demand model of the enterprise, and the method specifically comprises the following steps:
the recommendation system recommends a related industry community to the enterprise based on a supply and demand model of the enterprise, and guides enterprise users to log in the industry community;
recommending related industry communities to the enterprise specifically comprises the following steps:
recommending the related industry communities to the enterprise in sequence according to the priority of the industry communities;
if the plurality of industry communities are in the same priority, recommending the related industry communities to the enterprise according to the attention values of the industry communities;
wherein the concern value of the industry community is the frequency and frequency of the industry community being concerned by the enterprise user;
in the fifth step, after guiding the enterprise user to log in the industry community, analyzing and matching the supply and demand model of the enterprise with supply and demand models of other users of the community, and recommending the relevant supply and demand users of the community to the enterprise; the method specifically comprises the following steps:
analyzing and matching the supply and demand model of the enterprise with supply and demand models of other users of the community to obtain a matching degree mu; comparing the matching degree mu with a preset threshold value; if the matching degree mu is more than or equal to a preset threshold value, marking the corresponding community user as a related supply and demand user;
recommending the related supply and demand users to the enterprise users in sequence according to the matching degree mu;
after the enterprise user receives the recommended community-related supply and demand users, the recommendation system continuously pays attention to the recommendation conversion rate, and the method specifically comprises the following steps:
after the enterprise user receives the recommendation, the recommendation system continuously pays attention to subsequent behaviors of the enterprise user, wherein the subsequent behaviors comprise whether to contact or not, whether to transact or not and the like, if the transaction is performed later, the recommendation conversion is regarded as successful, and if not, the recommendation conversion is regarded as failed;
counting the number of times of successful recommended conversion and the number of times of failure recommended conversion, and calculating to obtain a recommended conversion rate, namely the ratio of the number of times of successful recommended conversion;
in this embodiment, the method further includes: further perfecting an enterprise supply and demand model according to the recommended conversion result and the recommended conversion rate, wherein the recommended conversion result comprises the recommended conversion success and the recommended conversion failure;
paying attention to the feedback of the user, and continuously optimizing the matching algorithm according to the feedback of the user;
the recommendation system is a common recommendation system in the prior art;
the method establishes the industry community according to the priority level batch, establishes and perfects the enterprise supply and demand model according to the enterprise information edited by the user and the behavior data of the enterprise user, can meet the optimal supply and demand of the enterprise, and reduces the purchasing cost and the selling cost of the enterprise; a transaction management and monitoring platform is provided, so that the transparency of project progress is realized, and the transaction execution requirements of the capacity requiring party and the supplying party are facilitated; after the enterprise user receives the recommendation, the recommendation system continuously pays attention to the subsequent behaviors of the enterprise user, further perfects the enterprise supply and demand model according to the recommendation conversion result and the recommendation conversion rate, pays attention to the feedback of the user, and continuously optimizes the matching algorithm according to the feedback of the user, so that the enterprise is concentrated on improving the product quality and promoting the benign competition of the enterprise;
as shown in fig. 2, a manufacturing enterprise supply and demand docking device based on recommendation includes an information acquisition module, a community creation module, a login registration module, a model generation module, a supply and demand matching module, a controller, and a supply and demand correction module;
the information acquisition module: the system comprises a community establishing module, a recommendation system and a user model acquisition module, wherein the community establishing module is used for acquiring an enterprise model of a user in the recommendation system and transmitting the acquired enterprise model to the community establishing module;
a community creation module: acquiring enterprise models of users in a recommendation system, analyzing and mining potential upstream and downstream of all enterprises, determining the priority of an industry community according to the number of associated enterprises, and creating the industry community according to priority batches; the method for acquiring the number of the associated enterprises comprises the following steps:
mining potential upstream and downstream of all enterprises, wherein the potential upstream and downstream are expressed as related industry communities;
counting the occurrence times of the same industry community according to the industry community and marking the occurrence times as the number of the associated enterprises of the industry community;
a login registration module: the system is used for registering a supply and demand platform by a user and completing enterprise real-name authentication; after a user enters a platform, enterprise real-name authentication is completed, and then enterprise information is edited;
a model generation module: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for acquiring enterprise information, generating a business field model from the enterprise information according to a formula, and calculating scores of all dimensions of the business field model according to values and weights of all variables;
the model generation module is also used for establishing and perfecting a supply and demand model of an enterprise according to the business field model and the behavior data of the enterprise user; the method comprises the following specific steps:
preliminarily obtaining a supply and demand model of an enterprise according to the business field model and the scores of all dimensions of the business field model;
behavior data of enterprise users are obtained, and the scores of all dimensions of the supply and demand model are recalculated according to the weight value of each behavior, so that the supply and demand model of an enterprise is further improved;
the model generation module is used for transmitting the enterprise supply and demand model to the controller for storage;
supply and demand matching module: the system is used for matching supply and demand according to the supply and demand model of the enterprise; the method specifically comprises the following steps:
based on a supply and demand model of an enterprise, a supply and demand matching module recommends a related industry community to the enterprise and guides enterprise users to log in the industry community;
after guiding enterprise users to log in the industry community, analyzing and matching the supply and demand model of the enterprise with supply and demand models of other users of the community, and recommending relevant supply and demand users of the community to the enterprise;
supply and demand correction module: the system is used for correcting the enterprise supply and demand model; the method specifically comprises the following steps:
after the enterprise user receives the recommended community-related supply and demand users, the follow-up behaviors of the enterprise user continue to be concerned, a recommended conversion result and a recommended conversion rate are obtained, and the supply and demand model is corrected according to the recommended conversion result and the recommended conversion rate;
wherein, the follow-up behavior of the enterprise user is continuously concerned to obtain the recommended conversion result and the recommended conversion rate, which specifically comprises the following steps:
the subsequent behaviors comprise whether contact is carried out or not, whether trade is carried out or not and the like, if the trade is carried out later, the recommendation conversion is considered to be successful, and if not, the recommendation conversion is considered to be failed;
counting the times of successful recommended conversion and the times of failure recommended conversion, and calculating to obtain a recommended conversion rate;
the supply and demand correction module is also used for paying attention to the feedback of the user and continuously optimizing the matching algorithm according to the feedback of the user.
The working principle of the invention is as follows:
during working, firstly, acquiring enterprise models of users in a recommendation system, analyzing and mining potential upstream and downstream of all enterprises according to the enterprise models, determining the priority of an industry community according to the number of associated enterprises, and creating the industry community in batches according to the priority; a user registers a supply and demand platform, completes enterprise real-name authentication and then edits enterprise information; the model generation module acquires enterprise information, generates a business field model from the enterprise information according to a formula, and calculates scores of all dimensions of the business field model according to the values and weights of all variables; then, establishing and perfecting a supply and demand model of the enterprise according to the business field model and the behavior data of the enterprise user; the supply and demand matching module is used for matching supply and demand according to a supply and demand model of an enterprise, firstly, based on the supply and demand model of the enterprise, the supply and demand matching module recommends a related industry community to the enterprise, and guides enterprise users to log in the industry community; then, analyzing and matching the supply and demand model of the enterprise with supply and demand models of other users of the community, and recommending relevant supply and demand users of the community to the enterprise;
the supply and demand correction module is used for correcting the enterprise supply and demand model, continuing to pay attention to the subsequent behaviors of the enterprise users after the enterprise users receive the recommended community-related supply and demand users to obtain a recommended conversion result and a recommended conversion rate, and correcting the supply and demand model according to the recommended conversion result and the recommended conversion rate; the feedback of the user is concerned, and the matching algorithm is continuously optimized according to the feedback of the user; the invention can meet the optimal supply and demand of enterprises, and reduce the purchasing cost and the selling cost of the enterprises; a transaction management and monitoring platform is provided, so that the transparency of project progress is realized, and the transaction execution requirements of a capability demand party and a supply party are facilitated; the enterprise is concentrated on improving the product quality, and the benign competition of the enterprise is promoted.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. A manufacturing enterprise supply and demand docking method based on recommendation is characterized by comprising the following steps:
the method comprises the following steps: creating an industry community according to the priority level batches;
step two: a user registers a supply and demand platform, completes enterprise real-name authentication and then edits enterprise information;
step three: the method for modeling the business field of the enterprise specifically comprises the following steps: the recommendation system generates a business field model according to a formula from enterprise information edited by a user, and calculates scores of all dimensions of the business field model according to values and weights of all variables; wherein the variables refer to each element in the enterprise information;
step four: perfecting a supply and demand model of an enterprise according to the behavior data of the enterprise user;
step five: supply and demand matching is carried out according to the supply and demand model of the enterprise, and the method specifically comprises the following steps:
the recommendation system recommends a related industry community to the enterprise based on a supply and demand model of the enterprise, and guides enterprise users to log in the industry community;
and after guiding the enterprise user to log in the industry community, analyzing and matching the supply and demand model of the enterprise with supply and demand models of other users of the community, and recommending the relevant supply and demand users of the community to the enterprise.
2. The recommendation-based supply and demand docking method for manufacturing enterprises according to claim 1, wherein the creating of the industry community according to the priority lot specifically comprises:
acquiring an enterprise model of a user in a recommendation system, and mining potential upstream and downstream of all enterprises by analyzing the enterprise model, wherein the potential upstream and downstream are expressed as associated industry communities;
counting the occurrence times of the same industry community according to the industry community and marking the occurrence times as the number of the associated enterprises of the industry community; and determining the priority of the corresponding industry community according to the number of the associated enterprises, and creating the industry community according to the priority batch.
3. The manufacturing enterprise supply and demand docking method based on recommendation according to claim 2, wherein the priority of the corresponding industry community is determined according to the number of the associated enterprises, specifically:
acquiring the number of associated enterprises of the industry community, and determining the number interval of the associated enterprises in which the number of the associated enterprises is positioned in the corresponding mapping relation table; and acquiring corresponding priority according to the number interval of the associated enterprises.
4. The manufacturing enterprise supply and demand docking method based on recommendation according to claim 1, wherein in the fourth step, an enterprise supply and demand model is refined according to the behavior data of the enterprise user, specifically:
preliminarily obtaining a supply and demand model of an enterprise according to the business field model and the scores of all dimensions of the business field model; acquiring behavior data of enterprise users, wherein the behavior data comprises an industry community concerned by the enterprise users, users concerned by the enterprise users and information contents browsed by the enterprise users, and the weight value of each behavior is different;
and recalculating the scores of all dimensions of the supply and demand model according to the weight value of each behavior, and further perfecting the supply and demand model of the enterprise.
5. The recommendation-based docking method for a manufacturing enterprise as claimed in claim 1, further comprising: after the enterprise user receives the recommended community-related supply and demand users, the enterprise user continues to pay attention to the recommendation conversion rate, specifically:
after receiving the recommendation, the enterprise user continuously pays attention to the follow-up behaviors of the enterprise user, wherein the follow-up behaviors comprise whether to contact and whether to transact;
if the transaction is carried out later, the recommended conversion is considered to be successful, otherwise, the recommended conversion is considered to be failed;
and counting the times of successful recommended transformation and the times of failure recommended transformation, and calculating to obtain a recommended transformation rate, namely the ratio of the times of successful recommended transformation.
6. The recommendation-based docking method for a manufacturing enterprise according to claim 5, further comprising: correcting the supply and demand model according to the recommended conversion result and the recommended conversion rate, and further perfecting the supply and demand model of the enterprise; wherein the recommended transformation result comprises a recommended transformation success and a recommended transformation failure.
7. The recommendation-based docking method for a manufacturing enterprise according to claim 5, further comprising: and (4) paying attention to the feedback of the user, and continuously optimizing the matching algorithm according to the feedback of the user.
8. A manufacturing enterprise supply and demand docking device based on recommendation is characterized by comprising an information acquisition module, a community creation module, a login registration module, a model generation module, a supply and demand matching module, a controller and a supply and demand correction module;
the information acquisition module: the system is used for collecting an enterprise model of a user in the recommendation system;
a community creation module: acquiring enterprise models of users in a recommendation system, analyzing and mining potential upstream and downstream of all enterprises, and creating an industry community according to priority levels in batches;
a login registration module: the system is used for registering a supply and demand platform by a user and completing enterprise real-name authentication; after a user enters a platform, enterprise real-name authentication is completed, and then enterprise information is edited;
a model generation module: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for acquiring enterprise information, generating a business field model from the enterprise information according to a formula, and calculating scores of all dimensions of the business field model according to values and weights of all variables;
the model generation module is also used for establishing and perfecting a supply and demand model of an enterprise according to the business field model and the behavior data of the enterprise user;
supply and demand matching module: the system is used for matching supply and demand according to the supply and demand model of the enterprise; the method specifically comprises the following steps:
based on a supply and demand model of an enterprise, a supply and demand matching module recommends a related industry community to the enterprise and guides enterprise users to log in the industry community;
after guiding enterprise users to log in the industry community, analyzing and matching the supply and demand model of the enterprise with supply and demand models of other users of the community, and recommending relevant supply and demand users of the community to the enterprise;
supply and demand correction module: the method is used for correcting the enterprise supply and demand model.
9. The recommendation-based supply and demand docking device for the manufacturing enterprise as claimed in claim 8, wherein the supply and demand modification module comprises the following specific modification steps:
after the enterprise user receives the recommended community-related supply and demand users, continuing to pay attention to the recommendation conversion rate;
correcting the supply and demand model according to the recommended conversion result and the recommended conversion rate, and further perfecting the supply and demand model of the enterprise;
and (4) paying attention to the feedback of the user, and continuously optimizing the matching algorithm according to the feedback of the user.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1870026A (en) * | 2006-06-20 | 2006-11-29 | 东华大学 | Method for supplying personality information based on trade supply-demand relations |
| US20110289010A1 (en) * | 2010-05-21 | 2011-11-24 | Rankin Jr Claiborne R | Apparatuses, methods and systems for an activity tracking and property transaction facilitating hub user interface |
| US20110289161A1 (en) * | 2010-05-21 | 2011-11-24 | Rankin Jr Claiborne R | Apparatuses, Methods and Systems For An Intelligent Inbox Coordinating HUB |
| CN106383894A (en) * | 2016-09-23 | 2017-02-08 | 深圳市由心网络科技有限公司 | Enterprise supply-demand information matching method and apparatus |
| CN108427695A (en) * | 2017-08-04 | 2018-08-21 | 平安科技（深圳）有限公司 | Method and application server are recommended by enterprise |
| CN110119466A (en) * | 2019-03-29 | 2019-08-13 | 五渡(杭州)科技有限责任公司 | A kind of big data intelligent marketing system and method |
| CN112017036A (en) * | 2020-09-02 | 2020-12-01 | 山东振大社区商业中心有限公司 | Community new retail supply chain finance implementation method and device |
| CN112037031A (en) * | 2020-09-02 | 2020-12-04 | 北京炎黄医养科技有限公司 | Method and device for realizing finance of agricultural and commercial interconnection supply chain |
| CN112348318A (en) * | 2020-10-19 | 2021-02-09 | 深圳前海微众银行股份有限公司 | Method and device for training and applying supply chain risk prediction model |
| CN112581010A (en) * | 2020-12-25 | 2021-03-30 | 政和科技股份有限公司 | System and method for supply and demand butt joint matching of industrial products |
| US20210216923A1 (en) * | 2020-01-14 | 2021-07-15 | VIMware, Inc. | System and method for recommending demand-supply agent combination pairs for transactions using machine learning |
| CN113158192A (en) * | 2021-02-05 | 2021-07-23 | 西安电子科技大学 | Batch construction and management method and system for anti-detection online social network virtual users |
| CN113313504A (en) * | 2021-06-28 | 2021-08-27 | 中国农业银行股份有限公司 | Supplier recommendation method and related device |
| CN113342844A (en) * | 2021-08-09 | 2021-09-03 | 北京能科瑞元数字技术有限公司 | Industrial intelligent search system |
| CN113379227A (en) * | 2021-06-08 | 2021-09-10 | 软通智慧信息技术有限公司 | Industrial park data processing method and device, computer equipment and storage medium |
-
2022
- 2022-01-11 CN CN202210028738.3A patent/CN114357308B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1870026A (en) * | 2006-06-20 | 2006-11-29 | 东华大学 | Method for supplying personality information based on trade supply-demand relations |
| US20110289010A1 (en) * | 2010-05-21 | 2011-11-24 | Rankin Jr Claiborne R | Apparatuses, methods and systems for an activity tracking and property transaction facilitating hub user interface |
| US20110289161A1 (en) * | 2010-05-21 | 2011-11-24 | Rankin Jr Claiborne R | Apparatuses, Methods and Systems For An Intelligent Inbox Coordinating HUB |
| CN106383894A (en) * | 2016-09-23 | 2017-02-08 | 深圳市由心网络科技有限公司 | Enterprise supply-demand information matching method and apparatus |
| CN108427695A (en) * | 2017-08-04 | 2018-08-21 | 平安科技（深圳）有限公司 | Method and application server are recommended by enterprise |
| CN110119466A (en) * | 2019-03-29 | 2019-08-13 | 五渡(杭州)科技有限责任公司 | A kind of big data intelligent marketing system and method |
| US20210216923A1 (en) * | 2020-01-14 | 2021-07-15 | VIMware, Inc. | System and method for recommending demand-supply agent combination pairs for transactions using machine learning |
| CN112017036A (en) * | 2020-09-02 | 2020-12-01 | 山东振大社区商业中心有限公司 | Community new retail supply chain finance implementation method and device |
| CN112037031A (en) * | 2020-09-02 | 2020-12-04 | 北京炎黄医养科技有限公司 | Method and device for realizing finance of agricultural and commercial interconnection supply chain |
| CN112348318A (en) * | 2020-10-19 | 2021-02-09 | 深圳前海微众银行股份有限公司 | Method and device for training and applying supply chain risk prediction model |
| CN112581010A (en) * | 2020-12-25 | 2021-03-30 | 政和科技股份有限公司 | System and method for supply and demand butt joint matching of industrial products |
| CN113158192A (en) * | 2021-02-05 | 2021-07-23 | 西安电子科技大学 | Batch construction and management method and system for anti-detection online social network virtual users |
| CN113379227A (en) * | 2021-06-08 | 2021-09-10 | 软通智慧信息技术有限公司 | Industrial park data processing method and device, computer equipment and storage medium |
| CN113313504A (en) * | 2021-06-28 | 2021-08-27 | 中国农业银行股份有限公司 | Supplier recommendation method and related device |
| CN113342844A (en) * | 2021-08-09 | 2021-09-03 | 北京能科瑞元数字技术有限公司 | Industrial intelligent search system |
Non-Patent Citations (3)
| Title |
|---|
| ELIZABETH COUZINEAU-ZEGWAARD等: "From smart grid to smart city business ecosystem: Strategy to define the proper legitimacy for an energy utility firm", 《2013 WORLD ELECTRIC VEHICLE SYMPOSIUM AND EXHIBITION (EVS27)》 * |
| 姜雅倩等: "基于供求关系及协同过滤技术的推荐模型研究", 《计算机技术与发展》 * |
| 徐新清: "绿色供应链合作伙伴的选择与协调研究", 《中国优秀硕士学位论文全文数据库经济与管理科学辑》 * |
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