CN116383511B - Method, system, terminal and medium for recommending campus recruits based on industry chain analysis - Google Patents
Method, system, terminal and medium for recommending campus recruits based on industry chain analysis Download PDFInfo
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Abstract
The invention discloses a method, a system, a terminal and a medium for recommending a campus recruiter based on industrial chain analysis, which relate to the technical field of data analysis and have the technical scheme that: establishing an industrial chain panoramic map meeting the industrial chain cooperative ecology; collecting enterprise data related to all nodes in the industrial chain panorama through a web crawler, and establishing an enterprise basic information base; carrying out enterprise value, enterprise supply chain and industrial space distribution analysis on each enterprise data in an enterprise basic information base, and establishing an enterprise tag database; dividing a target area of the sponsor in the industry chain panorama according to the information of the sponsor demand; and determining a recommended enterprise based on each node in the recruitment target area, and solving the final enterprise recommendation strategy by taking the maximum sum of the label priority values of all the recommended enterprises in the recruitment target area as an optimization target. The method and the system enable enterprises recommended by the campus tenderers to be more reasonable, and are beneficial to the collaborative development of the campus.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a method, a system, a terminal and a medium for recommending a campus vendor based on industrial chain analysis.
Background
The traditional method for realizing the campus recruitment generally relies on industry experts to combine the existing data and experience, judges whether the industry development direction of the campus and the intention recruitment enterprises accord with the development of the campus, has simple recruitment path and is not comprehensive enough to know the enterprises, so that the effect of the campus recruitment is not ideal.
Along with the continuous development of big data analysis technology, in the prior art, matching analysis is recorded according to the requirement of a campus and related information of an enterprise, specifically, a keyword set is determined according to the direction of the industrial field in the requirement of the campus, then feature word matching is performed between keywords recorded in related information of the enterprise and the keyword set, the related information of the enterprise such as the enterprise operating range, related documents issued by the enterprise and the like, and the higher the matching degree of the feature words, the more accords with the requirement of the campus.
However, for a plurality of enterprises in an industry chain, there may be an upstream-downstream enterprise relationship, and the development directions of the enterprises of the same type also have obvious differences, so that the enterprise of the tenderer determined according to the requirement of the tenderer in the above method is easy to have excessive concentration of the similar enterprises, and is difficult to form the situation of cooperation ecology of the industry chain, which is unfavorable for the development of the campus; in addition, the method for realizing the campus recruitment through the feature word matching simply ignores the advantages of the campus, such as geographic advantages and policy advantages, is easy to waste resources of the park, and also is easy to cause higher cost of the enterprise to stay in the park. Therefore, how to research and design a method, a system, a terminal and a medium for recommending a campus and a vendor based on industry chain analysis, which can overcome the defects, is a problem which needs to be solved in the present day.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a method, a system, a terminal and a medium for recommending a campus recruitment based on industrial chain analysis, which are used for determining all node enterprises for realizing local industrial chain collaborative ecology aiming at the information of the recruitment demands, and then carrying out whole-network enterprise optimization selection according to an enterprise tag set determined after 3 latitudes are analyzed according to enterprise value, an enterprise supply chain and an industrial space, so that the enterprises recommended by the campus recruitment are more reasonable and are beneficial to the collaborative development of the campus.
The technical aim of the invention is realized by the following technical scheme:
in a first aspect, there is provided a method of industrial chain analysis-based campus recruitment recommendation, comprising the steps of:
establishing an industry chain panoramic map meeting the industry chain cooperative ecology according to the industry planning information of the target park;
collecting enterprise data related to all nodes in the industrial chain panorama through a web crawler, and establishing an enterprise basic information base;
carrying out enterprise value, enterprise supply chain and industry space distribution analysis on each enterprise data in an enterprise basic information base, and establishing an enterprise tag database composed of enterprise tag sets corresponding to each enterprise;
dividing a target area of the sponsor in the industry chain panorama according to the information of the sponsor demand;
and determining a recommended enterprise based on each node in the recruitment target area, and solving the final enterprise recommendation strategy by taking the maximum sum of the label priority values of all the recommended enterprises in the recruitment target area as an optimization target.
Further, the construction process of the industrial chain panorama specifically comprises the following steps:
determining an industry overall plan of the park according to the resource information, the geographic information and the policy information of the target park;
constructing an industrial chain collaborative ecological sub-map in a mode of covering all enterprises on the upstream and downstream of a supply chain for each planning area in the industrial overall planning;
and combining all the sub-images and then establishing an industrial chain panoramic map.
Further, the enterprise value analysis process of the enterprise data specifically includes:
performing label classification training on enterprises by adopting a classification algorithm to obtain value labels of all enterprises;
the value tags comprise high yield, low pollution, multiple tax, excellent reputation, unicorn and high growth tags;
wherein each business corresponds to at least one value tag.
Further, the enterprise supply chain analysis process of the enterprise data specifically includes:
determining a supply chain label of a corresponding enterprise according to the upstream enterprise relationship and the downstream enterprise relationship of the supplier and/or the client relationship;
the total number of the supply chains with the relation between the upstream enterprise and the downstream enterprise corresponding to the enterprise is taken as the label value of the supply chain label.
Further, the industrial spatial distribution analysis process of the enterprise data specifically includes:
prioritizing the enterprises according to whether the enterprises are local enterprises and whether the enterprises accord with various preferential policies of the park, and taking the priorities as spatial distribution labels of the enterprises;
and taking the preference coefficient corresponding to the priority as a label value of the corresponding spatial distribution label, wherein the higher the priority is, the larger the corresponding preference coefficient is.
Further, the solving process of the enterprise recommendation strategy specifically includes:
the value labels in the enterprise label set are matched with the value orientations in the requirement information of the sponsor to be used as constraint conditions for recommending enterprise selection;
calculating the product of the label values of the enterprise label concentrated supply chain labels and the spatial distribution labels to obtain the label priority value of the corresponding enterprise;
and solving the optimization target by taking the maximum sum of the label priority values of the enterprises recommended by all the nodes in the business-recruitment target area as the optimization target to obtain the final enterprise recommendation strategy.
Further, the calculation formula of the optimization target is specifically:
；
wherein ,indicate->The label value of the supply chain label of the enterprise recommended by each node; />Indicate->Spatial distribution label of enterprise recommended by individual nodeA tag value of the tag; />Representing the number of nodes in the target area of the recruiter; />Indicate->Value tags of enterprises recommended by the individual nodes; />Representing a set of value orientations contained in the vendor demand information.
In a second aspect, there is provided a campus recruiter recommendation system based on industry chain analysis, comprising:
the map construction module is used for building an industry chain panoramic map meeting the cooperation ecology of the industry chain according to the industry planning information of the target park;
the information acquisition module is used for acquiring enterprise data related to all nodes in the industrial chain panoramic map through the web crawler and establishing an enterprise basic information base;
the label classification module is used for carrying out enterprise value, enterprise supply chain and industrial space distribution analysis on each enterprise data in the enterprise basic information base, and establishing an enterprise label database consisting of enterprise label sets corresponding to each enterprise;
the region dividing module is used for dividing a target region of the sponsor in the industry chain panorama according to the information of the sponsor requirement;
and the recommendation optimization module is used for determining a recommendation enterprise based on each node in the recruitment target area, and solving the recommendation target to obtain a final enterprise recommendation strategy by taking the sum of the label priority values of all recommendation enterprises in the recruitment target area as the maximum.
In a third aspect, a computer terminal is provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method for industrial chain analysis based on the recommendation of a campus facilitator according to any one of the first aspects when executing the program.
In a fourth aspect, there is provided a computer readable medium having stored thereon a computer program for execution by a processor to implement the industrial chain analysis based on campus recruitment recommendation method of any of the first aspects.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the industrial chain analysis-based campus recruitment recommendation method, panoramic display of all node enterprises in one industrial chain is achieved through establishment of the industrial chain panoramic map, the target areas of the recruits are divided in the industrial chain panoramic map through the recruit demand information, all node enterprises which realize local industrial chain cooperative ecology aiming at the recruit demand information are determined, and then whole-network enterprise optimization selection is conducted according to the enterprise label set determined after 3 latitudes are analyzed according to enterprise value, enterprise supply chain and industrial space distribution, so that enterprises recommended by the campus recruits are more reasonable, and collaborative development of the campus is facilitated;
2. according to the method, the value labels are used as constraint conditions for selecting the recommended enterprises, the overall matching of the development directions of the recommended enterprises and the target parks is achieved, the product of the label values of the supply chain labels and the spatial distribution labels is used as a label priority value, meanwhile, the ecological characteristics of the recommended enterprises and whether the ecological characteristics meet the advantages of the target parks or not are considered, then the sum of the label priority values is used as the maximum optimization target for solving, and the overall optimization of the enterprises selected by the target areas of the tenderers is achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention. In the drawings:
FIG. 1 is a flow chart in embodiment 1 of the present invention;
fig. 2 is a system block diagram in embodiment 2 of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1: the industrial chain analysis-based campus recruitment recommendation method, as shown in fig. 1, comprises the following steps:
step S1: establishing an industry chain panoramic map meeting the industry chain cooperative ecology according to the industry planning information of the target park;
step S2: collecting enterprise data related to all nodes in the industrial chain panorama through a web crawler, and establishing an enterprise basic information base;
step S3: carrying out enterprise value, enterprise supply chain and industry space distribution analysis on each enterprise data in an enterprise basic information base, and establishing an enterprise tag database composed of enterprise tag sets corresponding to each enterprise;
step S4: dividing a target area of the sponsor in the industry chain panorama according to the information of the sponsor demand;
step S5: and determining a recommended enterprise based on each node in the recruitment target area, and solving the final enterprise recommendation strategy by taking the maximum sum of the label priority values of all the recommended enterprises in the recruitment target area as an optimization target.
In this embodiment, the construction process of the industrial chain panorama specifically includes: determining the industrial overall planning of the park according to the resource information, the geographic information, the policy information and other information of the target park; constructing an industrial chain collaborative ecological sub-map in a mode of covering all enterprises on the upstream and downstream of a supply chain for each planning area in the industrial overall planning; and combining all the sub-images and then establishing an industrial chain panoramic map.
The enterprise value analysis process of the enterprise data specifically comprises the following steps: performing label classification training on enterprises by adopting a classification algorithm to obtain value labels of all enterprises; the value labels comprise high yield, low pollution, multiple tax pays, excellent reputation, unicorns and high growth labels; wherein each business corresponds to at least one value tag.
Classification algorithms include, but are not limited to, decision trees, random forests, and the like.
The enterprise supply chain analysis process of the enterprise data specifically comprises the following steps: determining a supply chain label of a corresponding enterprise according to the relationship between the upstream enterprise and the downstream enterprise; the total number of the supply chains with the relation between the upstream enterprise and the downstream enterprise corresponding to the enterprise is taken as the label value of the supply chain label. The upstream and downstream business relationships may be supplier relationships or customer relationships, and are not limited herein.
For example, if the number of upstream enterprises and the number of downstream enterprises in the enterprise a are 3 and 2, the label value of the supply chain label corresponding to the enterprise a is 5, and both the upstream enterprises and the downstream enterprises should have corresponding nodes in the industrial chain panorama.
The industrial space distribution analysis process of the enterprise data specifically comprises the following steps: prioritizing the enterprises according to whether the enterprises are local enterprises and whether the enterprises accord with various preferential policies of the park, and taking the priorities as spatial distribution labels of the enterprises; and taking the preference coefficient corresponding to the priority as a label value of the corresponding spatial distribution label, wherein the higher the priority is, the larger the corresponding preference coefficient is.
For example, marking local enterprises which meet various preference policies of a park as first priority, wherein the corresponding preference coefficient is 1; the local enterprise is marked as a second priority, and the corresponding preference coefficient is 0.9; the non-local enterprises which accord with various preferential policies of the park are marked with a third priority, and the corresponding preferential coefficient is 0.8; non-native enterprises are marked with a fourth priority, preferably a factor of 0.7.
The solving process of the enterprise recommendation strategy specifically comprises the following steps: the value labels in the enterprise label set are matched with the value orientations in the requirement information of the sponsor to be used as constraint conditions for recommending enterprise selection; calculating the product of the label values of the enterprise label concentrated supply chain labels and the spatial distribution labels to obtain the label priority value of the corresponding enterprise; and solving the optimization target by taking the maximum sum of the label priority values of the enterprises recommended by all the nodes in the business-recruitment target area as the optimization target to obtain the final enterprise recommendation strategy.
According to the method, the value labels are used as constraint conditions for selecting the recommended enterprises, the overall matching of the development directions of the recommended enterprises and the target parks is achieved, the product of the label values of the supply chain labels and the spatial distribution labels is used as a label priority value, meanwhile, the ecological characteristics of the recommended enterprises and whether the ecological characteristics meet the advantages of the target parks or not are considered, then the sum of the label priority values is used as the maximum optimization target for solving, and the overall optimization of the enterprises selected by the target areas of the tenderers is achieved.
As an alternative embodiment, the calculation formula of the optimization target is specifically:
；
wherein ,indicate->The label value of the supply chain label of the enterprise recommended by each node; />Indicate->The label value of the spatial distribution label of the enterprise recommended by each node; />Representing the number of nodes in the target area of the recruiter; />Indicate->Value tags of enterprises recommended by the individual nodes; />Representing a set of value orientations contained in the vendor demand information.
As another alternative, if the vendor demand information has a limit on the number of vendor businesses, the number of vendor businesses is less than the number of nodes in the vendor target area. Then the nodes may be ranked according to the upstream-downstream enterprise relationship, the more upstream the enterprise ranking. When the nodes are selected for enterprise recommendation, the node number with the lowest level can be reduced, or the equal proportion reduction is carried out according to the change of the node level, for example, the node with the lowest level is reduced by 5, the node with the middle level is reduced by 3, and the node with the highest level is reduced by 1.
Example 2: the industrial chain analysis-based campus-sponsor recommending system for implementing the industrial chain analysis-based campus-sponsor recommending method described in embodiment 1 includes, as shown in fig. 2, a map construction module, an information acquisition module, a tag classification module, a region division module, and a recommendation optimization module.
The map construction module is used for building an industry chain panoramic map meeting the industry chain cooperative ecology according to the industry planning information of the target park; the information acquisition module is used for acquiring enterprise data related to all nodes in the industrial chain panoramic map through the web crawler and establishing an enterprise basic information base; the label classification module is used for carrying out enterprise value, enterprise supply chain and industrial space distribution analysis on each enterprise data in the enterprise basic information base, and establishing an enterprise label database consisting of enterprise label sets corresponding to each enterprise; the region dividing module is used for dividing a target region of the sponsor in the industry chain panorama according to the information of the sponsor requirement; and the recommendation optimization module is used for determining a recommendation enterprise based on each node in the recruitment target area, and solving the recommendation target to obtain a final enterprise recommendation strategy by taking the sum of the label priority values of all recommendation enterprises in the recruitment target area as the maximum.
Working principle: according to the method, panoramic display of all node enterprises in an industrial chain is realized by establishing the industrial chain panoramic map, the target region of the recruitment is divided in the industrial chain panoramic map through the information of the recruitment requirements, all node enterprises which realize local industrial chain cooperative ecology aiming at the information of the recruitment requirements are determined, and then the whole-network enterprise optimization selection is performed according to the enterprise label set determined after 3 latitudes of enterprise value, enterprise supply chain and industrial space distribution are analyzed, so that the enterprises recommended by the recruitment of the park are more reasonable, and the collaborative development of the park is facilitated.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing detailed description of the invention has been presented for purposes of illustration and description, and it should be understood that the invention is not limited to the particular embodiments disclosed, but is intended to cover all modifications, equivalents, alternatives, and improvements within the spirit and principles of the invention.
Claims (9)
1. The industrial chain analysis-based campus recruitment recommendation method is characterized by comprising the following steps of:
establishing an industry chain panoramic map meeting the industry chain cooperative ecology according to the industry planning information of the target park;
collecting enterprise data related to all nodes in the industrial chain panorama through a web crawler, and establishing an enterprise basic information base;
carrying out enterprise value, enterprise supply chain and industry space distribution analysis on each enterprise data in an enterprise basic information base, and establishing an enterprise tag database composed of enterprise tag sets corresponding to each enterprise;
dividing a target area of the sponsor in the industry chain panorama according to the information of the sponsor demand;
determining a recommended enterprise based on each node in the recruitment target area, and solving a final enterprise recommendation strategy by taking the maximum sum of label priority values of all recommended enterprises in the recruitment target area as an optimization target;
the solving process of the enterprise recommendation strategy specifically comprises the following steps:
the value labels in the enterprise label set are matched with the value orientations in the requirement information of the sponsor to be used as constraint conditions for recommending enterprise selection;
calculating the product of the label values of the enterprise label concentrated supply chain labels and the spatial distribution labels to obtain the label priority value of the corresponding enterprise;
and solving the optimization target by taking the maximum sum of the label priority values of the enterprises recommended by all the nodes in the business-recruitment target area as the optimization target to obtain the final enterprise recommendation strategy.
2. The industrial chain analysis-based campus vendor recommendation method according to claim 1, wherein the construction process of the industrial chain panorama is specifically as follows:
determining an industry overall plan of the park according to the resource information, the geographic information and the policy information of the target park;
constructing an industrial chain collaborative ecological sub-map in a mode of covering all enterprises on the upstream and downstream of a supply chain for each planning area in the industrial overall planning;
and combining all the sub-images and then establishing an industrial chain panoramic map.
3. The industrial chain analysis-based campus vendor recommendation method according to claim 1, wherein the enterprise value analysis process of the enterprise data specifically comprises:
performing label classification training on enterprises by adopting a classification algorithm to obtain value labels of all enterprises;
the value tags comprise high yield, low pollution, multiple tax, excellent reputation, unicorn and high growth tags;
wherein each business corresponds to at least one value tag.
4. The industrial chain analysis-based campus vendor recommendation method according to claim 1, wherein the enterprise supply chain analysis process of the enterprise data specifically comprises:
determining a supply chain label of a corresponding enterprise according to the upstream enterprise relationship and the downstream enterprise relationship of the supplier and/or the client relationship;
the total number of the supply chains with the relation between the upstream enterprise and the downstream enterprise corresponding to the enterprise is taken as the label value of the supply chain label.
5. The industrial chain analysis-based campus vendor recommendation method according to claim 1, wherein the industrial spatial distribution analysis process of the enterprise data specifically comprises:
prioritizing the enterprises according to whether the enterprises are local enterprises and whether the enterprises accord with various preferential policies of the park, and taking the priorities as spatial distribution labels of the enterprises;
and taking the preference coefficient corresponding to the priority as a label value of the corresponding spatial distribution label, wherein the higher the priority is, the larger the corresponding preference coefficient is.
6. The industrial chain analysis-based campus vendor recommendation method according to claim 1, wherein the calculation formula of the optimization objective is specifically:
；
wherein ,indicate->The label value of the supply chain label of the enterprise recommended by each node; />Indicate->The label value of the spatial distribution label of the enterprise recommended by each node; />Representing the number of nodes in the target area of the recruiter; />Indicate->Value tags of enterprises recommended by the individual nodes; />Representing a set of value orientations contained in the vendor demand information.
7. The industrial chain analysis-based campus recruitment recommendation system is characterized by comprising:
the map construction module is used for building an industry chain panoramic map meeting the cooperation ecology of the industry chain according to the industry planning information of the target park;
the information acquisition module is used for acquiring enterprise data related to all nodes in the industrial chain panoramic map through the web crawler and establishing an enterprise basic information base;
the label classification module is used for carrying out enterprise value, enterprise supply chain and industrial space distribution analysis on each enterprise data in the enterprise basic information base, and establishing an enterprise label database consisting of enterprise label sets corresponding to each enterprise;
the region dividing module is used for dividing a target region of the sponsor in the industry chain panorama according to the information of the sponsor requirement;
the recommendation optimization module is used for determining a recommendation enterprise based on each node in the recruitment target area, and solving a final enterprise recommendation strategy by taking the sum of label priority values of all recommendation enterprises in the recruitment target area as the maximum;
the solving process of the enterprise recommendation strategy specifically comprises the following steps:
the value labels in the enterprise label set are matched with the value orientations in the requirement information of the sponsor to be used as constraint conditions for recommending enterprise selection;
calculating the product of the label values of the enterprise label concentrated supply chain labels and the spatial distribution labels to obtain the label priority value of the corresponding enterprise;
and solving the optimization target by taking the maximum sum of the label priority values of the enterprises recommended by all the nodes in the business-recruitment target area as the optimization target to obtain the final enterprise recommendation strategy.
8. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the industrial chain analysis based on-campus recruitment recommendation method according to any one of claims 1-6 when executing the program.
9. A computer readable medium having stored thereon a computer program, the computer program being executable by a processor to implement the industrial chain analysis based on-market recruitment recommendation method according to any one of claims 1-6.
Priority Applications (1)
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