An industrial resource intelligent matching method, system, medium and device

Abstract

The present application relates to the technical field of industrial resource management, and relates to an industrial resource intelligent matching method, system, medium and equipment.The scheme comprises establishing a standardized attribute database of industrial resources;the subject inputs information, analyzes the subject demand, assigns specific attributes, and the specific attributes include policy, talent, technology and product demand;according to the policy and talent demand, online analysis is carried out to obtain the current optimal demand recommendation;all historical optimal demand data are obtained, feature parameter analysis is carried out, and a group of target correction training coefficients are obtained;the attribute prompt matching degree of all resource and demand information is calculated;the user provides the corresponding matching information of policy, talent, technology and product to the subject according to the attribute prompt matching degree from large to small.The scheme adopts an intelligent matching mode, after the subject completes the matching demand input, data is automatically searched, the corresponding matching result can be quickly matched in the database, and resource information is provided to the subject.

Description

Technical Field

[0001] This invention relates to the field of industrial resource management technology, and more specifically, to an intelligent matching method, system, medium, and equipment for industrial resources. Background Technology

[0002] With the rapid development of the information age, more and more platforms and systems are being used for the allocation and planning of information and resources, thereby enabling the matching of policies, talent, technology, products, and services across different regions based on information. However, achieving efficient and accurate information matching is the core of ensuring the effective scheduling of information resources.

[0003] Prior to this invention, traditional industry resource matching required the subject to input matching requirements, and the matching system could only run when the matching page was open. When there was no matching data, the subject's matching requirements could not be stored, and the system could not perform matching retrieval after the matching page was closed. Each matching was independent, requiring the matching page to be opened manually and the matching requirements to be input. The operation process was cumbersome, and the system could not indicate when there were industry resources that met the matching requirements, making it impossible for the subject to grasp the data information in time, resulting in information lag. Summary of the Invention

[0004] In view of the above problems, the present invention proposes an intelligent matching method, system, medium and equipment for industrial resources. It adopts an intelligent matching mode, which automatically retrieves data after the subject completes the input of matching requirements, and can quickly match the corresponding matching results in the database to provide resource information to the subject.

[0005] According to a first aspect of the present invention, an intelligent matching method for industrial resources is provided.

[0006] In one or more embodiments, preferably, the intelligent matching method for industrial resources includes:

[0007] During the information matching process, an intelligent matching mode is adopted. After the subject completes the input of the matching requirements, the matching data is continuously and automatically retrieved. The retrieval can be carried out automatically after the subject closes the operation page, and the matching data can be retrieved and the subject is notified and the matching results are presented as soon as the matching data appears in the database.

[0008] The system facilitates bidirectional matching between resource providers and demanders, allowing for the submission of multiple matching requests simultaneously. This satisfies the entity's need for resources across various industries and its supply of resources to multiple demanders, ensuring timely market response. A standardized attribute database is used, and resources and demands are assigned normalized specific attributes. These attributes are then normalized to indicate the degree of matching, and the data presentation is based on the level of matching indicated by these attributes. The specific process includes:

[0009] Establish a standardized attribute database for industrial resources;

[0010] The subject inputs information, completes the analysis of the subject's needs, and assigns specific attributes, including policy needs, talent needs, product needs, technology needs, service needs, and resource needs;

[0011] Based on the policy needs, talent needs, product needs, technology needs, service needs, and resource needs, online analysis is conducted to obtain the current optimal demand recommendation;

[0012] Obtain all historical optimal demand data, perform feature parameter analysis, and obtain a set of target correction training coefficients;

[0013] Calculate the attribute matching degree of all resource and demand information based on the set of target-corrected training coefficients;

[0014] Users provide the subject with matching information on policies, talents, products, technologies, services, and resources, ranked from highest to lowest based on the matching degree indicated by the aforementioned attributes.

[0015] In one or more embodiments, preferably, establishing a standardized attribute database for industrial resources specifically includes:

[0016] Obtain all policy documents, technical personnel information, technical introductions, and product introductions as raw input information;

[0017] Based on the original input information, data is stored in the same form under the same type and saved as a standardized attribute database.

[0018] In one or more embodiments, preferably, the subject inputs information to complete the analysis of the subject's needs and assign specific attributes, the specific attributes including policy needs, talent needs, product needs, technology needs, service needs, and resource needs, specifically including:

[0019] The standardized attributes of industrial resources are provided in the standardized attribute database on the tree. The specific attributes are actively confirmed and assigned by the providers of industrial resources. All resource data are summarized, organized and stored in the standardized attribute database according to the attributes.

[0020] When a requirement is published, specific attributes are assigned according to the different types and contents of the requirement, and the requirement is summarized, organized and stored in a standardized attribute database.

[0021] The subject can independently select the attributes to be matched, and at the same time select the attributes of resources and needs, and then search in the standardized attribute database.

[0022] In one or more embodiments, preferably, the step of obtaining the current optimal demand recommendation through online analysis based on the policy needs, talent needs, product needs, technology needs, service needs, and resource needs specifically includes:

[0023] After obtaining the subject's input information, the system automatically extracts the vocabulary from the subject's input information to obtain the policy requirements, talent requirements, product requirements, technology requirements, service requirements, and resource requirements.

[0024] The policy dimension index of the subject input information is calculated using the first calculation formula, wherein the policy demand dimension includes at least department, time, level, region, keywords and application status;

[0025] The talent dimension index of the main input information is calculated using the second calculation formula, wherein the dimensions of talent demand include at least industry, posting time, price, region and supplier attributes;

[0026] The product dimension index of the main input information is calculated using a third calculation formula, wherein the dimensions of the product demand include at least industry, release time, price, region, and supplier attributes;

[0027] The fourth calculation formula is used to calculate the technical dimension index of the main input information, wherein the dimensions of the technical demand include at least industry, release time, price, region, supplier attributes and patent technology resources;

[0028] The service dimension index of the main input information is calculated using the fifth calculation formula, wherein the dimensions of the service demand include at least industry, release time, price, region, supplier attributes, and instrument and equipment sharing;

[0029] The resource dimension index of the main input information is calculated using the sixth calculation formula, wherein the dimensions of the resource demand include at least industry, release time, price, region, supplier attributes, service resources and shared resources;

[0030] The first calculation formula is:

[0031]

[0032] Among them, Z T Z is the policy dimension index input by the subject for the Tth time, where i is the policy dimension number. i_T Let n be the policy score corresponding to the i-th policy dimension number of the T-th subject input. i Total number of policy dimensions;

[0033] The second calculation formula is:

[0034]

[0035] Among them, R T Let R be the talent dimension index input by the subject for the Tth time, where x is the talent dimension number and R is the index for the Tth input. x_T For the x-th talent dimension ID input by the T-th subject, n is the talent dimension index corresponding to the talent dimension ID. x Total number of talents;

[0036] The third calculation formula is:

[0037]

[0038] Among them, C T For the Tth subject input, z is the product dimension index, and C is the product dimension number. z_T For the product dimension index corresponding to the z-th product dimension number input by the T-th subject, n z This represents the total number of product dimensions.

[0039] The fourth calculation formula is:

[0040]

[0041] Among them, J T Let y be the technical dimension index of the T-th input subject, and J be the technical dimension number. y_T For the y-th technical dimension number input by the T-th subject, n is the technical dimension index. y Total number of technical dimensions;

[0042] The fifth calculation formula is:

[0043]

[0044] Among them, F T For the Tth input subject, the service dimension index is p, where p is the service dimension number, and F is the service dimension index. p_T For the service dimension index corresponding to the p-th service dimension number input by the T-th subject, n p Total number of service dimensions;

[0045] The sixth calculation formula is:

[0046]

[0047] Among them, Y T Let J be the resource dimension index of the Tth input subject, l be the resource dimension number, and J be the index of the Tth input subject. l_T For the Tth input, the resource dimension index corresponding to the l-th resource dimension number, n l This represents the total number of resource dimensions.

[0048] In one or more embodiments, preferably, the step of obtaining all historical optimal demand data, performing feature parameter analysis, and obtaining a set of target correction training coefficients specifically includes:

[0049] Extract all historical data from the main input, and use the final recommended policies, talents, products, technologies, services and resources as a historical data set;

[0050] The first, second, third, fourth, fifth, and sixth target correction training coefficients are obtained by using the seventh calculation formula based on the historical data set. The first, second, third, fourth, fifth, and sixth target correction training coefficients are a set of target correction training coefficients.

[0051] The seventh calculation formula is:

[0052]

[0053] Where L represents the historical number of main inputs, j represents the number of main inputs, argmin() is the target correction training coefficient that minimizes the formula, and k 10 k 20 k 30 k 40 k 50 and k 60 To adjust the training coefficients Z for the first, second, third, fourth, fifth, and sixth objectives. T_j R represents the recommended value of the policy dimension index for the j-th subject input. T_j J represents the recommended value of the talent dimension index at the j-th subject input. T_j C represents the recommended value of the technology dimension index for the j-th subject input. T_j Y is the recommended value of the product dimension index for the j-th subject input. T_j F represents the recommended value of the resource dimension index for the j-th input. T_j Z is the recommended value of the service dimension index for the j-th subject input. X_j R represents the actual selected value of the policy dimension index at the j-th subject input. X_j J represents the actual selected value of the talent dimension index at the j-th subject input. X_j C represents the actual selected value of the technology dimension index at the j-th subject input. X_j Y represents the actual selected value of the product dimension index during the j-th subject input. T_j F represents the actual selected value of the resource dimension index at the j-th subject input. T_j is the actual selected value of the service dimension index when the subject inputs for the jth time, and k1, k2, k3, k4, k5 and k6 are the first, second, third, fourth, fifth and sixth corrected training coefficients.

[0054] In one or more embodiments, preferably, the step of calculating the attribute suggestion matching degree of all resource and demand information based on the set of target-corrected training coefficients specifically includes:

[0055] Obtain the corrected training coefficients for the first, second, third, fourth, fifth, and sixth targets;

[0056] The matching degree of the attribute hints is calculated using the eighth calculation formula;

[0057] The attribute suggestions are sorted from highest to lowest matching degree, and 20 consecutive alternative solutions are recommended to the subject.

[0058] The eighth calculation formula is:

[0059] T Z =k 10 Z T_j +k 20 R T_j +k 30 J T_j +k 40 C T_j

[0060] Among them, T Z The degree of matching is indicated for the attribute.

[0061] In one or more embodiments, preferably, the user provides the subject with matching information on policies, talent, products, technologies, services, and resources, ranked from highest to lowest according to the matching degree indicated by the attribute prompts, specifically including:

[0062] Obtain the feature margin of the main input;

[0063] The system selects the attribute suggestion matching degree based on the number of feature margins in the attribute suggestion matching degree sorted from largest to smallest, and recommends the corresponding policies, talents, products, technologies, services and resources to the subject.

[0064] According to a second aspect of the present invention, an intelligent matching system for industrial resources is provided.

[0065] In one or more embodiments, preferably, the intelligent matching system for industrial resources includes:

[0066] The resource collection module is used to establish a standardized attribute database for industry resources;

[0067] The subject confirmation module is used for subjects to input information, complete the analysis of subject needs and assign specific attributes, including policy needs, talent needs, product needs, technology needs, service needs and resource needs;

[0068] The demand publishing module is used to perform online analysis based on the policy demand, talent demand, product demand, technology demand, service demand, and resource demand to obtain the current optimal demand recommendation.

[0069] The attribute analysis module is used to obtain all historical optimal demand data, perform feature parameter analysis, and obtain a set of target correction training coefficients.

[0070] The attribute matching module is used to calculate the attribute matching degree of all resource and demand information based on the set of target-corrected training coefficients;

[0071] The information push module is used to provide users with matching information on policies, talents, products, technologies, services and resources to the subject in descending order of the matching degree indicated by the attribute prompts.

[0072] According to a third aspect of the present invention, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the method as described in any one of the first aspects of the present invention.

[0073] According to a fourth aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method described in any one aspect of the present invention.

[0074] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0075] The present invention allows the entity to perform bidirectional matching between resource providers and demanders, and can submit multiple different matching requests simultaneously, satisfying the entity's demand for multiple industry resources and the supply of resources to multiple demanders, ensuring that the entity responds to market information in a timely manner and seizes market opportunities.

[0076] The present invention employs a standardized attribute database, where resources and requirements can be assigned specific attributes. Based on unified attributes, the accuracy of matching can be better ensured. At the same time, the system will indicate the degree of matching and use the degree of matching as the basis for data presentation and arrangement.

[0077] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0078] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0079] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0080] Figure 1 This is a flowchart of an intelligent matching method for industrial resources according to an embodiment of the present invention.

[0081] Figure 2 This is a flowchart illustrating the establishment of a standardized attribute database for industrial resources in an intelligent matching method for industrial resources according to an embodiment of the present invention.

[0082] Figure 3 This is a flowchart illustrating an intelligent matching method for industrial resources according to an embodiment of the present invention, in which the subject inputs information, completes the analysis of the subject's needs, and assigns specific attributes, including policy needs, talent needs, product needs, technology needs, service needs, and resource needs.

[0083] Figure 4 This is a flowchart illustrating an intelligent matching method for industrial resources according to an embodiment of the present invention, which involves online analysis based on policy needs, talent needs, product needs, technology needs, service needs, and resource needs to obtain the current optimal demand recommendation.

[0084] Figure 5 This is a flowchart illustrating how an intelligent matching method for industrial resources, according to an embodiment of the present invention, obtains all historical optimal demand data, performs feature parameter analysis, and acquires a set of target correction training coefficients.

[0085] Figure 6 This is a flowchart illustrating the process of calculating the attribute-based matching degree of all resource and demand information based on a set of target-corrected training coefficients in an intelligent matching method for industrial resources according to an embodiment of the present invention.

[0086] Figure 7 This is a flowchart illustrating how, in an embodiment of the present invention, a user provides matching information on policies, talent, products, technologies, services, and resources to a subject based on the attribute-based matching degree, from highest to lowest.

[0087] Figure 8 This is a structural diagram of an intelligent matching system for industrial resources according to an embodiment of the present invention.

[0088] Figure 9This is a structural diagram of an electronic device according to one embodiment of the present invention. Detailed Implementation

[0089] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0090] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0091] With the rapid development of the information age, more and more platforms and systems are being used for the allocation and planning of information and resources, thereby enabling the matching of policies, talent, technology, products, and services across different regions based on information. However, achieving efficient and accurate information matching is the core of ensuring the effective scheduling of information resources.

[0092] Prior to this invention, traditional industry resource matching required the subject to input matching requirements, and the matching system could only run when the matching page was open. When there was no matching data, the subject's matching requirements could not be stored, and the system could not perform matching retrieval after the matching page was closed. Each matching was independent, requiring the matching page to be opened manually and the matching requirements to be input. The operation process was cumbersome, and the system could not indicate when there were industry resources that met the matching requirements, making it impossible for the subject to grasp the data information in time, resulting in information lag.

[0093] This invention provides a method, system, medium, and device for intelligent matching of industrial resources. The solution employs an intelligent matching mode, automatically retrieving data after the subject completes the input of matching requirements and quickly matching the corresponding results in the database, providing resource information to the subject.

[0094] According to a first aspect of the present invention, an intelligent matching method for industrial resources is provided.

[0095] Figure 1This is a flowchart of an intelligent matching method for industrial resources according to an embodiment of the present invention.

[0096] In one or more embodiments, preferably, the intelligent matching method for industrial resources includes:

[0097] During the information matching process, an intelligent matching mode is adopted. After the subject completes the input of the matching requirements, the matching data is continuously and automatically retrieved. The retrieval can be carried out automatically after the subject closes the operation page, and the matching data can be retrieved and the subject is notified and the matching results are presented as soon as the matching data appears in the database.

[0098] The system facilitates bidirectional matching between resource providers and demanders, allowing for the submission of multiple matching requests simultaneously. This satisfies the entity's need for resources across various industries and its supply of resources to multiple demanders, ensuring timely market response. A standardized attribute database is used, and resources and demands are assigned normalized specific attributes. These attributes are then normalized to indicate the degree of matching, and the data presentation is based on the level of matching indicated by these attributes. The specific process includes:

[0099] S101. Establish a standardized attribute database for industrial resources;

[0100] S102. The subject inputs information to complete the analysis of the subject's needs and assign specific attributes, including policy needs, talent needs, product needs, technology needs, service needs, and resource needs.

[0101] S103. Based on the policy requirements, talent requirements, product requirements, technology requirements, service requirements, and resource requirements, conduct online analysis to obtain the current optimal demand recommendation;

[0102] S104. Obtain all historical optimal demand data, perform feature parameter analysis, and obtain a set of target correction training coefficients.

[0103] S105. Calculate the attribute suggestion matching degree of all resource and demand information based on the set of target correction training coefficients;

[0104] S106. The user provides the subject with matching information on policies, talents, products, technologies, services and resources, in descending order of the matching degree indicated by the attribute prompts.

[0105] In the embodiments of the invention, the subject can be either a user or a customer, depending on the actual situation. In order to quickly match information, a standardized attribute database of industry resources is established. Then, relevant subjects actively confirm or are assigned specific attributes based on algorithms. When a demand is released, specific attributes are assigned, and resource and demand information are actively pushed to specific subjects whose attributes match. Based on a unified attribute prompt, the degree of matching is indicated, and the information matching results are presented.

[0106] Figure 2 This is a flowchart illustrating the establishment of a standardized attribute database for industrial resources in an intelligent matching method for industrial resources according to an embodiment of the present invention.

[0107] like Figure 2 As shown, in one or more embodiments, preferably, establishing a standardized attribute database for industrial resources specifically includes:

[0108] S201. Obtain all policy documents, technical personnel information, technical introductions, and product introductions as raw input information;

[0109] S202. Based on the original input information, store the data in the same form under the same type and save it as a standardized attribute database.

[0110] In this embodiment of the invention, in order to establish a standardized attribute database of industrial resources, after the subject inputs data, natural language analysis is performed on the subject input data to obtain the corresponding vocabulary, and the vocabulary is divided into several resource tables, which are then filled into the standardized attribute database. The standardized attribute database includes, but is not limited to, policies, talents, technologies and products. Under these categories, there are corresponding policies and policy numbers, talents and talents numbers, technologies and technologies numbers, and products and products numbers.

[0111] In this embodiment of the invention, information in the database can be classified in a standardized manner according to preset standards, and in combination with the information classification, precise content push can be set by user selection.

[0112] Figure 3 This is a flowchart illustrating an intelligent matching method for industrial resources according to an embodiment of the present invention, in which the subject inputs information, completes the analysis of the subject's needs, and assigns specific attributes, including policy needs, talent needs, product needs, technology needs, service needs, and resource needs.

[0113] like Figure 3 As shown, in one or more embodiments, preferably, the subject inputs information to complete the analysis of the subject's needs and assign specific attributes. These specific attributes include policy needs, talent needs, product needs, technology needs, service needs, and resource needs, specifically including:

[0114] S301. The standardized attributes of industrial resources provided in the tree-based standardized attribute database, the specific attributes actively confirmed and assigned by the provider of industrial resources, and all resource data are summarized, organized and stored in the standardized attribute database according to the attributes.

[0115] S302. When publishing requirements, assign specific attributes according to different types and contents of requirements, and summarize and store the requirements in a standardized attribute database.

[0116] S303. The subject independently selects the attributes to be matched, and selects both resource and demand attributes, and then searches in the standardized attribute database.

[0117] In this embodiment of the invention, in order to automatically identify industrial resources, all industrial resources were automatically retrieved, and the matching degree was presented based on the retrieval results. This process also specifically includes talent demand and policy demand.

[0118] Figure 4 This is a flowchart illustrating an intelligent matching method for industrial resources according to an embodiment of the present invention, which involves online analysis based on policy needs, talent needs, product needs, technology needs, service needs, and resource needs to obtain the current optimal demand recommendation.

[0119] like Figure 4 As shown, in one or more embodiments, preferably, the step of obtaining the current optimal demand recommendation through online analysis based on the policy needs, talent needs, product needs, technology needs, service needs, and resource needs specifically includes:

[0120] S401. After obtaining the subject input information, automatically extract the vocabulary from the subject input information to obtain the policy requirements, talent requirements, product requirements, technology requirements, service requirements, and resource requirements.

[0121] S402. Calculate the policy dimension index of the subject input information using the first calculation formula, wherein the policy demand dimension includes at least department, time, level, region, keywords and application status;

[0122] S403. Calculate the talent dimension index of the main input information using the second calculation formula, wherein the talent demand dimension includes at least industry, posting time, price, region and supplier attributes;

[0123] S404. Calculate the product dimension index of the main input information using the third calculation formula, wherein the dimensions of the product demand include at least industry, release time, price, region and supplier attributes.

[0124] S405. Calculate the technical dimension index of the main input information using the fourth calculation formula, wherein the dimensions of the technical requirements include at least industry, release time, price, region, supplier attributes, and patent technology resources.

[0125] S406. Calculate the service dimension index of the main input information using the fifth calculation formula, wherein the dimensions of the service demand include at least industry, release time, price, region, supplier attributes, and instrument and equipment sharing.

[0126] S407. Calculate the resource dimension index of the main input information using the sixth calculation formula, wherein the dimensions of the resource demand include at least industry, release time, price, region, supplier attributes, service resources and shared resources;

[0127] The first calculation formula is:

[0128]

[0129] Among them, Z T Z is the policy dimension index input by the subject for the Tth time, where i is the policy dimension number. i_T Let n be the policy score corresponding to the i-th policy dimension number of the T-th subject input. i Total number of policy dimensions;

[0130] The second calculation formula is:

[0131]

[0132] Among them, R T Let R be the talent dimension index input by the subject for the Tth time, where x is the talent dimension number and R is the index for the Tth input. x_T For the x-th talent dimension ID input by the T-th subject, n is the talent dimension index corresponding to the talent dimension ID. x Total number of talents;

[0133] The third calculation formula is:

[0134]

[0135] Among them, C T For the Tth subject input, z is the product dimension index, and C is the product dimension number. z_T For the product dimension index corresponding to the z-th product dimension number input by the T-th subject, n z This represents the total number of product dimensions.

[0136] The fourth calculation formula is:

[0137]

[0138] Among them, J T Let y be the technical dimension index of the T-th input subject, and J be the technical dimension number. y_T For the y-th technical dimension number input by the T-th subject, n is the technical dimension index. y Total number of technical dimensions;

[0139] The fifth calculation formula is:

[0140]

[0141] Among them, F T For the Tth input subject, the service dimension index is p, where p is the service dimension number, and F is the service dimension index. p_T For the service dimension index corresponding to the p-th service dimension number input by the T-th subject, n p Total number of service dimensions;

[0142] The sixth calculation formula is:

[0143]

[0144] Among them, Y T Let J be the resource dimension index of the Tth input subject, l be the resource dimension number, and J be the index of the Tth input subject. l_T For the Tth input, the resource dimension index corresponding to the l-th resource dimension number, n l This represents the total number of resource dimensions.

[0145] In this embodiment of the invention, after obtaining specific input data, the subject input for each instance is determined. It is then determined whether all the words include the corresponding indicator parameters in the resource dimension library. If so, the corresponding resource indicator index is set. For example, in the T-th subject input, assuming that the corresponding resource indicators include industry, release time, price, region, supplier attributes, service resources, and shared resources, and the corresponding word types are numbered 3, 4, 5, 6, 7, and 2 respectively, if the occurrence frequency of the corresponding words is 1, 1, 1, 1, 1, 2, then the corresponding resource dimension index is 3+4+5+6+7+2×2=29.

[0146] In the process of obtaining the aforementioned policy needs, talent needs, product needs, technology needs, service needs, and resource needs, there are binding influences on the talent needs and technology needs, and the policy needs and resource needs. The specific process includes:

[0147] Set talent demand thresholds and policy demand thresholds;

[0148] The technical dimension index for calculating the Tth subject input is updated using the first online correction formula.

[0149] The policy dimension index of the subject input for the Tth time is updated using the second online correction formula;

[0150] The first online correction formula is:

[0151]

[0152] Among them, J T 'L' represents the updated technical dimension index of the T-th subject input. 1 The threshold for talent demand;

[0153] The second online correction formula is:

[0154]

[0155] Among them, Y T 'L' is the updated resource dimension index for the T-th subject input. 2 This represents the threshold for policy demand.

[0156] In this embodiment of the invention, updates to the technology dimension index and resource dimension index based on talent demand threshold and policy demand threshold are provided. These updates are real-time and linked, and the technology dimension index and resource dimension index used in subsequent calculations will be the updated data.

[0157] In this embodiment of the invention, it should be noted that the updating of the technology dimension index and resource dimension index based on the talent demand threshold and policy demand threshold can also be reversed. Based on the margin of technology and policy, the updating of the talent dimension index and policy dimension index can also be based on the policy demand threshold to update the service dimension index and resource dimension index.

[0158] Figure 5 This is a flowchart illustrating how an intelligent matching method for industrial resources, according to an embodiment of the present invention, obtains all historical optimal demand data, performs feature parameter analysis, and acquires a set of target correction training coefficients.

[0159] like Figure 5 As shown, in one or more embodiments, preferably, the step of obtaining all historical optimal demand data, performing feature parameter analysis, and obtaining a set of target correction training coefficients specifically includes:

[0160] S501. Extract all historical data from the main input and use the final recommended policies, talents, products, technologies, services and resources as historical data groups;

[0161] S502. Based on the historical data set, the seventh calculation formula is used to calculate the first, second, third, fourth, fifth and sixth target correction training coefficients, which together form a set of target correction training coefficients.

[0162] The seventh calculation formula is:

[0163]

[0164] Where L represents the historical number of main inputs, j represents the number of main inputs, argmin() is the target correction training coefficient that minimizes the formula, and k 10 k 20 k 30 k 40 k 50 and k 60 To adjust the training coefficients Z for the first, second, third, fourth, fifth, and sixth objectives. T_j R represents the recommended value of the policy dimension index for the j-th subject input. T_j J represents the recommended value of the talent dimension index at the j-th subject input. T_j C represents the recommended value of the technology dimension index for the j-th subject input. T_j Y is the recommended value of the product dimension index for the j-th subject input. T_j F represents the recommended value of the resource dimension index for the j-th input. T_j Z is the recommended value of the service dimension index for the j-th subject input. X_j R represents the actual selected value of the policy dimension index at the j-th subject input. X_j J represents the actual selected value of the talent dimension index at the j-th subject input. X_j C represents the actual selected value of the technology dimension index at the j-th subject input. X_j Y represents the actual selected value of the product dimension index during the j-th subject input. T_j F represents the actual selected value of the resource dimension index at the j-th subject input. T_j is the actual selected value of the service dimension index when the subject inputs for the jth time, and k1, k2, k3, k4, k5 and k6 are the first, second, third, fourth, fifth and sixth corrected training coefficients.

[0165] In this embodiment of the invention, specific parameters are trained based on all historical data. The parameters obtained from the training are the first, second, third, fourth, fifth, and sixth target coefficients. These target coefficients can be used more accurately as resource feature parameters to recommend to users.

[0166] Figure 6This is a flowchart illustrating the process of calculating the attribute-based matching degree of all resource and demand information based on a set of target-corrected training coefficients in an intelligent matching method for industrial resources according to an embodiment of the present invention.

[0167] like Figure 6 As shown, in one or more embodiments, preferably, the step of calculating the attribute suggestion matching degree of all resource and demand information based on the set of target-corrected training coefficients specifically includes:

[0168] S601. Obtain the first, second, third, fourth, fifth, and sixth target correction training coefficients;

[0169] S602. Calculate the matching degree of the attribute hints using the eighth calculation formula;

[0170] S603. Sort the matching degree of the attribute prompts from largest to smallest, and recommend 20 consecutive alternative solutions to the subject.

[0171] The eighth calculation formula is:

[0172] T Z =k 10 Z T_j +k 20 R T_j +k 30 J T_j +k 40 C T_j

[0173] Among them, T Z The degree of matching is indicated for the attribute.

[0174] In this embodiment of the invention, after determining the optimal policy, talent, technology and product recommendations for each time, 20 consecutive alternative solutions are intelligently recommended to the user in descending order of the resource feature parameters.

[0175] Figure 7 This is a flowchart illustrating how, in an embodiment of the present invention, a user provides matching information on policies, talent, products, technologies, services, and resources to a subject based on the attribute-based matching degree, from highest to lowest.

[0176] like Figure 7 As shown, in one or more embodiments, preferably, the user provides the subject with matching information on policies, talent, products, technologies, services, and resources in descending order of the matching degree indicated by the attribute prompts, specifically including:

[0177] S701. Obtain the feature margin of the main input;

[0178] S702. Select the attribute suggestion matching degree with the number of feature margins in the sorting of attribute suggestion matching degree from large to small, and recommend the corresponding policies, talents, products, technologies, services and resources to the subject.

[0179] In this embodiment of the invention, in order to enable the subject to obtain the optimal alternative and automatically match the expected needs, the subject can select the total number of recommended options, sort them from largest to smallest according to the previously designed resource feature parameters, and obtain the final recommendations of different resources.

[0180] According to a second aspect of the present invention, an intelligent matching system for industrial resources is provided.

[0181] Figure 8 This is a structural diagram of an intelligent matching system for industrial resources according to an embodiment of the present invention.

[0182] In one or more embodiments, preferably, the intelligent matching system for industrial resources includes:

[0183] Resource collection module 801 is used to establish a standardized attribute database for industrial resources;

[0184] The subject confirmation module 802 is used for the subject to input information, complete the analysis of the subject's needs and assign specific attributes, including policy needs, talent needs, product needs, technology needs, service needs and resource needs;

[0185] The demand publishing module 803 is used to perform online analysis based on the policy demand, talent demand, product demand, technology demand, service demand and resource demand to obtain the current optimal demand recommendation;

[0186] The attribute analysis module 804 is used to obtain all historical optimal demand data, perform feature parameter analysis, and obtain a set of target correction training coefficients.

[0187] The attribute matching module 805 is used to calculate the attribute prompt matching degree of all resource and demand information based on the set of target correction training coefficients.

[0188] The information push module 806 is used to provide the subject with matching information on policies, talents, products, technologies, services and resources in descending order of the matching degree indicated by the attribute prompts.

[0189] In this embodiment of the invention, the collection, confirmation and publication of different resources are realized through rapid information recommendation. The final resource recommendation adopts a modular design to achieve efficient intelligent acquisition.

[0190] According to a third aspect of the present invention, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the method as described in any one of the first aspects of the present invention.

[0191] According to a fourth aspect of the present invention, an electronic device is provided. Figure 9 This is a structural diagram of an electronic device according to one embodiment of the present invention. Figure 9 The electronic device shown is a general-purpose intelligent matching device for industrial resources. This electronic device can be a smartphone, tablet, or other similar device. As shown, the electronic device 900 includes a processor 901 and a memory 902. The processor 901 and memory 902 are electrically connected. The processor 901 is the control center of the terminal 900, connecting various parts of the terminal through various interfaces and lines. By running or calling computer programs stored in the memory 902, and by calling data stored in the memory 902, it executes various functions of the terminal and processes data, thereby performing overall monitoring of the terminal.

[0192] In this embodiment, the processor 901 in the electronic device 900 loads the instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 runs the computer programs stored in the memory 902 to achieve various functions: establishing a standardized attribute database of industrial resources; the subject inputs information, completes the analysis of the subject's needs and assigns specific attributes, the specific attributes including policy needs, talent needs, product needs, technology needs, service needs and resource needs; based on the policy needs, talent needs, product needs, technology needs, service needs and resource needs, online analysis is performed to obtain the current optimal demand recommendation; all historical optimal demand data are obtained, feature parameter analysis is performed, and a set of target correction training coefficients are obtained; based on the set of target correction training coefficients, the attribute prompt matching degree of all resources and demand information is calculated; the user provides the subject with corresponding policy, talent, product, technology, service and resource matching information according to the attribute prompt matching degree from large to small.

[0193] Memory 902 can be used to store computer programs and data. The computer programs stored in memory 902 contain instructions that can be executed in the processor. Computer programs can be composed of various functional modules. Processor 901 executes various functional applications and data processing by calling the computer programs stored in memory 902.

[0194] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0195] The present invention allows the entity to perform bidirectional matching between resource providers and demanders, and can submit multiple different matching requests simultaneously, satisfying the entity's demand for multiple industry resources and the supply of resources to multiple demanders, ensuring that the entity responds to market information in a timely manner and seizes market opportunities.

[0196] The present invention employs a standardized attribute database, where resources and requirements can be assigned specific attributes. Based on unified attributes, the accuracy of matching can be better ensured. At the same time, the system will indicate the degree of matching and use the degree of matching as the basis for data presentation and arrangement.

[0197] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0198] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0199] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0200] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0201] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for intelligent matching of industrial resources, characterized in that, The method includes: During the information matching process, an intelligent matching mode is adopted. After the subject completes the input of the matching requirements, the matching data is continuously and automatically retrieved. The retrieval can be carried out automatically after the subject closes the operation page, and the matching data can be retrieved and the subject is notified and the matching results are presented as soon as the matching data appears in the database. The system facilitates bidirectional matching between resource providers and demanders, allowing for the submission of multiple matching requests simultaneously. This satisfies the entity's need for resources across various industries and its supply of resources to multiple demanders, ensuring timely market response. A standardized attribute database is used, and resources and demands are assigned normalized specific attributes. These attributes are then normalized to indicate the degree of matching, and the data presentation is based on the level of matching indicated by these attributes. The specific process includes: Establish a standardized attribute database for industrial resources; The subject inputs information, completes the analysis of the subject's needs, and assigns specific attributes, including policy needs, talent needs, product needs, technology needs, service needs, and resource needs; Based on the policy needs, talent needs, product needs, technology needs, service needs, and resource needs, online analysis is conducted to obtain the current optimal demand recommendation; Obtain all historical optimal demand data, perform feature parameter analysis, and obtain a set of target correction training coefficients; Calculate the attribute matching degree of all resource and demand information based on the set of target-corrected training coefficients; Users provide the subject with matching information on policies, talents, products, technologies, services, and resources, ranked from highest to lowest based on the matching degree indicated by the aforementioned attributes. Specifically, obtaining all historical optimal demand data, performing feature parameter analysis, and obtaining a set of target correction training coefficients includes: Extract all historical data from the main input, and use the final recommended policies, talents, products, technologies, services and resources as a historical data set; The first, second, third, fourth, fifth, and sixth target correction training coefficients are obtained by using the seventh calculation formula based on the historical data set. The first, second, third, fourth, fifth, and sixth target correction training coefficients are a set of target correction training coefficients. The seventh calculation formula is: in, L For the number of main inputs in history, j Input the number of times as the main body. argmin () is used to adjust the training coefficients to minimize the formula. k 10 , k 20 , k 30 , k 40 , k 50 and k 60 Adjust the training coefficients for the first, second, third, fourth, fifth, and sixth objectives. Z T_j For the first j Recommended values ​​for the policy dimension index when inputting secondary subjects. R T_j For the first j Recommended value for the talent dimension index when inputting secondary subjects. J T_j For the first j Recommended values ​​for the technical dimension index when inputting secondary subjects. C T_j For the first j Recommended values ​​for product dimension indices when inputting secondary user data. Y T_j For the first j Recommended values ​​for the resource dimension index when inputting secondary subjects. F T_j For the first j Recommended values ​​for the service dimension index when inputting secondary data. Z X_j For the first j The actual selected value of the policy dimension index when inputting secondary subjects. R X_j For the first j The actual selected value of the talent dimension index when inputting the secondary subject. J X_j For the first j The actual selected value of the technical dimension index when inputting the secondary subject. C X_j For the first j The actual selected value of the product dimension index when inputting the secondary subject. Y T_j For the first j The actual selected value of the resource dimension index when inputting the secondary subject. F T_j For the first j The actual selected value of the service dimension index when inputting the secondary subject. k 1. k 2. k 3. k 4. k 5 and k 6 represents the first, second, third, fourth, fifth, and sixth corrected training coefficients; Specifically, the step of calculating the attribute suggestion matching degree of all resource and demand information based on the set of target-corrected training coefficients includes: Obtain the corrected training coefficients for the first, second, third, fourth, fifth, and sixth targets; The matching degree of the attribute hints is calculated using the eighth calculation formula; The attribute suggestions are sorted from highest to lowest matching degree, and 20 consecutive alternative solutions are recommended to the subject. The eighth calculation formula is: T Z = k 10 Z T_j + k 20 R T_j + k 30 J T_j + k 40 C T_j in, T Z The degree of matching is indicated for the attribute.

2. The intelligent matching method for industrial resources as described in claim 1, characterized in that, The establishment of a standardized attribute database for industrial resources specifically includes: Obtain all policy documents, technical personnel information, technical introductions, and product introductions as raw input information; Based on the original input information, data is stored in the same form under the same type and saved as a standardized attribute database.

3. The intelligent matching method for industrial resources as described in claim 2, characterized in that, The entity inputs information to complete the analysis of its needs and assign specific attributes. These specific attributes include policy needs, talent needs, product needs, technology needs, service needs, and resource needs, specifically including: The standardized attributes of industrial resources provided in the standardized attribute database, the specific attributes actively confirmed and assigned by the providers of industrial resources, and all resource data are summarized, organized and stored in the standardized attribute database according to the attributes. When a requirement is published, specific attributes are assigned according to the different types and contents of the requirement, and the requirement is summarized, organized and stored in a standardized attribute database. The subject can independently select the attributes to be matched, and at the same time select the attributes of resources and needs, and then search in the standardized attribute database.

4. The intelligent matching method for industrial resources as described in claim 1, characterized in that, The process of obtaining the optimal demand recommendation through online analysis based on the policy needs, talent needs, product needs, technology needs, service needs, and resource needs specifically includes: After obtaining the subject's input information, the system automatically extracts the vocabulary from the subject's input information to obtain the policy requirements, talent requirements, product requirements, technology requirements, service requirements, and resource requirements. The policy dimension index of the subject input information is calculated using the first calculation formula, wherein the policy demand dimension includes at least department, time, level, region, keywords and application status; The talent dimension index of the main input information is calculated using the second calculation formula, wherein the dimensions of talent demand include at least industry, posting time, price, region and supplier attributes; The product dimension index of the main input information is calculated using a third calculation formula, wherein the dimensions of the product demand include at least industry, release time, price, region, and supplier attributes; The fourth calculation formula is used to calculate the technical dimension index of the main input information, wherein the dimensions of the technical demand include at least industry, release time, price, region, supplier attributes and patent technology resources; The service dimension index of the main input information is calculated using the fifth calculation formula, wherein the dimensions of the service demand include at least industry, release time, price, region, supplier attributes, and instrument and equipment sharing; The resource dimension index of the main input information is calculated using the sixth calculation formula, wherein the dimensions of the resource demand include at least industry, release time, price, region, supplier attributes, service resources and shared resources; The first calculation formula is: in, Z T For the first T The policy dimension index of secondary subject input. i Number the policy dimensions. Z i_T For the Tth input of the main body i Each policy dimension number corresponds to a policy score. n i Total number of policy dimensions; The second calculation formula is: in, R T For the first T The talent dimension index of the secondary subject input. x Number the talent dimension R x_T For the Tth input of the main body x Each talent dimension number corresponds to a talent dimension index. n x Total number of talents; The third calculation formula is: in, C T For the first T The product dimension index is input by the secondary subject. z Number the product dimensions. C z_T For the first T The second main input z The product dimension index corresponding to each product dimension number. n z This represents the total number of product dimensions. The fourth calculation formula is: in, J T For the first T The technical dimension index of the secondary subject input. y Number the technical dimensions. J y_T For the first T The second main input y Each technical dimension number corresponds to a technical dimension index. n y Total number of technical dimensions; The fifth calculation formula is: in, F T For the first T The service dimension index of the secondary subject input. p Number the service dimensions. F p_T For the first T The second main input p The service dimension index corresponding to each service dimension number. n p Total number of service dimensions; The sixth calculation formula is: in, Y T For the first T Resource dimension index of secondary subject input. l Number the resource dimensions. J l_T For the first T The second subject input to the first l The resource dimension index corresponding to each resource dimension number. n l This represents the total number of resource dimensions.

5. The intelligent matching method for industrial resources as described in claim 1, characterized in that, The user provides the subject with matching information on policies, talent, products, technologies, services, and resources, ranked from highest to lowest based on the matching degree indicated by the attribute prompts. Specifically, this includes: Obtain the feature margin of the main input; The system selects the attribute suggestion matching degree based on the number of feature margins in the attribute suggestion matching degree sorted from largest to smallest, and recommends the corresponding policies, talents, products, technologies, services and resources to the subject.

6. An intelligent matching system for industrial resources, characterized in that, The system is used to implement the method as described in any one of claims 1-5, the system comprising: The resource collection module is used to establish a standardized attribute database for industry resources; The subject confirmation module is used for subjects to input information, complete the analysis of subject needs and assign specific attributes, including policy needs, talent needs, product needs, technology needs, service needs and resource needs; The demand publishing module is used to perform online analysis based on the policy demand, talent demand, product demand, technology demand, service demand, and resource demand to obtain the current optimal demand recommendation. The attribute analysis module is used to obtain all historical optimal demand data, perform feature parameter analysis, and obtain a set of target correction training coefficients. The attribute matching module is used to calculate the attribute matching degree of all resource and demand information based on the set of target-corrected training coefficients; The information push module is used to provide users with matching information on policies, talents, products, technologies, services and resources to the subject in descending order of the matching degree indicated by the attribute prompts.

7. A computer-readable storage medium storing computer program instructions thereon, characterized in that, The computer program instructions, when executed by a processor, implement the method as described in any one of claims 1-5.

8. An electronic device comprising a memory and a processor, characterized in that, The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in any one of claims 1-5.

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