Path planning method, electronic device, storage medium and program product

Abstract

The embodiments of the present application provide a path planning method, electronic device, storage medium and program product. Relating to the field of talent introduction path planning, the method includes: establishing multiple sub-networks, the multiple sub-networks are generated based on the information of candidate objects provided by the corresponding cooperative institutions, and the multiple sub-networks are stored in different network storage nodes in the blockchain; when there is no single network path from the known object to the current target object in the multiple sub-networks, each cross-network node set in the blockchain obtains multiple sub-networks respectively; using each cross-network node, searching based on the multiple sub-networks to determine the target path from the known object to the current target object; based on the target paths obtained by the multiple cross-network nodes, the final path is determined by majority consensus. The present application solves the technical problem that it is difficult to balance the contradiction between data security and data sharing in the related art, and thus the accuracy of talent introduction path planning is not ideal.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a path planning method, electronic device, storage medium, and program product. Background Art

[0002] In today's fiercely competitive world, talent is the core driving force behind scientific and technological progress and industrial development. Accurately identifying teams with high potential and designing appropriate recruitment pathways for them is crucial. With the rapid advancement of technology, talent recruitment and recruitment pathway planning have shifted from a talent discovery approach based on experience and connections to data-driven, intelligent solutions.

[0003] On the one hand, the cost of trust between institutions in related technologies is high, and complex technical interfaces need to be established to ensure data authenticity. The sub-network data of each institution cannot be securely communicated with each other, and cross-network path planning needs to rely on cumbersome data synchronization protocols, which are inefficient and prone to errors. Therefore, there is a problem of unsatisfactory accuracy in talent path planning in related technologies.

[0004] To address the above problems, no solution has been proposed in the relevant technologies so far. Summary of the Invention

[0005] The embodiments of the present application provide a path planning method, electronic device, storage medium and program product to alleviate or resolve the technical problem in related technologies of difficulty in balancing data security and data sharing, and thus the unsatisfactory accuracy of talent path planning.

[0006] In a first aspect, an embodiment of the present application provides a path planning method, including:

[0007] Establish multiple sub-networks, each of which is generated based on the information of candidate objects provided by the corresponding cooperative institutions, and each of which is stored in different network storage nodes in the blockchain;

[0008] In the case where there is no single network path from the known object to the current target object in multiple sub-networks, each cross-network node set up in the blockchain obtains multiple sub-networks respectively, where the known object is a candidate object with a contact path among the multiple candidate objects, and the current target object is the object to be contacted among the multiple candidate objects;

[0009] Using each cross-network node, searching based on multiple sub-networks, determine the target path from the known object to the current target object;

[0010] Based on the target paths obtained by multiple cross-network nodes, the final path is determined by majority consensus.

[0011] In a second aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements any method of the embodiment of the present application when executing the computer program.

[0012] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of any one of the embodiments of the present application is implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements any method of the embodiments of the present application when executed by a processor.

[0014] Based on the path planning method of the first aspect described above, this application has at least the following beneficial effects or advantages: Subnetwork data maintained by each partner organization (such as collaboration records and educational background) is stored on-chain, and all modifications are subject to consensus verification. This prevents a single organization from forging or tampering with relationships, ensuring the credibility of the data underlying the talent recruitment path. Blockchain permission control allows cross-network nodes to securely access subnetwork data from other organizations. When cross-network nodes authorize access to heterogeneous subnetworks through smart contracts, zero-knowledge proof technology is used to verify query permissions without directly sharing the original database. Each cross-network node searches across multiple subnetworks to determine the target path from a known object to the current target object. Each cross-network node independently calculates the target path based on the local topology and selects the final path through majority consensus. A redundant calculation mechanism can identify and eliminate the possibility of tampering with guidance at individual cross-network nodes, thereby improving the robustness of the talent recruitment path. By incorporating a blockchain mechanism, all subnetwork information queries and cross-network node consensus votes are stored on-chain, enabling traceability of the path decision process and avoiding the drawbacks of closed-loop talent recruitment, such as recommendations from acquaintances.

[0015] The above description is only an overview of the technical solution of this application. In order to more clearly understand the technical means of this application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of this application more obvious and easy to understand, the specific implementation methods of this application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments according to the present application and should not be regarded as limiting the scope of the present application.

[0017] Figure 1 A flow chart showing a path planning method according to an embodiment of the present application is shown;

[0018] Figure 2 A schematic diagram showing a path planning method according to an embodiment of the present application is shown;

[0019] Figure 3 A schematic block diagram of a path planning device according to an embodiment of the present application is shown;

[0020] Figure 4 A block diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0021] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive.

[0022] To facilitate understanding of the technical solutions of the embodiments of the present application, the following describes the related technologies of the embodiments of the present application. The following related technologies can be combined with the technical solutions of the embodiments of the present application as optional solutions, and all of them fall within the scope of protection of the embodiments of the present application.

[0023] It should be noted that the application scenarios or application examples provided in the embodiments of this application are for ease of understanding, and the embodiments of this application do not specifically limit the application of the technical solution. In addition, the user information (including but not limited to user device information, user personal information, relevant information of multiple candidate objects, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, historical path records, data stored in the blockchain, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation portals for users to choose to authorize or refuse.

[0024] The following describes in detail the technical solution of this application and how it solves the aforementioned technical problems using specific embodiments. The specific embodiments listed may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. The following describes the embodiments of this application in detail with reference to the accompanying drawings.

[0025] Figure 1 A flow chart of the path planning method according to an embodiment of the present application is shown. Figure 1 As shown, the method may include steps S101 to S104.

[0026] Step S101: Establish multiple sub-networks, each of which is generated based on information about candidate objects provided by a corresponding cooperative organization, and each of which is stored in different network storage nodes in the blockchain;

[0027] Step S102: If there is no single network path from the known object to the current target object in the multiple sub-networks, each cross-network node set in the blockchain obtains the multiple sub-networks respectively, where the known object is a candidate object with a contact path among the multiple candidate objects, and the current target object is the object to be contacted among the multiple candidate objects;

[0028] Step S103: using each cross-network node to search based on multiple sub-networks to determine a target path from the known object to the current target object;

[0029] Step S104: Based on the target paths obtained by multiple cross-network nodes, a final path is determined by majority consensus.

[0030] In the embodiments of the present application, the aforementioned execution entity may be a control node in a blockchain. The blockchain ensures the reliability and immutability of path information through its consensus mechanism. The decentralized nature of the blockchain makes the path planning process more transparent and trustworthy. Furthermore, smart contracts enable automated and highly reliable dynamic updates of multiple subnetworks and automatic path planning. The permissions for each of the aforementioned cross-network nodes are set to allow access to any network storage node in the blockchain.

[0031] In an embodiment of the present application, multiple subnetworks are generated based on candidate object information provided by different partner organizations, and these subnetworks are stored on different network storage nodes of the blockchain. The information provided by different partner organizations, such as one organization providing academic cooperation data and another providing professional association data, generates corresponding partnership subnetworks and professional association subnetworks, respectively, to ensure the diversity and independence of data sources, and utilize blockchain storage to ensure data security and non-tamperability. When a path from a known object to the current target object does not exist within a single network, multiple cross-network nodes set up in the blockchain begin to work. These cross-network nodes have access to any network storage node in the blockchain and can obtain information from each subnetwork to prepare for cross-network path search.

[0032] During the path search phase, each cross-network node searches multiple subnetworks, starting from known objects, to find multiple candidate paths to the current target object. Known objects are candidates with existing connections among multiple candidate objects. If talent information is considered a database, known objects can be considered a talent inventory. To evaluate the quality of these candidate paths, a path scoring mechanism is introduced. The path score calculation takes into account the strength of the associations between candidate objects and a predetermined decay factor. The association strength is dynamically adjusted based on historical path records. This historical path record includes the paths used to successfully connect with the target object, which serves as prior knowledge of feasible paths. This dynamic adjustment mechanism enables the system to continuously optimize the weights of associations based on actual conditions. The decay factor represents how the path score decreases with increasing path length. As path length increases, the number of intermediate candidate objects increases, leading to a decrease in the trustworthiness of the target object. By balancing path length and path quality, and avoiding the problem of reduced trustworthiness caused by excessive intermediate links, multiple candidate paths are pruned based on the path score to select the optimal target path. After multiple cross-network nodes have determined their target paths, they use majority consensus to determine the final path. If the majority of cross-network nodes agree on the target path, or if a particular path appears the most times, that path is chosen as the final path. The results from multiple nodes are combined to improve the reliability and accuracy of the path.

[0033] For example, the above-mentioned cooperative institutions can be schools, enterprises, academic organizations, etc., and the above-mentioned multiple candidates can be represented as talents or experts in the application scenario of talent introduction. It should be noted that the sub-network data maintained by different cooperative institutions often involve different fields or types of information. Each cooperative institution can formulate personalized confidentiality measures for the edge server based on its own data characteristics and security requirements. The institution responsible for maintaining the educational background sub-network can adopt stricter access control policies for sensitive data such as student information. Only authorized personnel can access talent data of specific academic levels or institutions, reducing the risk of sensitive data leakage and protecting the privacy of cooperative institutions and related personnel.

[0034] When adding a new partner institution, simply deploy a new subnetwork smart contract and update the cross-node query interface, without having to restructure the global network. The modular design makes each subnetwork relatively independent. Each partner institution's data is encapsulated in its own subnetwork, and data is retrieved and queried through edge servers deployed on the partner institution's side. This encapsulation mechanism ensures that the addition of a new subnetwork will not impact existing subnetworks, allowing each subnetwork to operate and be maintained independently. For example, the addition of a corporate partner institution will not disrupt the subnetwork of an existing academic partner institution. The two interact through cross-network nodes, but their internal data structures and management logic remain independent.

[0035] According to the embodiment provided by this application, in step S101: establishing multiple sub-networks includes the following specific steps:

[0036] For the association relationship of the cooperative relationship type, based on the academic achievement information corresponding to the multiple candidate objects, determine the cooperative object corresponding to each candidate object from the multiple candidate objects;

[0037] Determine the intensity of cooperation between each candidate and its corresponding partner based on the number of collaborations and citations of each candidate's achievements;

[0038] A subnetwork of cooperative relationship type is established by taking multiple candidate objects as nodes, the connection between each candidate object and the corresponding cooperative object as an edge, and the cooperation intensity as the edge weight of the edge.

[0039] In the embodiment provided in the present application, multiple candidate objects are traversed, and for each candidate object, its academic achievement information (such as papers, projects, etc.) is analyzed to extract other objects that have completed academic achievements together with the candidate object. These objects are the collaborators of the candidate object. For each candidate object and its corresponding collaborator, the number of collaborations between the two is counted. The number of collaborations can be measured by the number of jointly published papers, the number of jointly participated projects, etc. Multiple candidate objects are used as nodes, and for each candidate object and its corresponding collaborator, the connection between them is used as an edge. The calculated cooperation intensity is used as the edge weight, and eventually a subnetwork is formed with the candidate objects as nodes, the connection between the collaborators as edges, and the cooperation intensity as the edge weight.

[0040] For example, in the process of constructing a cooperative relationship sub-network, author cooperation information is extracted from the paper data, which includes analyzing the author list of each academic achievement to determine the authors and their collaborators, and establishing the relationship between the authors and collaborators based on this. The above-mentioned academic achievements include at least papers, patents, academic works (including software works), research reports, technical standards and specifications formulated with the participation of experts, etc. When calculating the intensity of cooperation, the frequency of cooperation and the quality of the cooperative results are comprehensively considered for network modeling. In the cooperative relationship sub-network (i.e., in the form of a graph), each candidate object is defined as a node. If two nodes have cooperated in at least one paper or patent, an edge is established between the two nodes, and the weight of the edge is used to represent an indicator of the intensity of cooperation between the two nodes, providing data support for subsequent talent recruitment path planning.

[0041] For example, the calculation method for the above-mentioned collaboration intensity can be based on factors such as the frequency of collaboration and the quality of collaborative achievements (such as the number of paper citations and the impact of patents), so as to more accurately assess the strength of the relationship between collaborators. For example, the method of allocating contribution weights based on the ranking of candidates in academic achievements is illustrated. The specific calculation formula is as follows:

[0042]

[0043] in, represents the weight of candidate object i, The candidate's ranking position (counting starts from 1), and n represents the total number of authors of the academic work. This emphasizes that authors with higher rankings have made greater contributions.

[0044] The formula for calculating the average contribution of two authors in a single paper is as follows:

[0045]

[0046] in, and They are used to represent different candidate objects. and Indicates the average contribution.

[0047] The calculation method of the above cooperation intensity can be expressed as:

[0048] Cooperation intensity = number of collaborative papers × average number of cited papers × average contribution + number of collaborative patents × technological influence;

[0049] The above calculations measure the collaboration intensity between two collaborators on a specific collaborative project (paper or patent). The number of collaborative papers and the number of collaborative patents represent the number of collaborations on papers and patents, respectively. The average number of citations per paper and the average contribution per author represent the average number of citations per paper and the average contribution per author, respectively. The technical impact represents the technical influence of the patents.

[0050] For example, the intensity of cooperation can also be corrected by using a citation decay factor. The citation decay factor is used to represent the decay of the number of citations of academic achievements over time, which can more accurately reflect the timeliness and influence of academic achievements. As time goes by, some early research may gradually be replaced by new research results, and their citations will decrease accordingly. By introducing the decay factor, the actual influence of academic achievements in different time periods can be more reasonably evaluated. There are differences in citation behavior and citation half-life in different disciplines. The citation decay factor can be adjusted according to the characteristics of the discipline to better adapt to the citation patterns of different disciplines. The preferred setting is that the weight of cooperation in the past three years is 1, and the weight of 3-5 years is 0.7.

[0051] According to the embodiment provided by this application, in step S101: establishing multiple sub-networks includes the following specific steps:

[0052] For an association relationship of the educational background matching type, based on the educational background information corresponding to the multiple candidate objects, a background matching object associated with the educational background of each candidate object is determined from the multiple candidate objects;

[0053] Determining the strength of association between each candidate object and the corresponding background matching object based on the degree of overlap in educational background between each candidate object and the corresponding background matching object;

[0054] A subnetwork of educational background matching relationship is established by taking multiple candidate objects as nodes, the connection between each candidate object and the corresponding background matching object as an edge, and the association strength as the edge weight of the edge.

[0055] In the embodiment provided in the present application, based on the extracted educational background information, other candidate objects that have similarities or the same educational background as each candidate object are identified from multiple candidate objects. These objects are background matching objects. For each candidate object and its corresponding background matching object, the association strength is calculated based on the degree of overlap in educational background. The degree of overlap may include common graduation schools, the same majors, the existence of the same courses across majors, etc. The association strength can be quantified by a set algorithm. For example, different weights can be given based on the amount and importance of the common educational background, and then the numerical value of the association strength is calculated. Multiple candidate objects are regarded as nodes in the network. If there is an educational background matching relationship between two nodes (i.e., two candidate objects), an edge is established between the two nodes. The calculated association strength is used as the edge weight to represent the degree of educational background matching between the two candidate objects, and a sub-network of educational background matching is obtained.

[0056] For example, direct classmate relationships are extracted from educational background information. If two candidates attend the same school, major, grade, or class, they are considered directly classmate related. Indirect classmate relationships consider broader educational connections. For example, candidates from different majors or grades may also have an indirect classmate relationship if they participated in the same course, project, or activity.

[0057] Preferably, different weights can be set according to the relationship between classmates, for example, classmates are set to 1.0, students with the same tutor but different grades are set to 0.7, and students participating in the same project at school are set to 0.5.

[0058] In the embodiment provided in the present application, expert team discovery can also be performed based on the above-mentioned multiple sub-networks. The collected data is analyzed to discover talent teams with potential. By constructing an analysis model, applying scientific metrology methods, such as paper contribution, H-index, G-index, etc., and utilizing the graph structure characteristics of the knowledge graph, path analysis, community detection and other operations are performed to discover expert talent teams. The H-index and G-index are two indicators used to measure the influence of academic researchers, which are based on the number of papers published by the researchers and the frequency with which these papers are cited. Among them, the H-index represents a mixed quantitative indicator that takes into account both the number of papers published by the researchers and the number of citations of these papers. The G-index is an improved version of the H-index, which also takes into account the number of papers and the number of citations, but focuses more on the influence of highly cited papers. Talents whose contribution is greater than a certain threshold and who have published more than 30 papers / patents are regarded as expert talents. By adopting the graph traversal algorithm of depth-first search (DFS) and breadth-first search (BFS) to identify the three-degree relationship network of the expert, those whose contribution is greater than a certain threshold (the contribution threshold is dynamically calculated: the top 30% quantile in the field) are retained as the core talents of the team.

[0059] For example, the team contribution index can be expressed as follows: Contribution = 0.4 × H-index + 0.3 × patent authorization rate + 0.3 × proportion of international collaborative papers; the number of works statistics includes the number of papers published in the past five years, the number of patent authorizations, and the number of major project participations. The KeyBERT algorithm can be used to extract keywords from paper abstracts and patent claims to generate team technical labels. KeyBERT (Keyword Extraction with BERT) is a keyword extraction algorithm based on the BERT (Bidirectional Encoder Representations from Transformers) model. The keyword relevance score is calculated using the following formula:

[0060]

[0061] in, Indicates candidate keywords, Represents document content. A higher semantic similarity score calculated by the BERT model between word t and document d indicates a stronger thematic relevance between word t and document d. Leveraging the real-time processing capabilities of big data, we continuously monitor and update information about talent collaborators, ensuring the timeliness and accuracy of the collaborator network. As new data is added, the knowledge graph is continuously expanded and optimized, dynamically reflecting changes and development trends in the talent collaboration network.

[0062] Figure 2A schematic diagram of a path planning method according to an embodiment of the present application is shown. Figure 2 As shown, an example shows the relationship between a team with a contribution of 91.2, 32 papers, and 23 patents (including 5 PCT patents). The team's technical targets include: gene editing and immune cells. Figure 2 The size of the circles in the figure represents the importance of different objects, and object 7 represents the core person of the team.

[0063] For example, smart contracts will set expert certification standards (e.g., H-index ≥ 30, number of patents ≥ 20, proportion of internationally collaborative papers ≥ 40%), and those who meet these criteria will be automatically designated as expert nodes. Certified data (e.g., papers and patents) must be verified by blockchain consensus before being uploaded to the blockchain to ensure authenticity. The expert node's identity information, certification records, and technical tags are all stored on-chain, and any modifications require node verification through majority consensus.

[0064] In the integrated network, edge weights between expert nodes are automatically increased (e.g., normal collaboration strength × 1.5), prioritizing the construction of high-quality paths through the expert network. When searching across network nodes, if an expert node exists in the target path (e.g., A → Expert → B), a fast consensus channel is triggered, shortening voting time. This fast consensus channel is implemented through a smart contract and is only activated when the path includes an expert node. If the final path includes an expert node and the talent is successfully recruited, the smart contract automatically increases the expert node's association strength. If a path associated with an expert node fails a predetermined number of times (e.g., three no-response attempts), the expert node's authority is automatically devalued (weight × 0.8). Voting can also determine whether to revoke the expert's certification.

[0065] In the embodiment provided in the present application, multiple sub-networks respectively correspond to edge servers, and the permissions of the edge servers are set to allow access to the network storage nodes in the blockchain of the corresponding sub-networks. After executing step S101: establishing multiple sub-networks, the method includes the following specific steps:

[0066] Use multiple edge servers to search in the corresponding sub-networks to determine whether there is a single network path;

[0067] In the case that there is a unique single network path, the single network path is determined as the final path;

[0068] In the case where there are multiple single network paths, determining the path scores corresponding to the multiple single network paths respectively;

[0069] The path with the highest score among multiple single network paths is determined as the final path.

[0070] In the embodiment provided in the present application, a plurality of sub-networks respectively correspond to edge servers, and the edge servers are granted permissions to access the network storage nodes of the corresponding sub-networks in the blockchain. Each edge server searches in the corresponding sub-network to determine whether there is a single network path from a known object to the current target object. In the academic cooperation sub-network, it is determined whether there is a direct or indirect cooperation path from a known academic staff member to a target academic staff member. If, during the search process, it is found that there is only one single network path, then this path will be directly determined as the final path. When there are multiple single network paths, these paths need to be further evaluated to determine the corresponding path score for each single network path.

[0071] Through the above processing, the edge server searches directly in the corresponding sub-network, avoiding concentrating all search tasks on a central node for processing.

[0072] Exemplarily, the calculation of the above-mentioned path score can comprehensively consider multiple factors, such as the credibility of the association relationship between candidate objects, the strength of the association, the length of the path, etc. The priority of the known object as the starting point of the path can also be taken into account in the calculation of the path score. For example, for a known object with stable cooperation experience, it is more inclined to assist in the talent recruitment task. Therefore, the candidate path with it as the starting point can have a higher path score than the candidate path with a known object with no historical cooperation record as the starting point and other conditions are equal. After obtaining the score of each path, the path with the highest score is selected as the final path.

[0073] For example, each edge server can only access its corresponding blockchain subnetwork node through a dedicated channel (e.g., the education network edge server only connects to the education subnetwork node). This isolation at the physical network level ensures that even if a server is compromised, attackers cannot move laterally through the intranet to access data from other subnetworks. Compared to solutions offered by related technologies, in centralized server clusters, once perimeter protection is breached, all data is at risk of being leaked. Furthermore, edge servers in different subnetworks use independent communication certificates and VPN (Virtual Private Network) tunnels. Even if the same partner organization manages multiple servers, cryptographic isolation prevents crosstalk. This approach to edge server configuration effectively addresses the practical need for sharing minutes while maintaining isolation in cross-institutional data collaboration.

[0074] Exemplarily, the search priority of multiple sub-networks can also be determined according to the object type of the target object. Taking the sub-network of cooperative relationship and the sub-network of educational background matching as an example, when the object type of the target object is a student type, the sub-network of educational background matching is determined to be the highest priority among multiple sub-networks. When the object type of the target object is a non-student type, the sub-network of cooperative relationship is determined to be the highest priority among multiple sub-networks. Multiple sub-networks represent different association types, and a single network search can be performed in stages according to the above object types, reducing the number of edge servers that start calculations and effectively reducing the time waiting for calculation feedback.

[0075] For cross-network search needs, such as combining collaborator networks and classmate networks, multiple sub-networks can be integrated.

[0076] According to the embodiment provided by the present application, in step S103: each cross-network node is used to search based on multiple sub-networks to determine the target path from the known object to the current target object. Each cross-network node can similarly perform the following steps:

[0077] A comprehensive network is obtained by fusing multiple sub-networks; the nodes included in the comprehensive network are multiple candidate objects, and the edges included in the comprehensive network represent different types of association relationships among the multiple candidate objects;

[0078] A path search strategy is used to search the integrated network, and the paths passed during the search are recorded as multiple candidate paths. The path search strategy is determined based on the network scale of the integrated network.

[0079] Determining, for each cross-network node, a path score corresponding to each of the plurality of candidate paths based on the strength of associations between the plurality of candidate objects and a predetermined decay factor; the decay factor representing how the path score of the corresponding candidate path decreases as the path length increases, the path length representing the number of candidate objects along the path, and the association strength adjusted based on historical path records, the historical path records including historical paths used to successfully communicate with historical target objects;

[0080] Based on the path scores corresponding to the multiple candidate paths, the multiple candidate paths are pruned to determine the target path obtained by each cross-network node.

[0081] In the embodiments provided herein, the nodes in the integrated network represent multiple candidate objects, while the edges represent different types of relationships between these candidate objects. The integrated network can comprehensively reflect various relationships between candidate objects, such as collaborator relationships, educational background matching, and so on. Network scale involves factors such as the number of nodes and edge density, which influence the choice of search strategy. For example, for large-scale networks, a heuristic search algorithm may be used to improve efficiency, while for smaller networks, a more comprehensive search algorithm can be used to ensure accuracy. A path search strategy is used to search the integrated network, and the paths traversed during the search are recorded. These paths serve as multiple candidate paths. By fusing multiple subnetworks to form the integrated network, it is possible to comprehensively integrate different types of relationships, enabling a more accurate assessment of the quality of candidate paths and providing a richer information foundation for path search. Dynamically selecting a path search strategy based on the scale of the integrated network allows for flexible adjustment of the search method across networks of varying sizes. This dynamic adaptability mechanism ensures search efficiency while also improving path search accuracy.

[0082] By calculating path scores based on both association strength and a decay factor, path quality can be more accurately assessed. Association strength is adjusted based on historical path records, making path selection more informed and prioritizing proven, reliable paths. Furthermore, the inclusion of a decay factor prevents the selection of excessively long paths, improving both path practicality and accuracy. These historical path records provide examples of feasible paths. Intermediate objects, even those not targeting the same target object, have proven to be reliable, providing up-to-date information on the relationships between candidate objects included in the path. Certain intermediate objects have played a key role in past successes, suggesting they may still be highly reliable at this point in time. By analyzing the path characteristics of successful cases, it is possible to identify path structures, combinations of intermediate objects, or relationships that have proven effective in the past. Path pruning effectively reduces the search space, avoiding unnecessary path exploration and thus improving path search efficiency. By pre-determining the potential of nodes, the computational overhead of extensive analysis is avoided.

[0083] For example, historical path records can be considered to utilize backpropagation based on success cases. By analyzing the path characteristics of successful talent recruitment cases, weight parameters are adjusted inversely to optimize the path scoring model. If an intermediate object plays a key role in multiple success cases, its weight can be increased; conversely, if an intermediate object frequently appears in failure cases, its weight can be reduced. The weight update cycle can be set as needed, for example, dynamically adjusting weight parameters based on real-time data feedback to ensure the accuracy and timeliness of the path scoring model.

[0084] For example, the predetermined attenuation factor can be set in a variety of ways, such as exponential attenuation, linear attenuation, etc., and the attenuation function can also be customized according to needs. The linear attenuation formula can be expressed as: attenuation factor = 1-α × path length; where α is the attenuation coefficient, which is a positive number less than 1. The path score of linear attenuation decreases linearly with the increase of path length. The exponential function formula can be expressed as ,in, is the base of natural logarithms, represents the predetermined coefficient, The predetermined coefficient is preferably set to 0.15, which can be obtained by fitting data from historical path records and can better balance the relationship between path length and path quality.

[0085] For example, the integrated network is represented as ,in is a collection of nodes (representing talents), is a set of edges (representing cooperative relationships or classmates). The goal of the secondary path search is to find To the target node The path can be represented as a sequence of nodes ,in , and there are edges connecting adjacent nodes. The length of the path can be measured by the number of edges or the sum of the weights of the edges. The weight can represent the closeness of the cooperative relationship, etc.

[0086] For a network size smaller than a predetermined threshold, it is considered to be applied in a small-scale integrated network. In the optional embodiment provided in this application, a path search strategy is adopted to search the integrated network, including the following methods:

[0087] When the network size is smaller than a predetermined threshold, the path search strategy is determined as follows: starting from a start node representing a known object in the integrated network, searching for a target node representing a target object within a predetermined range;

[0088] If the target node does not exist within the predetermined range, searching the neighboring nodes of the nodes within the predetermined range to determine whether the target node exists among the neighboring nodes;

[0089] If the target node does not exist in the adjacent nodes, the adjacent nodes are used as new starting nodes to search for nodes in a predetermined range;

[0090] The above process is repeatedly executed until the target node exists within the predetermined range or among the adjacent nodes, and the search process is stopped.

[0091] In the embodiment provided herein, the search process begins with a start node representing a known object, and first searches within a predetermined range to see if a target node representing the target object exists. If the target node is not found within the predetermined range, the search continues based on the neighboring nodes of the node within the current range, which can be understood as a breadth-first search to determine whether the target node exists among these neighboring nodes. By using the set of nodes that can be reached by the breadth-first search, it is ensured that nodes within a certain range are quickly covered in the initial stage, that is, nodes within a predetermined number of hops from the start node are preferentially explored.

[0092] If the target node isn't found within the predetermined range, the search strategy shifts to searching the neighboring nodes within the current range. This process, based on the nodes at the current level, further explores the neighboring nodes. This is a depth-first search, prioritizing the in-depth exploration of a specific branch path rather than evenly distributing search resources across multiple branches. This allows for a deeper exploration of the network structure to find the target node.

[0093] If the target node is still not found among these adjacent nodes, then these adjacent nodes are used as new starting nodes and the nodes within the predetermined range are searched again. By continuously expanding the boundaries of the search, it combines the comprehensiveness of breadth-first search with the in-depth nature of depth-first search. By using adjacent nodes as new starting nodes, the search process can gradually expand the breadth of the search while exploring the depth, ensuring that no possible paths are missed. The above process will be repeated until the target node is found within the predetermined range or in the adjacent nodes, at which point the search process stops.

[0094] For example, searching nodes within a predetermined range is a breadth-first search (BFS), while searching branches where adjacent nodes are located is a depth-first search (DFS). DFS and BFS are two graph search algorithms.

[0095] Breadth-first search (BFS) starts at a starting node and expands nodes layer by layer to ensure the shortest path is found. The search process first initializes a queue and enqueues the starting node s. If the queue is not empty, each node v is dequeued and checked to see if it is the target node t. If it is the target node, the found path is returned; if not, all unvisited adjacent nodes of the node are enqueued for further exploration in subsequent steps. BFS's layer-by-layer expansion ensures that the path found when the target node is found is the shortest path, making it suitable for use in scenarios with short paths or shallow network structures.

[0096] Depth-first search (DFS) begins at a starting node and explores a path as deeply as possible until it finds the target node or reaches a node where it cannot proceed any further. During the search, if the current node v is the target node t, the found path is returned. Otherwise, DFS traverses all adjacent nodes u of the current node v and recursively performs DFS operations on each adjacent node. This depth-first search method can quickly and deeply explore paths and is suitable for finding possible paths in complex networks, especially when the paths are long or the network structure is deep.

[0097] In the embodiments provided in the present application, DFS and BFS can be used in combination to fully utilize their respective advantages. First, BFS is used to expand the nodes layer by layer to quickly cover the local range around the starting node to ensure that no possible paths are missed. In each step of BFS, for each node that is dequeued, DFS can be used to deeply explore the path of the node until the target node is found or it is impossible to continue. If the target node is found during the DFS process, the found path is returned; if the target node is still not found after all nodes in the current layer have been explored, the BFS layer-by-layer expansion is continued until the target node is found.

[0098] For example, during the search process, breadth-first search (BFS) and depth-first search (DFS) are alternately used according to certain heuristic rules, and the switching strategy can also be determined based on the current search status. For example, the condition for switching from BFS to DFS can be further set to meet any of the following: when a predetermined number (e.g., three) of high-weight nodes (e.g., cooperation strength greater than 80) appear consecutively in the path, or when the target is not found after BFS reaches a predetermined range (e.g., a node range within four hops).

[0099] The condition for switching from DFS to BFS can be further set to satisfy any one of the following: the DFS recursion depth is greater than a predetermined number of hops, or the path score growth is less than a predetermined amplitude (considered as path growth stagnation).

[0100] To facilitate understanding, let's use the following example to switch from BFS to DFS. For example, if BFS discovers three consecutive nodes with a cooperation strength greater than 80, it considers it a strong relationship chain. This means that three consecutive high-weight connections ("cooperation strength 85 → 92 → 88") appear on the search path. The search immediately switches from breadth-first to depth-first, exploring subsequent nodes along this high-quality path. Alternatively, if BFS fails to find the target after expanding four hops (i.e., after four hops of node expansion from the starting node), the search switches to DFS mode, prioritizing the highest-weighted branch within the existing path.

[0101] In order to avoid invalid penetration, when the DFS recursion depth is greater than 10 hops, it is considered that three layers of relationships (such as 1→2→3) have been traced along a certain path. If the target node is still not found, it is judged as excessive penetration and automatically switches back to BFS mode to find a new direction.

[0102] Alternatively, if the path score growth stagnates (e.g., the cumulative score growth over the last three hops is less than 5%), this is considered a case of a path extending from 210 to 215 (a 2.3% increase) due to the continuous increase in "cooperation intensity 62 → 58 → 65." The system then determines that the path quality has deteriorated, terminates DFS, and reverts to BFS.

[0103] For a network size greater than or equal to a predetermined threshold, it is considered to be applied in a large-scale integrated network. In the optional embodiment provided in this application, a path search strategy is adopted to search the integrated network, including the following methods:

[0104] In the case where the network size is greater than or equal to a predetermined threshold, determining a path search strategy as follows: starting the search from a start node representing a known object in the integrated network;

[0105] Based on the actual path cost from the starting node to the current node and the remaining path cost, determine the next node that minimizes the cumulative path cost from the starting node to the target node; the target node is used to represent the target object in the integrated network, and the remaining path cost is used to estimate the cost required to reach the target node from the current node;

[0106] The process is performed in a manner of determining the next node until the new next node is the target node, and the search process is stopped.

[0107] In the embodiment provided in the present application, each cross-network node guides the search direction by dynamically evaluating the path cost to ensure that the optimal talent attraction path is found in the most efficient way. The search process starts from the starting node representing the known object, and continuously tracks the actual path cost from the starting point to the current node. The actual path cost comprehensively considers key factors such as the strength of the association relationship and the timeliness of the relationship. By combining the actual path cost with the remaining estimated cost, the selection priority of each next node is determined. In each step of the search, the node that minimizes the cumulative path cost is selected as the next search target. The above process continues to iterate until the target node representing the target talent is finally reached. Through the above method, paths can be effectively found and optimized in complex networks, providing strong support for talent discovery and talent attraction path planning.

[0108] For example, the above-mentioned remaining path cost is calculated by using a heuristic search algorithm, and the above-mentioned heuristic search algorithm can be multiple, such as A* algorithm, IDA* algorithm (iterative deepening A* algorithm), etc. The A* algorithm uses the heuristic function Estimate the cost from the current node v to the target node t. The IDA* algorithm searches for a path by limiting the search depth and gradually increasing the depth limit. In each iteration, IDA* uses the heuristic function To limit the total cost of the current path , thereby ensuring the shortest path while reducing memory usage, and has good adaptability for integrated networks with a certain scale. Preferably, the above-mentioned A* algorithm is applied to integrated networks with less than 100,000 nodes, and the above-mentioned IDA algorithm is applied to integrated networks with more than 100,000 nodes.

[0109] The following is an example of the A* algorithm. The A* algorithm uses a heuristic function Estimation Node To the target node The cost, combined with the actual path cost Calculate the total estimated cost:

[0110]

[0111] in, is the actual path cost from the starting node s to the node v, Is a heuristic function that can be based on the Euclidean distance between nodes, the accumulation of cooperation strength, etc. During the search process, priority is given to expanding The smallest node is used as the next node, so as to find the optimal path more efficiently.

[0112] According to the embodiments provided herein, during a single pathfinding process for a node representing a target object, the search range is gradually expanded in the integrated network according to a certain number of hops, and path pruning is performed on multiple candidate paths based on the path scores corresponding to the multiple candidate paths to obtain the target path. The following steps may be included:

[0113] For the current extended search, multiple candidate paths are sorted according to their corresponding path scores;

[0114] The candidate paths whose rankings meet the predetermined conditions are retained to obtain the current search result of the current extended search; the current search result is the starting path of the next extended search;

[0115] Until there is any path including the target node in the new search results, the retained candidate path is used as the target path.

[0116] In the current extended search phase, each candidate path is ranked according to its path score. The path score is a comprehensive metric that reflects the overall quality of the path. Candidate paths whose rankings meet the predetermined criteria are retained and constitute the search results for the current extended search. They will serve as the starting paths for the next extended search phase. The search process is iterative, with each round of search building on the results of the previous round. This process repeats until at least one path in the new search results contains the target node.

[0117] For example, the above screening of multiple candidate paths according to the predetermined conditions is a pruning process. Dynamic pruning is performed based on the path scores, and the top few paths with the highest scores are retained. The path score can be defined as:

[0118]

[0119] in, Representation node To Node The edge weights can comprehensively consider factors such as the strength of the partnership, the quantity and quality of the collaborative outcomes, and other factors. During the search process, a priority queue is maintained, sorted by path score, and the highest-scoring path is expanded each time. Once a path to the target node is found, the top k paths with the highest scores are retained.

[0120] It should be noted that each cross-network node has its own independent digital identity, generated and stored using blockchain encryption technology. An authentication mechanism ensures that the cross-network node's identity is tamper-proof and easily verifiable. Cross-network node permissions are managed through smart contracts. Smart contracts define the subnetworks a node can access, the data types, and the operational permissions. Each time a node accesses a subnetwork, it must pass authorization verification by the smart contract. To further enhance security, cross-network nodes utilize end-to-end encryption when transmitting data. All data is encrypted before transmission, and only the destination node can decrypt it, ensuring that data cannot be eavesdropped or tampered with during transmission. In some cases, cross-network nodes need to perform computations on encrypted data. Homomorphic encryption allows nodes to perform computations without decrypting the data, thus protecting data privacy. During the cross-network node path search process, all operations are automatically executed by smart contracts. Each time a cross-network node accesses data or performs path computations, the smart contract automatically verifies that the operation complies with predefined rules. This automated verification mechanism ensures compliance with node behavior and prevents malicious operations. The code of smart contracts is open and transparent on the blockchain, ensuring the fairness and transparency of operations.

[0121] In an embodiment of the present application, the historical path record is stored in a historical node set in the blockchain, and the method further includes the following steps:

[0122] If the talent recruitment result for the current target object is successful according to the final path, the credibility of the talent recruitment result will be verified;

[0123] When the recruitment result passes the credibility verification, the final path will be stored in the historical node and the historical path record will be updated.

[0124] In the embodiment provided by this application, historical path records are stored in a specially set historical node in the blockchain. The characteristics of the blockchain ensure the security, immutability and traceability of these historical data. The path information of past successful talent recruitment is properly preserved to provide a basis for subsequent association strength adjustment and path planning. When the talent recruitment result of the current target object is displayed as successful according to the final path, the path is not immediately recorded in the historical node. Instead, the credibility of the talent recruitment result is first verified. This verification process can be implemented in a variety of ways, such as verifying various key information in the talent recruitment process, such as whether a cooperation intention has been truly reached with the target object, whether the relevant agreement is authentic and valid, etc. If the talent recruitment result passes the credibility verification, it means that the talent recruitment path is authentic and valid. At this time, the final path is stored in the historical node and the historical path record is updated so that the latest successful talent recruitment experience can be taken into account when performing path planning and association strength calculation in the future.

[0125] Historical path records are an important basis for adjusting the strength of associations between candidate candidates, more accurately reflecting the actual degree of association between different candidates. When new successful talent recruitment paths are correctly recorded and updated in the historical path records, the impact of these successes on the associations can be more accurately reflected when adjusting the strength of associations, ensuring that the strength of associations is more consistent with actual conditions and thus optimizing path planning results. Using blockchain historical nodes to store and manage talent recruitment path records, combined with a credibility verification mechanism, further enhances the security and reliability of the entire system. The immutability of blockchain ensures that historical records cannot be maliciously tampered with, while credibility verification ensures the authenticity of the records. This dual guarantee makes the system more stable and reliable when handling important tasks such as talent recruitment path planning.

[0126] According to some embodiments provided by the present application, there are multiple known objects, and the identity information of the multiple known objects is stored in the inventory talent node in the blockchain in a predetermined order. The method further includes the following steps:

[0127] Determine the priority of the multiple known objects based on their corresponding email domains, default records, and response delays. The priority indicates the order of path searches based on the multiple known objects. Default records are predetermined default events recorded by smart contracts in the blockchain.

[0128] Update the predetermined order stored in the inventory talent node according to the priorities of multiple known objects.

[0129] In the embodiments provided herein, different email domain names represent different organizations, reputations, or levels of activity. For example, email domain names of professional organizations suggest that known entities are more professional and reliable, making them more worthy of priority consideration in path searches. Breach records are pre-determined breach of trust events recorded by smart contracts in the blockchain. The immutability of smart contracts ensures the authenticity and reliability of breach records. Known entities with breach records will be prioritized lower in the ranking to minimize their impact on the effectiveness and reliability of the talent recruitment path. Response latency reflects the speed with which a known entity responds to relevant information. Known entities with lower response latency are more proactive and can participate in the talent recruitment process more quickly, thus receiving a higher priority ranking. Based on the determined known entity priority, the predetermined order stored in the talent inventory node is updated. This allows subsequent path searches to use known entities in descending order of priority, improving the efficiency and success rate of path searches.

[0130] By prioritizing known candidates and conducting path searches based on their priority, we prioritize the most reliable and proactive known candidates. This reduces the time and resources wasted on low-quality known candidates, allowing path searches to find appropriate paths more quickly and improving overall search efficiency. By considering factors such as default records, we avoid using known candidates with poor track records, thereby reducing the potential for risk in the talent recruitment process. Prioritizing reputable and responsive known candidates improves the reliability and success rate of the talent recruitment path.

[0131] For example, priorities can be adjusted based on changes in information such as email addresses, default records, and response delays within a predetermined verification cycle. This allows the predetermined order of known objects in the talent inventory node to dynamically adapt to changes in actual conditions, maintaining the optimal path search order and further improving system performance and adaptability.

[0132] According to the embodiments provided herein, in step S104, a final path is determined using majority consensus based on the target paths obtained by multiple cross-network nodes. Cross-network nodes can be considered special nodes (with the highest authority in the blockchain) authorized to access all sub-network data, and their identities can be verified through smart contracts. Each cross-network node performs a parallel search based on the fused sub-network data (i.e., the integrated network) to obtain one or more target paths. For example, node α submits path P1 (score 92) and path P2 (score 85), while node β submits path P1 (score 90) and path P3 (score 88).

[0133] The smart contract collects target paths and their scores from each cross-network node and writes them to the blockchain. Using majority consensus voting, the target path with the highest vote count is recorded as the final path. Each cross-network node individually votes for / against each target path. The target path that receives a weighted approval vote greater than a first predetermined threshold (e.g., greater than 50%) and a weighted rejection vote less than a second predetermined threshold (e.g., less than 30%) is determined to be the final path. If no path meets the criteria after voting, the first and / or second predetermined thresholds are modified to allow the next highest-scoring target path to be submitted for re-voting. The resulting final path is then uploaded to the blockchain, optionally storing the following information: a list of participating nodes, detailed scores for each target path, and voting weight distribution. The blockchain's finality mechanism ensures that the final path result cannot be rolled back.

[0134] Based on the above embodiment, this application also provides an optional implementation method to achieve secure sharing and intelligent collaboration of multi-institutional data through blockchain technology. The system adopts a layered architecture design, which includes three core components: data layer, computing layer and application layer. At the data layer, each partner institution maintains an independent professional sub-network, including a partnership network, an educational background network and a project cooperation network. These sub-network data are standardized and stored in dedicated nodes of the blockchain to form physically isolated data storage units. Each sub-network node is equipped with a dedicated edge server, which is responsible for the path search task within the network. This design not only protects data privacy but also achieves a reasonable distribution of computing resources.

[0135] The system workflow consists of two main phases: network initialization and path discovery. During the initialization phase, each partner institution submits expert data through a standardized interface. The smart contract automatically verifies the data format and constructs a connection network. The collaboration network uses scholars as nodes, calculating edge weights based on metrics such as the number of collaborative papers and citations. The education network constructs an alumni relationship map, distinguishing the strength of connections at different levels. All network data is encrypted and distributed across blockchain nodes. Any data update requires consensus confirmation from a majority of validating nodes.

[0136] When a talent recruitment request is initiated, the system first performs a local search on the edge servers of the relevant subnetworks. If no single network path is found, a cross-network node search is triggered. Each cross-network node integrates multiple subnetworks into a comprehensive network. Based on the network size of the comprehensive network, a hybrid search strategy combining depth-first and breadth-first search is adopted for comprehensive networks with a network size less than a predetermined threshold. For comprehensive networks with a network size greater than or equal to the predetermined threshold, a modified A* search algorithm can be used to comprehensively consider the actual path cost and the residual path cost to find the target path.

[0137] To ensure the credibility of the results, the system designs a multi-level consensus verification mechanism. The target paths submitted by each cross-network node need to go through cluster analysis and weighted voting. Only the target paths recognized by the majority of verification nodes will be confirmed as the final results. The entire decision-making process and its basis are completely recorded on the blockchain, forming an immutable audit trail. The system establishes a dynamic adjustment mechanism. The paths of successful talent introduction will be incorporated into the historical records and used to optimize the calculation of future association strength, forming a continuously improving closed-loop system.

[0138] In the following example, each cross-network node is processed in the manner provided by this optional embodiment. When planning to introduce the team of Wang XX in the field of life and health, the system reveals potential talent introduction paths by analyzing the association network of the existing talent Zhang XX. Zhang XX has a classmate relationship with Wu XX, and Wu XX has a cooperation relationship with Wang XX.

[0139] The first-level path analysis shows a direct cooperation relationship: Wang XX and Wu XX have co-authored 8 papers in the past 5 years. These papers have been cited a total of 623 times, and their cooperation intensity score has reached 92 points (out of 100).

[0140] MATCH (w:Wang XX)-[r:COAUTHOR]-(k: Wu XX)

[0141] WHERE r.strength>85

[0142] RETURN r.papers, r.citations

[0143] The MATCH keyword is used to specify a matching pattern and attempt to find matching nodes and relationships in the network. (w: Wang XX) represents a node with the label "Wang XX". (k: Wu XX) represents a node with the label "Wu XX", which is automatically filled in after the search results are obtained and is the result of program execution. During the search process, k can represent multiple candidate nodes. [r: COAUTHOR] represents a relationship of type "COAUTHOR" that connects two nodes, indicating that these two candidate objects are co-authors. The WHERE keyword is used to filter the matching results and only select records that meet specific conditions. r.strength > 85 is a conditional expression, indicating that only records with a cooperation strength "strength" greater than 85 are selected. The RETURN keyword is used to specify the fields to be returned in the query results. r.papers and r.citations are attributes of the relationship r, representing the number of papers co-published and the number of citations respectively. Return the number of papers and the number of citations corresponding to all cooperation relationships that meet the conditions. Running the above processing shows that Wang XX and Wu XX co-published 8 papers in the past 5 years (with a total of 623 citations), and the cooperation strength score is 92 / 100.

[0144] The secondary path analysis expands the classmate network: Zhang and Wu XX are not only alumni of the university but also studied for a doctorate in the same cohort from 2008 to 2012. In addition, the two jointly applied for 2 patents in the field of life and health.

[0145] MATCH (z: Zhang)-[edu: ALUMNI]-(w: Wu XX)

[0146] WHERE edu.school = "XX University" AND edu.duration OVERLAPS 2008 - 2012

[0147] WITH z, w

[0148] MATCH (w)-[proj: SHARED_PROJECT]-(Wang XX)

[0149] WHERE proj.field = "Life and Health"

[0150] RETURN z, w, Wang XX

[0151] (z: Zhang Mou) represents the node with the matching label "Zhang Mou", which is the result retrieved and automatically filled in after the code execution. During the search process, z can represent multiple candidate nodes. [edu:ALUMNI] represents the educational relationship of the matching type "ALUMNI", indicating that the two candidate objects are alumni. "xx University" AND edu.duration OVERLAPS 2008 - 2012 is a conditional expression that filters out the alumni relationships where the school is "xx University" and the study period overlaps between 2008 and 2012.

[0152] WITH z,w means passing the nodes z and w matched according to the alumni conditions to the next part of the query. MATCH (w)-[proj:SHARED_PROJECT]-(Wang Moumou) means finding the node connected to the node w (which is not determined as Wu Moumou during the search process) through the relationship proj of the type SHARED_PROJECT (shared project). The SHARED_PROJECT relationship indicates that the two candidate objects have jointly participated in a certain project. WHERE proj.field = "Life and Health" is a conditional expression that filters out the shared project relationships where the project field is "Life and Health".

[0153] Based on this information, the system discovered a key talent introduction path: Zhang Mou can serve as a bridge connecting the three through the classmate relationship with Wu Moumou and the cooperation relationship between Wu Moumou and Wang Moumou, and a target path can be obtained.

[0154] In this process, Zhang Mou can utilize his resources in the university to actively contact Wu Moumou, express the intention of talent introduction to Wang Moumou's team, and explore possible cooperation opportunities. At the same time, Wu Moumou can act as an intermediary to promote the communication and cooperation between Zhang Mou and Wang Moumou, thereby reducing the communication cost and trust barrier, and creating favorable conditions for the successful introduction of Wang Moumou's team.

[0155] According to the above optional implementation manners, through the distributed characteristics of the blockchain, the secure sharing of cross-institutional data is achieved, and the data island problem in the related technologies is solved. The consensus mechanism of multiple cross-network nodes ensures the objectivity and fairness of the results and prevents the risk of single-point manipulation. The whole-process blockchain evidence preservation meets the compliance requirements and provides reliable data support for talent introduction decisions. Compared with the centralized solutions provided by the related technologies, this optional implementation manner has significantly improved in terms of data security, path quality, and system reliability, providing a new technical paradigm for intelligent talent introduction.

[0156] Corresponding to the application scenario and method of the method provided in the embodiment of the present application, Figure 3 The schematic block diagram of the path planning device according to the embodiment of the present application is shown, as Figure 3 As shown, the embodiment of the present application further provides a path planning device, comprising:

[0157] A sub-network generation module 301 is used to establish multiple sub-networks, each of which is generated based on information about candidate objects provided by a corresponding partner organization, and each of which is stored in different network storage nodes in the blockchain;

[0158] A cross-network acquisition module 302 is configured to, when there is no single network path from a known object to a current target object in multiple sub-networks, obtain multiple sub-networks from each cross-network node set in the blockchain, where the known object is a candidate object among the multiple candidate objects for which a contact path exists, and the current target object is an object among the multiple candidate objects for which a contact is to be established;

[0159] A cross-network search module 303 is configured to use each cross-network node to search based on multiple sub-networks to determine a target path from a known object to a current target object;

[0160] The consensus voting module 304 is used to determine the final path by majority consensus based on the target paths obtained by multiple cross-network nodes.

[0161] The functions of each module in each device in the embodiment of the present application can be referred to the corresponding description in the above method, and have corresponding beneficial effects, which will not be repeated here.

[0162] Figure 4 FIG. 1 is a block diagram of an electronic device for implementing an embodiment of the present application. Figure 4 As shown, the electronic device includes: a memory 401 and a processor 402. The memory 401 stores a computer program that can be executed on the processor 402. When the processor 402 executes the computer program, the method of the above embodiment is implemented. The number of memory 401 and processor 402 can be one or more. In a specific implementation, the electronic device may also include a communication interface 403 for communicating with external devices and exchanging data.

[0163] In a specific implementation, if the memory 401, processor 402, and communication interface 403 are implemented independently, the memory 401, processor 402, and communication interface 403 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0164] Optionally, in a specific implementation, if the memory 401 , the processor 402 , and the communication interface 403 are integrated on a chip, the memory 401 , the processor 402 , and the communication interface 403 may communicate with each other through an internal interface.

[0165] An embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the path planning method provided in the embodiment of the present application.

[0166] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the path planning method provided in the embodiment of the present application.

[0167] An embodiment of the present application also provides a chip, which includes a processor for calling and executing instructions stored in the memory from the memory, so that a communication device equipped with the chip executes the path planning method provided in the embodiment of the present application.

[0168] An embodiment of the present application also provides a chip, including: an input interface, an output interface, a processor and a memory. The input interface, the output interface, the processor and the memory are connected through an internal connection path. The processor is used to execute the code in the memory. When the code is executed, the processor is used to execute the path planning method provided in the embodiment of the application.

[0169] It should be understood that the processor described above may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. It is worth noting that the processor may be a processor that supports the Advanced RISC Machines (ARM) architecture.

[0170] Furthermore, optionally, the aforementioned memory may include read-only memory and random access memory. The memory may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache memory. By way of example and not limitation, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM) and direct memory bus random access memory (DR RAM).

[0171] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0172] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0173] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0174] Any process or method described in the flowchart or otherwise described herein can be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process. The scope of the preferred embodiments of the present application includes other implementations in which the functions may be performed in a different order than shown or discussed, including performing the functions substantially simultaneously or in reverse order depending on the functions involved.

[0175] The logic and / or steps described in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from and execute instructions on an instruction execution system, apparatus or device), or used in conjunction with such instruction execution systems, apparatuses or devices.

[0176] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0177] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the aforementioned integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.

[0178] The above are merely exemplary embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various modifications or substitutions within the technical scope described in this application, and such modifications or substitutions should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A path planning method, characterized in that: include: Establishing multiple sub-networks, each of which is generated based on information about candidate objects provided by a corresponding partner organization, and each of which is stored in different network storage nodes in the blockchain; When there is no single network path from the known object to the current target object in the multiple sub-networks, each cross-network node set in the blockchain obtains multiple sub-networks respectively; The known object is a candidate object with which a contact path exists among multiple candidate objects, and the current target object is an object with which contact needs to be established among the multiple candidate objects; Using each cross-network node, searching according to the multiple sub-networks, to determine a target path from the known object to the current target object; Based on the target paths obtained by multiple cross-network nodes, the final path is determined by majority consensus; The step of using each cross-network node to search based on the multiple sub-networks to determine a target path from the known object to the current target object includes: A comprehensive network is obtained by fusing the multiple sub-networks; the nodes included in the comprehensive network are multiple candidate objects, and the edges included in the comprehensive network represent different types of association relationships among the multiple candidate objects; Using a path search strategy to search the integrated network, recording paths traversed during the search as multiple candidate paths, wherein the path search strategy is determined based on the network scale of the integrated network; Determining, using each cross-network node, path scores corresponding to each of the plurality of candidate paths according to the strength of association between the plurality of candidate objects and a predetermined attenuation factor; the attenuation factor representing how the path score of the corresponding candidate path decreases as path length increases, the path length representing the number of candidate objects along the path; and the association strength adjusted based on historical path records, the historical path records including historical paths used to successfully communicate with historical target objects. Based on the path scores corresponding to the multiple candidate paths, path pruning is performed on the multiple candidate paths to determine the target path obtained by each cross-network node.

2. The method according to claim 1, characterized in that The multiple sub-networks respectively correspond to edge servers, and the permissions of the edge servers are set to allow access to the network storage nodes in the blockchain of the corresponding sub-networks. After establishing the multiple sub-networks, the method further includes: Using multiple edge servers to search in corresponding sub-networks respectively to determine whether the single network path exists; In the case that there is only one single network path, determining the single network path as the final path; In the case where there are multiple single network paths, determining the path scores corresponding to the multiple single network paths respectively; The path with the highest score among the multiple single network paths is determined as the final path.

3. The method according to claim 1, characterized in that There are multiple known objects, and identity information of the multiple known objects is stored in the inventory talent node in the blockchain in a predetermined order. The method further includes: Determining priorities of the multiple known objects based on email domain names, default records, and response delays corresponding to the multiple known objects, wherein the priorities are used to indicate a path search order based on the multiple known objects, wherein the default records are predetermined default events recorded by the smart contract in the blockchain; According to the priorities of the multiple known objects, the predetermined order stored in the inventory talent node is updated.

4. The method according to claim 1, wherein The historical path record is stored in a historical node set in the blockchain, and the method further includes: If the talent recruitment result for the current target object according to the final path is successful, verify the credibility of the talent recruitment result; When the talent recruitment result passes the credibility verification, the final path is stored in the historical node and the historical path record is updated.

5. The method according to claim 1, characterized in that The adopting of a path search strategy to search the integrated network includes: When the network size is smaller than a predetermined threshold, determining the path search strategy as follows: starting from a start node representing the known object in the integrated network, searching for a target node representing the target object within a predetermined range; If the target node does not exist within the predetermined range, searching based on adjacent nodes of the nodes within the predetermined range to determine whether the target node exists among the adjacent nodes; If the target node does not exist in the adjacent nodes, starting with the adjacent nodes as new starting nodes, searching for nodes in the predetermined range; The path search strategy is repeatedly executed when the network size is smaller than the predetermined threshold until the target node exists within the predetermined range or among the adjacent nodes, and the search process is stopped.

6. The method according to claim 1, characterized in that The adopting of a path search strategy to search the integrated network includes: In the case where the network size is greater than or equal to a predetermined threshold, determining the path search strategy as: starting the search from a start node representing the known object in the integrated network; Determining a next node that minimizes the cumulative path cost from the starting node to the target node based on the actual path cost from the starting node to the current node and the remaining path cost; the target node is used to represent the target object in the integrated network, and the remaining path cost is used to estimate the cost required to reach the target node from the current node; The process is performed in a manner of determining the next node until the new next node is the target node, and the search process is stopped.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

8. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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