A human resources data processing method and system based on recursive blockchain

Abstract

The present invention proposes a human resource data processing method and system based on a recursive blockchain. It belongs to the technical field of human resource information management; the human resource data processing method comprises: after receiving human resource information, the blockchain node obtains dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself; the target blockchain node corresponding to the blockchain node is determined by combining the dynamic evaluation parameters with the dynamic evaluation parameters of the candidate sub-nodes of the consensus chain of the blockchain node, and the human resource information is shared. By adopting the recursive blockchain method, data is shared and stored on multiple nodes, which enhances the security of the system; even if a node fails or is attacked, other nodes can still maintain the integrity and availability of the data to ensure that the data will not be lost or tampered with.

Description

Technical Field

[0001] The present invention proposes a human resources data processing method and system based on recursive blockchain, belonging to the technical field of human resources information management. Background Art

[0002] With the rapid development of information technology, human resource management has gradually shifted from traditional paper-based file management to digital and networked management. However, in the process of processing and sharing human resource data, how to ensure the accuracy, security and efficiency of data has become an urgent problem to be solved. The traditional centralized database management method has security risks such as data tampering and single point failure. At the same time, when processing a large amount of human resource information, it also faces performance bottlenecks and resource waste.

[0003] In recent years, blockchain technology has been widely used in many fields due to its decentralized, tamper-proof and distributed characteristics. In the field of human resource management, blockchain technology is expected to solve many problems existing in traditional management methods. However, directly applying blockchain technology to human resource data processing also faces some challenges. For example, in a blockchain network, each node needs to process and store a large amount of data, which will cause the node to be overloaded and the resource utilization rate to be high; at the same time, as the scale of the network expands, the consensus efficiency will gradually decrease. Summary of the invention

[0004] The present invention provides a human resources data processing method and system based on recursive blockchain, which is used to solve the problem in the prior art that the resource occupancy rate of human resources information in the blockchain network is too large, resulting in a large load on the blockchain nodes and insufficient security performance of the nodes. The technical solutions adopted are as follows:

[0005] A human resources data processing method based on a recursive blockchain, the human resources data processing method comprising:

[0006] After receiving the human resources information, the blockchain node obtains the dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself;

[0007] Determine the target blockchain node corresponding to the blockchain node by combining the dynamic evaluation parameters of the candidate sub-nodes of the consensus chain of the blockchain node, and share the human resources information;

[0008] Real-time monitoring is performed to determine whether a human resources retrieval instruction appears. When the blockchain node receives a human resources retrieval instruction, the target blockchain subnode provides the human resources information that needs to be retrieved.

[0009] Furthermore, after receiving the human resources information, the blockchain node obtains dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself, including:

[0010] The blockchain node monitors in real time whether human resources information is received, and uses the blockchain node that has received the human resources information as a parent node;

[0011] Retrieving the historical operation record of the parent node, and obtaining the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient corresponding to the parent node through the historical operation record of the parent node;

[0012] The dynamic evaluation parameter of the parent node is acquired using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient.

[0013] Further, retrieving the historical operation record of the parent node, and obtaining the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient corresponding to the parent node through the historical operation record of the parent node, including:

[0014] Collect node performance indicator parameters in the historical operation record of the parent node, and use the node performance indicator parameters to obtain a first dynamic evaluation coefficient; wherein the node performance indicator parameters include node response time information, node throughput information and node availability information; and use a first dynamic evaluation coefficient model to obtain the first dynamic evaluation coefficient;

[0015] Collecting node security index parameters in the historical operation record of the parent node, and using the node security index parameters to obtain a second dynamic evaluation coefficient; wherein the node security index parameters include the node's recognition rate of malicious behavior, the node's error rate of processing requests, and the node's strength information for resisting attacks, and using a second dynamic evaluation coefficient model to obtain the second dynamic evaluation coefficient;

[0016] Collect data synchronization consensus indicator parameters in the historical operation records of the parent node, and use the data synchronization consensus indicator parameters to obtain a third dynamic evaluation coefficient; wherein the data synchronization consensus indicator parameters include data synchronization speed and consensus mechanism efficiency; and use a third dynamic evaluation coefficient model to obtain the third dynamic evaluation coefficient.

[0017] Further, obtaining the dynamic evaluation parameter of the parent node by using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient includes:

[0018] Retrieving the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient;

[0019] The first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient obtain the dynamic evaluation parameter of the parent node, wherein the dynamic evaluation parameter of the parent node is obtained by the following formula:

[0020]

[0021] in, F Represents the dynamic evaluation parameters of the parent node; F 0 indicates the preset parameter baseline value; F 01 Represents the first dynamic evaluation coefficient of the parent node; F 02 Represents the second dynamic evaluation coefficient of the parent node; F 03 Indicates the third dynamic evaluation coefficient of the parent node.

[0022] Furthermore, the target blockchain node corresponding to the blockchain node is determined by combining the dynamic evaluation parameters with the dynamic evaluation parameters of the candidate sub-nodes of the consensus chain of the blockchain node, and human resources information is shared, including:

[0023] Extracting blockchain nodes that have a consensus chain with the parent node as candidate child nodes;

[0024] Retrieving the historical operation records of the candidate child node;

[0025] Obtaining a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate subnode by using the historical operation record of the candidate subnode;

[0026] Acquire a dynamic evaluation parameter of the candidate subnode using a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate subnode;

[0027] The target blockchain child node is determined by utilizing the parameter relationship between the dynamic evaluation parameters of the candidate child node and the dynamic evaluation parameters of the parent node.

[0028] Further, using the historical operation record of the candidate child node to obtain the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient and the fourth dynamic evaluation coefficient of the candidate child node includes:

[0029] Collecting node performance indicator parameters in the historical operation records of the candidate child nodes, and using the node performance indicator parameters to obtain a first dynamic evaluation coefficient;

[0030] Collecting node safety index parameters in the historical operation records of the candidate child node, and using the node safety index parameters to obtain a second dynamic evaluation coefficient;

[0031] Collecting data synchronization consensus indicator parameters in the historical operation records of the candidate child node, and using the data synchronization consensus indicator parameters to obtain a third dynamic evaluation coefficient;

[0032] The communication quality indicator parameters in the historical operation records of the candidate subnode are collected, and the fourth dynamic evaluation coefficient is obtained by using the communication quality indicator parameters.

[0033] Further, obtaining the dynamic evaluation parameters of the candidate subnode using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient, and the fourth dynamic evaluation coefficient of the candidate subnode includes:

[0034] Retrieving a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate child node;

[0035] The dynamic evaluation parameters of the candidate child node are obtained by using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient and the fourth dynamic evaluation coefficient; wherein the dynamic evaluation parameters of the candidate child node are obtained by the following formula:

[0036]

[0037] in, Z Represents the dynamic evaluation parameters of the candidate child nodes; Z 0 indicates the preset parameter reference value corresponding to the candidate child node; Z 01 Represents the first dynamic evaluation coefficient of the candidate child node; Z 02 A second dynamic evaluation coefficient representing a candidate child node; Z 03 represents the third dynamic evaluation coefficient of the candidate child node; Z 04 Indicates the fourth dynamic evaluation coefficient of the candidate child node.

[0038] Further, determining the target blockchain child node using the parameter relationship between the dynamic evaluation parameters of the candidate child node and the dynamic evaluation parameters of the parent node includes:

[0039] Determine whether the dynamic evaluation parameters of the candidate child node and the dynamic evaluation parameters of the parent node meet preset parameter constraints;

[0040] The candidate subnodes whose dynamic evaluation parameters satisfy the parameter constraints are selected as the target blockchain subnodes.

[0041] Furthermore, whether a human resources retrieval instruction appears is monitored in real time. When the blockchain node receives the human resources retrieval instruction, the target blockchain subnode provides the human resources information to be retrieved, including:

[0042] Monitor in real time whether there are any human resources retrieval instructions;

[0043] When the blockchain node receives the human resources retrieval instruction, it extracts the human resources information to be retrieved from the human resources retrieval instruction;

[0044] Acquire, according to the human resources information to be retrieved, a blockchain node storing the human resources information to be retrieved;

[0045] According to the principle of shortest network communication distance, the blockchain node storing the human resources information to be retrieved is retrieved as the target blockchain subnode for retrieving the information;

[0046] The human resources information that needs to be retrieved is provided through the target blockchain subnode.

[0047] A human resources data processing system based on a recursive blockchain, the human resources data processing system comprising:

[0048] A dynamic evaluation parameter acquisition module, used for the blockchain node to acquire dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself after receiving the human resources information;

[0049] A target blockchain node acquisition module is used to determine the target blockchain node corresponding to the blockchain node by combining the dynamic evaluation parameters of the candidate subnodes of the consensus chain of the blockchain node, and to share human resources information;

[0050] The human resources information retrieval module is used to monitor in real time whether a human resources retrieval instruction appears. When the blockchain node receives the human resources retrieval instruction, it provides the human resources information to be retrieved through the target blockchain sub-node.

[0051] Beneficial effects of the present invention:

[0052] The human resources data processing method and system based on recursive blockchain proposed by the present invention uses cryptography and decentralized characteristics through blockchain technology to ensure the security of human resources information during transmission and storage. Since the data is distributed and stored on multiple nodes, and each block contains the hash value of the previous block, the integrity and immutability of the data are guaranteed; blockchain technology eliminates centralized management agencies, making the human resources management process more fair and transparent. All participants can monitor the exchange and sharing of human resources information in real time through blockchain nodes, reducing information asymmetry and trust problems; the method can select the most suitable target blockchain child node according to parameters such as node performance indicators, security indicators, synchronization consensus indicators and communication quality indicators through dynamic evaluation of parent nodes and candidate child nodes. In this way, the performance and stability of the system can be optimized and the efficiency of human resources management can be improved; when the blockchain node receives the human resources retrieval instruction, it quickly locates the blockchain node storing the required human resources information through the principle of the shortest network communication distance, and provides the corresponding human resources information through the target blockchain child node. This process is real-time and can quickly meet the needs of human resources deployment; since all operations on the blockchain are recorded and cannot be tampered with, the human resources management process has a high degree of transparency and traceability. This makes it easier to conduct audits and supervision, and reduce fraud and misconduct in human resource management. At the same time, by using the parent node to use the local blockchain node in the blockchain network as a data sharing node, the data sharing of human resource information data can effectively reduce the operating load and resource occupancy of the overall blockchain network. At the same time, by adopting the recursive blockchain method, data is shared and stored on multiple nodes, which enhances the security of the system. Even if a node fails or is attacked, other nodes can still maintain the integrity and availability of the data, ensuring that the data will not be lost or tampered with. By dynamically selecting the target node and performing the sharing / consensus process based on the evaluation parameters, the efficiency and accuracy of the consensus can be improved. Each target node participates in the consensus process to ensure the consistency and credibility of the data, thereby improving the effect of the overall consensus indicator. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 The system block diagram of the system of the present invention. DETAILED DESCRIPTION

[0055] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0056] The embodiment of the present invention proposes a human resources data processing method based on recursive blockchain, such as Figure 1 As shown, the human resources data processing method includes:

[0057] S1. After receiving the human resources information, the blockchain node obtains the dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself;

[0058] S2. Determine the target blockchain node corresponding to the blockchain node by combining the dynamic evaluation parameters of the candidate sub-nodes of the consensus chain of the blockchain node, and share the human resources information;

[0059] S3. Real-time monitoring of whether a human resources retrieval instruction appears. When the blockchain node receives the human resources retrieval instruction, the target blockchain sub-node provides the human resources information that needs to be retrieved.

[0060] The working principle of the above technical solution is as follows: after receiving human resources information, the blockchain node uses its own first dynamic evaluation coefficient, second dynamic evaluation coefficient and third dynamic evaluation coefficient to obtain dynamic evaluation parameters. The dynamic evaluation parameters may include but are not limited to information such as node performance, credibility, historical performance, etc., to evaluate the ability and reliability of the node in processing and sharing human resources information; by combining the dynamic evaluation parameters with the dynamic evaluation parameters of the candidate sub-nodes in the blockchain node consensus chain, the target blockchain node can be determined and the human resources information can be shared. This node selection mechanism based on dynamic evaluation parameters can ensure that information is shared between reliable and high-performance nodes, improving the security and processing efficiency of information. Finally, real-time monitoring is performed to determine whether a human resources retrieval instruction appears. When a human resources retrieval instruction is received, the human resources information to be retrieved is provided through the target blockchain sub-node, which can make the information retrieval fast and accurate, further improving the response speed and processing efficiency of the system.

[0061] The effects of the above technical solution are as follows: Blockchain technology has the characteristics of decentralization and distributed storage, and each block contains the hash value of the previous block, so that the data cannot be tampered with in the blockchain network. This mechanism can ensure the security and immutability of human resources data; the data on the blockchain is open and transparent, and each node can view and verify the information on the blockchain. This makes the human resources data processing process more transparent and traceable, reducing information asymmetry and potential fraud; through dynamic evaluation parameters, the blockchain node can determine the target blockchain node based on its own evaluation coefficient and the evaluation parameters of the candidate sub-nodes of the consensus chain. In this way, the optimal node can be selected for data sharing based on the trust and performance of the node, improving the overall efficiency and performance; the blockchain node can monitor in real time whether there is a human resources call instruction, and provide the required human resources information through the target blockchain sub-node. In this way, it can respond to user needs in a timely manner and improve the real-time and accuracy of data processing. At the same time, the above technical solution introduces the first, second and third dynamic evaluation coefficients, so that when processing human resources information, the blockchain nodes can be dynamically adjusted according to real-time situations, historical data and other relevant factors, thereby reducing the load and resource occupancy of a single node, and significantly improving the processing efficiency of the entire system. At the same time, by adopting a recursive blockchain structure, the scalability and adaptability of the above technical solution are significantly increased. As the business scale grows or demand changes, the system can expand new nodes or functions without large-scale reconstruction or adjustment.

[0062] In one embodiment of the present invention, after receiving the human resources information, the blockchain node obtains the dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself, including:

[0063] The blockchain node monitors in real time whether human resources information is received, and uses the blockchain node that has received the human resources information as a parent node;

[0064] Retrieving the historical operation record of the parent node, and obtaining the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient corresponding to the parent node through the historical operation record of the parent node;

[0065] The dynamic evaluation parameter of the parent node is acquired using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient.

[0066] The working principle of the above technical solution is as follows: the blockchain node will monitor in real time whether human resources information is received. When the node receives human resources information, the node will become a new blockchain parent node; the new blockchain parent node will retrieve the historical operation records of its parent node. The historical operation records contain information such as the performance and trustworthiness of the parent node in its past operations; by analyzing the historical operation records of the parent node, the new blockchain parent node can obtain the corresponding first dynamic evaluation coefficient, second dynamic evaluation coefficient and third dynamic evaluation coefficient. These dynamic evaluation coefficients reflect the performance and capabilities of the parent node in different aspects; the new blockchain parent node uses the first dynamic evaluation coefficient, second dynamic evaluation coefficient and third dynamic evaluation coefficient obtained to calculate and obtain its own dynamic evaluation parameters. These parameters can be used in subsequent data processing and sharing processes.

[0067] The effect of the above technical solution is: by obtaining the historical operation records and dynamic evaluation coefficients of the parent node, the blockchain node can calculate and obtain its own dynamic evaluation parameters based on real data and indicators. In this way, the performance and capabilities of the node can be evaluated more accurately, thereby optimizing the selection and use of the node; according to the dynamic evaluation parameters, the blockchain node can select the optimal parent node for data processing and sharing. By selecting nodes with good performance as parent nodes, the performance and efficiency of the entire blockchain network can be improved, and delays and congestion can be reduced; by obtaining dynamic evaluation parameters, the blockchain node can screen out parent nodes with high trust and stable operation for data interaction. In this way, the impact of unreliable nodes on the system can be reduced, and the security and reliability of data can be increased; the dynamic evaluation parameters can be adjusted and updated in real time according to the actual situation of the node. This makes the blockchain network adaptive and flexible, able to adapt to different scenarios and needs, and make corresponding node selection and adjustment according to the actual situation. By selecting the best performance and most reliable nodes for processing and storage, the security of the data can be further enhanced, and the accurate evaluation of the nodes can help avoid the waste of resources and improve the operating efficiency of the entire system.

[0068] In one embodiment of the present invention, the historical operation record of the parent node is retrieved, and the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient corresponding to the parent node are obtained through the historical operation record of the parent node, including:

[0069] Collect node performance indicator parameters in the historical operation record of the parent node, and use the node performance indicator parameters to obtain a first dynamic evaluation coefficient; wherein the node performance indicator parameters include node response time information, node throughput information, and node availability information; and use a first dynamic evaluation coefficient model to obtain the first dynamic evaluation coefficient; wherein the first dynamic evaluation coefficient model is as follows:

[0070]

[0071] in, F 01 Represents the first dynamic evaluation coefficient of the parent node; P Represents the node availability evaluation factor; m Indicates the number of unit times contained in the historical operation record, and the unit time is 1s; n Indicates the number of request processing in the historical running record of the current parent node; C i Indicates the parent node i The amount of data processed per unit time; C max Indicates the maximum amount of data processing per unit time of the parent node allowed by the node load; T i Indicates that the parent node processes i The length of time it takes to process data for a request or transaction; T 0 means i The theoretical processing time for a request or transaction; Δ T Indicates the maximum allowable delay time corresponding to the normal operation of the parent node; S represents the success rate of data processing in the historical operation records of the parent node; L represents the failure rate of the parent node in the historical operation records; T h Indicates the total recovery time of the parent node failure; T z Indicates the total running time of the parent node;

[0072] Collect node security index parameters in the historical operation record of the parent node, and use the node security index parameters to obtain a second dynamic evaluation coefficient; wherein the node security index parameters include the node's recognition rate of malicious behavior, the node's error rate of processing requests, and the node's strength information for resisting attacks, and use a second dynamic evaluation coefficient model to obtain the second dynamic evaluation coefficient; wherein the second dynamic evaluation coefficient model is as follows:

[0073]

[0074] in, F 02 Represents the second dynamic evaluation coefficient of the parent node; L a Indicates the recognition rate of malicious behavior by the node; L b Indicates the error rate of node processing requests; Q Indicates the strength information of the node in resisting attacks; λ represents the weight coefficient, and λ The value range is 0.32-0.39; N Indicates the number of child nodes corresponding to the parent node; N z Indicates the total number of nodes in the blockchain network; Y c Represents the average number of floating-point operations per unit time for the parent node and its corresponding child nodes; Y z Represents the average number of floating-point operations per unit time of the blockchain network; L h Indicates the node hash rate corresponding to the parent node, that is, the number of hash collisions that the parent node can perform per second; f x Indicates the defense coefficient of the parent node; f g Indicates the attack frequency of the attacks suffered by the parent node; H It represents the difficulty adjustment coefficient, and the difficulty adjustment coefficient is obtained by the following formula;

[0075]

[0076] in, H w Represents the network hash rate, and the network hash rate refers to the average hash rate of the entire blockchain network; H n It represents the difficulty target hash rate, and the difficulty target hash rate refers to the difficulty target set by the blockchain network.

[0077] Collect the data synchronization consensus indicator parameters in the historical operation records of the parent node, and use the data synchronization consensus indicator parameters to obtain the third dynamic evaluation coefficient; wherein the data synchronization consensus indicator parameters include data synchronization speed and consensus mechanism efficiency; and use the third dynamic evaluation coefficient model to obtain the third dynamic evaluation coefficient. wherein the third dynamic evaluation coefficient model is as follows:

[0078]

[0079] in, F 03 Represents the third dynamic evaluation coefficient of the parent node; R 01 Indicates the minimum value of the parent node's consensus mechanism efficiency; k Indicates the number of data synchronizations of the parent node; T bi Indicates i Data synchronization duration for each data synchronization; e Indicates the number of data consensus of the parent node; Tgi Indicates i The duration of data consensus corresponding to each data consensus; R 02 Indicates the data synchronization success rate of the parent node; Δ T z Indicates the shortest time that the parent node consumes in its historical operation record for both data synchronization and data consensus with the blockchain node with consensus chain;

[0080] The working principle of the above technical solution is as follows: the blockchain node retrieves the historical operation record of the designated parent node, obtains the relevant parameter information such as the node performance index, security index and data synchronization consensus index; the first dynamic evaluation coefficient of the parent node is calculated using the first dynamic evaluation coefficient model through the collected node performance index parameters. Among them, indicators such as node response time information, node throughput information and node availability information will affect the calculation result of the first dynamic evaluation coefficient; through the collected node security index parameters, the second dynamic evaluation coefficient of the parent node is calculated using the second dynamic evaluation coefficient model. Among them, indicators such as the node's recognition rate of malicious behavior, the node's error rate in processing requests and the node's strength information in resisting attacks will affect the calculation result of the second dynamic evaluation coefficient; through the collected data synchronization consensus index parameters, the third dynamic evaluation coefficient of the parent node is calculated using the third dynamic evaluation coefficient model. Among them, indicators such as data synchronization speed and consensus mechanism efficiency will affect the calculation result of the third dynamic evaluation coefficient; through the calculated first, second and third dynamic evaluation coefficients, the dynamic evaluation coefficient corresponding to the parent node is obtained. This dynamic evaluation coefficient can be used as the basis for selecting the optimal parent node to ensure the performance and security of the entire blockchain network; blockchain nodes need to update the dynamic evaluation coefficient of the parent node in real time to ensure the accuracy and real-time nature of node selection.

[0081] The effects of the above technical solutions are as follows: by collecting node performance indicator parameters, such as response time, throughput and availability, the performance of the parent node can be dynamically evaluated. This helps to select nodes with better performance as parent nodes and improve the performance of the entire blockchain network; by collecting node security indicator parameters, such as the recognition rate of malicious behavior, the error rate of processing requests and the strength of resisting attacks, the security of the parent node can be dynamically evaluated. This helps to select nodes with higher security as parent nodes and enhance the security of the entire blockchain network; by collecting data synchronization consensus indicator parameters, such as data synchronization speed and consensus mechanism efficiency, the data synchronization consensus efficiency of the parent node can be dynamically evaluated. This helps to select nodes with higher data synchronization efficiency as parent nodes and improve the consensus efficiency of the blockchain network; by updating the dynamic evaluation coefficient of the parent node in real time, the latest status and performance of the node can be reflected in time, and corresponding node selection adjustments can be made. The accuracy and real-time performance of node selection can be maintained, and the operation effect of the entire blockchain network can be optimized. Through the above technical solutions, more efficient, secure and reliable information exchange and data sharing can be achieved, providing strong support for the digitalization and intelligent upgrading of the industry. F 01 The first dynamic evaluation coefficient of the parent node is described, and the coefficient is calculated based on the performance indicator parameters of the node. These performance indicator parameters include information such as node response time, throughput and availability. By calculating the first dynamic evaluation coefficient, the node performance can be evaluated based on the node's historical operation records, and the evaluation results can be used to dynamically adjust the parameters. For example, the processing efficiency of the node can be evaluated by the response time information, the processing capacity of the node can be evaluated by the throughput information, and the reliability of the node can be evaluated by the availability information. By adjusting the dynamic evaluation parameters, the operating status and performance of the node can be more accurately evaluated, and at the same time, the node can be helped to achieve a more efficient and stable operating state. The above F 02 The second dynamic evaluation coefficient of the parent node is described, which is calculated based on the security index parameters of the node. These security index parameters include information such as the node's recognition rate of malicious behavior, the error rate of processing requests, and the strength of resisting attacks. The security of the node can be evaluated by calculating the second dynamic evaluation coefficient, and the evaluation results can be used to dynamically adjust the parameters. For example, the security capability of the node can be evaluated by the recognition rate of malicious behavior, the stability of the node can be evaluated by the error rate of processing requests, and the security capability of the node can be evaluated by the strength of resisting attacks. By adjusting the dynamic evaluation parameters, the security status and anti-attack capability of the node can be more accurately evaluated, thereby improving the security and robustness of the system. The above F 03The formula describes the third dynamic evaluation coefficient of the parent node, which is calculated based on the data synchronization consensus indicator parameters of the node. These data synchronization consensus indicator parameters include information such as data synchronization speed and consensus mechanism efficiency. By calculating the third dynamic evaluation coefficient, the data synchronization consensus capability of the node can be evaluated, and the evaluation results can be used to dynamically adjust the parameters. For example, the data synchronization efficiency of the node can be evaluated by the data synchronization speed, and the consensus performance of the node can be evaluated by the consensus mechanism efficiency. By adjusting the dynamic evaluation parameters, the data synchronization consensus status of the node can be more accurately evaluated.

[0082] In one embodiment of the present invention, the method of obtaining the dynamic evaluation parameter of the parent node by using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient includes:

[0083] Retrieving the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient;

[0084] The first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient obtain the dynamic evaluation parameter of the parent node, wherein the dynamic evaluation parameter of the parent node is obtained by the following formula:

[0085]

[0086] in, F Represents the dynamic evaluation parameters of the parent node; F 0 indicates the preset parameter baseline value; F 01 Represents the first dynamic evaluation coefficient of the parent node; F 02 Represents the second dynamic evaluation coefficient of the parent node; F 03 Indicates the third dynamic evaluation coefficient of the parent node.

[0087] The working principle of the above technical solution is: retrieve the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient stored in the blockchain network. These coefficients can reflect the comprehensive situation of the node's performance, security and data synchronization consensus efficiency; according to the retrieved dynamic evaluation coefficients, the dynamic evaluation parameters of the parent node are calculated using the corresponding dynamic evaluation coefficient model. These parameters can reflect the current performance, security and data synchronization consensus efficiency of the parent node, and provide a basis for subsequent node selection; according to the calculated dynamic evaluation parameters of the parent node, node selection can be performed. Select a parent node with better performance, higher security and higher data synchronization consensus efficiency as the parent node of the current node.

[0088] The effect of the above technical solution is: by calculating the dynamic evaluation parameters of the parent node, the operating status and efficiency of the parent node can be understood more accurately, so as to select a suitable parent node. This can improve the performance, security and consensus efficiency of the entire blockchain network; by adopting a dynamic evaluation coefficient model to evaluate the parent node, and select a suitable parent node based on the evaluation results. At the same time, the dynamic evaluation coefficient is also updated regularly to ensure the accuracy and real-time nature of the evaluation results. This can realize the dynamic evaluation and optimization of the parent node, and further improve the performance, security and consensus efficiency of the blockchain network; selecting a parent node with better performance, higher security, and higher data synchronization consensus efficiency as the parent node of the current node can avoid network congestion and delays, and improve the efficiency of the entire blockchain network; through dynamic evaluation and optimization, it is possible to avoid selecting parent nodes with poor performance, low security, and low data synchronization consensus efficiency, avoid waste of resources, and improve resource utilization. The above F The formula describes the dynamic evaluation parameters of the parent node, which are calculated by combining the first, second, and third dynamic evaluation coefficients. By calculating the dynamic evaluation parameters, factors such as the node's performance, security, and data synchronization consensus capabilities can be comprehensively considered to comprehensively evaluate the node's overall operating status. The benefit of dynamic evaluation parameters is that the node's parameters can be adjusted in real time to adapt to the ever-changing operating environment and needs. For example, by adjusting the parameters, the node's performance, security, and data synchronization consensus efficiency can be optimized. By adjusting the dynamic evaluation parameters, the parent node can be more adaptable and responsive to the needs and changes of the blockchain network.

[0089] In one embodiment of the present invention, a target blockchain node corresponding to the blockchain node is determined by combining dynamic evaluation parameters with dynamic evaluation parameters of candidate child nodes of the blockchain node having a consensus chain, and human resources information is shared, including:

[0090] Extracting blockchain nodes that have a consensus chain with the parent node as candidate child nodes;

[0091] Retrieving the historical operation records of the candidate child node;

[0092] Obtaining a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate subnode by using the historical operation record of the candidate subnode;

[0093] Acquire a dynamic evaluation parameter of the candidate subnode using a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate subnode;

[0094] The target blockchain child node is determined by utilizing the parameter relationship between the dynamic evaluation parameters of the candidate child node and the dynamic evaluation parameters of the parent node.

[0095] The working principle of the above technical solution is: select candidate child nodes related to the parent node from the blockchain nodes with consensus chain; obtain the historical operation records of the candidate child nodes, including the running time, status, performance indicators and other information of the node; use the historical operation records of the candidate child nodes to calculate the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient and the fourth dynamic evaluation coefficient of the candidate child nodes. These coefficients can reflect the performance, stability and reliability of the node; use the dynamic evaluation coefficient of the candidate child node, combined with other indicators, to calculate the dynamic evaluation parameters of the candidate child node. These parameters can comprehensively evaluate the overall performance of the node; compare and analyze the dynamic evaluation parameters of the candidate child node with the dynamic evaluation parameters of the parent node to determine the most suitable target blockchain child node. According to the human resource information of the node, information sharing is carried out to improve the efficiency and security of the entire blockchain network.

[0096] The effect of the above technical solution is: through the calculation and comparison of dynamic evaluation parameters, suitable target blockchain subnodes can be selected more accurately. The historical operation records and dynamic evaluation coefficients of candidate subnodes provide objective data basis to help determine the best nodes; by selecting target blockchain subnodes with excellent performance, stability and reliability, the performance and stability of the entire blockchain network can be improved. The dynamic evaluation parameters can comprehensively consider various indicators of the node, so as to select the best performing node, effectively improve the throughput and response speed of the network; after determining the target blockchain subnode, human resource information can be shared. This means that not only can the technical capabilities and experience of the nodes be shared, but also human resources can be deployed so that each node can give full play to its expertise and improve the overall operation efficiency; through the calculation and comparison of dynamic evaluation parameters, nodes with excellent performance, stability and reliability can be selected as target subnodes. This helps to reduce the impact of malicious nodes and improve the security and protection capabilities of the blockchain network. Through the comprehensive analysis of the historical operation records and dynamic evaluation parameters of candidate subnodes, a scientific and objective selection basis can be provided, thereby avoiding subjective bias and arbitrariness and improving the accuracy and reliability of node selection.

[0097] In one embodiment of the present invention, obtaining a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate subnode by using the historical operation record of the candidate subnode includes:

[0098] Collecting node performance indicator parameters in the historical operation records of the candidate child nodes, and using the node performance indicator parameters to obtain a first dynamic evaluation coefficient;

[0099] Collecting node safety index parameters in the historical operation records of the candidate child node, and using the node safety index parameters to obtain a second dynamic evaluation coefficient;

[0100] Collecting data synchronization consensus indicator parameters in the historical operation records of the candidate child node, and using the data synchronization consensus indicator parameters to obtain a third dynamic evaluation coefficient;

[0101] The communication quality index parameters in the historical operation records of the candidate child node are collected, and the fourth dynamic evaluation coefficient is obtained by using the communication quality index parameters. The communication quality index parameters include packet loss rate information, communication delay information and bandwidth utilization information; and the fourth dynamic evaluation coefficient is obtained by using the fourth dynamic evaluation coefficient model. Z 04 ; Among them, the fourth dynamic evaluation coefficient model is as follows:

[0102]

[0103] in, Z 04 A fourth dynamic evaluation coefficient representing the candidate child node; B Indicates the packet loss rate information of the candidate child node; L t Indicates the ratio of the number of communications with data communication delay during the data communication process of the candidate child node to the total number of communications of the candidate child node; r Indicates the total number of unit times included in the running time of the candidate node; L ui Indicates i Bandwidth utilization per unit time; W Indicates the ratio of the number of times the candidate child node reaches data consensus with the parent node to the total number of times the parent node reaches data consensus.

[0104] The working principle of the above technical solution is: extracting node performance-related index parameters, such as node throughput, response time, etc., from the historical operation records of the candidate subnodes. Using the collected node performance index parameters, through certain calculations or models, obtain the first dynamic evaluation coefficient; extracting node security-related index parameters, such as node protection capabilities, vulnerability conditions, etc., from the historical operation records of the candidate subnodes; using the collected node security index parameters, through certain calculations or models, obtain the second dynamic evaluation coefficient; extracting data synchronization consensus-related index parameters, such as node synchronization speed, consensus algorithm performance, etc., from the historical operation records of the candidate subnodes; using the collected data synchronization consensus index parameters, through certain calculations or models, obtain the third dynamic evaluation coefficient; extracting communication quality-related index parameters, such as packet loss rate, communication delay, and bandwidth utilization, etc., from the historical operation records of the candidate subnodes; using the collected communication quality index parameters, through certain calculations or models, obtain the fourth dynamic evaluation coefficient.

[0105] The effect of the above technical solution is: by collecting the node performance index parameters in the historical operation records of the candidate subnodes, the performance of the node can be reflected in real time. By using these index parameters to obtain the first dynamic evaluation coefficient, the performance of the candidate subnodes can be evaluated according to the actual situation to ensure the selection of nodes with better performance; by collecting the node security index parameters in the historical operation records of the candidate subnodes, the protection capabilities and vulnerabilities of the nodes can be understood. By obtaining the second dynamic evaluation coefficient, the security of the candidate subnodes can be evaluated to ensure the selection of nodes with higher security; by collecting the data synchronization consensus index parameters in the historical operation records of the candidate subnodes, the data synchronization capability and consensus performance of the nodes can be understood. By obtaining the third dynamic evaluation coefficient, the data synchronization consensus capability of the candidate subnodes can be evaluated to ensure the selection of nodes with good data synchronization consensus capabilities; by collecting the communication quality index parameters in the historical operation records of the candidate subnodes, such as packet loss rate, communication delay and bandwidth utilization, the communication performance and reliability of the nodes can be understood. By obtaining the fourth dynamic evaluation coefficient, the communication quality of the candidate subnodes can be evaluated to ensure the selection of nodes with better communication quality. At the same time, by considering the security index parameters of the node and incorporating them into the calculation of the dynamic evaluation coefficient, the ability of the entire blockchain network to resist attacks can be improved and the security of the data can be guaranteed. By optimizing the node selection process, the scalability of the entire blockchain network can be improved, enabling it to better cope with the growing transaction volume and number of users, thereby promoting the widespread application and development of blockchain technology. The fourth dynamic evaluation coefficient model mentioned above can help the blockchain system select the optimal candidate child nodes, improve the performance, security and stability of the system, and increase the credibility and reliability of the system. The formula can comprehensively evaluate the candidate child nodes by comprehensively considering the performance indicators, security indicators, data synchronization consensus indicators and communication quality indicators of the nodes. It can ensure that the selected nodes perform well in many aspects and improve the overall performance and reliability of the entire blockchain system; the dynamic evaluation coefficient in the formula is calculated based on the historical operation records of the candidate child nodes, so it is dynamic. By continuously updating the evaluation coefficient of the node, the latest status and performance of the node can be reflected in time, so as to adjust the participation weight of the node in time or exclude unqualified nodes; the evaluation coefficient in the formula can reflect the credibility of the candidate child nodes. By considering indicators such as node performance, security, data synchronization and communication quality, nodes with good performance and high credibility can be screened out. This helps improve the overall security and stability of the blockchain system; according to the evaluation coefficient calculated by the formula, the candidate sub-nodes can be sorted and compared, and the best nodes can be selected to participate in the work of the blockchain network. This can improve the efficiency and performance of the entire system and ensure the contribution and reliability of the nodes.

[0106] In one embodiment of the present invention, the dynamic evaluation parameters of the candidate subnode are obtained by using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient and the fourth dynamic evaluation coefficient of the candidate subnode, including:

[0107] Retrieving a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate child node;

[0108] The dynamic evaluation parameters of the candidate child node are obtained by using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient and the fourth dynamic evaluation coefficient; wherein the dynamic evaluation parameters of the candidate child node are obtained by the following formula:

[0109]

[0110] in, Z Represents the dynamic evaluation parameters of the candidate child nodes; Z 0 indicates the preset parameter reference value corresponding to the candidate child node; Z 01 Represents the first dynamic evaluation coefficient of the candidate child node; Z 02 A second dynamic evaluation coefficient representing a candidate child node; Z 03 represents the third dynamic evaluation coefficient of the candidate child node; Z 04 Indicates the fourth dynamic evaluation coefficient of the candidate child node.

[0111] The working principle of the above technical solution is: after the candidate sub-node joins the network, the system will dynamically evaluate it and obtain the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient and the fourth dynamic evaluation coefficient; when it is necessary to retrieve information, the system will select the candidate sub-node according to the network distance and obtain its dynamic evaluation parameters. By retrieving the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient and the fourth dynamic evaluation coefficient of the candidate sub-node and combining them for calculation, the dynamic evaluation parameters of the candidate sub-node can be obtained; as the network changes and the node status changes, the dynamic evaluation parameters of the candidate sub-node will also be continuously updated.

[0112] The effect of the above technical solution is that by using different dynamic evaluation coefficients, personalized dynamic evaluation parameters can be customized for each candidate subnode according to the specific situation. This can more accurately measure and evaluate the specific characteristics and performance of each candidate subnode; the use of dynamic evaluation parameters can help optimize the decision-making process. By comprehensively considering the dynamic evaluation coefficients and other relevant parameters of the candidate subnodes, their potential value and contribution can be more accurately evaluated. This helps to make more informed decisions and choices; by analyzing the dynamic evaluation parameters, resources and tasks can be allocated more efficiently. According to the dynamic evaluation parameters of the candidate subnodes, it can be determined which candidate subnodes are most worthy of resource investment to improve overall benefits and performance; the dynamic evaluation parameters can be adjusted and updated over time and as the environment changes. This enables the system to better adapt to changing conditions and needs, thereby improving overall robustness and adaptability. By calculating the dynamic evaluation parameters using the dynamic evaluation coefficients of the candidate subnodes, a comprehensive evaluation of the node's capabilities and credibility can be achieved, and a basis for the selection and sorting of nodes can be provided. It helps to improve the overall performance, security and stability of the blockchain system. At the same time, through dynamic evaluation parameters, it can quantitatively evaluate factors such as node performance, security, data synchronization consensus and communication quality, and select the most suitable node based on the evaluation results; it can reduce the dependence on node hardware requirements, so that even relatively weak nodes can participate in the blockchain network. By calculating the dynamic evaluation parameters of candidate child nodes, multiple aspects of node indicators can be comprehensively considered and converted into a specific value. This makes it easy to compare and screen nodes and select the best nodes to participate in the blockchain network; since the dynamic evaluation coefficient in the formula is calculated based on the historical operation records of the node, it is flexible and real-time. By regularly updating the evaluation coefficient of the node and recalculating the dynamic evaluation parameters, the latest status and performance of the node can be reflected in a timely manner, and the accuracy of the evaluation results can be maintained.

[0113] In one embodiment of the present invention, determining a target blockchain child node using a parameter relationship between a dynamic evaluation parameter of the candidate child node and a dynamic evaluation parameter of the parent node includes:

[0114] Determine whether the dynamic evaluation parameters of the candidate child node and the dynamic evaluation parameters of the parent node meet preset parameter constraints; wherein the parameter constraints are as follows:

[0115]

[0116] in, σ represents the sign adjustment coefficient, Z 01p , Z 02P and Z 03PThey respectively represent the average values ​​of the first dynamic evaluation coefficients, the second dynamic evaluation coefficients, and the third dynamic evaluation coefficients of all candidate child nodes corresponding to the parent node, and the values ​​of the sign adjustment coefficients are as follows:

[0117]

[0118] The candidate subnodes whose dynamic evaluation parameters satisfy the parameter constraints are selected as the target blockchain subnodes.

[0119] The working principle of the above technical solution is as follows: define preset parameter constraints, which will be used to determine whether the dynamic evaluation parameters of the candidate child nodes meet the requirements of the dynamic evaluation parameters of the parent node. The parameter constraints may include performance indicators, resource utilization, security and other requirements; compare the dynamic evaluation parameters of the candidate child nodes with the dynamic evaluation parameters of the parent node, and determine whether they meet the preset parameter constraints; based on the results of parameter comparison and judgment, select the candidate child nodes that meet the parameter constraints as the target blockchain child nodes. These target child nodes are considered to be the most appropriate choices under given conditions.

[0120] The effect of the above technical solution is: by comparing the dynamic evaluation parameters of the candidate child node with the dynamic evaluation parameters of the parent node, the target blockchain child node can be determined according to the preset parameter constraints. In this way, the most suitable child node can be selected according to specific needs to improve overall performance and efficiency; because the blockchain network is dynamic, the parameters of the candidate child node and the parent node will change with time and network status. Using dynamic evaluation parameters to determine the target child node can better adapt to different environments and changing conditions, maintain the stability of the system and optimize performance; by screening the candidate child nodes that meet the parameter constraints as the target child nodes, it can be ensured that the selected child nodes have appropriate performance, resource utilization and security. This helps to improve the reliability and security of the entire blockchain system and reduce potential risks and vulnerabilities; through dynamic evaluation parameters, the resource utilization of child nodes can be better monitored and managed. By screening the target child nodes according to the parameter constraints, load balancing can be achieved, and excessive concentration or waste of resources can be avoided, thereby optimizing the overall resource utilization efficiency. By judging whether the dynamic evaluation parameters of the candidate child node and the dynamic evaluation parameters of the parent node meet the preset parameter constraint model, the candidate child nodes can be screened and filtered according to the preset parameter constraints. By controlling the symbol adjustment coefficient σThe value of can adjust the strictness of the constraint conditions to meet specific needs. By using this model, it is possible to effectively determine whether the candidate child node meets the preset parameter constraint conditions. If the difference between the dynamic evaluation parameter of the candidate child node and the corresponding average value of the parent node is within the allowable range, that is, the constraint conditions are met, then the candidate child node can be considered to meet the requirements. This judgment can help select suitable candidate child nodes and optimize the performance and effect of the entire system. In practical applications, the values ​​of parameter constraints and symbol adjustment coefficients can be determined according to specific circumstances to meet needs and requirements. On the other hand, the dynamic evaluation parameters obtained by the above method combine the actual operating parameters and operating safety parameters of the candidate child node, which can effectively improve the accuracy of the dynamic operation instruction evaluation of the candidate child node. At the same time, the above method can maximize the adaptive follow-up between the dynamic evaluation of the candidate child node and the real-time dynamic operation changes of the candidate child node, thereby effectively improving the timeliness of the adaptive changes of the dynamic evaluation parameters of the candidate child node. By setting parameter constraints, it is possible to ensure that the dynamic evaluation parameters of the target blockchain child node are within a certain range with the parameters of the parent node, avoiding abnormal situations or attacks; it can enhance the security of the blockchain network and prevent malicious nodes from attacking the network or tampering with information. By selecting candidate child nodes that meet the parameter constraints as the target blockchain child nodes, resources can be effectively utilized and network load can be reduced; it can optimize the allocation and utilization of resources and improve the efficiency and performance of the entire blockchain network.

[0121] In one embodiment of the present invention, whether a human resources retrieval instruction appears is monitored in real time. When the blockchain node receives the human resources retrieval instruction, the target blockchain subnode provides the human resources information to be retrieved, including:

[0122] Monitor in real time whether there are any human resources retrieval instructions;

[0123] When the blockchain node receives the human resources retrieval instruction, it extracts the human resources information to be retrieved from the human resources retrieval instruction;

[0124] Acquire, according to the human resources information to be retrieved, a blockchain node storing the human resources information to be retrieved;

[0125] According to the principle of shortest network communication distance, the blockchain node storing the human resources information to be retrieved is retrieved as the target blockchain subnode for retrieving the information;

[0126] The human resources information that needs to be retrieved is provided through the target blockchain subnode.

[0127] The working principle of the above technical solution is as follows: the blockchain node needs to monitor in real time whether there is a human resource retrieval instruction. This can be achieved by monitoring network messages or other mechanisms to ensure that the node can perceive the emergence of the human resource retrieval instruction in time; when the blockchain node receives the human resource retrieval instruction, it needs to extract the human resource information to be retrieved from the instruction. This can be obtained by parsing the instruction data format and extracting the corresponding fields or parameters to obtain the human resource information to be retrieved; according to the human resource information to be retrieved, the blockchain node needs to determine the blockchain node that stores this information. This can be done by querying the blocks in the blockchain network or using a specific search algorithm to locate the blockchain node containing the required information; after determining the blockchain node that stores the human resource information to be retrieved, the blockchain subnode closest to the current node is selected as the target node according to the principle of the shortest network communication distance. This can be achieved through network topology and distance calculation to ensure the efficiency and speed of information retrieval and can adapt to human resource management needs of different scales and complexities; through the target blockchain subnode, the current node can request and obtain the required human resource information. The target node will provide the corresponding information, which may be a way of directly returning data or exchanging data. By extracting and transmitting only the required human resource information, the risk of spreading and exposing sensitive data can be reduced, and the security of data can be further improved. By selecting the target blockchain subnode for retrieval according to the principle of shortest network communication distance, network load balancing and optimal resource utilization can be achieved; network congestion and resource competition can be reduced, and the performance and stability of the entire blockchain network can be improved.

[0128] The effects of the above technical solution are: by real-time monitoring of human resources retrieval instructions, user requests can be responded to in a timely manner to improve the real-time and efficiency of the system; by extracting the human resources information to be retrieved from the instructions, unnecessary information can be avoided and data retrieval efficiency and security can be improved; by querying the blocks in the blockchain network or using a specific search algorithm to locate the blockchain node containing the required information, the required information can be quickly located and the data retrieval efficiency can be improved; by selecting the blockchain subnode closest to the current node as the target node according to the principle of shortest network communication distance, the impact of data transmission delays and network congestion on data retrieval can be reduced, thereby improving data retrieval efficiency and quality; through the target blockchain subnode, the required human resources information can be requested and obtained to ensure the accuracy and reliability of data retrieval.

[0129] The embodiment of the present invention proposes a human resources data processing system based on recursive blockchain, such as Figure 2 As shown, the human resources data processing system includes:

[0130] A dynamic evaluation parameter acquisition module, used for the blockchain node to acquire dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself after receiving the human resources information;

[0131] A target blockchain node acquisition module is used to determine the target blockchain node corresponding to the blockchain node by combining the dynamic evaluation parameters of the candidate subnodes of the consensus chain of the blockchain node, and to share human resources information;

[0132] The human resources information retrieval module is used to monitor in real time whether a human resources retrieval instruction appears. When the blockchain node receives the human resources retrieval instruction, it provides the human resources information to be retrieved through the target blockchain sub-node.

[0133] The working principle of the above technical solution is: after receiving human resources information, the blockchain node uses its own first dynamic evaluation coefficient, second dynamic evaluation coefficient and third dynamic evaluation coefficient to obtain dynamic evaluation parameters. The dynamic evaluation parameters may include but are not limited to information such as node performance, credibility, historical performance, etc., to evaluate the node's ability and reliability in processing and sharing human resources information.

[0134] Determination of target blockchain nodes and sharing of human resources information: By combining dynamic evaluation parameters with the dynamic evaluation parameters of candidate subnodes in the blockchain node consensus chain, the target blockchain node can be determined and human resources information can be shared. This node selection mechanism based on dynamic evaluation parameters can ensure that information is shared between reliable and high-performance nodes, improving information security and processing efficiency. Finally, real-time monitoring is performed to determine whether human resources retrieval instructions appear. When a human resources retrieval instruction is received, the target blockchain subnode provides the human resources information that needs to be retrieved, which can make information retrieval fast and accurate, further improving the system's response speed and processing efficiency.

[0135] The effects of the above technical solution are as follows: Blockchain technology has the characteristics of decentralization and distributed storage, and each block contains the hash value of the previous block, so that the data cannot be tampered with in the blockchain network. This mechanism can ensure the security and immutability of human resources data; the data on the blockchain is open and transparent, and each node can view and verify the information on the blockchain. This makes the human resources data processing process more transparent and traceable, reducing information asymmetry and potential fraud; through dynamic evaluation parameters, the blockchain node can determine the target blockchain node based on its own evaluation coefficient and the evaluation parameters of the candidate sub-nodes of the consensus chain. In this way, the optimal node can be selected for data sharing based on the trust and performance of the node, improving the overall efficiency and performance; the blockchain node can monitor in real time whether there is a human resources call instruction, and provide the required human resources information through the target blockchain sub-node. In this way, it can respond to user needs in a timely manner and improve the real-time and accuracy of data processing. At the same time, the above technical solution introduces the first, second and third dynamic evaluation coefficients, so that when processing human resources information, the blockchain nodes can be dynamically adjusted according to real-time situations, historical data and other relevant factors, thereby reducing the load and resource occupancy of a single node, and significantly improving the processing efficiency of the entire system. At the same time, by adopting a recursive blockchain structure, the scalability and adaptability of the above technical solution are significantly increased. As the business scale grows or demand changes, the system can expand new nodes or functions without large-scale reconstruction or adjustment.

[0136] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A human resources data processing method based on recursive blockchain, characterized in that: The human resources data processing method comprises: After receiving the human resources information, the blockchain node obtains the dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself; Determine the target blockchain node corresponding to the blockchain node by combining the dynamic evaluation parameters of the candidate sub-nodes of the consensus chain of the blockchain node, and share the human resources information; Real-time monitoring of whether a human resources retrieval instruction appears. When the blockchain node receives the human resources retrieval instruction, the target blockchain subnode provides the human resources information to be retrieved; After receiving the human resources information, the blockchain node obtains dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself, including: The blockchain node monitors in real time whether human resources information is received, and uses the blockchain node that has received the human resources information as a parent node; Retrieving the historical operation record of the parent node, and obtaining the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient corresponding to the parent node through the historical operation record of the parent node; Acquire a dynamic evaluation parameter of the parent node using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient; Retrieving the historical operation record of the parent node, and obtaining the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient corresponding to the parent node through the historical operation record of the parent node, including: Collect node performance indicator parameters in the historical operation record of the parent node, and use the node performance indicator parameters to obtain a first dynamic evaluation coefficient; wherein the node performance indicator parameters include node response time information, node throughput information, and node availability information; and use a first dynamic evaluation coefficient model to obtain the first dynamic evaluation coefficient; wherein the first dynamic evaluation coefficient model is as follows: Among them, F 01 represents the first dynamic evaluation coefficient of the parent node; P represents the node availability evaluation factor; m represents the number of unit times contained in the historical operation record, and the unit time is 1s; n represents the number of request processing in the historical operation record of the current parent node; C i represents the data processing amount of the parent node per unit time; C max Indicates the maximum amount of data processing per unit time of the parent node allowed by the node load; T i represents the data processing time length of the parent node processing the i-th request or transaction; T0 represents the theoretical processing time length of the i-th request or transaction; ΔT represents the maximum allowable delay length corresponding to the normal operation of the parent node; S represents the success rate of data processing in the historical operation record of the parent node; L represents the failure rate of the parent node in the historical operation record; T h Indicates the total recovery time of the parent node failure; T z Indicates the total running time of the parent node; Collecting node security index parameters in the historical operation records of the parent node, and using the node security index parameters to obtain a second dynamic evaluation coefficient; wherein the node security index parameters include the node's recognition rate of malicious behavior, the node's error rate of processing requests, and the node's strength information for resisting attacks, and using a second dynamic evaluation coefficient model to obtain the second dynamic evaluation coefficient; Collect data synchronization consensus indicator parameters in the historical operation records of the parent node, and use the data synchronization consensus indicator parameters to obtain a third dynamic evaluation coefficient; wherein the data synchronization consensus indicator parameters include data synchronization speed and consensus mechanism efficiency; and use a third dynamic evaluation coefficient model to obtain the third dynamic evaluation coefficient.

2. The human resources data processing method based on recursive blockchain according to claim 1 is characterized in that: Acquiring the dynamic evaluation parameter of the parent node using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient includes: Retrieving the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient; The first dynamic evaluation coefficient, the second dynamic evaluation coefficient, and the third dynamic evaluation coefficient obtain the dynamic evaluation parameter of the parent node, wherein the dynamic evaluation parameter of the parent node is obtained by the following formula: Where F represents the dynamic evaluation parameter of the parent node; F0 represents the preset parameter benchmark value; F 01 Represents the first dynamic evaluation coefficient of the parent node; F 02 Represents the second dynamic evaluation coefficient of the parent node; F 03 Indicates the third dynamic evaluation coefficient of the parent node.

3. The human resources data processing method based on recursive blockchain according to claim 1 is characterized in that: Determine the target blockchain node corresponding to the blockchain node by combining the dynamic evaluation parameters with the dynamic evaluation parameters of the candidate sub-nodes of the consensus chain of the blockchain node, and share the human resources information, including: Extract the blockchain nodes that have a consensus chain with the parent node as candidate child nodes; Retrieving the historical operation records of the candidate child node; Obtaining a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate subnode by using the historical operation record of the candidate subnode; Acquire a dynamic evaluation parameter of the candidate subnode using a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate subnode; The target blockchain child node is determined by utilizing the parameter relationship between the dynamic evaluation parameters of the candidate child node and the dynamic evaluation parameters of the parent node.

4. The human resources data processing method based on recursive blockchain according to claim 3 is characterized in that: Acquiring a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate subnode by using the historical operation record of the candidate subnode includes: Collecting node performance indicator parameters in the historical operation records of the candidate child nodes, and using the node performance indicator parameters to obtain a first dynamic evaluation coefficient; Collecting node safety index parameters in the historical operation records of the candidate child node, and using the node safety index parameters to obtain a second dynamic evaluation coefficient; Collecting data synchronization consensus indicator parameters in the historical operation records of the candidate child node, and using the data synchronization consensus indicator parameters to obtain a third dynamic evaluation coefficient; The communication quality indicator parameters in the historical operation records of the candidate subnode are collected, and the fourth dynamic evaluation coefficient is obtained by using the communication quality indicator parameters.

5. The human resources data processing method based on recursive blockchain according to claim 3 is characterized in that: Acquiring a dynamic evaluation parameter of the candidate subnode using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient, and the fourth dynamic evaluation coefficient of the candidate subnode includes: Retrieving a first dynamic evaluation coefficient, a second dynamic evaluation coefficient, a third dynamic evaluation coefficient, and a fourth dynamic evaluation coefficient of the candidate child node; The dynamic evaluation parameters of the candidate child node are obtained by using the first dynamic evaluation coefficient, the second dynamic evaluation coefficient, the third dynamic evaluation coefficient and the fourth dynamic evaluation coefficient; wherein the dynamic evaluation parameters of the candidate child node are obtained by the following formula: Wherein, Z represents the dynamic evaluation parameter of the candidate child node; Z0 represents the preset parameter reference value corresponding to the candidate child node; Z 01 Represents the first dynamic evaluation coefficient of the candidate child node; Z 02 Represents the second dynamic evaluation coefficient of the candidate child node; Z 03 Represents the third dynamic evaluation coefficient of the candidate child node; Z 04 Indicates the fourth dynamic evaluation coefficient of the candidate child node.

6. The human resources data processing method based on recursive blockchain according to claim 3 is characterized in that: Determining a target blockchain child node using a parameter relationship between a dynamic evaluation parameter of the candidate child node and a dynamic evaluation parameter of the parent node includes: Determine whether the dynamic evaluation parameters of the candidate child node and the dynamic evaluation parameters of the parent node meet preset parameter constraints; The candidate subnodes whose dynamic evaluation parameters satisfy the parameter constraints are selected as the target blockchain subnodes.

7. The human resources data processing method based on recursive blockchain according to claim 1 is characterized in that: Real-time monitoring of whether a human resources retrieval instruction appears. When the blockchain node receives the human resources retrieval instruction, the target blockchain subnode provides the human resources information to be retrieved, including: Monitor in real time whether there are any human resources retrieval instructions; When the blockchain node receives the human resources retrieval instruction, it extracts the human resources information to be retrieved from the human resources retrieval instruction; Acquire, according to the human resources information to be retrieved, a blockchain node storing the human resources information to be retrieved; According to the principle of shortest network communication distance, the blockchain node storing the human resources information to be retrieved is retrieved as the target blockchain subnode for retrieving the information; The human resources information that needs to be retrieved is provided through the target blockchain subnode.

8. A system for implementing the human resources data processing method based on recursive blockchain as claimed in claim 1, characterized in that: The system comprises: A dynamic evaluation parameter acquisition module is used for the blockchain node to acquire dynamic evaluation parameters through the first dynamic evaluation coefficient, the second dynamic evaluation coefficient and the third dynamic evaluation coefficient of the blockchain node itself after receiving the human resources information; A target blockchain node acquisition module is used to determine the target blockchain node corresponding to the blockchain node by combining the dynamic evaluation parameters of the candidate subnodes of the consensus chain of the blockchain node, and to share human resources information; The human resources information retrieval module is used to monitor in real time whether a human resources retrieval instruction appears. When the blockchain node receives the human resources retrieval instruction, it provides the human resources information to be retrieved through the target blockchain sub-node.

Images