A Distributed Credit Database Load Balancing Adjustment Method and System

Abstract

This invention provides a load balancing adjustment method and system for a distributed credit reporting database, relating to the field of load adjustment technology. The method includes: distributing encrypted communication parameters to each distributed node in the distributed credit reporting database; performing bidirectional security authentication on each node based on the encrypted communication parameters to establish an encrypted communication channel; calculating the load rate of each node and identifying overloaded nodes based on the load rate; overloaded nodes issuing migration requests, generating encrypted request packets and broadcasting them to neighboring nodes; neighboring nodes parsing the request packets and determining whether they meet the migration criteria; if so, they construct encrypted response packets and send them to the overloaded nodes; otherwise, they ignore the request packets; the overloaded nodes receiving and parsing the encrypted response packets, determining candidate migration nodes based on the load status of neighboring nodes, and transmitting the data to be migrated to the candidate migration nodes; after migration is completed, calculating the overall resource utilization rate and fine-tuning the overload threshold through a negative feedback mechanism.

Description

Technical Field

[0001] This invention relates to the field of load balancing technology, and in particular to a method and system for load balancing of a distributed credit database. Background Technology

[0002] With the widespread application of technologies such as big data and blockchain, credit reporting databases have gradually evolved from a centralized architecture to a distributed deployment model. Distributed credit reporting systems improve the system's scalability, fault tolerance, and service availability by distributing data and task processing capabilities to multiple geographically dispersed nodes.

[0003] Currently, several methods are commonly used to address load imbalance in distributed systems. One approach is based on a centralized scheduler controller, which monitors the load status of each node and allows the central node to allocate tasks or migrate loads according to a specific strategy. Another approach uses static load sharding or predefined task allocation rules, dividing tasks based on node capacity, preset weights, and other metrics at the initial stage of the task allocation process.

[0004] However, existing distributed credit databases lack real-time perception and dynamic adjustment mechanisms for the load status of each node, making it difficult for the system to identify and alleviate node overload in a timely manner, resulting in limited load balancing effectiveness. Furthermore, the lack of robust security measures during load migration, coupled with the absence of two-way authentication and encrypted communication channels, makes it difficult to effectively guarantee the security and reliability of credit data during the migration process. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a load balancing adjustment method for a distributed credit reporting database. This method addresses the shortcomings of existing distributed credit reporting databases, which lack real-time perception and dynamic adjustment mechanisms for the load status of each node. This results in the system's inability to promptly identify and alleviate node overload, leading to limited load balancing effectiveness. Furthermore, the lack of robust security measures during load migration, coupled with the absence of bidirectional authentication and encrypted communication channels, makes it difficult to effectively guarantee the security and reliability of credit reporting data during migration.

[0006] A first aspect of this invention provides a load balancing adjustment method for a distributed credit reporting database, comprising:

[0007] S1: The distributed credit database distributes encrypted communication parameters to each distributed node;

[0008] S2: Based on the encrypted communication parameters, perform bidirectional security authentication on each of the distributed nodes, and establish an encrypted communication channel for interaction between the authenticated nodes.

[0009] S3: Calculate the load rate of each of the distributed nodes, and determine the overloaded nodes among the distributed nodes based on the load rate.

[0010] S4: The overloaded node sends a load migration request, generates an encrypted request packet, and broadcasts it to its neighboring nodes;

[0011] S5: The neighboring node parses the encrypted request packet and determines whether its own load meets the migration conditions; if so, it constructs an encrypted response packet and sends it to the overloaded node, proceeding to S6; otherwise, it ignores the encrypted request packet.

[0012] S6: The overloaded node parses the encrypted response packet and determines candidate load migration nodes based on the load status of the neighboring nodes;

[0013] S7: Migrate the data to be migrated from the overloaded node to the candidate load migration node;

[0014] S8: Calculate the resource utilization rate of the distributed credit database after load migration, and based on the resource utilization rate, fine-tune the overload threshold through a negative feedback adaptive mechanism, and return to S3.

[0015] A second aspect of this invention provides a distributed credit reporting database load balancing adjustment system, comprising: a processor and a memory;

[0016] The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the distributed credit database load balancing adjustment method as described in the first aspect.

[0017] A third aspect of the present invention provides a readable storage medium on which a program or instructions are stored, wherein when the program or instructions are executed by a processor, the steps of the distributed credit database load balancing adjustment method as described in the first aspect are implemented.

[0018] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0019] In this embodiment of the invention, by real-time calculation and overload identification of the load status of each node, and by utilizing an encrypted communication request and response mechanism, a suitable migration target is collaboratively selected among neighboring nodes to execute data migration. This effectively enhances the system's load regulation capability and improves the flexibility and efficiency of resource allocation. Combining the migration results with resource utilization evaluation, the system continuously optimizes the adjustment strategy through a negative feedback mechanism, further improving overall adaptive performance. Simultaneously, through encrypted communication parameter allocation, two-way authentication, and end-to-end encrypted transmission, the security of the data migration process is enhanced, improving the reliability and protection strength of credit data transmission in a distributed environment. Attached Figure Description

[0020] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0021] Figure 1 This is a flowchart illustrating a distributed credit database load balancing adjustment method provided in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the structure of a distributed credit database load balancing adjustment system provided in an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] The distributed credit reporting database load balancing adjustment method provided by the present invention will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0025] Reference manual attached Figure 1 The diagram shows a flowchart of a distributed credit database load balancing adjustment method provided by an embodiment of the present invention.

[0026] This invention provides a distributed credit reporting database load balancing adjustment method, which may include the following steps:

[0027] S1: The distributed credit database distributes encrypted communication parameters to each distributed node.

[0028] Among them, the distributed credit reporting database is a data system that combines distributed computing architecture with credit reporting business scenarios. It introduces key technologies such as distributed nodes, encryption authentication mechanisms, and load balancing strategies on the basis of the traditional centralized credit reporting model, aiming to improve the security, scalability, anti-tampering ability of credit reporting data, and overall fault tolerance of the system.

[0029] In one possible implementation, the encrypted communication parameters specifically include: a unique identifier, a node private key, and a shared cloud key.

[0030] In this embodiment of the invention, the allocation of encrypted communication parameters enables the entire distributed credit reporting system to have a secure and reliable identity authentication foundation, efficient encrypted communication capabilities, and strong scalability and risk resistance, which is a key prerequisite for the system to achieve "distributed trust and collaboration".

[0031] S2: Based on encrypted communication parameters, perform two-way security authentication on each distributed node and establish an encrypted communication channel for interaction between the authenticated nodes.

[0032] In one possible implementation, S2 specifically includes:

[0033] S201: Each distributed node uses a shared cloud key to encrypt its own distributed identity and distributed key, constructing a secure authentication request packet:

[0034]

[0035] in, This indicates a security authentication request packet. This indicates an encryption function that uses a shared cloud key for encryption. Represents distributed identity identification. This represents the data concatenation operator. This represents a distributed key.

[0036] It should be noted that by encrypting sensitive identity information and the private key, it is ensured that even if the authentication request is intercepted during broadcast, it cannot be parsed or forged. This guarantees the privacy and integrity of the authentication request.

[0037] S202: Broadcast the security authentication request packet to neighboring nodes.

[0038] It should be noted that broadcasting allows authentication requests to spread rapidly across the local network, making the authentication process efficient, parallel, and without requiring single points of communication.

[0039] S203: Neighboring nodes use the shared cloud key to decrypt the security authentication request packet and extract the distributed identity and distributed key of the distributed node.

[0040] S204: The neighboring node sends an identity verification request for the distributed node to the distributed credit database and determines whether the identity verification is successful. If yes, the distributed node is marked as an authenticated node, and the process proceeds to S205. Otherwise, the distributed node is ignored.

[0041] In this embodiment of the invention, introducing a central database for secondary identity verification can effectively prevent fake or unregistered nodes from participating in communication, fundamentally improving the credibility of all nodes in the network and realizing the maintenance of a dynamic trust system.

[0042] S205: The neighboring node concatenates its own neighborhood identity and neighborhood key, encrypts them using the distributed key to generate a response packet, and sends it to the distributed node.

[0043] Optionally, the response packet specifically includes:

[0044]

[0045] in, Indicates a response packet. This indicates an encryption function that uses distributed key encryption. Indicates the identity of neighboring nodes. This represents the neighboring private key.

[0046] It should be noted that the encrypted response packet provides peer-to-peer information exchange for authentication, meaning that the requesting node can also verify the identity of its neighboring nodes, laying the foundation for two-way authentication, and using the other party's key for encryption to ensure that the response information can only be read by the target node.

[0047] S206: Distributed nodes use a distributed key to decrypt the primary response packet, extract the node information of neighboring nodes along with their own identity identifier, and use a shared cloud key to encrypt and generate a secondary authentication packet, which is then sent to the distributed credit database.

[0048] Optionally, the two-factor authentication package specifically includes:

[0049]

[0050] in, This indicates a two-factor authentication package. This indicates an encryption function that uses a shared cloud key for encryption.

[0051] It should be noted that this layer of nested cryptographic verification ensures that the responses from neighboring nodes are not forged.

[0052] In this embodiment of the invention, a dual verification mechanism is implemented by interacting with the database, which also enhances the system's resistance to "man-in-the-middle forgery responses".

[0053] S207: The distributed credit database decrypts the secondary authentication packet, extracts the node information and neighborhood key of neighboring nodes, and performs secondary authentication on the neighboring nodes. The node information and neighborhood key of the successfully authenticated neighboring nodes are then encrypted using the distributed key to generate a return packet, which is returned to the distributed node.

[0054] Optionally, the return packet specifically includes:

[0055]

[0056] in, This indicates the return packet.

[0057] It should be noted that by participating in the verification process again through a centralized database, stronger authority endorsement can be provided to neighboring nodes, ensuring the final reliability of the authentication results, and ensuring the secure transmission of packets through keys.

[0058] S208: The distributed node decrypts the returned packet and compares the decrypted neighborhood key with the original neighborhood key. If the comparison is successful, it is considered that the two-way authentication is successful, and the establishment of the encrypted communication channel between the two parties is completed.

[0059] It should be noted that all authenticated nodes will form an authenticated node database, which can be used to query information about authenticated nodes.

[0060] S209: Each successfully authenticated node generates a public key and a private key pair, and broadcasts each public key to its corresponding neighboring nodes for encrypted transmission of subsequent task payloads.

[0061] It should be noted that the broadcast public key provides a reusable encryption tool for subsequent tasks, data transmission, and other operations, avoiding the need for re-authentication for each communication.

[0062] In this embodiment of the invention, a two-way security authentication mechanism based on encrypted communication parameters is designed to achieve security guarantees at multiple levels among distributed nodes, including identity trust, information encryption, and interaction verification. This ensures that each node participating in communication undergoes rigorous verification, preventing forgery and unauthorized access. A dynamic, trusted chain of trust is constructed through multi-layered nested encryption and database verification. Simultaneously, a high-security encrypted communication channel is established for subsequent task collaboration, data migration, and other operations, thereby significantly improving the security, reliability, and collaborative efficiency of the entire distributed credit reporting system.

[0063] S3: Calculate the load rate of each distributed node, and based on the load rate, determine the overloaded nodes among the distributed nodes.

[0064] In one possible implementation, S3 specifically includes:

[0065] S301: Collects the running status data of each distributed node, including the current number of tasks and the maximum task processing capacity.

[0066] S302: Calculate the load rate of each distributed node based on the current number of tasks and the maximum number of tasks that can be processed.

[0067]

[0068] in, Indicates the first a The load rate of each distributed node. Indicates the first a The current number of tasks on each distributed node. Indicates the first a The maximum number of tasks that can be processed by a distributed node.

[0069] S303: Identify distributed nodes whose load rate is greater than the first preset load rate as overloaded nodes.

[0070] Optionally, distributed nodes with a load rate less than or equal to a first preset load rate are treated as ordinary nodes, which only need to complete their own tasks and do not participate in load migration.

[0071] It should be noted that those skilled in the art can set the size of the first preset load rate according to actual needs, and the present invention does not limit this.

[0072] In this embodiment of the invention, by periodically collecting the number of tasks and maximum processing capacity of each node and calculating the load rate, high-load nodes can be identified in a timely manner, thereby enabling real-time monitoring and refined management of the entire distributed system's operating status.

[0073] S4: The overloaded node issues a load migration request, generates an encrypted request packet, and broadcasts it to neighboring nodes.

[0074] In one possible implementation, S4 specifically includes:

[0075] S401: Overloaded nodes collect data information for load migration requests, including their own identity, the amount of load to be migrated, and the task deadline.

[0076] It should be noted that by collecting all migration-related parameters, we can clearly identify who needs to migrate, how much needs to be migrated, and when it must be completed, providing quantifiable and decision-making data for subsequent load scheduling.

[0077] S402: Encapsulate its own identity, the amount of workload to be migrated, and the task deadline into a structured control packet:

[0078]

[0079] in, This represents a structured control package. This indicates the identity of the overloaded node. Indicates the amount of load to be migrated. Indicates the task deadline.

[0080] It should be noted that structured encapsulation gives request information a unified data format, which facilitates system parsing and processing, improves communication efficiency and protocol compatibility between nodes, and also facilitates data verification and version control, reducing the risk of misprocessing.

[0081] S403: Using the public key of the neighboring node, encrypt the structured control packet and generate an encrypted request packet:

[0082]

[0083] in, This indicates an encrypted request packet. This represents an encryption function that uses the public key of a neighboring node for encryption. This represents the data concatenation operator.

[0084] It should be noted that by using an asymmetric encryption mechanism, the migration request can only be decrypted and read by the target neighbor node, which avoids data leakage or tampering during the broadcast process, improves the security and reliability of information transmission, and can prevent man-in-the-middle attacks or interference from malicious nodes.

[0085] S404: Broadcast the encrypted request packet to neighboring nodes via the distributed network.

[0086] In this embodiment of the invention, a secure, controllable, and collaborative task migration request mechanism is constructed by standardizing, encrypting, and efficiently broadcasting load migration information. This not only ensures the confidentiality and integrity of migration data during network transmission but also improves the flexibility and response speed of system load adjustment through a multi-node response mechanism, laying a solid foundation for dynamic load balancing and efficient resource utilization in the entire distributed system.

[0087] S5: The neighboring node parses the encrypted request packet and determines whether its own load meets the migration conditions. If so, it constructs an encrypted response packet and sends it to the overloaded node, proceeding to S6. Otherwise, it ignores the encrypted request packet.

[0088] In one possible implementation, S5 specifically includes:

[0089] S501: The neighboring node uses its own private key to decrypt the encrypted request packet and extract the identity of the overloaded node, the load to be migrated, and the task deadline.

[0090] In this embodiment of the invention, the encrypted request packet is parsed using the user's private key, ensuring that only legitimate target neighbor nodes can read the request content. This protects the privacy and targeting of the migration request, while also ensuring the consistency of the identities of the communicating parties, effectively preventing malicious nodes from forging requests or illegally eavesdropping.

[0091] S502: Query the authenticated node database to determine if the overloaded node's identity is present in the database. If yes, proceed to S503. Otherwise, ignore the encrypted request packet.

[0092] In this embodiment of the invention, an identity verification mechanism is used to ensure that neighboring nodes only respond to requests from trusted and authenticated nodes, thereby fundamentally eliminating malicious requests with forged identities and improving the overall trust level and anti-attack capability of the system.

[0093] S503: Calculate the current load rate of neighboring nodes.

[0094] It should be noted that calculating the current load rate can effectively prevent tasks from being assigned to nodes that are already nearing saturation, enabling a more intelligent and refined load adjustment strategy.

[0095] S504: Determine whether the current load rate of the neighboring node is less than the second preset load rate and whether the available resources of the neighboring node are greater than the load to be migrated. If yes, proceed to S505. Otherwise, ignore the encrypted request packet.

[0096] It should be noted that those skilled in the art can set the second preset load rate according to actual needs, and this invention does not limit it.

[0097] In this embodiment of the invention, a dual judgment mechanism (load rate + available resources) is used to ensure that only nodes with sufficient processing capacity participate in task reception, thereby avoiding a new round of local overload or system jitter caused by migration and improving the stability and success rate of task distribution.

[0098] S505: Encrypt the identity, association key, and current load rate of the neighboring node using the public key of the overloaded node, generate an encrypted response packet, and send the encrypted response packet to the overloaded node.

[0099] It should be noted that neighboring nodes provide overloaded nodes with accurate and reliable resource information, providing reliable data support for overloaded nodes to make subsequent migration decisions.

[0100] S6: The overloaded node parses the encrypted response packet and determines the candidate load migration node based on the load status of the neighboring nodes.

[0101] In one possible implementation, S6 specifically includes:

[0102] S601: Overloaded nodes decrypt encrypted response packets and extract the identity, association key, and load rate of neighboring nodes.

[0103] It should be noted that by parsing the encrypted response packet, the overloaded node can securely and accurately obtain the key resource status information of the candidate node, providing basic data for subsequent screening and scheduling, and protecting the privacy and data security of the response node.

[0104] S602: Query the authenticated node database to determine if the neighboring node's identity is present in the database. If yes, proceed to S603. Otherwise, ignore the encrypted response packet.

[0105] S603: Retrieve the association key of the neighboring node from the authenticated node database and compare it with the association key extracted from the encrypted response packet. If they match, proceed to S604. Otherwise, ignore the encrypted response packet.

[0106] In this embodiment of the invention, the dual verification mechanism can prevent unauthorized nodes with forged identities from entering the system and prevent registered nodes from using tampered or misused keys to participate in task migration, effectively improving the system's security, trustworthiness, and anti-forgery capabilities, and ensuring that the entire communication and scheduling process is conducted on a trustworthy basis.

[0107] S604: Calculate the priority score of each neighboring node:

[0108]

[0109]

[0110]

[0111] in, Indicates the first k Priority scores of each neighboring node. Indicates the weighting coefficient. Indicates the first k The normalized value of the minimum remaining time of each neighboring node's own task. Indicates the first k The normalized value of the load rate of each neighboring node. This represents the remaining time for neighboring nodes. Represents a constant. This represents the load rate of neighboring nodes. Represents all neighboring nodes The maximum value, Represents all neighboring nodes The minimum value, Represents all neighboring nodes The maximum value, Represents all neighboring nodes The maximum value.

[0112] In this embodiment of the invention, the urgency of the task and the status of node resources are taken into account, and adjustable weights are used to implement flexible scheduling strategy configuration, avoiding resource mismatch caused by single-dimensional decision-making, and making the task migration process more intelligent and goal-oriented.

[0113] S605: Sort the priority scores in descending order and determine the neighbor node with the highest priority score as the candidate load migration node.

[0114] S7: Migrate the data to be migrated from the overloaded node to the candidate load migration node.

[0115] In one possible implementation, the process after S7 includes:

[0116] S7A: Calculate the task default rate of each neighboring node.

[0117] S7B: Determine if the default rate exceeds the preset default rate. If yes, proceed to S7C. Otherwise, proceed to S8.

[0118] It should be noted that those skilled in the art can set the default rate according to actual needs, and this invention does not limit this.

[0119] S7C: Increase the weight of the remaining time item when selecting candidate load migration nodes, proceed to S8.

[0120] In this embodiment of the invention, when the execution performance after task migration is found to be unsatisfactory, the system dynamically identifies an upward trend in the default rate and automatically adjusts the migration strategy parameters. This enables the system to have self-learning and self-adjustment capabilities, allowing it to continuously optimize its behavior under different business scenarios and node status changes. Simultaneously, when it detects that task timeliness is being neglected due to over-reliance on low-load nodes, it proactively increases the influence of the indicator corresponding to the task's "remaining time of neighboring nodes," significantly reducing the default risk of migration tasks and improving the overall on-time completion rate and service quality.

[0121] S8: Calculate the resource utilization of the distributed credit reporting database after load migration, and based on the resource utilization, fine-tune the overload threshold through a negative feedback adaptive mechanism, and return to S3.

[0122] It should be noted that when low resource utilization is detected (such as when nodes are generally lightly loaded), the overload threshold is lowered to make it easier for the system to identify "overloaded nodes", thereby prompting more tasks to migrate between nodes.

[0123] In one possible implementation, S8 specifically includes:

[0124] S801: Calculate the resource utilization rate of the distributed credit reporting database:

[0125]

[0126] in, Indicates resource utilization rate, N Indicates the total number of nodes. Indicates the first a The current number of tasks on each distributed node. Indicates the first a The maximum number of tasks that can be processed by a distributed node.

[0127] S802: Determine if the resource utilization rate is less than the preset resource utilization rate. If yes, proceed to S803. Otherwise, return to S3.

[0128] It should be noted that those skilled in the art can set the preset resource utilization rate according to actual needs, and this invention does not limit this.

[0129] S803: Update the overload threshold through a negative feedback adaptive mechanism, return to S3, and apply the updated overload threshold to the subsequent node status monitoring process.

[0130] Optionally, the formula for updating the overload threshold is:

[0131]

[0132] in, This indicates the updated overload threshold. Indicates the current overload threshold. Indicates the learning rate. Indicates the target resource utilization rate. This represents the current resource utilization rate.

[0133] Optionally, The target resource utilization rate is 0.8. The current resource utilization rate is between (0, 0.9).

[0134] For example, the target resource utilization rate is 0.8, and the current overload threshold is 0.7.

[0135] Scenario 1: The current utilization rate is too low (the threshold needs to be lowered). The current resource utilization rate is 0.5, so the overload threshold needs to be lowered to make more nodes identified as overloaded nodes, thereby improving the utilization rate. That is, the updated overload threshold = 0.7 × (1 - 0.1 × (0.8 - 0.55)) = 0.6825.

[0136] Scenario 2: Current utilization is too high (threshold needs to be increased). The current resource utilization is 0.87, so the overload threshold needs to be increased to reduce the number of nodes identified as overloaded, thereby reducing utilization and alleviating system pressure. The new overload threshold is calculated as: 0.7 × (1 - 0.1 × (0.8 - 0.87)) = 0.7049.

[0137] In this embodiment of the invention, a resource utilization-driven negative feedback adaptive mechanism is introduced to dynamically adjust the overload threshold, enabling the system to automatically optimize task scheduling strategies based on the current operating state. This mechanism not only improves the utilization efficiency of network resources but also enhances the system's flexibility and self-regulation capabilities in the face of load fluctuations, thereby achieving an efficient and balanced distributed resource management and regulation system.

[0138] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0139] In this embodiment of the invention, by real-time calculation and overload identification of the load status of each node, and by utilizing an encrypted communication request and response mechanism, a suitable migration target is collaboratively selected among neighboring nodes to execute data migration. This effectively enhances the system's load regulation capability and improves the flexibility and efficiency of resource allocation. Combining the migration results with resource utilization evaluation, the system continuously optimizes the adjustment strategy through a negative feedback mechanism, further improving overall adaptive performance. Simultaneously, through encrypted communication parameter allocation, two-way authentication, and end-to-end encrypted transmission, the security of the data migration process is enhanced, improving the reliability and protection strength of credit data transmission in a distributed environment.

[0140] Reference manual attached Figure 2The diagram shows a structural schematic of a distributed credit database load balancing adjustment system provided in an embodiment of the present invention.

[0141] This invention provides a distributed credit database load balancing adjustment system 20, including: a processor 201 and a memory 202;

[0142] The memory 202 stores programs or instructions that can run on the processor 201. When the program or instructions are executed by the processor 201, they implement the steps of the above-described distributed credit database load balancing adjustment method and achieve the same technical effect. To avoid repetition, the present invention will not elaborate further.

[0143] It should be understood that the processor 201 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0144] It should also be understood that the memory 202 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be 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. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0145] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0146] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0147] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0148] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0149] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0152] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0153] This invention provides a readable storage medium comprising: storing a program or instructions on the readable storage medium, wherein when the program or instructions are executed by a processor, the program or instructions implement the steps of the above-described distributed credit database load balancing adjustment method and achieve the same technical effect. To avoid repetition, this invention will not elaborate further.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A method for load balancing adjustment of a distributed credit database, characterized in that, The method comprises the following steps: S1: a distributed credit investigation database distributes encryption communication parameters to each distributed node; S2: based on the encryption communication parameters, bidirectional security authentication is performed on each distributed node, and an encryption communication channel for interaction between each node that passes the authentication is established; S3: the load rate of each distributed node is calculated, and an overloaded node in each distributed node is determined according to each load rate; S4: the overloaded node sends a load migration request, generates an encryption request package and broadcasts it to a neighbor node; S5: the neighbor node parses the encryption request package and judges whether the load of the neighbor node meets the migration condition; if yes, an encryption response package is constructed and sent to the overloaded node, and S6 is entered; otherwise, the encryption request package is ignored; S6: the overloaded node parses the encryption response package and determines a candidate load migration node based on the load state of the neighbor node; S7: the data to be migrated of the overloaded node is migrated to the candidate load migration node; S8: the resource utilization rate of the distributed credit investigation database after load migration is calculated, and the overload threshold is fine-tuned through a negative feedback adaptive mechanism based on the resource utilization rate, and S3 is returned; wherein S8 specifically comprises: S801: the resource utilization rate of the distributed credit investigation database is calculated; S802: it is judged whether the resource utilization rate is less than a preset resource utilization rate; if yes, S803 is entered; otherwise, S3 is returned; S803: the overload threshold is updated through the negative feedback adaptive mechanism, S3 is returned, and the updated overload threshold is applied to the subsequent node state monitoring process; wherein the formula for updating the overload threshold is: wherein, represents an updated overload threshold, represents a current overload threshold, represents a learning rate, represents a target resource utilization, represents a current resource utilization.

2. The method of claim 1, wherein, The encryption communication parameters specifically comprise a unique identity, a node private key and a shared cloud key.

3. The method of claim 2, wherein, S2 specifically comprises: S201: each distributed node encrypts its own distributed identity and distributed key using the shared cloud key, and constructs a security authentication request package; S202: the security authentication request package is broadcasted to the neighbor node; S203: the neighbor node decrypts the security authentication request package using the shared cloud key, and extracts the distributed identity and the distributed key of the distributed node; S204: the neighbor node sends an identity verification request for the distributed node to the distributed credit investigation database, and judges whether the identity verification is successful; if yes, the distributed node is marked as an authenticated node, and S205 is entered; otherwise, the distributed node is ignored; S205: the neighbor node splices its own neighborhood identity and neighborhood key, and encrypts them using the distributed key to generate a one-time response package, which is sent to the distributed node; S206: the distributed node decrypts the one-time response package using the distributed key, extracts the node information of the neighbor node together with its own identity, and encrypts them using the shared cloud key to generate a secondary authentication package, which is sent to the distributed credit investigation database; S207: The distributed credit investigation database decrypts the secondary authentication package, extracts the node information and neighborhood key of the neighbor node, and performs secondary identity authentication on the neighbor node; the node information and neighborhood key of the neighbor node whose secondary identity authentication is successful are used to generate a return package by using the distributed key encryption and returned to the distributed node; S208: The distributed node decrypts the return package, compares the decrypted neighborhood key with the original neighborhood key, and if the comparison is successful, it is considered that the two-way authentication is successful, and the establishment of the encrypted communication channel between the two parties is completed; S209: Each authenticated node generates a pair of public key and private key, and broadcasts each public key to the corresponding neighbor node for subsequent task load encrypted transmission.

4. The method of claim 1, wherein, The S3 specifically comprises: S301: Collecting running state data of each distributed node, wherein the running state data comprises a current task quantity and a maximum task processing capacity; S302: Calculating a load rate of each distributed node based on the current task quantity and the maximum task processing quantity; S303: Determining a distributed node whose load rate is greater than a first preset load rate as the overload node.

5. The method of claim 1, wherein, The S4 specifically comprises: S401: The overload node collects data information for a load migration request, wherein the data information comprises a self-identity, a to-be-migrated load quantity, and a task deadline; S402: Encapsulating the self-identity, the to-be-migrated load quantity, and the task deadline into a structured control package; S403: Encrypting the structured control package using a public key of the neighbor node to generate the encrypted request package; S404: Broadcasting the encrypted request package to the neighbor node through a distributed network.

6. The method of claim 1, wherein, The S5 specifically comprises: S501: The neighbor node decrypts the encrypted request package using its own private key to extract the identity of the overload node, the to-be-migrated load quantity, and the task deadline; S502: Querying an authenticated node database to determine whether the identity of the overload node is in the authenticated node database; if yes, proceeding to S503; otherwise, ignoring the encrypted request package; S503: Calculating a current load rate of the neighbor node; S504: Determining whether the current load rate of the neighbor node is less than a second preset load rate and whether an available resource quantity of the neighbor node is greater than the to-be-migrated load quantity; if yes, proceeding to S505; otherwise, ignoring the encrypted request package; S505: Encrypting the identity of the neighbor node, the associated key, and the current load rate using the public key of the overload node to generate the encrypted response package, and sending the encrypted response package to the overload node.

7. The method of claim 1, wherein, The S6 specifically comprises: S601: The overload node decrypts the encrypted response package to extract the identity of the neighbor node, the associated key, and the load rate; S602: Query the authenticated node database to determine whether the identity of the neighbor node is in the authenticated node database; if yes, go to S603; otherwise, ignore the encrypted response package; S603: Obtain the association key of the neighbor node from the authenticated node database, and compare the association key extracted from the encrypted response package with the association key obtained from the authenticated node database; if they are consistent, go to S604; otherwise, ignore the encrypted response package; S604: Calculate the priority score of each neighbor node; S605: Sort each priority score in descending order, and determine the neighbor node with the highest priority score as the candidate load migration node.

8. The method of claim 1, wherein, After S7, further comprising: S7A: Calculate the task default rate of each neighbor node; S7B: Determine whether the default rate exceeds a preset default rate; if yes, go to S7C; otherwise, go to S8; S7C: Increase the weight of the remaining time item when selecting the candidate load migration node, and go to S8.

9. A distributed credit database load balancing regulation system, characterized in that, Comprise: A processor and a memory; The memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the distributed credit investigation database load balancing adjustment method according to any one of claims 1 to 8.

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