Distributed collaborative remote operation and maintenance management and control method and system

Abstract

The invention relates to the technical field of distributed collaboration, and discloses a distributed collaboration remote operation and maintenance management and control method and system, and the method comprises the steps: monitoring the resource use state of each distributed node in a distributed system, carrying out the distribution of the obtained resources, preferentially distributing the resources until the remaining capacity of the resources meets the distribution, and carrying out the management and control of the distributed collaboration. The distributed nodes are closest to the resources; the distributed resources are calculated through the distributed nodes; after calculation is completed, the calculation process is disassembled, and disassembled calculation resources are distributed to other distributed nodes for verification; and after verification is correct, a calculation result is uploaded to the block chain, so that cooperation of the distributed nodes is realized. And the resource utilization rate and the calculation efficiency of the system are improved. And the problem of unreliable calculation result caused by single-node fault or error is avoided. The overall design improves the fault tolerance of the system and the stability of task processing, and is suitable for a large-scale distributed collaborative operation and maintenance scene.

Description

Technical Field

[0001] The present invention relates to the field of distributed collaborative technology, and specifically to a distributed collaborative remote operation and maintenance control method and system. Background Art

[0002] With the rapid development of information technology, distributed systems and remote operation and maintenance technologies have been widely used in various industries. Traditional centralized operation and maintenance methods can no longer meet the needs of modern complex systems. Especially when facing large-scale distributed nodes, how to efficiently manage resources, coordinate computing tasks, and ensure the stability and reliability of the system has become an important challenge. Distributed collaborative systems can achieve parallel processing of tasks through the collaborative work of multiple nodes, improving the fault tolerance and scalability of the system. However, in practical applications, distributed systems also face many problems, such as uneven resource allocation, inconsistent computing results, and inefficient task scheduling. Especially in remote operation and maintenance scenarios, the system's task complexity and the coordination requirements between nodes are constantly increasing, and existing technologies have shortcomings in resource utilization, task allocation, and data security.

[0003] In recent years, blockchain technology, as a decentralized data management method, has gradually been applied to distributed systems, providing a new solution for the security and credibility of data in the operation and maintenance process. By introducing blockchain technology, data consistency between distributed nodes and the immutability of calculation results can be ensured, thereby improving the overall security of the system. At the same time, the development of distributed collaborative systems has also brought about the need for intelligent resource scheduling, task decomposition and verification mechanisms to cope with complex remote operation and maintenance environments. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing distributed collaboration method has problems such as unstable calculation accuracy and easy to be tampered with.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a distributed collaborative remote operation and maintenance control method, comprising: In a distributed system, monitor the resource usage status of each distributed node, allocate the acquired resources, and give priority to the distributed nodes whose remaining capacity meets the allocation and are closest to the resources; Calculate the allocated resources through distributed nodes; After the calculation is completed, the calculation process is disassembled and the disassembled computing resources are allocated to other distributed nodes for verification; After verification, the calculation results are uploaded to the blockchain to achieve coordination of distributed nodes.

[0007] As a preferred solution of the distributed collaborative remote operation and maintenance control method of the present invention, the resource usage status includes: CPU usage rate: refers to the occupancy of the computing processing capacity of each distributed node, which is used to evaluate the computing load of the node; Memory usage: refers to the usage of node memory resources, reflecting the memory pressure when the node processes tasks; Network bandwidth utilization rate: refers to the network bandwidth currently used by the node, which evaluates whether the node's data transmission capacity can meet the task requirements; Storage capacity: The storage resources available to a node, used to determine whether the node is capable of receiving and processing large amounts of data.

[0008] As a preferred solution of the distributed collaborative remote operation and maintenance control method described in the present invention, wherein: the allocation of the acquired resources includes: , calculate its remaining resource capacity: CPU remaining capacity , remaining memory capacity ,Storage remaining capacity ; The normalized percentage of available resources, ranging from [0,1], where 1 means that the node resources are completely idle and 0 means that the resources are full; For each node , define the comprehensive remaining resource capacity ; , For each task , calculates CPU requirements based on its resource requirements ,Memory Requirements ,Storage Requirements ; Normalized to the percentage of resources required for the task, ranging from [0,1], indicating the required occupancy ratio, defining the task Comprehensive resource requirements ; , Traverse all distributed nodes and filter out all node sets S that meet the requirements; , For the filtered node set that meets the conditions , according to the distance from the resource requesting node Sort by giving priority to the nodes closest to each other; the sorted node set is: , Among them, n represents the number of nodes; assign task j to the first node after sorting .

[0009] As a preferred solution of the distributed collaborative remote operation and maintenance control method of the present invention, wherein: the calculation of the allocated resources includes, in the distributed nodes, according to the current task, calculating the resources step by step according to the steps in the calculation module and the algorithm in each step; During the calculation process, record the input and output of each step and the algorithm used in step B: , Among them, Bp represents the record of the pth step of algorithm B, R represents the input of the algorithm, C represents the output of the algorithm; S represents the algorithm itself.

[0010] As a preferred solution of the distributed collaborative remote operation and maintenance control method described in the present invention, the decomposition of the calculation process includes: the node DQ converts the input and algorithm of each calculation step in the current task into , allocated as a new resource, randomly allocated to V other distributed nodes whose remaining capacity satisfies the allocation and are closest to the resource; Among them, V is the number of steps in the node DQ, and the first V are selected from the distance sort for allocation.

[0011] As a preferred solution of the distributed collaborative remote operation and maintenance control method of the present invention, wherein: the disassembling of computing resources to other distributed nodes for verification includes recalculating the new resources through other distributed nodes, and returning the calculated verification results to the node DQ; The recalculation includes using the new resource , the scheduling algorithm S operates on the input R; When the recalculated result is returned to the node DQ, if the recalculated output result is the same as C, the verification result is correct; if the recalculated output result is different from C, the verification result is failed; When the verification results calculated in each step are returned to the node DQ, if all the verification results are correct, the calculation results of the node DQ are output; if there are any verification results that fail, the node DQ recalculates and verifies the current task; If all the re-verification results are correct, the calculation results of the node DQ are output; if there are any re-verification results that fail, the resources are packaged and uploaded to the central node for calculation.

[0012] As a preferred solution of the distributed collaborative remote operation and maintenance control method described in the present invention, wherein: uploading the calculation results to the blockchain includes recording and encrypting the final output and packaged resources of the node DQ through the blockchain; The verification process between node DQ and other distributed nodes is carried out by exchanging data through wireless communication, and the verification process is not recorded.

[0013] A distributed collaborative remote operation and maintenance control system using any method described in the present invention, comprising: The allocation unit monitors the resource usage status of each distributed node in the distributed system, allocates the acquired resources, and gives priority to the distributed nodes whose remaining capacity meets the allocation and are closest to the resources. The computing unit computes the allocated resources through distributed nodes; After the verification unit completes the calculation, it disassembles the calculation process and distributes the disassembled computing resources to other distributed nodes for verification; The transmission unit, after verification, uploads the calculation results to the blockchain, thereby achieving the coordination of distributed nodes.

[0014] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0015] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0016] Beneficial effects of the present invention: The distributed collaborative remote operation and maintenance control method provided by the present invention has the following beneficial effects: First, by monitoring the resource usage status of each distributed node and dynamically allocating task resources, it is ensured that tasks are preferentially allocated to nodes with sufficient resources and the nearest distance, thereby improving the resource utilization and computing efficiency of the system. Secondly, the calculation process disassembly and distributed verification mechanism are adopted to ensure the accuracy and consistency of the calculation results, and avoid the problem of unreliable calculation results caused by single node failure or error. By randomly allocating data to other nodes in steps, the data does not need to be encrypted, reducing the computing power occupied. In addition, by uploading the verified calculation results to the blockchain for encrypted recording, the risk of data tampering is effectively prevented, and the security and credibility of the system are enhanced. The overall design improves the fault tolerance of the system and the stability of task processing, and is suitable for large-scale distributed collaborative operation and maintenance scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 An overall flow chart of a distributed collaborative remote operation and maintenance control method provided for the first embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a distributed collaborative remote operation and maintenance control method, including: S1: In a distributed system, monitor the resource usage status of each distributed node, allocate the acquired resources, and give priority to the distributed nodes whose remaining capacity meets the allocation and are closest to the resources.

[0021] Furthermore, the resource usage status includes: CPU usage: refers to the occupancy of the computing processing capacity of each distributed node, which is used to evaluate the computing load of the node. Memory usage: refers to the occupancy of the node's memory resources, reflecting the memory pressure of the node when processing tasks. Network bandwidth usage: refers to the network bandwidth currently used by the node, which evaluates whether the node's data transmission capacity can meet the task requirements. Storage capacity: The storage resources available to the node, used to determine whether the node can receive and process large-scale data.

[0022] For each node , calculate its remaining resource capacity: CPU remaining capacity , remaining memory capacity ,Storage remaining capacity The normalized percentage of available resources is in the range of [0,1], where 1 means that the node resources are completely idle and 0 means that the resources are full.

[0023] For each node , define the comprehensive remaining resource capacity .

[0024] , For each task , calculates CPU requirements based on its resource requirements ,Memory Requirements ,Storage Requirements ; Normalized to the percentage of resources required for the task, ranging from [0,1], indicating the required occupancy ratio, defining the task Comprehensive resource requirements .

[0025] , Traverse all distributed nodes and filter out all node sets S that meet the requirements.

[0026] , For the filtered node set that meets the conditions , according to the distance from the resource requesting node Sort by giving priority to the nodes closest to each other; the sorted node set is: , Among them, n represents the number of nodes; assign task j to the first node after sorting .

[0027] S2: Calculate the allocated resources through distributed nodes.

[0028] Specifically, in the distributed node, according to the current task, the resources are calculated step by step according to the steps in the calculation module and the algorithm in each step. During the calculation process, the input and output of each step and the algorithm used in step B are recorded: , Among them, Bp represents the record of the pth step of algorithm B, R represents the input of the algorithm, C represents the output of the algorithm, and S represents the algorithm itself.

[0029] S3: After the calculation is completed, the calculation process is disassembled and the disassembled computing resources are allocated to other distributed nodes for verification.

[0030] The decomposition of the computing process includes: node DQ divides the input and algorithm of each computing step in the current task into , is allocated as a new resource, and is randomly allocated to V other distributed nodes whose remaining capacity satisfies the allocation and are closest to the resource. Among them, V is the number of steps in the node DQ, and the first V are selected from the distance sorting for allocation.

[0031] Furthermore, the new resource is recalculated through other distributed nodes, and the calculated verification result is returned to the node DQ.

[0032] The recalculation includes using the new resource , the scheduling algorithm S operates on the input R.

[0033] When the recalculated result is returned to the node DQ, if the output result of the recalculation is the same as C, the verification result is correct; if the output result of the recalculation is different from C, the verification result is failed.

[0034] When the verification results calculated at each step are returned to the node DQ, if all the verification results are correct, the calculation results of the node DQ are output; if there are any verification results that fail, the node DQ recalculates and verifies the current task. If all the re-verified results are correct, the calculation results of the node DQ are output; if there are any re-verified results that fail, the resources are packaged and uploaded to the central node for calculation.

[0035] S4: After verification, the calculation results are uploaded to the blockchain to achieve coordination of distributed nodes.

[0036] The uploading of the calculation results to the blockchain includes recording and encrypting the final output and packaged resources of the node DQ through the blockchain. The verification process of the node DQ and other distributed nodes is carried out by wireless communication to exchange data, and the verification process is not recorded.

[0037] On the other hand, this embodiment also provides a distributed collaborative remote operation and maintenance control system, which includes: The allocation unit, in a distributed system, monitors the resource usage status of each distributed node, allocates the acquired resources, and gives priority to the distributed nodes whose remaining capacity meets the allocation and are closest to the resources.

[0038] The computing unit performs calculations on the allocated resources through distributed nodes.

[0039] After the verification unit completes the calculation, it disassembles the calculation process and distributes the disassembled computing resources to other distributed nodes for verification.

[0040] The transmission unit, after verification, uploads the calculation results to the blockchain, thereby achieving the coordination of distributed nodes.

[0041] If the above functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0042] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0043] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0044] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0045] Example 2 is an embodiment of the present invention, which provides a distributed collaborative remote operation and maintenance control method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0046] The experiment selected 10 distributed nodes in the simulation environment. The task type was a complex computing task involving large-scale data processing. The experiment simulated an actual remote operation and maintenance scenario. The resource distribution of each node in the environment was different. The resources included CPU processing power, memory capacity, network bandwidth, and storage space. In addition, the physical distance between nodes simulated the communication delay between nodes in reality. The goal of the experiment was to test the performance of resource allocation, computation execution, and result verification under this distributed collaboration method.

[0047] First, the system collects the resource usage of each node in real time by monitoring the resource usage status of each distributed node, including CPU usage, memory usage, network bandwidth and storage capacity. To ensure the accuracy of resource allocation, the task is broken down into multiple subtasks and dynamically allocated to the appropriate node based on the remaining resource capacity of each node and the distance priority principle.

[0048] Next, the distributed nodes start to perform the calculation of the subtasks. Each node performs the calculation step by step according to the modular steps of the task, and records the input, output and algorithm used in each step. After the task is completed, the system disassembles each calculation step of the task and distributes it to other nodes for verification. The node verifies the execution result of the task by recalculating. If the calculation results are consistent, the verification is considered to be passed, and the final calculation results are uploaded to the blockchain to ensure its security and immutability.

[0049] In order to ensure the objectivity of the experiment, a traditional centralized computing system was specially selected as the control group, using the same computing tasks and resource allocation model. The experimental data was collected through multiple runs of the experiment and summarized in a table to analyze the performance of the distributed system in terms of resource utilization, task execution time, and computing accuracy.

[0050] Experimental data table , It can be clearly seen from the table data that the distributed collaborative remote operation and maintenance management method of the present invention has obvious advantages and innovations compared to the traditional centralized system. First, from the distribution of CPU, memory and storage remaining capacity, the resource occupancy of nodes 4 and 6 is relatively small, which conforms to the principle of priority allocation of resource remaining capacity. The execution time of tasks on these nodes is 100 seconds and 90 seconds respectively, which is much lower than the nodes with relatively tight resources, such as 150 seconds of node 3. This reflects the rationality and efficiency of the inventive method in dynamic resource allocation.

[0051] Secondly, from the perspective of bandwidth utilization, node 6 has the lowest bandwidth utilization (18%), which further reduces the delay of the task during execution. Combined with the distance data, node 6 has the fastest response time, which is only 15 milliseconds, which reflects that this method significantly reduces network latency by giving priority to the distributed nodes closest to the requesting node, thereby optimizing the overall time of task execution.

[0052] Furthermore, during the calculation verification process, the verification result differences of all nodes were kept within 0.3%, especially the verification difference of node 6 was the lowest, only 0.02%, which shows that the consistency and accuracy of the calculation results can be effectively ensured through the calculation process disassembly and distributed verification mechanism. Compared with the single-point verification method of the traditional centralized system, the method of the present invention is more fault-tolerant and reliable, and can still ensure high accuracy of calculation even when multiple nodes are involved.

[0053] In addition, the task execution time shown in the table further verifies the advantages of the present invention. Nodes with higher resource utilization, such as Node 1 and Node 3, have relatively longer execution times of 120 seconds and 150 seconds respectively due to their larger resource occupancy rates, while nodes with lower resource occupancy rates, such as Node 6, only need 90 seconds to complete the same task, indicating that through optimized resource scheduling, the present invention can make dynamic adjustments according to the real-time resource status, thereby improving the execution efficiency of tasks.

[0054] In summary, the present invention can effectively overcome the problems of computational instability, resource waste, and easy data tampering in the prior art through intelligent resource allocation, optimized computational verification process, and blockchain-based result storage. Experimental data fully proves that the invention has extremely high resource utilization efficiency, accuracy of computational results, and better task execution time, demonstrating the innovation and novelty of the technical solution.

[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed collaborative remote operation and maintenance control method, characterized in that: include: In a distributed system, monitor the resource usage status of each distributed node, allocate the acquired resources, and give priority to the distributed nodes whose remaining capacity meets the allocation and are closest to the resources; Calculate the allocated resources through distributed nodes; After the calculation is completed, the calculation process is disassembled and the disassembled computing resources are allocated to other distributed nodes for verification; After verification, the calculation results are uploaded to the blockchain to achieve coordination of distributed nodes.

2. The distributed collaborative remote operation and maintenance control method according to claim 1, characterized in that: The resource usage status includes: CPU usage rate: refers to the occupancy of the computing processing capacity of each distributed node, which is used to evaluate the computing load of the node; Memory usage: refers to the usage of node memory resources, reflecting the memory pressure when the node processes tasks; Network bandwidth utilization rate: refers to the network bandwidth currently used by the node, which evaluates whether the node's data transmission capacity can meet the task requirements; Storage capacity: The storage resources available to a node, used to determine whether the node is capable of receiving and processing large amounts of data.

3. The distributed collaborative remote operation and maintenance control method according to claim 2, characterized in that: The allocation of the acquired resources includes: , calculate its remaining resource capacity: CPU remaining capacity , remaining memory capacity ,Storage remaining capacity ; The normalized percentage of available resources, ranging from [0,1], where 1 means that the node resources are completely idle and 0 means that the resources are full; For each node , define the comprehensive remaining resource capacity ; ， For each task , calculates CPU requirements based on its resource requirements ,Memory Requirements ,Storage Requirements ; Normalized to the percentage of resources required for the task, ranging from [0,1], indicating the required occupancy ratio, defining the task Comprehensive resource requirements ; ， Traverse all distributed nodes and filter out all node sets S that meet the requirements; ， For the filtered node set that meets the conditions , according to the distance from the resource requesting node Sort by giving priority to the nodes closest to each other; the sorted node set is: ， Among them, n represents the number of nodes; assign task j to the first node after sorting .

4. The distributed collaborative remote operation and maintenance control method according to claim 3, characterized in that: The calculating of the allocated resources includes, in the distributed nodes, calculating the resources step by step according to the steps in the calculation module and the algorithm in each step according to the current task; During the calculation process, record the input and output of each step and the algorithm used in step B: ， Among them, Bp represents the record of the pth step of algorithm B, R represents the input of the algorithm, C represents the output of the algorithm; S represents the algorithm itself.

5. The distributed collaborative remote operation and maintenance control method according to claim 4, characterized in that: The decomposition of the computing process includes: node DQ divides the input and algorithm of each computing step in the current task into , allocated as a new resource, randomly allocated to V other distributed nodes whose remaining capacity satisfies the allocation and are closest to the resource; Among them, V is the number of steps in the node DQ, and the first V are selected from the distance sort for allocation.

6. The distributed collaborative remote operation and maintenance control method according to claim 5, characterized in that: The distributing the disassembled computing resources to other distributed nodes for verification includes recalculating the new resources through other distributed nodes and returning the calculated verification results to the node DQ; The recalculation includes using the new resource , the scheduling algorithm S operates on the input R; When the recalculated result is returned to node DQ, if the recalculated result is the same as C, the verification result is correct; If the recalculated output result is different from C, the verification result is failed; When the verification results calculated in each step are returned to the node DQ, if all the verification results are correct, the calculation results of the node DQ are output; if there are any verification results that fail, the node DQ recalculates and verifies the current task; If all the re-verification results are correct, the calculation results of the node DQ are output; If the re-verification result fails, the resources will be packaged and uploaded to the central node for calculation.

7. The distributed collaborative remote operation and maintenance control method according to claim 6, characterized in that: Uploading the calculation results to the blockchain includes recording and encrypting the final output and packaged resources of the node DQ through the blockchain; The verification process between node DQ and other distributed nodes is carried out by exchanging data through wireless communication, and the verification process is not recorded.

8. A distributed collaborative remote operation and maintenance control system using the method according to any one of claims 1 to 7, characterized in that: The allocation unit monitors the resource usage status of each distributed node in the distributed system, allocates the acquired resources, and gives priority to the distributed nodes whose remaining capacity meets the allocation and are closest to the resources. The computing unit computes the allocated resources through distributed nodes; After the verification unit completes the calculation, it disassembles the calculation process and distributes the disassembled computing resources to other distributed nodes for verification; The transmission unit, after verification, uploads the calculation results to the blockchain, thereby achieving the coordination of distributed nodes.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the distributed collaborative remote operation and maintenance control method are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program implements the steps of a distributed collaborative remote operation and maintenance control method when executed by a processor.

Images