A distributed collaborative remote operation and maintenance management method and system

Abstract

This invention relates to the field of distributed collaborative technology, and discloses a distributed collaborative remote operation and maintenance management method and system, comprising: monitoring the resource usage status of each distributed node in a distributed system; allocating acquired resources, prioritizing allocation to the distributed node with sufficient remaining capacity and closest proximity to the resource; performing calculations on the allocated resources through the distributed nodes; after completing the calculations, decomposing the calculation process and allocating the decomposed calculation resources to other distributed nodes for verification; after verification, uploading the calculation results to the blockchain, thereby realizing the collaboration of distributed nodes. This improves the system's resource utilization and computational efficiency. It avoids the problem of unreliable calculation results caused by single-node failures or errors. The overall design improves the system's fault tolerance and task processing stability, making it suitable for large-scale distributed collaborative operation and maintenance scenarios.

Description

Technical Field

[0001] This invention relates to the field of distributed collaborative technology, specifically to a distributed collaborative remote operation and maintenance management method and system. Background Technology

[0002] With the rapid development of information technology, distributed systems and remote operation and maintenance technologies have been widely applied across 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. Efficiently managing resources, coordinating computing tasks, and ensuring system stability and reliability have become significant challenges. Distributed collaborative systems, through the collaborative work of multiple nodes, can achieve parallel processing of tasks, improving system fault tolerance and scalability. However, in practical applications, distributed systems also face many problems, such as uneven resource allocation, inconsistent computation results, and low task scheduling efficiency. Particularly in remote operation and maintenance scenarios, the complexity of system tasks 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 been increasingly applied to distributed systems, providing new solutions for data security and trustworthiness during operation and maintenance. By introducing blockchain technology, data consistency among distributed nodes and the immutability of computation results can be ensured, improving the overall security of the system. Simultaneously, 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 this invention is that existing distributed collaborative methods suffer from unstable computational accuracy and are easily tampered with.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a distributed collaborative remote operation and maintenance management method, comprising:

[0007] In a distributed system, the resource usage status of each distributed node is monitored, and the acquired task resources are allocated, prioritizing allocation to the distributed node with sufficient remaining capacity and closest to the task resources.

[0008] The allocated task resources are computed through distributed nodes;

[0009] After the calculation is completed, the calculation process is broken down, and the decomposed computing resources are allocated to other distributed nodes for verification.

[0010] After verification, the calculation results are uploaded to the blockchain, thereby enabling the collaboration of distributed nodes.

[0011] As a preferred embodiment of the distributed collaborative remote operation and maintenance management method described in this invention, the resource usage status includes: CPU utilization rate: refers to the occupancy of the computing power of each distributed node, used to assess the computing load of the node;

[0012] Memory utilization: refers to the usage of node memory resources, reflecting the memory pressure when the node processes tasks;

[0013] Network bandwidth utilization: refers to the network bandwidth currently used by the node, used to assess whether the node's data transmission capacity can meet the task requirements;

[0014] Storage capacity: The storage resources available to a node, used to determine whether a node can receive and process large-scale data.

[0015] As a preferred embodiment of the distributed collaborative remote operation and maintenance management method described in this invention, the allocation of acquired task resources includes, for each distributed node i, calculating its remaining resource capacity: CPU remaining capacity C cpu,i Remaining memory capacity C mem,i Remaining storage capacity C stor,i ;

[0016] The percentage of normalized available resources, ranging from [0,1], where 1 indicates that the node's resources are completely idle, and 0 indicates that the resources are full;

[0017] For each distributed node i, define the total remaining resource capacity C. res,i ;

[0018] C res,i ={C cpu,i , C mem,i , C stor,i}

[0019] For each task j, calculate the CPU requirement R based on its resource requirements. cpu,j Memory requirements R mem,j Storage requirements R stor,j Normalized to the percentage of resources required by the task, ranging from [0,1], representing the required proportion of resources, the comprehensive resource requirement R for task j is defined. req,j ;

[0020]

[0021] Iterate through all distributed nodes and select the set S of all nodes that meet the requirements;

[0022] S={i∣C cpu,i ≥R cpu,j And C mem,i ≥R mem,j And C stor,i ≥R stor,j}

[0023] For the selected set S of nodes that meet the criteria, they are ranked according to their distance D from the node requesting the task resource. i,j The distributed nodes are sorted, prioritizing those closest to the nearest node; the sorted set of distributed nodes is as follows:

[0024]

[0025] Where n represents the number of nodes; task j is assigned to the first node i1 after sorting.

[0026] As a preferred embodiment of the distributed collaborative remote operation and maintenance management method of the present invention, the calculation of the allocated task resources includes defining the distributed node currently performing the task calculation action as a node DQ, and in the node DQ, calculating the task resources step by step according to the steps in the calculation module and the algorithm in each step according to the current task.

[0027] During the calculation process, the input, output, and algorithm used at each step are recorded:

[0028] Bp = {R, C, S}

[0029] Where Bp represents the record of any step, R represents the input of the algorithm in the current step, C represents the output of the algorithm in the current step, and S represents the algorithm itself in the current step.

[0030] As a preferred embodiment of the distributed collaborative remote operation and maintenance management method of the present invention, the step of decomposing the calculation process includes: the node DQ allocates the input and algorithm of each calculation step in the current task as a new record resource Bp' = {R, S}, and randomly allocates them to the V other distributed nodes that have the remaining capacity of the resource and are closest to the resource; wherein, V is the number of steps in the node DQ, and the top V are selected from the distance sorting for allocation.

[0031] As a preferred embodiment of the distributed collaborative remote operation and maintenance management method of the present invention, the step of allocating the decomposed computing resources to other distributed nodes for verification includes recalculating the new resources through other distributed nodes and returning the verification results of the calculation to node DQ.

[0032] The recalculation includes using Bp' in the new record resource and the scheduling algorithm S to operate on the input R;

[0033] 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.

[0034] When the verification results calculated in each step are returned to node DQ, if all verification results are correct, the calculation results of node DQ are output; if there are any verification results that fail, node DQ recalculates and verifies the current task.

[0035] If all re-verification results are correct, the calculation result of node DQ is output; if any re-verification results fail, the resources are packaged and uploaded to the central node for calculation.

[0036] As a preferred embodiment of the distributed collaborative remote operation and maintenance management method described in this invention, the step of uploading the calculation results to the blockchain includes recording and encrypting the final output of node DQ and the packaged resources through the blockchain.

[0037] The verification process between node DQ and other distributed nodes is conducted via wireless communication, and the verification process is not recorded.

[0038] A distributed collaborative remote operation and maintenance management system employing any of the methods described in this invention includes:

[0039] In a distributed system, the allocation unit monitors the resource usage status of each distributed node, allocates the acquired resources, and prioritizes allocating them to the distributed node with sufficient remaining capacity and closest proximity to the resource.

[0040] The computing unit performs calculations on the allocated resources through distributed nodes;

[0041] The verification unit, after completing the calculation, breaks down the calculation process and allocates the decomposed computing resources to other distributed nodes for verification.

[0042] After verification, the transmission unit uploads the calculation results to the blockchain, thereby enabling the collaboration of distributed nodes.

[0043] A computer device includes: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, it implements the steps of the method described in any one of the present invention.

[0044] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the present invention.

[0045] The beneficial effects of this invention are as follows: The distributed collaborative remote operation and maintenance management method provided by this invention has the following beneficial effects: First, by monitoring the resource usage status of each distributed node and dynamically allocating task resources, it ensures that tasks are preferentially assigned to nodes with sufficient resources and the closest proximity, thereby improving the system's resource utilization and computational efficiency. Second, by adopting a computation process decomposition and distributed verification mechanism, the accuracy and consistency of computation results are ensured, avoiding the problem of unreliable computation results caused by single-node failures or errors. Randomly assigning tasks to other nodes in steps eliminates the need for data encryption, reducing the consumption of computational power. Furthermore, by uploading the verified computation results to the blockchain for encrypted recording, the risk of data tampering is effectively prevented, enhancing the system's security and credibility. The overall design improves the system's fault tolerance and task processing stability, making it suitable for large-scale distributed collaborative operation and maintenance scenarios. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 The first embodiment of the present invention provides an overall flowchart of a distributed collaborative remote operation and maintenance management method. Detailed Implementation

[0048] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. 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 protection scope of the present invention.

[0049] Example 1, referring to Figure 1 As an embodiment of the present invention, a distributed collaborative remote operation and maintenance management method is provided, comprising:

[0050] S1: In a distributed system, monitor the resource usage status of each distributed node, allocate the acquired task resources, and prioritize allocating them to the distributed node with sufficient remaining capacity and closest to the task resources.

[0051] Furthermore, the resource usage status includes: CPU utilization: refers to the computing power occupied by each distributed node, used to assess the node's computational load. Memory utilization: refers to the node's memory resource usage, reflecting the memory pressure when the node processes tasks. Network bandwidth utilization: refers to the network bandwidth currently used by the node, assessing whether the node's data transmission capacity can meet task requirements. Storage capacity: the available storage resources of the node, used to determine whether the node can receive and process large-scale data.

[0052] For each distributed node i, calculate its remaining resource capacity: CPU remaining capacity C cpu,i Remaining memory capacity C mem,i Remaining storage capacity C stor,i .

[0053] The percentage of normalized available resources, ranging from [0,1], where 1 indicates that the node's resources are completely idle, and 0 indicates that the resources are full.

[0054] For each distributed node i, define the total remaining resource capacity C. res,i :

[0055] C res,i ={C cpu,i , C mem,i , C stor,i}

[0056] For each task j, calculate the CPU requirement R based on its resource requirements. cpu,j Memory requirements R mem,j Storage requirements R stor,j Normalized to the percentage of resources required by the task, ranging from [0,1], representing the required proportion of resources, the comprehensive resource requirement R for task j is defined. req,j :

[0057] R req,j ={R cpu,j R mem,j R stor,j}

[0058] Iterate through all distributed nodes and select the set S of all nodes that meet the requirements:

[0059] S={i∣C cpu,i ≥R cpu,j And C mem,i ≥R mem,j And C stor,i ≥R stor,j}

[0060] For the selected set S of nodes that meet the criteria, they are ranked according to their distance D from the node requesting the task resource. i,jThe distributed nodes are sorted, prioritizing those closest to the nearest node; the sorted set of distributed nodes is as follows:

[0061]

[0062] Where n represents the number of nodes; task j is assigned to the first node i1 after sorting.

[0063] S2: Calculates the allocated task resources through distributed nodes.

[0064] Specifically, the distributed node currently performing task computation is defined as node DQ. Within node DQ, task resources are computed step-by-step according to the steps and algorithms within the computation module, based on the current task. During the computation process, the input, output, and algorithm used for each step are recorded.

[0065] Bp = {R, C, S}

[0066] Where Bp represents the record of any step, R represents the input of the algorithm in the current step, C represents the output of the algorithm in the current step, and S represents the algorithm itself in the current step.

[0067] S3: After the calculation is completed, the calculation process is broken down and the decomposed computing resources are allocated to other distributed nodes for verification.

[0068] Specifically, the decomposition of the computation process includes the following: the node DQ allocates the input and algorithm of each computation step in the current task as a new record resource Bp' = {R, S}, and randomly assigns them to the V other distributed nodes that have the remaining capacity of the resource and are closest to the resource; where V is the number of steps in the node DQ, and the top V are selected from the distance sorting for allocation.

[0069] Furthermore, the new resources are recalculated using other distributed nodes, and the verification results are returned to node DQ.

[0070] The recalculation includes using Bp' in the new record resources and the scheduling algorithm S to operate on the input R.

[0071] 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 unsuccessful.

[0072] When the verification results calculated in each step are returned to node DQ, if all verification results are correct, the calculation results of node DQ are output; if there are any verification results that fail, node DQ recalculates and verifies the current task.

[0073] If all re-verification results are correct, the calculation result of node DQ is output; if any re-verification results fail, the resources are packaged and uploaded to the central node for calculation.

[0074] S4: After verification, the calculation results are uploaded to the blockchain to achieve collaboration among distributed nodes.

[0075] Uploading the calculation results to the blockchain includes recording and encrypting the final output of node DQ and the packaged resources through the blockchain.

[0076] The verification process between node DQ and other distributed nodes is conducted via wireless communication, and the verification process is not recorded.

[0077] On the other hand, this embodiment also provides a distributed collaborative remote operation and maintenance management system, which includes:

[0078] In a distributed system, the allocation unit monitors the resource usage status of each distributed node, allocates the acquired resources, and prioritizes allocating them to the distributed node with sufficient remaining capacity and closest proximity to the resource.

[0079] The computing unit performs calculations on the allocated resources through distributed nodes.

[0080] The verification unit, after completing the calculation, breaks down the calculation process and allocates the decomposed computing resources to other distributed nodes for verification.

[0081] After verification, the transmission unit uploads the calculation results to the blockchain, thereby enabling the collaboration of distributed nodes.

[0082] If the above 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, or the part that contributes to the prior art, or a part 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.

[0083] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0084] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be 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 processing in another suitable manner as necessary, and then stored in a computer memory.

[0085] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0086] Example 2 is an embodiment of the present invention, which provides a distributed collaborative remote operation and maintenance management method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0087] The experiment selected 10 distributed nodes in a simulated environment, and the task type was a complex computational task involving large-scale data processing. The experiment simulated a real-world remote operation and maintenance scenario, where the resource distribution of each node varied, including CPU processing power, memory capacity, network bandwidth, and storage space. Furthermore, the physical distance between nodes simulated the communication latency 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 collaborative method.

[0088] First, the system monitors the resource usage status of each distributed node, including CPU utilization, memory utilization, network bandwidth, and storage capacity, collecting real-time data on each node's resource consumption. To ensure accurate resource allocation, tasks are broken down into multiple sub-tasks and dynamically assigned to suitable nodes based on each node's remaining resource capacity and distance priority.

[0089] Next, the distributed nodes begin calculating the subtasks. Each node performs the calculations step-by-step according to the modular steps of the task, recording the inputs, outputs, and algorithms used at each step. After the task is completed, the system breaks down each calculation step and distributes them to other nodes for verification. Nodes recalculate and verify the task's execution result. If the results match, the verification is considered successful, and the final result is uploaded to the blockchain to ensure its security and immutability.

[0090] To ensure the objectivity of the experiment, a traditional centralized computing system was specifically chosen as the control group, using the same computing tasks and resource allocation model. Experimental data was collected through multiple runs of the experiment and summarized in tables to analyze the performance of the distributed system in terms of resource utilization, task execution time, and computational accuracy.

[0091] Experimental Data Table

[0092]

[0093]

[0094] The data in the table clearly shows that the distributed collaborative remote operation and maintenance management method of this invention has significant advantages and innovations compared to traditional centralized systems. Firstly, considering the distribution of remaining CPU, memory, and storage capacity, nodes 4 and 6 consume fewer resources, conforming to the principle of prioritizing the allocation of remaining resource capacity. The execution time of tasks on these nodes is 100 seconds and 90 seconds respectively, far lower than the 150 seconds for resource-constrained nodes, such as node 3. This demonstrates the rationality and efficiency of the invention's method in dynamic resource allocation.

[0095] Secondly, in terms of bandwidth utilization, node 6 has the lowest bandwidth utilization (18%), which further reduces the latency during task execution. Combined with distance data, node 6 has the fastest response time, at only 15 milliseconds. This reflects that the method significantly reduces network latency by prioritizing the selection of the distributed node closest to the requesting node, thereby optimizing the overall task execution time.

[0096] Furthermore, during the computation verification process, the difference in verification results across all nodes remained within 0.3%, with node 6 exhibiting the lowest difference at only 0.02%. This demonstrates that the computation process decomposition and distributed verification mechanism effectively ensure the consistency and accuracy of the computation results. Compared to the single-point verification method of traditional centralized systems, the method of this invention is more fault-tolerant and reliable, maintaining high computational accuracy even with the participation of multiple nodes.

[0097] Furthermore, the task execution times shown in the table further validate the advantages of this invention. Nodes with higher resource utilization, such as node 1 and node 3, have relatively longer execution times due to their larger resource occupancy, at 120 seconds and 150 seconds respectively. In contrast, nodes with lower resource occupancy, such as node 6, only need 90 seconds to complete the same task. This demonstrates that through optimized resource scheduling, this invention can dynamically adjust based on real-time resource status, thereby improving task execution efficiency.

[0098] In summary, this invention, through intelligent resource allocation, optimized computational verification processes, and blockchain-based result storage, effectively overcomes the problems of computational instability, resource waste, and data tampering in existing technologies. Experimental data fully demonstrates that this invention has extremely high resource utilization efficiency, accurate computational results, and superior task execution time, showcasing the innovation and novelty of the technical solution.

[0099] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed, collaborative, remote operation and maintenance management method, characterized in that, include: In a distributed system, the resource usage status of each distributed node is monitored, and the acquired task resources are allocated, prioritizing allocation to the distributed node with sufficient remaining capacity and closest to the node that requested the task resources. The allocated task resources are computed through distributed nodes; After the calculation is completed, the calculation process is broken down, and the decomposed computing resources are allocated to other distributed nodes for verification. After verification, the calculation results are uploaded to the blockchain, thereby enabling the collaboration of distributed nodes; The calculation of the allocated task resources includes defining the distributed node currently performing the task calculation as a node DQ. In the node DQ, the task resources are calculated step by step according to the steps in the calculation module and the algorithm in each step, based on the current task. During the calculation process, the input, output, and algorithm used at each step are recorded: Bp = {R, C, S} Where Bp represents the record of any step, R represents the input of the algorithm in the current step, C represents the output of the algorithm in the current step, and S represents the algorithm itself in the current step. The decomposition of the computation process includes the node DQ allocating the input and algorithm of each computation step in the current task as new computation resources Bp' = {R, S}, and randomly allocating them to V other distributed nodes whose remaining resource capacity meets the allocation and which are closest to the task resource request node. Where V is the number of steps in node DQ, and the first V steps are selected from the distance sorting for allocation; The step of allocating the decomposed computing resources to other distributed nodes for verification includes recalculating the new computing resources through other distributed nodes and returning the verification results of the calculation to node DQ. The recalculation includes using Bp' in the new computing resources and scheduling algorithm S to operate 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 result is different from C, the verification result is unsuccessful. When the verification results calculated in each step are returned to node DQ, if all verification results are correct, the calculation results of node DQ are output; if there are any verification results that fail, node DQ recalculates and verifies the current task. If all re-verification results are correct, the calculation results of node DQ are output; if any re-verification results fail, the computing resources are packaged and uploaded to the central node for calculation.

2. The distributed collaborative remote operation and maintenance management method as described in claim 1, characterized in that: The resource usage status includes CPU utilization: which refers to the utilization of the computing power of each distributed node and is used to assess the computing load of the distributed nodes. Memory utilization: refers to the memory resource usage of distributed nodes, reflecting the memory pressure when distributed nodes process tasks; Network bandwidth utilization: refers to the network bandwidth currently used by distributed nodes, used to assess whether the data transmission capacity of distributed nodes can meet the task requirements; Storage capacity: The storage resources available to distributed nodes, used to determine whether distributed nodes can receive and process large-scale data.

3. The distributed collaborative remote operation and maintenance management method as described in claim 2, characterized in that: The allocation of acquired task resources includes, for each distributed node i, calculating its remaining resource capacity: CPU remaining capacity C cpu,i Remaining memory capacity C mem,i Remaining storage capacity C stor,i ; The percentage of normalized available resources, ranging from [0,1], where 1 indicates that the node's resources are completely idle, and 0 indicates that the resources are full; For each distributed node i, define the total remaining resource capacity C. res,i ; C res,i ={C cpu,i ,C mem,i ,C stor,i } For each task j, calculate the CPU requirement R based on its resource requirements. cpu,j Memory requirements R mem,j Storage requirements R stor,j Normalized to the percentage of resources required by the task, ranging from [0,1], representing the required proportion of resources, the comprehensive resource requirement R for task j is defined. req,j ; R req,j ={R cpu,j ,R mem,j ,R stor,j } Iterate through all distributed nodes and select the set S of all distributed nodes that meet the requirements; S={i∣C cpu,i ≥R cpu,j And C mem,i ≥R mem,j And C stor,i ≥R stor,j } For the selected set S of distributed nodes that meet the criteria, they are ranked according to their distance D from the node requesting the task resources. i,j The distributed nodes are sorted, prioritizing those closest to the nearest node; the sorted set of distributed nodes is as follows: Where n represents the number of nodes; task j is assigned to the first node i1 after sorting.

4. The distributed collaborative remote operation and maintenance management method as described in claim 3, characterized in that: Uploading the calculation results to the blockchain includes recording and encrypting the final output of node DQ and the packaged calculation results through the blockchain. The verification process between node DQ and other distributed nodes is conducted via wireless communication, and the verification process is not recorded.

5. A distributed collaborative remote operation and maintenance management system employing the method described in any one of claims 1-4, characterized in that: Including the allocation unit, in a distributed system, it monitors the resource usage status of each distributed node, allocates the acquired task resources, and prioritizes allocating them to the distributed node with sufficient remaining capacity and closest to the node that requested the task resources. The computing unit performs calculations on the allocated task resources through distributed nodes; The verification unit, after completing the calculation, breaks down the calculation process and allocates the decomposed computing resources to other distributed nodes for verification. After verification, the transmission unit uploads the calculation results to the blockchain, thereby enabling the collaboration of distributed nodes.

6. 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, it implements the steps of a distributed collaborative remote operation and maintenance management method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the distributed collaborative remote operation and maintenance management method according to any one of claims 1 to 4.

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