Internet-based collaborative service method and system

Abstract

The invention discloses a collaborative service method and system based on the Internet, and relates to the technical field of the Internet. The cooperative service method based on the Internet comprises the following steps: after receiving a user cooperative service request, a master control device analyzes and extracts service demand parameters, sets a difficulty coefficient of a cooperative service task, screens adaptive target slave cooperative devices according to the difficulty coefficient, establishes a cooperative service cluster, and sends the cooperative service cluster to the master control device; the master control device establishes a communication link with each target slave cooperative device, synchronizes service demand parameters and initial data, splits a total task into a plurality of sub-tasks based on service core logic, and distributes the sub-tasks in combination with hardware and software capability differences of each slave cooperative device. According to the cooperative service method based on the Internet, standardization and high efficiency of the cooperative service are realized, and continuity and reliability of the cooperative service are guaranteed at the same time.

Description

Technical Field

[0001] This invention relates to the field of Internet technology, and in particular to an Internet-based collaborative service method and system. Background Technology

[0002] With the rapid development of Internet technology, collaborative services have been widely used in many fields such as cloud computing, industrial data processing, and online services. Its core requirement is to efficiently complete complex service tasks that are difficult for a single device to handle through the collaboration of multiple devices.

[0003] However, existing internet-based collaborative service methods suffer from a core problem: task allocation lacks a scientific and quantitative basis, leading to low collaborative efficiency and insufficient service stability. Specifically, in current technologies, when allocating collaborative tasks, the main control device often simply assigns tasks based on the device's basic hardware configuration, without accurately quantifying the difficulty of the collaborative tasks or considering the comprehensive capabilities of the collaborative devices (including computing power, processing power, network stability, etc.) for targeted allocation. This results in high-difficulty tasks being assigned to devices with insufficient capabilities, while low-difficulty tasks waste the resources of high-performance devices. Summary of the Invention

[0004] The purpose of this invention is to provide an Internet-based collaborative service method and system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an Internet-based collaborative service method, comprising the following steps: S1: After receiving the user's collaborative service request, the main control device parses and extracts the service requirement parameters, evaluates the rationality of the parameters in combination with the actual application scenario, and sets the difficulty coefficient of the collaborative service task. S2: Based on the difficulty coefficient, select suitable targets from collaborative devices and build a collaborative service cluster; S3: The main control device establishes a communication link with each target slave device to synchronize service requirement parameters and initial data; S4: Based on the core service logic, the total task is divided into several sub-tasks, and the sub-tasks are allocated according to the differences in the hardware and software capabilities of each collaborative device. S5: Each slave device executes sub-tasks and provides real-time feedback on execution status and intermediate data; S6: After all subtasks are completed, the main control device integrates the results of each subtask, generates the final result of the collaborative service, and feeds it back to the user.

[0006] Preferably, in step S1, the service requirements are evaluated based on the integrity of comprehensive parameters, task complexity, and real-time performance. The difficulty coefficient is based on a 1-10 scale, and is determined by combining four dimensions: computational load, data transmission volume, task relevance, and fault tolerance requirements. The final difficulty coefficient is the weighted average score of each dimension, with the following weights: computational load 32%, data transmission volume 23%, task relevance 27%, and fault tolerance requirements 18%.

[0007] Preferably, the criteria for determining the difficulty level in each dimension are as follows: In terms of computational load, a score of 1-2 is given when the single task computation load is less than 12 million times; a score of 3-7 is given when the computation load is between 12 million and 950 million times; and a score of 8-10 is given when the computation load is ≥ 950 million times. In terms of data volume, the score is 1-2 points when the data volume is less than 120MB; 3-7 points when the data volume is between 120MB and 9.8GB; and 8-10 points when the data volume is ≥9.8GB.

[0008] Preferably, the criteria for determining the difficulty level also include: In the task relevance dimension, the score is 1-2 points when there are no related subtasks; 3-4 points when the number of related subtasks is ≤3; 5-7 points when the number of related subtasks is 4-9; and 8-10 points when the number of related subtasks is ≥10 and real-time interaction is required. In terms of fault tolerance requirements, the following scores are given: 1-2 points for allowing an error ≥ 5.2% and without a fault tolerance response mechanism or subtask result correction; 3-4 points for allowing an error 2.1%-5.2% and using a post-fault tolerance mechanism, with a one-time correction completed within 30 seconds after the subtask is executed; 5-7 points for allowing an error < 2.1% and using an in-process fault tolerance mechanism, with incremental verification and correction completed within 10 seconds during subtask execution; and 8-10 points for not allowing any error and using a real-time fault tolerance mechanism, with second-level correction and re-execution completed within 1 second during subtask execution.

[0009] Preferably, in step S2, the main control device obtains the hardware configuration, software support capabilities, network bandwidth, historical collaborative service success rate and current load rate of the candidate devices, sets screening conditions in combination with the difficulty coefficient, and determines the target collaborative devices and forms a cluster after preliminary screening, multi-dimensional capability scoring and matching degree verification.

[0010] Preferably, the screening conditions are matched with the difficulty level, and the hardware configuration meets the following requirements: When the difficulty level is ≥7, the candidate device must have at least 8 CPU cores and at least 15.6GB of memory; when the difficulty level is 4-6, the device must have at least 4 CPU cores and at least 7.8GB of memory; when the difficulty level is 1-3, the device must have at least 2 CPU cores and at least 4.2GB of memory. The software support capabilities must comply with the technical specifications of this collaborative service, and its software operating environment must be able to meet the execution requirements of the corresponding sub-tasks.

[0011] Preferably, the filtering conditions further include: Network bandwidth should be adapted to real-time transmission requirements, with a minimum of 98Mbps for a difficulty level of ≥7 minutes, a minimum of 52Mbps for a difficulty level of 4-6 minutes, and a minimum of 11Mbps for a difficulty level of 1-3 minutes. Historically, the success rate of collaborative services should be no less than 91% when the difficulty level is ≥5 and no less than 82% when the difficulty level is <5; the current load rate should be controlled within 58%, and resources should be reserved to ensure service stability.

[0012] Preferably, the subtask splitting and allocation method is as follows: The subtasks are independent of each other but can be coordinated. The difficulty coefficient of each subtask is a weighted average of its computational load, data transmission load, task correlation, and fault tolerance requirements. The difficulty coefficient of a single subtask does not exceed the total task difficulty coefficient, and the sum of the difficulty coefficients of all subtasks deviates from the total task difficulty coefficient by ≤10%. Based on the overall capabilities of collaborative devices and the allocation of individual scores, the specific rules are as follows: Individual scoring criteria: The computing power score is based on the CPU computing speed and floating-point computing power assessment. A computing speed ≥1000MIPS is scored 8-10 points, a computing speed between 500-1000MIPS is scored 5-7 points, and a computing speed <500MIPS is scored 1-4 points. Processing capability score is based on data parsing speed. A parsing speed ≥100MB / s is scored 8-10 points, a parsing speed between 50-100MB / s is scored 5-7 points, and a parsing speed <50MB / s is scored 1-4 points. Network stability scores are based on network fluctuation amplitude assessment. Fluctuation amplitude ≤ 5% is scored as 8-10 points, fluctuation amplitude between 5% and 15% is scored as 5-7 points, and fluctuation amplitude > 15% is scored as 1-4 points.

[0013] The overall ability score is a weighted average of four scores: computing power, processing power, network stability, and software compatibility. The weights of the four scores are 35%, 25%, 25%, and 15%, respectively.

[0014] Allocation rules: For collaborative devices with a computing power score of ≥8, priority will be given to sub-tasks with a difficulty coefficient of ≥7 and a large amount of computation; for collaborative devices with a processing power score of ≥8, priority will be given to data parsing sub-tasks; for collaborative devices with a network stability score of ≥8, priority will be given to real-time transmission sub-tasks.

[0015] Preferably, during the execution of a subtask, the master control device monitors the operating status of each slave collaborative device in real time. When abnormal device load, network interruption, or subtask execution error is detected, the master control device immediately reassigns the subtask to other suitable slave collaborative devices in the cluster to ensure the continuity of collaborative services.

[0016] An Internet-based collaborative service system, the system being used to implement a collaborative service method, includes a master control device and several candidate slave collaborative devices, which establish a communication connection through the Internet; The main control device includes a demand analysis and evaluation module, a cluster screening and building module, a communication synchronization module, a task splitting and allocation module, a real-time fault-tolerant scheduling module, and a result fusion and feedback module. The demand analysis and evaluation module is used to analyze demand parameters and set difficulty coefficients according to regulations. The cluster screening and building module is used to build a collaborative cluster by combining the difficulty coefficient and screening conditions. The communication synchronization module is used to establish communication and synchronize relevant data. The task splitting and allocation module is used to split and allocate sub-tasks according to rules. The real-time fault-tolerant scheduling module is used to monitor the device status and reallocate abnormal sub-tasks. The result fusion and feedback module is used to fuse the results of sub-tasks and provide feedback to the user. The candidate slave collaborative device includes a subtask execution module, a status feedback module, and a data interaction module. The status feedback module is used to provide real-time feedback on the execution status and intermediate data, and the data interaction module is used to realize data interaction with the main control device.

[0017] The technical effects and advantages of this invention are as follows: This internet-based collaborative service method solves the problems of unfounded and inefficient collaborative task allocation in existing technologies through a closed-loop design that includes difficulty level setting, device selection, task splitting and allocation, fault-tolerant scheduling, and result fusion. It achieves standardization and efficiency of collaborative services, while ensuring the continuity and reliability of collaborative services and improving user experience.

[0018] This internet-based collaborative service method sets corresponding hardware configuration and software support requirements for different difficulty levels, making equipment selection more targeted. It ensures that high-difficulty tasks are assigned to high-performance devices, while low-difficulty tasks make reasonable use of ordinary device resources, thus optimizing the allocation of device resources. At the same time, it ensures software compatibility and avoids collaborative failures caused by software incompatibility.

[0019] This internet-based collaborative service method clarifies the principles and difficulty control standards for subtask decomposition, avoiding difficulties in collaborative connection caused by unreasonable subtask decomposition. At the same time, by combining the comprehensive capabilities and individual scores of collaborative devices to allocate subtasks, it achieves a precise match between task difficulty and device capabilities, maximizes the advantages of each collaborative device, improves the efficiency of subtask execution, and thus improves the efficiency of the entire collaborative service, while avoiding resource waste. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 This is a schematic diagram illustrating the difficulty level of the present invention; Figure 3 This is a schematic diagram of the device screening process of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] This invention provides, for example Figures 1-3 The illustrated method for internet-based collaborative services includes the following steps: S1: After receiving the user's collaborative service request, the main control device parses and extracts the service requirement parameters, evaluates the rationality of the parameters in combination with the actual application scenario, and sets the difficulty coefficient of the collaborative service task. S2: Based on the difficulty level, select suitable targets from collaborative devices and build a collaborative service cluster; S3: The main control device establishes a communication link with each target slave device to synchronize service requirement parameters and initial data; S4: Based on the core service logic, the total task is divided into several sub-tasks, and the sub-tasks are allocated according to the differences in the hardware and software capabilities of each collaborative device. S5: Each slave device executes sub-tasks and provides real-time feedback on execution status and intermediate data; S6: After all subtasks are completed, the main control device integrates the results of each subtask, generates the final result of the collaborative service, and feeds it back to the user.

[0023] By implementing a closed-loop design that includes setting difficulty levels, selecting equipment, splitting and allocating tasks, fault-tolerant scheduling, and merging results, the problem of unfounded and inefficient collaborative task allocation in existing technologies has been solved. This has enabled the standardization and efficiency of collaborative services, while ensuring the continuity and reliability of collaborative services and improving the user experience.

[0024] Furthermore, in step S1, the service requirements are evaluated based on the integrity of comprehensive parameters, task complexity, and real-time performance. The difficulty coefficient is based on a 1-10 scale, and is determined by combining four dimensions: computational load, data transmission volume, task relevance, and fault tolerance requirements. The final difficulty coefficient is the weighted average score of each dimension, with the following weights: computational load 32%, data transmission volume 23%, task relevance 27%, and fault tolerance requirements 18%.

[0025] By using four fixed dimensions and clearly defined weights, the problem of ambiguous task difficulty determination in existing technologies is avoided, making the setting of difficulty coefficients more objective and repeatable. This provides a precise quantitative basis for subsequent equipment selection and task allocation, ensuring the adaptability of collaborative services.

[0026] Furthermore, the criteria for determining the difficulty level in each dimension are as follows: In terms of computational load, a score of 1-2 is given when the single task computation load is less than 12 million times; a score of 3-7 is given when the computation load is between 12 million and 950 million times; and a score of 8-10 is given when the computation load is ≥ 950 million times. In terms of data volume, the score is 1-2 points when the data volume is less than 120MB; 3-7 points when the data volume is between 120MB and 9.8GB; and 8-10 points when the data volume is ≥9.8GB.

[0027] The criteria for judging the two core dimensions of difficulty coefficient—calculation volume and data transmission volume—have been quantified and refined, and the scoring standards corresponding to different numerical ranges have been clarified. This makes the calculation of the difficulty coefficient more accurate and operable, further improving the objectivity of the difficulty coefficient and providing more reliable support for the advancement of subsequent collaborative processes.

[0028] Furthermore, the criteria for determining the difficulty level also include: In the task relevance dimension, the score is 1-2 points when there are no related subtasks; 3-4 points when the number of related subtasks is ≤3; 5-7 points when the number of related subtasks is 4-9; and 8-10 points when the number of related subtasks is ≥10 and real-time interaction is required. In terms of fault tolerance requirements, the following scores are given: 1-2 points for allowing an error ≥ 5.2% and without a fault tolerance response mechanism or subtask result correction; 3-4 points for allowing an error 2.1%-5.2% and using a post-fault tolerance mechanism, with a one-time correction completed within 30 seconds after the subtask is executed; 5-7 points for allowing an error < 2.1% and using an in-process fault tolerance mechanism, with incremental verification and correction completed within 10 seconds during subtask execution; and 8-10 points for not allowing any error and using a real-time fault tolerance mechanism, with second-level correction and re-execution completed within 1 second during subtask execution.

[0029] The system supplements the quantitative judgment criteria for two dimensions: task relevance and fault tolerance requirements. The fault tolerance requirement dimension clarifies the fault tolerance mechanism corresponding to different error ranges, which solves the problem of vague fault tolerance requirements and lack of clear execution standards in the existing technology. This not only improves the difficulty coefficient judgment system, but also provides a clear basis for the fault tolerance scheduling of subsequent sub-tasks, thereby enhancing the stability of collaborative services.

[0030] Furthermore, in step S2, the main control device obtains the hardware configuration, software support capabilities, network bandwidth, historical collaborative service success rate and current load rate of the candidate devices, sets screening conditions in combination with the difficulty coefficient, and determines the target collaborative devices and forms a cluster after preliminary screening, multi-dimensional capability scoring and matching degree verification.

[0031] Through initial screening, capability scoring, and matching verification, the selected equipment is ensured to be suitable for the task difficulty, avoiding inefficiency or resource waste caused by mismatch between equipment capabilities and task difficulty, and providing equipment support for the efficient execution of collaborative services.

[0032] Furthermore, the screening criteria are matched with the difficulty level, and the hardware configuration meets the requirements: When the difficulty level is ≥7, the candidate device must have at least 8 CPU cores and at least 15.6GB of memory; when the difficulty level is 4-6, the device must have at least 4 CPU cores and at least 7.8GB of memory; when the difficulty level is 1-3, the device must have at least 2 CPU cores and at least 4.2GB of memory. The software support capabilities must comply with the technical specifications of this collaborative service, and its software operating environment must be able to meet the execution requirements of the corresponding sub-tasks.

[0033] For different levels of difficulty, corresponding hardware configuration and software support requirements were set to make equipment selection more targeted. This ensures that high-difficulty tasks are assigned to high-performance equipment, while low-difficulty tasks make reasonable use of ordinary equipment resources, thus optimizing the allocation of equipment resources. At the same time, it ensures software compatibility and avoids collaborative failures caused by software incompatibility.

[0034] Furthermore, the screening criteria also include: Network bandwidth should be adapted to real-time transmission requirements, with a minimum of 98Mbps for a difficulty level of ≥7 minutes, a minimum of 52Mbps for a difficulty level of 4-6 minutes, and a minimum of 11Mbps for a difficulty level of 1-3 minutes. Historically, the success rate of collaborative services should be no less than 91% when the difficulty level is ≥5 and no less than 82% when the difficulty level is <5; the current load rate should be controlled within 58%, and resources should be reserved to ensure service stability.

[0035] The selection criteria have been supplemented with network bandwidth, historical success rate, and load rate, and are strongly tied to the task difficulty coefficient. This ensures that the selected devices have stable network transmission capabilities and high collaborative reliability. At the same time, sufficient load resources are reserved to avoid collaborative service interruptions caused by excessive device load or network instability, thereby further improving the stability and reliability of collaborative services.

[0036] Furthermore, the subtask splitting and allocation method is as follows: The subtasks are independent of each other but can be coordinated. The difficulty coefficient of each subtask is a weighted average of its computational load, data transmission load, task correlation, and fault tolerance requirements. The difficulty coefficient of a single subtask does not exceed the total task difficulty coefficient, and the sum of the difficulty coefficients of all subtasks deviates from the total task difficulty coefficient by ≤10%. Based on the overall capabilities of collaborative devices and the allocation of individual scores, the specific rules are as follows: Individual scoring criteria: The computing power score is based on the CPU computing speed and floating-point computing power assessment. A computing speed ≥1000MIPS is scored 8-10 points, a computing speed between 500-1000MIPS is scored 5-7 points, and a computing speed <500MIPS is scored 1-4 points. Processing capability score is based on data parsing speed. A parsing speed ≥100MB / s is scored 8-10 points, a parsing speed between 50-100MB / s is scored 5-7 points, and a parsing speed <50MB / s is scored 1-4 points. Network stability scores are based on network fluctuation amplitude assessment. Fluctuation amplitude ≤ 5% is scored as 8-10 points, fluctuation amplitude between 5% and 15% is scored as 5-7 points, and fluctuation amplitude > 15% is scored as 1-4 points.

[0037] The overall ability score is a weighted average of four scores: computing power, processing power, network stability, and software compatibility. The weights of the four scores are 35%, 25%, 25%, and 15%, respectively.

[0038] Allocation rules: For collaborative devices with a computing power score of ≥8, priority will be given to sub-tasks with a difficulty coefficient of ≥7 and a large amount of computation; for collaborative devices with a processing power score of ≥8, priority will be given to data parsing sub-tasks; for collaborative devices with a network stability score of ≥8, priority will be given to real-time transmission sub-tasks.

[0039] The principles and difficulty control standards for subtask decomposition were clearly defined, avoiding difficulties in collaboration caused by unreasonable subtask decomposition. At the same time, by combining the comprehensive capabilities and individual scores of the collaborative devices to allocate subtasks, the task difficulty and device capabilities were accurately matched, maximizing the advantages of each collaborative device, improving the efficiency of subtask execution, and thus improving the efficiency of the entire collaborative service, while avoiding resource waste.

[0040] Furthermore, during the execution of subtasks, the main control device monitors the operating status of each slave collaborative device in real time. The criteria for determining device anomalies are as follows: a load rate exceeding 58% for more than 10 seconds is considered a load anomaly; a network interruption lasting more than 3 seconds is considered a network interruption; and subtask execution errors are considered execution errors, specifically including data parsing failure, computation timeout, and abnormal feedback data. When the above anomalies are detected, the subtask is immediately reassigned to other suitable slave collaborative devices within the cluster. The reassignment priority rule is: priority is given to slave collaborative devices whose comprehensive capability score matches the difficulty coefficient of the subtask, whose current load rate is less than 30%, and whose network stability score is ≥7. If no suitable device is found, a suitable device is randomly selected from within the cluster for allocation, ensuring the continuity of collaborative services.

[0041] By monitoring the operating status of equipment in real time and promptly reallocating abnormal sub-tasks, the problem of collaborative services being easily interrupted due to the failure of a single device in existing technologies has been solved. This achieves real-time fault tolerance for collaborative services, ensures the continuity of collaborative services, reduces the risk of collaborative service failure, and improves the reliability and user satisfaction of collaborative services.

[0042] An Internet-based collaborative service system, which implements collaborative service methods, includes a master control device and several candidate slave collaborative devices, which establish a communication connection through the Internet. The main control equipment includes a demand analysis and evaluation module, a cluster screening and building module, a communication synchronization module, a task splitting and allocation module, a real-time fault-tolerant scheduling module, and a result fusion and feedback module. The demand analysis and evaluation module is used to analyze demand parameters and set difficulty coefficients according to regulations. The cluster screening and building module is used to build a collaborative cluster by combining the difficulty coefficient and screening conditions. The communication synchronization module is used to establish communication and synchronize relevant data. The task splitting and allocation module is used to split and allocate sub-tasks according to rules. The real-time fault-tolerant scheduling module is used to monitor the equipment status and reallocate abnormal sub-tasks. The result fusion and feedback module is used to fuse the results of sub-tasks and provide feedback to the user. The candidate collaborative device includes a subtask execution module, a status feedback module, and a data interaction module. The status feedback module is used to provide real-time feedback on the execution status and intermediate data, and the data interaction module is used to realize data interaction with the main control device.

[0043] The system implementation corresponding to the collaborative service method is provided, which clarifies the module composition and function of the master control device and candidate slave collaborative devices, enabling the collaborative service method to be implemented. The clear division of labor and cooperation among the modules ensure the smooth progress of the collaborative service process, while improving the scalability and maintainability of the system, and providing support for the large-scale application of collaborative services.

[0044] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative service method based on the Internet, characterized in that, Includes the following steps: S1: After receiving the user's collaborative service request, the main control device parses and extracts the service requirement parameters, evaluates the rationality of the parameters in combination with the actual application scenario, and sets the difficulty coefficient of the collaborative service task. S2: Based on the difficulty coefficient, select suitable targets from collaborative devices and build a collaborative service cluster; S3: The main control device establishes a communication link with each target slave device to synchronize service requirement parameters and initial data; S4: Based on the core service logic, the total task is divided into several sub-tasks, and the sub-tasks are allocated according to the differences in the hardware and software capabilities of each collaborative device. S5: Each slave device executes sub-tasks and provides real-time feedback on execution status and intermediate data; S6: After all subtasks are completed, the main control device integrates the results of each subtask, generates the final result of the collaborative service, and feeds it back to the user.

2. The Internet-based collaborative service method according to claim 1, characterized in that, In step S1, the service requirements are assessed based on the integrity of comprehensive parameters, task complexity, and real-time performance. The difficulty coefficient is based on a 1-10 scale, and is determined by combining four dimensions: computational load, data transmission volume, task relevance, and fault tolerance requirements. The final difficulty coefficient is the weighted average score of each dimension, with the following weights: computational load 32%, data transmission volume 23%, task relevance 27%, and fault tolerance requirements 18%.

3. The Internet-based collaborative service method according to claim 2, characterized in that, The criteria for determining the difficulty level in each dimension are as follows: In terms of computational load, a score of 1-2 is given when the single task computation load is less than 12 million times; a score of 3-7 is given when the computation load is between 12 million and 950 million times; and a score of 8-10 is given when the computation load is ≥ 950 million times. In terms of data volume, the score is 1-2 points when the data volume is less than 120MB; 3-7 points when the data volume is between 120MB and 9.8GB; and 8-10 points when the data volume is ≥9.8GB.

4. The Internet-based collaborative service method according to claim 1, characterized in that, The criteria for determining the difficulty level also include: In the task relevance dimension, the score is 1-2 points when there are no related subtasks; 3-4 points when the number of related subtasks is ≤3; 5-7 points when the number of related subtasks is 4-9; and 8-10 points when the number of related subtasks is ≥10 and real-time interaction is required. In terms of fault tolerance requirements, the following scores are given: 1-2 points for allowing an error ≥ 5.2% and without a fault tolerance response mechanism or subtask result correction; 3-4 points for allowing an error 2.1%-5.2% and using a post-fault tolerance mechanism, with a one-time correction completed within 30 seconds after the subtask is executed; 5-7 points for allowing an error < 2.1% and using an in-process fault tolerance mechanism, with incremental verification and correction completed within 10 seconds during subtask execution; and 8-10 points for not allowing any error and using a real-time fault tolerance mechanism, with second-level correction and re-execution completed within 1 second during subtask execution.

5. The Internet-based collaborative service method according to claim 1, characterized in that, In step S2, the main control device obtains the hardware configuration, software support capabilities, network bandwidth, historical collaborative service success rate and current load rate of the candidate devices, sets the screening conditions in combination with the difficulty coefficient, and determines the target collaborative devices and builds a cluster after preliminary screening, multi-dimensional capability scoring and matching degree verification.

6. The Internet-based collaborative service method according to claim 5, characterized in that, The screening criteria are matched with the difficulty level, and the hardware configuration meets the requirements: When the difficulty level is ≥7, the candidate device must have at least 8 CPU cores and at least 15.6GB of memory; when the difficulty level is 4-6, the device must have at least 4 CPU cores and at least 7.8GB of memory; when the difficulty level is 1-3, the device must have at least 2 CPU cores and at least 4.2GB of memory. The software support capabilities must comply with the technical specifications of this collaborative service, and its software operating environment must be able to meet the execution requirements of the corresponding sub-tasks.

7. The Internet-based collaborative service method according to claim 5, characterized in that, The filtering criteria also include: Network bandwidth should be adapted to real-time transmission requirements, with a minimum speed of 98Mbps for a difficulty level of ≥7 minutes, a minimum speed of 52Mbps for a difficulty level of 4-6 minutes, and a minimum speed of 11Mbps for a difficulty level of 1-3 minutes. Historically, the success rate of collaborative services should be no less than 91% when the difficulty level is ≥5 and no less than 82% when the difficulty level is <5. The current load rate should be controlled within 58%, and resources should be reserved to ensure service stability.

8. The Internet-based collaborative service method according to claim 1, characterized in that, The method for splitting and allocating subtasks is as follows: The subtasks are independent of each other but can be coordinated. The difficulty coefficient of each subtask is a weighted average of its computational load, data transmission load, task correlation, and fault tolerance requirements. The difficulty coefficient of a single subtask does not exceed the total task difficulty coefficient, and the sum of the difficulty coefficients of all subtasks deviates from the total task difficulty coefficient by ≤10%. Based on the overall capabilities of collaborative devices and individual score allocation, sub-tasks with a computing power score ≥8 and a difficulty coefficient ≥7 and a large amount of computation are prioritized for allocation; data parsing sub-tasks are prioritized for allocation; and real-time transmission sub-tasks are prioritized for allocation when the processing power score ≥8 and the network stability score ≥8.

9. The Internet-based collaborative service method according to claim 1, characterized in that, During the execution of a subtask, the master control device monitors the operating status of each slave collaborative device in real time. When abnormal device load, network interruption, or subtask execution error is detected, the master control device immediately reassigns the subtask to other suitable slave collaborative devices in the cluster to ensure the continuity of collaborative services.

10. An Internet-based collaborative service system, characterized in that, The system is used to implement the collaborative service method as described in any one of claims 1-9, including a master control device and a plurality of candidate slave collaborative devices, which establish a communication connection through the Internet; The main control device includes a demand analysis and evaluation module, a cluster screening and building module, a communication synchronization module, a task splitting and allocation module, a real-time fault-tolerant scheduling module, and a result fusion and feedback module. The demand analysis and evaluation module is used to analyze demand parameters and set difficulty coefficients according to regulations. The cluster screening and building module is used to build a collaborative cluster by combining the difficulty coefficient and screening conditions. The communication synchronization module is used to establish communication and synchronize relevant data. The task splitting and allocation module is used to split and allocate sub-tasks according to rules. The real-time fault-tolerant scheduling module is used to monitor the device status and reallocate abnormal sub-tasks. The result fusion and feedback module is used to fuse the results of sub-tasks and provide feedback to the user. The candidate slave collaborative device includes a subtask execution module, a status feedback module, and a data interaction module. The status feedback module is used to provide real-time feedback on the execution status and intermediate data, and the data interaction module is used to realize data interaction with the main control device.

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