Cloud computing and edge computing collaborative system integration architecture and collaborative computing method

Abstract

The invention relates to the technical field of distributed computing and networks, in particular to a cloud computing and edge computing collaborative system integration architecture and a collaborative computing method. The method comprises the following steps: an edge processing cache unit filters and caches received original data based on a self-adaptive data filtering strategy and an intelligent cache strategy; the cloud management cooperation unit constructs a time delay energy consumption optimization objective function based on the current cloud node and edge node states, solves the time delay energy consumption optimization objective function by using a multi-objective optimization algorithm, and dynamically cooperates task scheduling and resource allocation; the heterogeneous hardware acceleration unit adopts a hierarchical accelerator scheduling strategy to calculate an optimal hardware matching degree, introduces a calculation acceleration efficiency coefficient, and schedules heterogeneous accelerator resources. According to the cloud computing and edge computing collaborative system integration architecture and the collaborative computing method, efficient collaborative scheduling between cloud computing and edge computing is realized by combining intelligent matching of a multi-objective optimization algorithm and a heterogeneous accelerator.

Description

Technical Field

[0001] The present invention relates to the field of distributed computing and network technology, and in particular to a system integration architecture and collaborative computing method for collaborative cloud computing and edge computing. Background Art

[0002] The system integration architecture and collaborative computing method of cloud computing and edge computing are designed to minimize the overall execution delay and energy consumption of the system and optimize resource utilization. Through multi-objective optimization algorithms and dynamic scheduling strategies, task allocation and resource allocation are controlled to achieve efficient task scheduling and resource management, thereby improving system performance and energy efficiency.

[0003] The existing collaborative architecture of cloud computing and edge computing usually finds it difficult to efficiently and dynamically schedule computing tasks under high load conditions, and flexibly select the most suitable computing resources according to the characteristics of the task. In addition, due to the complexity of tasks and the heterogeneity of computing resources, when different tasks are assigned, there will be problems such as low accelerator resource utilization, long task execution delay and low system energy efficiency. Therefore, a system integration architecture and collaborative computing method for the collaboration of cloud computing and edge computing are provided. Summary of the invention

[0004] The purpose of the present invention is to provide a system integration architecture and method for the collaboration of cloud computing and edge computing, so as to solve the problems raised in the above background technology that due to the existence of task complexity and computing resource heterogeneity, accelerator resource utilization is low, task execution delay is too long, and system energy efficiency is low when different tasks are assigned.

[0005] To achieve the above object, the present invention aims to provide a system integration architecture for cloud computing and edge computing collaboration, including: an edge processing cache unit, which performs filtering and caching on the received raw data based on an adaptive data filtering strategy and an intelligent caching strategy; It also includes a cloud management collaboration unit, which is used to construct a delay energy consumption optimization objective function and set constraints between the edge and the cloud based on the current cloud node and edge node status, solve the delay energy consumption optimization objective function using a multi-objective optimization algorithm, and dynamically coordinate task scheduling and resource allocation; It also includes a heterogeneous hardware acceleration unit, which adopts a hierarchical accelerator scheduling strategy to calculate the optimal hardware matching degree, introduces a calculation acceleration efficiency coefficient, and schedules heterogeneous accelerator resources.

[0006] As a further improvement of the technical solution, the edge processing cache unit includes an adaptive data filtering module and an intelligent cache management module; Wherein, the adaptive data filtering module uses an adaptive data filtering strategy to filter the collected raw data; The intelligent cache management module uses an intelligent cache strategy to store the filtered data into the cache.

[0007] As a further improvement of the present technical solution, the adaptive data filtering strategy is implemented based on machine learning algorithms and statistical analysis techniques, and is used to identify the received raw data in real time and filter out noise and irrelevant data in the data: The adaptive data filtering module uses an adaptive data filtering strategy to filter the collected raw data. The specific method steps are as follows: S1.1.1. Receive raw data from various terminal devices, obtain the business demand weight vector, and use the data feature extraction algorithm to extract the raw data feature vector, and adaptively select the filtering strategy based on the business demand weight vector and the raw data feature vector: ; in, for the moment; For the moment The selected filtering strategy; A collection of all optional filtering strategies; For filtering strategy; is the business demand weight vector; For the moment The original data feature vector of S1.1.2 Application time The selected filtering strategy is used to filter the original data to obtain the filtered data: ; in, For the moment Filter data volume; is the original data; For the moment Data filtering ratio.

[0008] As a further improvement of the technical solution, the intelligent caching strategy is implemented based on the least recently used algorithm and predictive caching technology to optimize data storage and access; The intelligent cache management module uses the intelligent cache strategy to store the filtered data in the cache. The specific method steps are as follows: S1.2.1, compressing the filtered data and storing it in the cache; S1.2.2. Calculate the current cache hit rate based on the number of cache hit accesses and the total number of accesses: ; in, For the moment Cache hit rate; For the moment The number of cache hit accesses; For the moment Total number of visits; S1.2.3. Adjust cache strategy parameters based on cache hit rate and target hit rate: ; in, For the moment Cache strategy parameters; The cache strategy parameters updated at time t+1; is the learning rate; is the target cache hit rate; S1.2.4. Calculate the amount of data to be migrated based on the current cache capacity and the preset cache limit, and migrate the amount of data to be migrated to the cloud and edge nodes: ; in, For the moment The amount of data that needs to be migrated; For the moment The total amount of data currently cached; The maximum capacity of the cache.

[0009] As a further improvement of the technical solution, the cloud management collaboration unit includes a task scheduling and allocation module, a delay and energy consumption solution module, and a network path optimization module; The task scheduling and allocation module is used to construct a delay and energy consumption optimization objective function and set constraints between the edge node and the cloud based on the current cloud node and edge node status using a multi-objective optimization algorithm; The delay energy consumption solving module solves the delay energy consumption optimization objective function based on a multi-objective optimization algorithm, and dynamically coordinates task scheduling and resource allocation; The network path optimization module uses the Dijkstra algorithm to optimize the data transmission path and bandwidth allocation between the edge node and the cloud.

[0010] As a further improvement of the technical solution, the multi-objective optimization algorithm is implemented based on task delay requirements and energy consumption evaluation, and is used for dynamic collaborative task scheduling and resource allocation between edge nodes and the cloud to minimize the overall execution delay and energy consumption of the system and optimize resource utilization; The task scheduling allocation module is used to construct a delay energy consumption optimization objective function and set constraints between the edge node and the cloud based on the current cloud node and edge node status using a multi-objective optimization algorithm. The specific method steps are as follows: S2.1.1. Based on the collected raw data, a multi-objective optimization algorithm is used to construct the delay energy consumption optimization objective function: ; in, is the task number, indicating the tasks; is the label of the edge node, indicating the edge nodes; is the number of the cloud node, indicating the cloud nodes; is a marginal binary decision variable; is a binary decision variable in the cloud; is the weight factor of delay; is the weight factor of energy consumption; For the The task is in Execution time on edge nodes; For the The task is in Execution time on each cloud node; For the The task is in Power consumption on edge nodes; For the The task is in Power consumption on each cloud node; is the total number of tasks; is the total number of edge nodes; is the total number of cloud nodes; S2.1.2. Add constraints to the latency energy consumption optimization objective function: Task unique assignment constraints: ; Node resource capacity constraints: ; ; in, For the Tasks at time The amount of computing resources required; For the The edge at the moment The maximum available computing resources; For the Cloud nodes at time The maximum available computing resources; Binary variable constraints: ; .

[0011] As a further improvement of the technical solution, the delay energy consumption solution module solves the delay energy consumption optimization objective function based on a multi-objective optimization algorithm, dynamically coordinates task scheduling and resource allocation, and the specific method steps are as follows: S2.2.1. According to the total number of tasks, the total number of edge nodes and the total number of cloud nodes, based on the optimization algorithm selection strategy, select the optimization algorithm for solving the delay energy consumption optimization objective function applicable to the current moment; S2.2.2. Based on the selected optimization algorithm, solve the delay energy consumption optimization objective function at the current moment to obtain the optimal edge binary decision variables and the optimal cloud binary decision variables; S2.2.3. Based on the optimal edge binary decision variables and the optimal cloud binary decision variables, construct the optimal task scheduling and resource allocation solution; The optimization algorithm selection strategy is based on the integration of linear programming algorithm, mixed integer programming algorithm, genetic algorithm and particle swarm optimization algorithm, and is used to select the optimization algorithm for solving the delay energy consumption optimization objective function applicable to the current moment according to the total number of tasks, the total number of edge nodes and the total number of cloud nodes. The optimization algorithm selection strategy is as follows: If the delay energy consumption optimization objective function and constraints are both linear, the linear programming algorithm is selected; If the delay energy consumption optimization objective function involves integer decision variables, a mixed integer programming algorithm is selected; If the delay energy consumption optimization objective function involves a global optimal solution, a genetic algorithm is selected; If the delay energy consumption optimization objective function seeks an efficient approximate solution, the particle swarm optimization algorithm is selected.

[0012] As a further improvement of the technical solution, the heterogeneous hardware acceleration unit includes an accelerator matching module and an accelerator scheduling module; The accelerator matching module is used to collect accelerator performance data, calculate the acceleration efficiency coefficient of each task on different accelerators, and determine the optimal accelerator matching solution. The specific method steps are as follows: S3.1.1. Collect performance metrics of the accelerator, including computing power, memory bandwidth, and power consumption; Obtain the characteristic indicators of each task, including data volume, computational intensity, and real-time requirements; S3.1.2. Based on the performance indicators of the accelerator and the characteristic indicators of each task, calculate the acceleration efficiency coefficient of each task: ; in, is the number of the accelerator, indicating the accelerator; is the task number, indicating the tasks; For the The task is in The acceleration efficiency coefficient of each accelerator; For the The computing power of an accelerator; For the Memory bandwidth of each accelerator; For the The power consumption of each accelerator; For the The computational intensity of each task; For the The amount of data for each task; S3.1.3. Based on the acceleration efficiency coefficient, match the optimal accelerator for each task: ; And the first The task is in The acceleration efficiency coefficient of the first accelerator becomes The acceleration efficiency coefficient of the task in the optimal accelerator ; For the The optimal accelerator for each task; S3.1.4. Store the optimal accelerator matching solution for each task in the cache and transmit it to the accelerator scheduling module.

[0013] As a further improvement of the technical solution, the accelerator scheduling module dynamically schedules the allocation of accelerator resources based on the optimal accelerator matching solution through hierarchical scheduling strategy and real-time resource monitoring. The specific method steps are as follows: S3.2.1. Calculate the task priority based on the real-time requirements and acceleration efficiency coefficient of the task: ; in, For the The priority of each task; For the The acceleration efficiency coefficient of each task on the optimal accelerator; For the The real-time requirements of each task; S3.2.2. Select the task with the highest priority: Define the decision variables: ; in, is the set of all accelerators; Construct resource allocation optimization objective function: ; in, For the Assign tasks to The decision variables of the accelerator; Build constraints: Task unique assignment constraints: ; Accelerator resource capacity constraints: ; in, For the The amount of computing resources required by each task; For the The maximum available computing resources of an accelerator; Binary variable constraints: ; S3.2.3. Based on the selected optimization algorithm, solve the resource allocation optimization objective function to obtain the optimal decision variables , perform heterogeneous accelerator scheduling.

[0014] On the other hand, the present invention provides a collaborative computing method for cloud computing and edge computing, based on the above-mentioned system integration architecture of collaborative cloud computing and edge computing, comprising the following steps: S10.1. Filter and cache the received raw data based on an adaptive data filtering strategy and an intelligent caching strategy; S10.2. Based on the current status of cloud nodes and edge nodes, construct the latency and energy consumption optimization objective function and set constraints, use the multi-objective optimization algorithm to solve the latency and energy consumption optimization objective function, and dynamically coordinate task scheduling and resource allocation; S10.3. Use a hierarchical accelerator scheduling strategy to calculate the optimal hardware matching degree, introduce a calculation acceleration efficiency coefficient, and schedule heterogeneous accelerator resources.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the system integration architecture and collaborative computing method of cloud computing and edge computing, based on multi-objective optimization algorithms and dynamic scheduling strategies, the optimal computing path can be intelligently selected and resource allocation can be dynamically adjusted according to the real-time requirements of the task and the heterogeneous characteristics of computing resources, thereby achieving efficient computing task scheduling and resource utilization between different nodes.

[0016] 2. In the system integration architecture and collaborative computing method of cloud computing and edge computing, by introducing accelerator performance matching and task characteristic analysis mechanism, the optimized scheduling of computing tasks between cloud and edge nodes is realized, ensuring that computing tasks are allocated according to the optimal matching degree of accelerators, thereby improving the overall computing efficiency of the system, reducing latency and optimizing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; The meaning of each number in the figure is: 1. Edge processing cache unit; 2. Cloud management collaboration unit; 3. Heterogeneous hardware acceleration unit; 11. Adaptive data filtering module; 12. Intelligent cache management module; 21. Task scheduling and allocation module; 22. Delay and energy consumption solution module; 23. Network path optimization module; 31. Accelerator matching module; 32. Accelerator scheduling module. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1: Please refer to Figure 1 As shown, a system integration architecture for collaborative cloud computing and edge computing is provided, including: an edge processing cache unit 1, wherein the edge processing cache unit 1 performs filtering and caching on the received raw data based on an adaptive data filtering strategy and an intelligent caching strategy; The edge processing cache unit 1 includes an adaptive data filtering module 11 and an intelligent cache management module 12; In this embodiment, the adaptive data filtering module 11 uses an adaptive data filtering strategy to filter the collected raw data; The intelligent cache management module 12 uses an intelligent cache strategy to store the filtered data into the cache.

[0020] The adaptive data filtering strategy is implemented based on machine learning algorithms and statistical analysis techniques, and is used to identify the received raw data in real time and filter out noise and irrelevant data in the data: In this embodiment, the adaptive data filtering module 11 uses an adaptive data filtering strategy to filter the collected raw data. The specific method steps are as follows: S1.1.1. Receive raw data from various terminal devices, obtain the business demand weight vector, and use the data feature extraction algorithm to extract the raw data feature vector, and adaptively select the filtering strategy based on the business demand weight vector and the raw data feature vector: ; in, for the moment; For the moment The selected filtering strategy; A collection of all optional filtering strategies; For filtering strategy; is the business demand weight vector; For the moment The original data feature vector of S1.1.2 Application time The selected filtering strategy is used to filter the original data to obtain the filtered data: ; in, For the moment Filter data volume; is the original data; For the moment Data filtering ratio.

[0021] In this embodiment, the data feature extraction algorithm uses principal component analysis to extract key features from high-dimensional raw data, reduce the data dimension, and retain as much data information as possible to support the selection of adaptive filtering strategies; A collection of all available filter policies The specific filtering strategies include the following: Threshold-based filtering strategy: simply filter the data using a preset threshold; Filtering strategies based on pattern recognition: Deciding whether to keep or not by identifying specific patterns or trends in the data; Machine learning-based filtering strategy: Use existing machine learning models to intelligently filter data; Filtering strategy based on statistical analysis: Evaluate the abnormality or importance of data through statistical analysis methods to decide whether to retain the data item.

[0022] In this embodiment, the intelligent cache strategy is implemented based on the least recently used algorithm and predictive cache technology to optimize data storage and access; In this embodiment, the intelligent cache management module 12 uses an intelligent cache strategy to store the filtered data in the cache. The specific method steps are as follows: S1.2.1, compressing the filtered data and storing it in the cache; S1.2.2. Calculate the current cache hit rate based on the number of cache hit accesses and the total number of accesses: ; in, For the moment Cache hit rate; For the moment The number of cache hit accesses; For the moment Total number of visits; S1.2.3. Adjust cache strategy parameters based on cache hit rate and target hit rate: ; in, For the moment Cache strategy parameters; The cache strategy parameters updated at time t+1; is the learning rate; is the target cache hit rate; S1.2.4. Calculate the amount of data to be migrated based on the current cache capacity and the preset cache limit, and migrate the amount of data to be migrated to the cloud and edge nodes: ; in, For the moment The amount of data that needs to be migrated; For the moment The total amount of data currently cached; The maximum capacity of the cache.

[0023] It also includes a cloud management collaboration unit 2, which is used to construct a delay energy consumption optimization objective function and set constraints between the edge and the cloud based on the current cloud node and edge node status, and use a multi-objective optimization algorithm to solve the delay energy consumption optimization objective function, dynamically coordinate task scheduling and resource allocation; In this embodiment, the cloud management collaboration unit 2 includes a task scheduling and allocation module 21, a delay and energy consumption solving module 22, and a network path optimization module 23; The task scheduling and allocation module 21 is used to construct a delay and energy consumption optimization objective function and set constraints between the edge node and the cloud based on the current cloud node and edge node status using a multi-objective optimization algorithm; The delay energy consumption solving module 22 solves the delay energy consumption optimization objective function based on a multi-objective optimization algorithm, and dynamically coordinates task scheduling and resource allocation; The network path optimization module 23 uses the Dijkstra algorithm to optimize the data transmission path and bandwidth allocation between the edge node and the cloud.

[0024] In this embodiment, the Dijkstra algorithm is a classic algorithm for calculating the shortest path in a graph, and its advantages for optimizing the data transmission path and bandwidth allocation between the edge node and the cloud are as follows: The Dijkstra algorithm can find the shortest path between any two nodes in a graph in polynomial time, and is suitable for real-time network path optimization needs; it is applicable to network topologies of different scales, and can be effectively applied in both small-scale and large-scale network environments; the Dijkstra algorithm can adjust path selection in real time according to dynamic changes in network status, adapt to the real-time requirements of the network, and improve the reliability and efficiency of data transmission; combined with real-time network monitoring data, the Dijkstra algorithm can continuously optimize transmission paths and bandwidth allocation strategies.

[0025] The multi-objective optimization algorithm is implemented based on task delay requirements and energy consumption evaluation, and is used to dynamically coordinate task scheduling and resource allocation between edge nodes and the cloud to minimize the overall execution delay and energy consumption of the system and optimize resource utilization; In this embodiment, the task scheduling allocation module 21 is used to construct a delay energy consumption optimization objective function and set constraints between the edge node and the cloud based on the current cloud node and edge node status using a multi-objective optimization algorithm. The specific method steps are as follows: S2.1.1. Based on the collected raw data, a multi-objective optimization algorithm is used to construct the delay energy consumption optimization objective function: ; in, is the task number, indicating the tasks; is the label of the edge node, indicating the edge nodes; is the number of the cloud node, indicating the cloud nodes; is a marginal binary decision variable; is a binary decision variable in the cloud; is the weight factor of delay; is the weight factor of energy consumption; For the The task is in Execution time on edge nodes; For the The task is in Execution time on each cloud node; For the The task is in Power consumption on edge nodes; For the The task is in Power consumption on each cloud node; is the total number of tasks; is the total number of edge nodes; is the total number of cloud nodes; S2.1.2. Add constraints to the latency energy consumption optimization objective function: Task unique assignment constraints: ; Node resource capacity constraints: ; ; in, For the Tasks at time The amount of computing resources required; For the The edge at the moment The maximum available computing resources; For the Cloud nodes at time The maximum available computing resources; Binary variable constraints: ; .

[0026] In this embodiment, the delay energy consumption solving module 22 solves the delay energy consumption optimization objective function based on a multi-objective optimization algorithm, dynamically coordinates task scheduling and resource allocation, and the specific method steps are as follows: S2.2.1. According to the total number of tasks, the total number of edge nodes and the total number of cloud nodes, based on the optimization algorithm selection strategy, select the optimization algorithm for solving the delay energy consumption optimization objective function applicable to the current moment; S2.2.2. Based on the selected optimization algorithm, solve the delay energy consumption optimization objective function at the current moment to obtain the optimal edge binary decision variables and the optimal cloud binary decision variables; S2.2.3. Based on the optimal edge binary decision variables and the optimal cloud binary decision variables, construct the optimal task scheduling and resource allocation solution; The optimization algorithm selection strategy is based on the integration of linear programming algorithm, mixed integer programming algorithm, genetic algorithm and particle swarm optimization algorithm, and is used to select the optimization algorithm for solving the delay energy consumption optimization objective function applicable to the current moment according to the total number of tasks, the total number of edge nodes and the total number of cloud nodes. The optimization algorithm selection strategy is as follows: If the delay energy consumption optimization objective function and constraints are both linear, the linear programming algorithm is selected; If the delay energy consumption optimization objective function involves integer decision variables, a mixed integer programming algorithm is selected; If the delay energy consumption optimization objective function involves a global optimal solution, a genetic algorithm is selected; If the delay energy consumption optimization objective function seeks an efficient approximate solution, the particle swarm optimization algorithm is selected.

[0027] It also includes a heterogeneous hardware acceleration unit 3, which uses a hierarchical accelerator scheduling strategy to calculate the optimal hardware matching degree, introduces a calculation acceleration efficiency coefficient, and schedules heterogeneous accelerator resources; The heterogeneous hardware acceleration unit 3 includes an accelerator matching module 31 and an accelerator scheduling module 32; In this embodiment, the accelerator matching module 31 is used to collect accelerator performance data, calculate the acceleration efficiency coefficient of each task on different accelerators, and determine the optimal accelerator matching solution. The specific method steps are as follows: S3.1.1. Collect performance metrics of the accelerator, including computing power, memory bandwidth, and power consumption; Obtain the characteristic indicators of each task, including data volume, computational intensity, and real-time requirements; S3.1.2. Based on the performance indicators of the accelerator and the characteristic indicators of each task, calculate the acceleration efficiency coefficient of each task: ; in, is the number of the accelerator, indicating the accelerator; is the task number, indicating the tasks; For the The task is in The acceleration efficiency coefficient of each accelerator; For the The computing power of an accelerator; For the Memory bandwidth of each accelerator; For the The power consumption of each accelerator; For the The computational intensity of each task; For the The amount of data for each task; S3.1.3. Based on the acceleration efficiency coefficient, match the optimal accelerator for each task: ; And the first The task is in The acceleration efficiency coefficient of the first accelerator becomes The acceleration efficiency coefficient of the task in the optimal accelerator ; For the The optimal accelerator for each task; S3.1.4. Store the optimal accelerator matching solution for each task in the cache and transmit it to the accelerator scheduling module 32.

[0028] In this embodiment, the accelerator scheduling module 32 dynamically schedules the allocation of accelerator resources based on the optimal accelerator matching solution through hierarchical scheduling strategy and real-time resource monitoring. The specific method steps are as follows: S3.2.1. Calculate the task priority based on the real-time requirements and acceleration efficiency coefficient of the task: ; in, For the The priority of each task; For the The acceleration efficiency coefficient of each task on the optimal accelerator; For the The real-time requirements of each task; S3.2.2. Select the task with the highest priority: Define the decision variables: ; in, is the set of all accelerators; Construct resource allocation optimization objective function: ; in, For the Assign tasks to The decision variables of the accelerator; Build constraints: Task unique assignment constraints: ; Accelerator resource capacity constraints: ; in, For the The amount of computing resources required by each task; For the The maximum available computing resources of an accelerator; Binary variable constraints: ; S3.2.3. Based on the selected optimization algorithm, solve the resource allocation optimization objective function to obtain the optimal decision variables , perform heterogeneous accelerator scheduling. Embodiment 2:

[0029] The collaborative computing method of cloud computing and edge computing, based on the above-mentioned system integration architecture of collaborative cloud computing and edge computing, includes the following steps: S10.1. Filter and cache the received raw data based on an adaptive data filtering strategy and an intelligent caching strategy; S10.2. Based on the current status of cloud nodes and edge nodes, construct the latency and energy consumption optimization objective function and set constraints, use the multi-objective optimization algorithm to solve the latency and energy consumption optimization objective function, and dynamically coordinate task scheduling and resource allocation; S10.3. Use a hierarchical accelerator scheduling strategy to calculate the optimal hardware matching degree, introduce a calculation acceleration efficiency coefficient, and schedule heterogeneous accelerator resources.

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. The system integration architecture of cloud computing and edge computing collaboration is characterized by: include: An edge processing cache unit (1), wherein the edge processing cache unit (1) performs filtering processing and caching on the received original data based on an adaptive data filtering strategy and an intelligent caching strategy; The edge processing cache unit (1) comprises an adaptive data filtering module (11) and an intelligent cache management module (12); The adaptive data filtering module (11) uses an adaptive data filtering strategy to filter the collected raw data; The intelligent cache management module (12) uses an intelligent cache strategy to store the filtered data into a cache; A cloud management collaboration unit (2), the cloud management collaboration unit (2) is used to construct a delay energy consumption optimization objective function and set constraints between the edge and the cloud based on the current cloud node and edge node status, solve the delay energy consumption optimization objective function using a multi-objective optimization algorithm, and dynamically coordinate task scheduling and resource allocation; The cloud management collaboration unit (2) comprises a task scheduling and allocation module (21), a delay and energy consumption solution module (22) and a network path optimization module (23); The task scheduling and allocation module (21) is used to construct a delay and energy consumption optimization objective function and set constraint conditions between the edge node and the cloud based on the current cloud node and edge node status using a multi-objective optimization algorithm; The delay energy consumption solving module (22) solves the delay energy consumption optimization objective function based on a multi-objective optimization algorithm, and dynamically coordinates task scheduling and resource allocation; The network path optimization module (23) uses the Dijkstra algorithm to optimize the data transmission path and bandwidth allocation between the edge node and the cloud; A heterogeneous hardware acceleration unit (3), wherein the heterogeneous hardware acceleration unit (3) adopts a hierarchical accelerator scheduling strategy to calculate the optimal hardware matching degree, introduces a calculation acceleration efficiency coefficient, and schedules heterogeneous accelerator resources; The heterogeneous hardware acceleration unit (3) comprises an accelerator matching module (31) and an accelerator scheduling module (32); The accelerator matching module (31) is used to collect accelerator performance data, calculate the acceleration efficiency coefficient of each task on different accelerators, and determine the optimal accelerator matching solution; The accelerator scheduling module (32) dynamically schedules the allocation of accelerator resources based on the optimal accelerator matching solution through a hierarchical scheduling strategy and real-time resource monitoring.

2. The system integration architecture for collaborative cloud computing and edge computing according to claim 1 is characterized by: The adaptive data filtering strategy is implemented based on machine learning algorithms and statistical analysis techniques, and is used to identify the received raw data in real time and filter out noise and irrelevant data in the data; The intelligent cache strategy is implemented based on the least recently used algorithm and predictive cache technology to optimize data storage and access.

3. The system integration architecture for collaborative cloud computing and edge computing according to claim 2 is characterized by: The adaptive data filtering module (11) uses an adaptive data filtering strategy to filter the collected raw data. The specific method steps are as follows: S1.1.

1. Receive raw data from various terminal devices, obtain the business demand weight vector, and use the data feature extraction algorithm to extract the raw data feature vector, and adaptively select the filtering strategy based on the business demand weight vector and the raw data feature vector: ； in, for the moment; For the moment The selected filtering strategy; A collection of all optional filtering strategies; For filtering strategy; is the business demand weight vector; For the moment The original data feature vector of S1.1.2 Application time The selected filtering strategy is used to filter the original data to obtain the filtered data: ； in, For the moment Filter data volume; is the original data; For the moment Data filtering ratio.

4. The system integration architecture for collaborative cloud computing and edge computing according to claim 3 is characterized by: The intelligent cache management module (12) uses an intelligent cache strategy to store the filtered data in the cache. The specific method steps are as follows: S1.2.1, compressing the filtered data and storing it in the cache; S1.2.

2. Calculate the current cache hit rate based on the number of cache hit accesses and the total number of accesses: ； in, For the moment Cache hit rate; For the moment The number of cache hit accesses; For the moment Total number of visits; S1.2.

3. Adjust cache strategy parameters based on cache hit rate and target hit rate: ； in, For the moment Cache strategy parameters; The cache strategy parameters updated at time t+1; is the learning rate; is the target cache hit rate; S1.2.

4. Calculate the amount of data to be migrated based on the current cache capacity and the preset cache limit, and migrate the amount of data to be migrated to the cloud and edge nodes: ； in, For the moment The amount of data that needs to be migrated; For the moment The total amount of data currently cached; The maximum capacity of the cache.

5. The system integration architecture for collaborative cloud computing and edge computing according to claim 4, characterized in that: The multi-objective optimization algorithm is implemented based on task delay requirements and energy consumption evaluation, and is used to dynamically coordinate task scheduling and resource allocation between edge nodes and the cloud to minimize the overall execution delay and energy consumption of the system and optimize resource utilization.

6. The system integration architecture for collaborative cloud computing and edge computing according to claim 5, characterized in that: The task scheduling allocation module (21) is used to construct a delay energy consumption optimization objective function and set constraint conditions between the edge node and the cloud based on the current cloud node and edge node status using a multi-objective optimization algorithm. The specific method steps are as follows: S2.1.

1. Based on the collected raw data, a multi-objective optimization algorithm is used to construct the delay energy consumption optimization objective function: ； in, is the task number, indicating the tasks; is the label of the edge node, indicating the edge nodes; is the number of the cloud node, indicating the cloud nodes; is a marginal binary decision variable; is a binary decision variable in the cloud; is the weight factor of delay; is the weight factor of energy consumption; For the The task is in Execution time on edge nodes; For the The task is in Execution time on each cloud node; For the The task is in Power consumption on edge nodes; For the The task is in Power consumption on each cloud node; is the total number of tasks; is the total number of edge nodes; is the total number of cloud nodes; S2.1.

2. Add constraints to the latency energy consumption optimization objective function: Task unique assignment constraints: ； Node resource capacity constraints: ； ； in, For the Tasks at time The amount of computing resources required; For the The edge at the moment The maximum available computing resources; For the Cloud nodes at time The maximum available computing resources; Binary variable constraints: ； 。 7. The system integration architecture for collaborative cloud computing and edge computing according to claim 6, characterized in that: The delay energy consumption solving module (22) solves the delay energy consumption optimization objective function based on a multi-objective optimization algorithm, dynamically coordinates task scheduling and resource allocation, and the specific method steps are as follows: S2.2.

1. According to the total number of tasks, the total number of edge nodes and the total number of cloud nodes, based on the optimization algorithm selection strategy, select the optimization algorithm for solving the delay energy consumption optimization objective function applicable to the current moment; S2.2.

2. Based on the selected optimization algorithm, solve the delay energy consumption optimization objective function at the current moment to obtain the optimal edge binary decision variables and the optimal cloud binary decision variables; S2.2.

3. Based on the optimal edge binary decision variables and the optimal cloud binary decision variables, construct the optimal task scheduling and resource allocation solution; The optimization algorithm selection strategy is based on the integration of linear programming algorithm, mixed integer programming algorithm, genetic algorithm and particle swarm optimization algorithm, and is used to select the optimization algorithm for solving the delay energy consumption optimization objective function applicable to the current moment according to the total number of tasks, the total number of edge nodes and the total number of cloud nodes. The optimization algorithm selection strategy is as follows: If the delay energy consumption optimization objective function and constraints are both linear, the linear programming algorithm is selected; If the delay energy consumption optimization objective function involves integer decision variables, a mixed integer programming algorithm is selected; If the delay energy consumption optimization objective function involves a global optimal solution, a genetic algorithm is selected; If the delay energy consumption optimization objective function seeks an efficient approximate solution, the particle swarm optimization algorithm is selected.

8. The system integration architecture of cloud computing and edge computing collaboration according to claim 7 is characterized by: The specific method steps for the accelerator matching module (31) to determine the optimal accelerator matching solution are as follows: S3.1.

1. Collect performance metrics of the accelerator, including computing power, memory bandwidth, and power consumption; Obtain the characteristic indicators of each task, including data volume, computational intensity, and real-time requirements; S3.1.

2. Based on the performance indicators of the accelerator and the characteristic indicators of each task, calculate the acceleration efficiency coefficient of each task: ； in, is the number of the accelerator, indicating the accelerator; is the task number, indicating the tasks; For the The task is in The acceleration efficiency coefficient of each accelerator; For the The computing power of an accelerator; For the Memory bandwidth of each accelerator; For the The power consumption of each accelerator; For the The computational intensity of each task; For the The amount of data for each task; S3.1.

3. Based on the acceleration efficiency coefficient, match the optimal accelerator for each task: ； And the first The task is in The acceleration efficiency coefficient of the first accelerator becomes The acceleration efficiency coefficient of the task in the optimal accelerator ; For the The optimal accelerator for each task; S3.1.

4. The optimal accelerator matching solution for each task is stored in a cache and transmitted to the accelerator scheduling module (32).

9. The system integration architecture for collaborative cloud computing and edge computing according to claim 8, characterized in that: The specific method steps of the accelerator scheduling module (32) for dynamically scheduling accelerator resource allocation are as follows: S3.2.

1. Calculate the task priority based on the real-time requirements and acceleration efficiency coefficient of the task: ； in, For the The priority of each task; For the The acceleration efficiency coefficient of each task on the optimal accelerator; For the The real-time requirements of each task; S3.2.

2. Select the task with the highest priority: Define the decision variables: ； in, is the set of all accelerators; Construct resource allocation optimization objective function: ； in, For the Assign tasks to The decision variables of the accelerator; Build constraints: Task unique assignment constraints: ； Accelerator resource capacity constraints: ； in, For the The amount of computing resources required by each task; For the The maximum available computing resources of an accelerator; Binary variable constraints: ； S3.2.

3. Based on the selected optimization algorithm, solve the resource allocation optimization objective function to obtain the optimal decision variables , perform heterogeneous accelerator scheduling.

10. A collaborative computing method for cloud computing and edge computing, based on a system integration architecture for collaborative cloud computing and edge computing as claimed in any one of claims 1 to 9, characterized in that: The steps include: S10.

1. Filter and cache the received raw data based on an adaptive data filtering strategy and an intelligent caching strategy; S10.

2. Based on the current status of cloud nodes and edge nodes, construct the latency and energy consumption optimization objective function and set constraints, use the multi-objective optimization algorithm to solve the latency and energy consumption optimization objective function, and dynamically coordinate task scheduling and resource allocation; S10.

3. Use a hierarchical accelerator scheduling strategy to calculate the optimal hardware matching degree, introduce a calculation acceleration efficiency coefficient, and schedule heterogeneous accelerator resources.

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