Heterogeneous computing power integration and dynamic optimization allocation method and system

Abstract

The present invention discloses a method and system for the fusion and dynamic optimization allocation of heterogeneous computing power, which relates to the field of computing power allocation technology, in order to solve the problem that the received tasks cannot be better integrated with heterogeneous computing power. The present invention performs real-time data analysis on the monitoring indicators of each node, which helps to achieve more refined computing power resource management. By analyzing the data, problems such as unbalanced resource utilization and excessive node load can be discovered. By clarifying the key fields in the computing power task allocation data and the computing power node connection data, accurate mapping between the data is achieved. This helps to ensure that tasks are correctly assigned to nodes with appropriate resources and status. After the data mapping is completed, network layer fusion is performed, which helps to achieve more efficient task execution and resource utilization in computing power allocation.

Description

Technical Field

[0001] The present invention relates to the field of computing power allocation technology, and specifically to a method and system for heterogeneous computing power integration and dynamic optimization allocation. Background Art

[0002] Computing power allocation refers to the process of rationally allocating the processing power required for computing tasks to available computing resources.

[0003] Chinese patent publication number CN116862126A discloses a method and apparatus for allocating computing power in the context of a fusion of an energy network and a computing power network. The method primarily utilizes the green power ratio and remaining computing power of each computing power node in the energy computing power allocation. The computing demand instruction includes the task to be calculated and computing power requirement data. Based on the green power ratio and remaining computing power of each computing power node in the energy computing power allocation, the current allocable node is determined. The computing demand instruction is then allocated to the allocable node, causing it to perform calculations based on the computing demand instruction. While this patent document addresses the computing power allocation issue, the following issues remain:

[0004] 1. Failure to adjust the load strategy based on the specific circumstances of the actual request task, resulting in reduced data load capacity.

[0005] 2. The received tasks are not integrated with the current network layer in a targeted manner, resulting in poor computing power data integration.

[0006] 3. There is no real-time monitoring of the implementation process, which results in the inability to quickly issue warnings for abnormal data during the implementation process. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for the fusion and dynamic optimization of heterogeneous computing power, and to perform real-time data analysis on the monitoring indicators of each node, which helps to achieve more refined computing power resource management. By analyzing the data, problems such as unbalanced resource utilization and excessive node load can be discovered. By clarifying the key fields in the computing power task allocation data and the computing power node connection data, accurate mapping between the data is achieved. This helps to ensure that tasks are correctly assigned to nodes with appropriate resources and status. After the data mapping is completed, network layer fusion is performed, which helps to achieve more efficient task execution and resource utilization in computing power allocation, and can solve the problems in the existing technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The heterogeneous computing power integration and dynamic optimization allocation method includes the following steps:

[0010] S1: Confirmation of central computing power: Collect hardware configuration information from the acquisition interface, process the collected hardware configuration information and extract key parameters, perform benchmark testing based on the extracted key parameters, and obtain real-time computing power data after the benchmark testing;

[0011] S2: Load balancing strategy formulation: Load analysis is performed based on real-time computing power data and standard computing power data. A load balancing strategy is formulated based on the load situation. After the load balancing strategy is formulated, the real-time computing power strategy data is obtained.

[0012] S3: Node task allocation: Analyze the task requirements of the received task requests and use the real-time computing power strategy data to allocate the analyzed tasks to the nodes. After the node task allocation is completed, the computing power task allocation data is obtained.

[0013] S4: Node information collaborative processing: Establish a node communication mechanism for each node in the computing power task allocation data. After the communication mechanism is established, the computing power node connection data is obtained;

[0014] S5: Processing task network fusion management: Map the computing power task allocation data with the computing power node connection data. After the data mapping is completed, perform network layer fusion to obtain the standard computing power task allocation data.

[0015] Preferably, in step S1, the hardware configuration information in the acquisition interface is collected, the collected hardware configuration information is processed and key parameters are extracted, and a benchmark test is performed based on the extracted key parameters, including:

[0016] The hardware configuration information in the acquisition interface includes processor information, memory information, storage device information, network interface information, computing unit and resource usage information;

[0017] After the hardware configuration information is collected, the key parameters of the hardware configuration information are extracted;

[0018] Among them, the key parameters of processor information include the number of cores, number of threads, main frequency, maximum acceleration frequency and cache size; the key parameters of memory information include total capacity, type, speed and timing; the key parameters of storage device information include type, capacity, read and write speed and interface type; the key parameters of network interface information include type, speed, MAC address and IP address; the key parameters of computing unit include model, number of CUDA cores, video memory size, video memory type and computing power; the key parameters of resource usage information include CPU usage, memory usage, disk frequency, disk speed and network traffic;

[0019] Extract key parameters of hardware configuration information and then conduct benchmark tests;

[0020] The test results are confirmed based on the benchmark test results, and the confirmed test results are used to generate real-time computing power data.

[0021] Preferably, the load situation analysis is performed based on the real-time computing power data and the standard computing power data in step S2, and a load balancing strategy is formulated based on the load situation, including:

[0022] Retrieving standard computing capacity data from a database, where the standard computing capacity data is a standard expected performance indicator or a statistical average of historical performance data;

[0023] Compare the real-time computing capability data with the performance indicators with the same attributes in the standard computing capability data, and calculate the difference value of each performance indicator based on the data comparison results;

[0024] Perform load analysis based on the difference value;

[0025] The load conditions are divided into normal load, light load and heavy load. When the percentage of the difference value is within the range of 80%-100%, it is normal load; when the percentage of the difference value is less than 80%, it is light load; when the percentage of the difference value is greater than 100%, it is heavy load.

[0026] Construct a load analysis matrix based on the analyzed load conditions, and mark load balancing operations based on the constructed load matrix;

[0027] Formulate load balancing strategies based on the load balancing operations marked in the load analysis matrix. Load balancing strategies include task migration, traffic regulation, and resource reallocation.

[0028] After the load balancing strategy is formulated, real-time computing power strategy data is generated.

[0029] Preferably, performing task demand analysis on the task request received in step S3 and allocating the analyzed tasks to nodes using real-time computing capability strategy data includes:

[0030] When the computing network receives a task request from an upper-layer application, it analyzes the task requirements of the task request;

[0031] The parsed task requirements include task type, resource requirements, execution time, priority, and data dependencies;

[0032] The task requirement data is matched and calculated using the task matching calculation method, and a matching plan for the task requirement data is obtained after the matching calculation is completed;

[0033] Organize the matching data in the real-time computing capacity strategy data, including node ID, available resources, current load, and node status;

[0034] Use greedy algorithm to assign matching plan to the node with the least load in matching data;

[0035] When the nodes in the matching data are overloaded, dynamic load balancing is used to redistribute some tasks to nodes with lower loads;

[0036] After the matching plan is assigned to the matching data, the computing power task allocation data is obtained.

[0037] Preferably, establishing a node communication mechanism for each node in the computing task allocation data in S4 includes:

[0038] Identify the node ID of each node in the computing power task allocation data;

[0039] Select the communication protocol based on the node ID. The communication protocols include TCP, UDP and MPI.

[0040] After the communication protocol is selected, the parameters of the selected communication protocol will be confirmed, including the port number, communication mode and data transmission format;

[0041] After the parameters of the communication protocol are confirmed, a communication connection is established between the nodes. The communication connection includes a heartbeat mechanism connection or a bidirectional connection.

[0042] The heartbeat mechanism connection is to send heartbeat packets to each node at regular intervals and confirm the node's survival status based on the heartbeat packet parameters; the bidirectional connection is to establish a two-way real-time transmission of tasks and data between nodes;

[0043] After the communication connection is confirmed, the connection data between the receiving task and the computing power node in the computing network is obtained.

[0044] Preferably, setting the heartbeat packet sending time interval corresponding to the heartbeat mechanism includes:

[0045] Extract the total number of nodes;

[0046] Extract the data transmission time of the data packet of the unit data volume corresponding to each node, wherein the value range of the unit data volume is 10BM-100BM;

[0047] Extract the data transmission rate corresponding to each node;

[0048] Under the current data transmission environment corresponding to the node, the data transmission duration of the node for a data packet of unit data volume and the data transmission rate corresponding to each node are obtained to obtain the heartbeat packet sending time interval coefficient corresponding to each node;

[0049] The heartbeat packet sending time interval coefficient corresponding to each node is obtained by the following formula:

[0050]

[0051] Among them, F represents the heartbeat packet sending time interval coefficient corresponding to each node; n represents the number of data transmission times corresponding to each node; B i Indicates the data transmission rate corresponding to the i-th data transmission of each node; B e T represents the data transmission rate of each node corresponding to the unit data volume received; i T represents the data transmission duration of the i-th data transmission corresponding to each node; e Indicates the data transmission duration of the unit data packet corresponding to the node; x i Indicates the number of unit data contained in the data volume of the ith data transmission corresponding to each node; T s Indicates the theoretical data transmission time of a data packet of unit data volume corresponding to the maximum data transmission speed;

[0052] Obtain the heartbeat packet sending time interval corresponding to all nodes according to the heartbeat packet sending time interval coefficient;

[0053] The heartbeat packet sending time interval is obtained by the following formula:

[0054]

[0055] Where G represents the heartbeat packet sending interval; G0 represents the preset initial heartbeat packet sending interval; m represents the total number of nodes; F i F represents the heartbeat packet sending time interval coefficient corresponding to the i-th node; z F represents the median value of the heartbeat packet sending time interval coefficient corresponding to m nodes; cmax F represents the maximum value of the coefficient difference of the heartbeat packet sending time interval between every two nodes with data transmission interactive connection corresponding to m nodes; b represents the standard deviation of the heartbeat packet sending time interval coefficient corresponding to m nodes; F cb It represents the standard deviation of the coefficient difference of the heartbeat packet sending time interval between every two nodes with data transmission interactive connection;

[0056] Send a heartbeat packet to each node according to the heartbeat packet sending time interval;

[0057] The node survival rate and node resurrection rate corresponding to each heartbeat packet sent are monitored in real time, and the heartbeat packet sending time interval is adjusted according to the node survival rate and node resurrection rate.

[0058] Preferably, real-time monitoring of the node survival rate and node resurrection rate corresponding to each heartbeat packet sent, and adjusting the heartbeat packet sending time interval according to the node survival rate and node resurrection rate, includes:

[0059] Extract the node survival rate and node resurrection rate corresponding to each heartbeat packet sent;

[0060] Comparing the node survival rate with a preset node survival rate threshold;

[0061] When the node survival rate is lower than a preset node survival rate threshold, the heartbeat packet sending time interval is adjusted using the node survival rate and the node revival rate to obtain the adjusted heartbeat packet sending time interval;

[0062] The adjusted heartbeat packet sending time interval is obtained by the following formula:

[0063]

[0064] Among them, G t represents the adjusted heartbeat packet sending interval; G represents the heartbeat packet sending interval; G0 represents the preset initial heartbeat packet sending interval; k represents the number of heartbeat packets sent before the node survival rate falls below the preset node survival rate threshold; P c Indicates the node survival rate that is lower than the preset node survival rate threshold; P f Indicates the node resurrection rate when the node survival rate is lower than the preset node survival rate threshold; P ci represents the node survival rate corresponding to the sending of the i-th heartbeat packet; P fi represents the node revival rate corresponding to the sending of the i-th heartbeat packet; P cy Indicates the preset node survival rate threshold;

[0065] A heartbeat packet is sent to each node according to the adjusted heartbeat packet sending time interval.

[0066] Preferably, data mapping is performed on the computing task allocation data and the computing node connection data in S5, and network layer fusion is performed after the data mapping is completed, including:

[0067] Verify the key fields in the computing task allocation data and computing node connection data. The key fields in the computing task allocation data include task ID, assigned node ID, task type, required resources, priority, and data dependencies; the key fields in the computing node connection data include node ID, available resource data, current load status, node status, and communication information.

[0068] Retrieve mapping rules from the database and perform data mapping between computing task allocation data and computing node connection data according to the mapping rules;

[0069] The data mapping process involves extracting task information from the computing task allocation data based on the task ID and the assigned node ID, then finding the corresponding node data from the computing node connection data and comparing them.

[0070] When the comparison result meets the mapping rules, the task is mapped to the node, and the task mapping data is obtained after the mapping is completed;

[0071] When the comparison result does not meet the mapping rules, the mapping task is retried from the standby node or the task allocation strategy is re-evaluated until the comparison result meets the mapping rules.

[0072] Preferably, in S5, data mapping is performed between the computing task allocation data and the computing node connection data, and network layer fusion is performed after the data mapping is completed, further comprising:

[0073] The task mapping data is integrated into a network layer. Before the network layer integration, the task mapping data is first organized into a fusion data set. The fusion data set includes extracting the relevant information of each task from the mapped task mapping data, and extracting the latest status and resource utilization of each node from the computing power node connection data.

[0074] Based on the task execution status in the fused dataset, the node data is updated. The node data includes the number of tasks assigned to each node, the current load, and the available resources. When the node load exceeds the preset range, dynamic adjustments are made.

[0075] The node data is updated or dynamically adjusted to perform data set integration, and the standard computing power task allocation data is obtained after the data set integration.

[0076] Heterogeneous computing power integration and dynamic optimization allocation system, including:

[0077] Task execution monitoring unit, used to:

[0078] When the standard computing power task allocation data is being implemented, the standard computing power task allocation data is monitored in real time;

[0079] Before real-time monitoring of standard computing power task allocation data, the monitoring indicators should be confirmed. The monitoring indicators include task status, execution progress, resource usage, node load, and abnormal events.

[0080] During the implementation of the standard computing power task allocation data, the monitoring indicators of each node are monitored in real time;

[0081] Perform real-time data analysis of standard computing power task allocation data based on the monitoring indicators of each node;

[0082] Issue early warning prompts based on the analyzed real-time data. If the threshold of real-time data exceeds the preset range, the real-time data exceeding the preset range will be marked as early warning data.

[0083] Adjust the warning data according to the degree of abnormality and generate a report;

[0084] Finally, the warning data and adjustment report are transmitted to the display terminal for visual display.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

[0086] 1. The heterogeneous computing power fusion and dynamic optimization allocation method and system provided by the present invention select a communication protocol with a reliable transmission mechanism and error recovery capability, which can ensure the integrity and accuracy of data during transmission. Through security measures such as encryption and authentication, the security of data transmission can be further enhanced, and user privacy and data security can be protected. By adjusting the parameters and configuration of the communication protocol, it can be easily adapted to computing power networks of different sizes and types, and dynamic load balancing technology can be used to reallocate some tasks to nodes with lower loads. The dynamic adjustment mechanism helps to maintain the stability and efficiency of the computing power network and avoid performance bottlenecks caused by single-point overload.

[0087] 2. The heterogeneous computing power fusion and dynamic optimization allocation method and system provided by this invention achieves precise data mapping by clarifying key fields in computing power task allocation data and computing power node connection data. This helps ensure that tasks are correctly assigned to nodes with appropriate resources and status. After data mapping is completed, network-layer fusion is performed, which helps achieve more efficient task execution and resource utilization in computing power allocation.

[0088] 3. The heterogeneous computing power integration and dynamic optimization allocation method and system provided by this invention performs real-time data analysis on each node's monitoring indicators, which facilitates more refined computing power resource management. This data analysis can identify problems such as uneven resource usage and excessive node load, thus providing strong support for optimizing computing power allocation and improving resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 Schematic diagram of the steps of heterogeneous computing power integration and dynamic optimization allocation of the present invention;

[0090] Figure 2 This is a schematic diagram of the heterogeneous computing power integration and dynamic optimization allocation process of the present invention. DETAILED DESCRIPTION

[0091] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0092] In order to solve the problem that the existing technology does not adjust the load strategy according to the specific situation of the actual request task, which leads to the reduction of data load capacity, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0093] The heterogeneous computing power integration and dynamic optimization allocation method includes the following steps:

[0094] S1: Confirmation of central computing power: Collect hardware configuration information from the acquisition interface, process the collected hardware configuration information and extract key parameters, perform benchmark testing based on the extracted key parameters, and obtain real-time computing power data after the benchmark testing;

[0095] Among them, by collecting detailed hardware configuration information and performing benchmark tests, potential performance issues or hardware failures can be discovered and resolved in a timely manner;

[0096] S2: Load balancing strategy formulation: Load analysis is performed based on real-time computing power data and standard computing power data. A load balancing strategy is formulated based on the load situation. After the load balancing strategy is formulated, the real-time computing power strategy data is obtained.

[0097] Among them, through the formulation and implementation of load balancing strategies, computing resources can be reasonably allocated and scheduled to avoid resource waste and bottlenecks;

[0098] S3: Node task allocation: Analyze the task requirements of the received task requests and use the real-time computing power strategy data to allocate the analyzed tasks to the nodes. After the node task allocation is completed, the computing power task allocation data is obtained.

[0099] Among them, the dynamic adjustment mechanism helps maintain the stability and efficiency of the computing network and avoids performance bottlenecks caused by single-point overload;

[0100] S4: Node information collaborative processing: Establish a node communication mechanism for each node in the computing power task allocation data. After the communication mechanism is established, the computing power node connection data is obtained;

[0101] Among them, by adjusting the parameters and configuration of the communication protocol, it can easily adapt to computing networks of different sizes and types, and can intelligently dispatch computing resources according to the performance and load of the nodes;

[0102] S5: Processing task network fusion management: Map the computing power task allocation data with the computing power node connection data. After the data mapping is completed, perform network layer fusion to obtain the standard computing power task allocation data.

[0103] Among them, the adjustability of mapping rules enables the system to be customized according to actual needs, thereby improving the flexibility and accuracy of task allocation.

[0104] In step S1, the hardware configuration information in the acquisition interface is collected, and the collected hardware configuration information is processed and key parameters are extracted. Benchmark tests are performed based on the extracted key parameters, including:

[0105] The hardware configuration information in the acquisition interface includes processor information, memory information, storage device information, network interface information, computing unit and resource usage information;

[0106] After the hardware configuration information is collected, the key parameters of the hardware configuration information are extracted;

[0107] Among them, the key parameters of processor information include the number of cores, number of threads, main frequency, maximum acceleration frequency and cache size; the key parameters of memory information include total capacity, type, speed and timing; the key parameters of storage device information include type, capacity, read and write speed and interface type; the key parameters of network interface information include type, speed, MAC address and IP address; the key parameters of computing unit include model, number of CUDA cores, video memory size, video memory type and computing power; the key parameters of resource usage information include CPU usage, memory usage, disk frequency, disk speed and network traffic;

[0108] Extract key parameters of hardware configuration information and then conduct benchmark tests;

[0109] The test results are confirmed based on the benchmark test results, and the confirmed test results are used to generate real-time computing power data.

[0110] Specifically, by collecting hardware configuration information in detail through the acquisition interface and extracting key parameters, we can accurately understand the specific configuration and capabilities of each computing node, which helps to make more refined resource allocation in computing power allocation and ensure that tasks are assigned to the most appropriate computing nodes, thereby improving the utilization of computing resources and the efficiency of the overall computing power network. Benchmarking based on the key parameters of the hardware configuration information can evaluate the actual performance of each computing node. The benchmark test results can serve as an important basis for computing power allocation, ensuring that computing power resources can be dynamically adjusted and optimized according to task requirements to meet the needs of AI applications of different scales and types. By collecting hardware configuration information in detail and conducting benchmark tests, potential performance problems can be discovered and resolved in a timely manner. or hardware failure, which helps to improve the reliability and stability of the computing network, ensure that tasks can be completed smoothly, and avoid computing interruptions or task failures caused by hardware problems. By continuously collecting and analyzing hardware configuration information and benchmark results, it can promote the continuous innovation and development of computing network technology, improve the overall computing power level and service quality, and accurately understand the performance and configuration of each computing node. Resource scheduling and allocation can be more effective to avoid resource waste. Among them, the processor is benchmarked using the CPU-Benchmark tool; the memory is benchmarked using the memtest tool; the storage device information is benchmarked using the CrystalDiskMark tool; the network interface is benchmarked using the Unigine Heaven tool; the computing unit is benchmarked using the 3DMark tool; and the resource usage monitoring tool is benchmarked.

[0111] In step S2, load analysis is performed based on real-time computing power data and standard computing power data, and a load balancing strategy is formulated based on the load situation, including:

[0112] Retrieving standard computing capacity data from a database, where the standard computing capacity data is a standard expected performance indicator or a statistical average of historical performance data;

[0113] Compare the real-time computing capability data with the performance indicators with the same attributes in the standard computing capability data, and calculate the difference value of each performance indicator based on the data comparison results;

[0114] Perform load analysis based on the difference value;

[0115] The load conditions are divided into normal load, light load and heavy load. When the percentage of the difference value is within the range of 80%-100%, it is normal load; when the percentage of the difference value is less than 80%, it is light load; when the percentage of the difference value is greater than 100%, it is heavy load.

[0116] Construct a load analysis matrix based on the analyzed load conditions, and mark load balancing operations based on the constructed load matrix;

[0117] Formulate load balancing strategies based on the load balancing operations marked in the load analysis matrix. Load balancing strategies include task migration, traffic regulation, and resource reallocation.

[0118] After the load balancing strategy is formulated, real-time computing power strategy data is generated.

[0119] Specifically, by comparing real-time computing power data with standard computing power data, the current load situation can be quickly identified, allowing timely adjustments to be made. The formulation and execution of load balancing policies are based on precise data analysis and judgment, ensuring efficient resource utilization. Using statistical averages or standard expected performance indicators as standard computing power data provides a reliable benchmark for comparing real-time computing power data. By calculating the percentage of difference values, load conditions (normal load, light load, heavy load) can be precisely classified, providing accurate information for subsequent load balancing policy formulation. Based on different load conditions, various load balancing policies, including task migration, traffic regulation, and resource reallocation, have been developed to adapt to various complex network environments and business needs. The flexibility of load balancing policies is also reflected in the ability to continuously adjust and optimize policies based on the generation of real-time computing power policy data to adapt to dynamic changes in the network environment. Through the formulation and execution of load balancing policies, computing resources can be rationally allocated and scheduled, avoiding resource waste and bottlenecks. When the load is light, idle resources can be fully utilized through task migration or traffic regulation; when the load is heavy, resource reallocation can be used to reduce pressure and ensure the normal operation of the business. By building a load analysis matrix and marking load balancing operations, intelligent decision-making support is provided for subsequent load balancing strategy formulation.

[0120] Perform task demand analysis on the task requests received in step S3, and use real-time computing power strategy data to allocate the analyzed tasks to nodes, including:

[0121] When the computing network receives a task request from an upper-layer application, it analyzes the task requirements of the task request;

[0122] The parsed task requirements include task type, resource requirements, execution time, priority, and data dependencies;

[0123] The task requirement data is matched and calculated using the task matching calculation method, and a matching plan for the task requirement data is obtained after the matching calculation is completed;

[0124] Organize the matching data in the real-time computing capacity strategy data, including node ID, available resources, current load, and node status;

[0125] Use greedy algorithm to assign matching plan to the node with the least load in matching data;

[0126] When the nodes in the matching data are overloaded, dynamic load balancing is used to redistribute some tasks to nodes with lower loads;

[0127] After the matching plan is assigned to the matching data, the computing power task allocation data is obtained.

[0128] Specifically, the solution performs a detailed task requirement analysis on received task requests, including key information such as task type, resource requirements, execution time, priority, and data dependencies. This comprehensive analysis helps ensure more accurate and efficient subsequent task allocation. Using a task matching algorithm, the solution quickly generates a matching plan based on task requirement data. Subsequently, a greedy algorithm is used to assign the matching plan to the least loaded node. This helps optimize computing resource utilization and reduce wait time and computational cost. If a node in the matching data becomes overloaded, the solution leverages dynamic load balancing to reallocate some tasks to less loaded nodes. This dynamic adjustment mechanism helps maintain the stability and efficiency of the computing network, avoiding performance bottlenecks caused by single-point overload. Through precise task matching and allocation, as well as dynamic load balancing adjustments, the solution significantly improves computing resource utilization. This helps reduce computing costs and improves overall computing efficiency. Dynamically adjusting computing resource allocation based on changing task requirements enhances system flexibility and scalability. This enables the computing network to better adapt to the needs of AI applications of varying scales and types.

[0129] In order to solve the problem in existing technologies that the received tasks are not integrated with the current network layer in a targeted manner, resulting in poor computing power data integration, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0130] Establish a node communication mechanism for each node in the computing task allocation data in S4, including:

[0131] Identify the node ID of each node in the computing power task allocation data;

[0132] Select the communication protocol based on the node ID. The communication protocols include TCP, UDP and MPI.

[0133] After the communication protocol is selected, the parameters of the selected communication protocol will be confirmed, including the port number, communication mode and data transmission format;

[0134] After the parameters of the communication protocol are confirmed, a communication connection is established between the nodes. The communication connection includes a heartbeat mechanism connection or a bidirectional connection.

[0135] The heartbeat mechanism connection is to send heartbeat packets to each node at regular intervals and confirm the node's survival status based on the heartbeat packet parameters; the bidirectional connection is to establish a two-way real-time transmission of tasks and data between nodes;

[0136] After the communication connection is confirmed, the connection data between the receiving task and the computing power node in the computing network is obtained.

[0137] Specifically, by identifying the node ID of each node in the computing power task allocation data, the system can accurately identify and manage each node to ensure the efficient use of computing power resources. It selects communication protocols (such as TCP, UDP and MPI) according to the node ID and confirms the parameters of the communication protocol (such as port number, communication mode and data transmission format). It can flexibly adapt to different application scenarios and computing power requirements. The establishment of the heartbeat mechanism connection enables the system to send heartbeat packets to each node at a regular interval, promptly confirm the survival status of the node, and ensure the reliability and stability of computing power allocation. The bidirectional connection realizes the two-way real-time transmission of tasks and data between nodes, improves the response speed and flexibility of computing power allocation, and the heartbeat mechanism connection can promptly detect and handle node failures to ensure the stability and availability of the computing power network.

[0138] When a node fails, the system can quickly adjust the computing power allocation strategy and schedule tasks to other available nodes to avoid task interruption and data loss. The selected communication protocol has a reliable transmission mechanism and error recovery capability, which can ensure the integrity and accuracy of data during transmission. In addition, through security measures such as encryption and authentication, the security of data transmission can be further enhanced, and user privacy and data security can be protected. By adjusting the parameters and configuration of the communication protocol, it can easily adapt to computing power networks of different sizes and types. It can intelligently schedule computing power resources according to the performance and load of the nodes to ensure that tasks are processed efficiently and evenly.

[0139] Specifically, setting the heartbeat packet sending time interval corresponding to the heartbeat mechanism includes:

[0140] Extract the total number of nodes;

[0141] Extract the data transmission time of the data packet of the unit data volume corresponding to each node, wherein the value range of the unit data volume is 10BM-100BM;

[0142] Extract the data transmission rate corresponding to each node;

[0143] Under the current data transmission environment corresponding to the node, the data transmission duration of the node for a data packet of unit data volume and the data transmission rate corresponding to each node are obtained to obtain the heartbeat packet sending time interval coefficient corresponding to each node;

[0144] The heartbeat packet sending time interval coefficient corresponding to each node is obtained by the following formula:

[0145]

[0146] Among them, F represents the heartbeat packet sending time interval coefficient corresponding to each node; n represents the number of data transmission times corresponding to each node; B i Indicates the data transmission rate corresponding to the i-th data transmission of each node; B e T represents the data transmission rate of each node corresponding to the unit data volume received; i T represents the data transmission duration of the i-th data transmission corresponding to each node; e Indicates the data transmission duration of the unit data packet corresponding to the node; x i Indicates the number of unit data contained in the data volume of the ith data transmission corresponding to each node; T s Indicates the theoretical data transmission time of a data packet of unit data volume corresponding to the maximum data transmission speed;

[0147] Obtain the heartbeat packet sending time interval corresponding to all nodes according to the heartbeat packet sending time interval coefficient;

[0148] The heartbeat packet sending time interval is obtained by the following formula:

[0149]

[0150] Where G represents the heartbeat packet sending interval; G0 represents the preset initial heartbeat packet sending interval; m represents the total number of nodes; F i F represents the heartbeat packet sending time interval coefficient corresponding to the i-th node; z F represents the median value of the heartbeat packet sending time interval coefficient corresponding to m nodes; cmax F represents the maximum value of the coefficient difference of the heartbeat packet sending time interval between every two nodes with data transmission interactive connection corresponding to m nodes; b represents the standard deviation of the heartbeat packet sending time interval coefficient corresponding to m nodes; F cb It represents the standard deviation of the coefficient difference of the heartbeat packet sending time interval between every two nodes with data transmission interactive connection;

[0151] Send a heartbeat packet to each node according to the heartbeat packet sending time interval;

[0152] The node survival rate and node resurrection rate corresponding to each heartbeat packet sent are monitored in real time, and the heartbeat packet sending time interval is adjusted according to the node survival rate and node resurrection rate.

[0153] The technical effect of the above technical solution is that by dynamically adjusting the heartbeat packet transmission interval based on factors such as the number of nodes, data transmission rate, and data transmission duration, the network can maintain a connected state while avoiding unnecessary heartbeat packet transmission, thereby improving network communication efficiency. The main purpose of the heartbeat mechanism is to monitor the survival status of nodes. By monitoring the node survival rate and node resurrection rate in real time and dynamically adjusting the heartbeat packet transmission interval based on this information, this solution helps to promptly detect and address node failures, thereby enhancing network stability and reliability. Frequent transmission of heartbeat packets consumes network resources. By dynamically adjusting the heartbeat packet transmission interval, this solution can reduce the frequency of heartbeat packet transmission while ensuring network stability, thereby optimizing network resource utilization. This solution considers the data transmission rate and data transmission duration of different nodes and can dynamically adjust the heartbeat packet transmission interval based on the actual situation of each node. This enables the mechanism to adapt to different network environments and improve the flexibility and adaptability of network communication. By optimizing the heartbeat packet transmission strategy, network latency and bandwidth usage can be reduced, thereby improving the user experience. This optimization is particularly important in application scenarios with high real-time requirements.

[0154] In summary, this technical solution optimizes network communication efficiency, stability, resource utilization, network adaptability, and user experience by dynamically adjusting the heartbeat packet transmission interval. This helps improve the overall performance of network communication and meet the growing demand for network communication.

[0155] Specifically, real-time monitoring of the node survival rate and node resurrection rate corresponding to each heartbeat packet sent, and adjusting the heartbeat packet sending time interval according to the node survival rate and node resurrection rate, including:

[0156] Extract the node survival rate and node resurrection rate corresponding to each heartbeat packet sent;

[0157] Comparing the node survival rate with a preset node survival rate threshold;

[0158] When the node survival rate is lower than a preset node survival rate threshold, the heartbeat packet sending time interval is adjusted using the node survival rate and the node revival rate to obtain the adjusted heartbeat packet sending time interval;

[0159] The adjusted heartbeat packet sending time interval is obtained by the following formula:

[0160]

[0161] Among them, G t represents the adjusted heartbeat packet sending interval; G represents the heartbeat packet sending interval; G0 represents the preset initial heartbeat packet sending interval; k represents the number of heartbeat packets sent before the node survival rate falls below the preset node survival rate threshold; P c Indicates the node survival rate that is lower than the preset node survival rate threshold; P f Indicates the node resurrection rate when the node survival rate is lower than the preset node survival rate threshold; P ci represents the node survival rate corresponding to the sending of the i-th heartbeat packet; P fi represents the node revival rate corresponding to the sending of the i-th heartbeat packet; P cy Indicates the preset node survival rate threshold;

[0162] A heartbeat packet is sent to each node according to the adjusted heartbeat packet sending time interval.

[0163] The technical effect of the above-mentioned technical solution is that, by monitoring node survival rates and node resurrection rates in real time, the solution can promptly detect network anomalies, such as node failures or network instability. When the node survival rate falls below a preset threshold, the heartbeat packet transmission interval is adjusted to monitor the node status more frequently, allowing timely measures to restore network connectivity and improve network communication reliability. When the network is stable and the node survival rate is high, reducing the frequency of heartbeat packets can conserve network resources. When the node survival rate drops, increasing the frequency of heartbeat packets can more quickly detect node failures and avoid wasting resources on ineffective communication. This dynamic adjustment mechanism helps optimize network resource utilization. The solution dynamically adjusts the heartbeat packet transmission interval based on the actual node survival rate and resurrection rate, demonstrating the system's adaptability. This adaptability enables the system to automatically adjust its policies based on changes in the network environment, thereby more flexibly responding to various network conditions. By promptly detecting and handling node failures, the solution helps reduce network interruptions and latency, improving the user experience. This optimization is particularly important in application scenarios with high real-time requirements, such as online gaming and video conferencing. By automatically adjusting the heartbeat packet transmission interval, this solution can reduce the frequency of manual intervention and lower network maintenance costs. Furthermore, by enabling timely detection and resolution of network issues, it also helps reduce downtime and losses caused by network failures.

[0164] In summary, this technical solution improves network communication reliability, optimizes resource utilization, enhances system adaptability, improves user experience, and reduces maintenance costs by dynamically adjusting the heartbeat packet transmission interval. These technical benefits make this solution promising for broad application in the field of network communications.

[0165] In S5, the computing task allocation data and computing node connection data are mapped. After the data mapping is completed, network layer fusion is performed, including:

[0166] Verify the key fields in the computing task allocation data and computing node connection data. The key fields in the computing task allocation data include task ID, assigned node ID, task type, required resources, priority, and data dependencies; the key fields in the computing node connection data include node ID, available resource data, current load status, node status, and communication information.

[0167] Retrieve mapping rules from the database and perform data mapping between computing task allocation data and computing node connection data according to the mapping rules;

[0168] The data mapping process involves extracting task information from the computing task allocation data based on the task ID and the assigned node ID, then finding the corresponding node data from the computing node connection data and comparing them.

[0169] When the comparison result meets the mapping rules, the task is mapped to the node, and the task mapping data is obtained after the mapping is completed;

[0170] When the comparison result does not meet the mapping rules, the mapping task is retried from the standby node or the task allocation strategy is re-evaluated until the comparison result meets the mapping rules.

[0171] The task mapping data is integrated into a network layer. Before the network layer integration, the task mapping data is first organized into a fusion data set. The fusion data set includes extracting the relevant information of each task from the mapped task mapping data, and extracting the latest status and resource utilization of each node from the computing power node connection data.

[0172] Based on the task execution status in the fused dataset, the node data is updated. The node data includes the number of tasks assigned to each node, the current load, and the available resources. When the node load exceeds the preset range, dynamic adjustments are made.

[0173] The node data is updated or dynamically adjusted to perform data set integration, and the standard computing power task allocation data is obtained after the data set integration.

[0174] Specifically, by clarifying key fields in computing task allocation data and computing node connection data, precise data mapping is achieved. This helps ensure that tasks are correctly assigned to nodes with appropriate resources and status. After data mapping is completed, network-level fusion is performed, which helps achieve more efficient task execution and resource utilization in computing task allocation. The adjustable mapping rules enable the system to be customized according to actual needs, thereby improving the flexibility and accuracy of task allocation. If the matching results do not meet the mapping rules, the system can retry mapping the task from an alternative node or re-evaluate the task allocation strategy, enhancing the system's adaptability and robustness. The fused dataset contains the latest task information and node status, helping the system to understand the network status in real time and make more informed decisions. When node load exceeds the preset range, the system dynamically adjusts, helping to prevent overload and performance degradation while improving resource utilization. By integrating task mapping data and node data, the system can generate standardized computing task allocation data, which helps achieve more efficient computing task allocation. Optimized computing task allocation reduces resource waste and improves task execution efficiency, thereby meeting the computing power needs of more users.

[0175] In order to solve the problem in the existing technology that when receiving a task and implementing it, the implementation process is not monitored in real time, resulting in the inability to quickly warn of abnormal data during the implementation process, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0176] Heterogeneous computing power integration and dynamic optimization allocation system, including:

[0177] Task execution monitoring unit, used to:

[0178] When the standard computing power task allocation data is being implemented, the standard computing power task allocation data is monitored in real time;

[0179] Before real-time monitoring of standard computing power task allocation data, the monitoring indicators should be confirmed. The monitoring indicators include task status, execution progress, resource usage, node load, and abnormal events.

[0180] During the implementation of the standard computing power task allocation data, the monitoring indicators of each node are monitored in real time;

[0181] Perform real-time data analysis of standard computing power task allocation data based on the monitoring indicators of each node;

[0182] Issue early warning prompts based on the analyzed real-time data. If the threshold of real-time data exceeds the preset range, the real-time data exceeding the preset range will be marked as early warning data.

[0183] Adjust the warning data according to the degree of abnormality and generate a report;

[0184] Finally, the warning data and adjustment report are transmitted to the display terminal for visual display.

[0185] Specifically, by real-time monitoring of standard computing task allocation data, the system can instantly obtain key information such as task status, execution progress, and resource usage, thereby achieving effective management and scheduling of computing resources. This real-time monitoring mechanism helps to promptly identify and resolve potential issues, ensuring that tasks are completed on time and efficiently. Real-time data can be analyzed according to preset thresholds, and if data exceeds the range, an immediate warning prompt will be issued. This warning mechanism helps to identify and address potential risks in advance, preventing problems from escalating. The generation of warning data and adjustment reports provides timely decision-making support for managers, helping them to quickly take measures to mitigate risks. Warning data and adjustment reports are visualized on display terminals, allowing managers to intuitively understand the system's operating status and existing problems. This visual display not only improves the readability of information, but also reduces the difficulty of understanding and operation, allowing managers to make decisions more quickly. Real-time data analysis of monitoring indicators for each node facilitates more refined computing resource management. By analyzing data, we can identify problems such as uneven resource usage and excessive node load, providing strong support for optimizing computing power allocation and improving resource utilization. Real-time monitoring and early warning mechanisms help promptly detect and repair system failures and anomalies, thereby improving system stability and reliability. This mechanism can also reduce losses caused by system crashes or data loss, ensuring business continuity and data integrity.

[0186] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0187] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. Heterogeneous computing power integration and dynamic optimization allocation method, characterized by: The steps include: S1: Confirmation of central computing power: Collect hardware configuration information from the acquisition interface, process the collected hardware configuration information and extract key parameters, perform benchmark testing based on the extracted key parameters, and obtain real-time computing power data after the benchmark testing; S2: Load balancing strategy formulation: Load analysis is performed based on real-time computing power data and standard computing power data. A load balancing strategy is formulated based on the load situation. After the load balancing strategy is formulated, the real-time computing power strategy data is obtained. S3: Node task allocation: Analyze the task requirements of the received task requests and use the real-time computing power strategy data to allocate the analyzed tasks to the nodes. After the node task allocation is completed, the computing power task allocation data is obtained. S4: Node information collaborative processing: Establish a node communication mechanism for each node in the computing power task allocation data. After the communication mechanism is established, the computing power node connection data is obtained; S5: Processing task network fusion management: Map the computing power task allocation data with the computing power node connection data. After the data mapping is completed, perform network layer fusion to obtain the standard computing power task allocation data. In S5, the computing task allocation data and computing node connection data are mapped. After the data mapping is completed, network layer fusion is performed, including: Verify the key fields in the computing task allocation data and computing node connection data. The key fields in the computing task allocation data include task ID, assigned node ID, task type, required resources, priority, and data dependencies; the key fields in the computing node connection data include node ID, available resource data, current load status, node status, and communication information. Retrieve mapping rules from the database and perform data mapping between computing task allocation data and computing node connection data according to the mapping rules; The data mapping process involves extracting task information from the computing task allocation data based on the task ID and the assigned node ID, then finding the corresponding node data from the computing node connection data and comparing them. When the comparison result meets the mapping rules, the task is mapped to the node, and the task mapping data is obtained after the mapping is completed; When the comparison result does not meet the mapping rules, the mapping task is retried from the standby node or the task allocation strategy is re-evaluated until the comparison result meets the mapping rules.

2. The heterogeneous computing power integration and dynamic optimization allocation method according to claim 1 is characterized in that: In step S1, the hardware configuration information in the acquisition interface is collected, and the collected hardware configuration information is processed and key parameters are extracted. Benchmark tests are performed based on the extracted key parameters, including: The hardware configuration information in the acquisition interface includes processor information, memory information, storage device information, network interface information, computing unit and resource usage information; After the hardware configuration information is collected, the key parameters of the hardware configuration information are extracted; Among them, the key parameters of processor information include the number of cores, number of threads, main frequency, maximum acceleration frequency and cache size; the key parameters of memory information include total capacity, type, speed and timing; the key parameters of storage device information include type, capacity, read and write speed and interface type; the key parameters of network interface information include type, speed, MAC address and IP address; the key parameters of computing unit include model, number of CUDA cores, video memory size, video memory type and computing power; the key parameters of resource usage information include CPU usage, memory usage, disk frequency, disk speed and network traffic; Extract key parameters of hardware configuration information and then conduct benchmark tests; The test results are confirmed based on the benchmark test results, and the confirmed test results are used to generate real-time computing power data.

3. The heterogeneous computing power integration and dynamic optimization allocation method according to claim 2 is characterized in that: In step S2, load analysis is performed based on real-time computing power data and standard computing power data, and a load balancing strategy is formulated based on the load situation, including: Retrieving standard computing capacity data from a database, where the standard computing capacity data is a standard expected performance indicator or a statistical average of historical performance data; Compare the real-time computing capability data with the performance indicators with the same attributes in the standard computing capability data, and calculate the difference value of each performance indicator based on the data comparison results; Perform load analysis based on the difference value; The load conditions are divided into normal load, light load and heavy load. When the percentage of the difference value is within the range of 80%-100%, it is normal load; when the percentage of the difference value is less than 80%, it is light load; when the percentage of the difference value is greater than 100%, it is heavy load. Construct a load analysis matrix based on the analyzed load conditions, and mark load balancing operations based on the constructed load matrix; Formulate load balancing strategies based on the load balancing operations marked in the load analysis matrix. Load balancing strategies include task migration, traffic regulation, and resource reallocation. After the load balancing strategy is formulated, real-time computing power strategy data is generated.

4. The heterogeneous computing power integration and dynamic optimization allocation method according to claim 3 is characterized in that: Perform task demand analysis on the task requests received in step S3, and use real-time computing power strategy data to allocate the analyzed tasks to nodes, including: When the computing network receives a task request from an upper-layer application, it analyzes the task requirements of the task request; The parsed task requirements include task type, resource requirements, execution time, priority, and data dependencies; The task requirement data is matched and calculated using the task matching calculation method, and a matching plan for the task requirement data is obtained after the matching calculation is completed; Organize the matching data in the real-time computing capacity strategy data, including node ID, available resources, current load, and node status; Use greedy algorithm to assign matching plan to the node with the least load in matching data; When the nodes in the matching data are overloaded, dynamic load balancing is used to redistribute some tasks to nodes with lower loads; After the matching plan is assigned to the matching data, the computing power task allocation data is obtained.

5. The heterogeneous computing power integration and dynamic optimization allocation method according to claim 4 is characterized in that: Establish a node communication mechanism for each node in the computing task allocation data in S4, including: Identify the node ID of each node in the computing power task allocation data; Select the communication protocol based on the node ID. The communication protocols include TCP, UDP and MPI. After the communication protocol is selected, the parameters of the selected communication protocol will be confirmed, including the port number, communication mode and data transmission format; After the parameters of the communication protocol are confirmed, a communication connection is established between the nodes. The communication connection includes a heartbeat mechanism connection or a bidirectional connection. The heartbeat mechanism connection is to send heartbeat packets to each node at regular intervals and confirm the node's survival status based on the heartbeat packet parameters; the bidirectional connection is to establish a two-way real-time transmission of tasks and data between nodes; After the communication connection is confirmed, the connection data between the receiving task and the computing power node in the computing network is obtained.

6. The method for heterogeneous computing power integration and dynamic optimization allocation according to claim 5 is characterized in that: Setting the heartbeat packet sending interval corresponding to the heartbeat mechanism includes: Extract the total number of nodes; Extract the data transmission time of the data packet of the unit data volume corresponding to each node, wherein the value range of the unit data volume is 10BM-100BM; Extract the data transmission rate corresponding to each node; Under the current data transmission environment corresponding to the node, the data transmission duration of the node for a data packet of unit data volume and the data transmission rate corresponding to each node are obtained to obtain the heartbeat packet sending time interval coefficient corresponding to each node; The heartbeat packet sending time interval coefficient corresponding to each node is obtained by the following formula: Among them, F represents the heartbeat packet sending time interval coefficient corresponding to each node; n represents the number of data transmission times corresponding to each node; B i Indicates the data transmission rate corresponding to the i-th data transmission of each node; B e T represents the data transmission rate of each node corresponding to the unit data volume received; i T represents the data transmission duration of the i-th data transmission corresponding to each node; e Indicates the data transmission duration of the unit data packet corresponding to the node; x i Indicates the number of unit data contained in the data volume of the ith data transmission corresponding to each node; T s Indicates the theoretical data transmission time of a data packet of unit data volume corresponding to the maximum data transmission speed; Obtain the heartbeat packet sending time interval corresponding to all nodes according to the heartbeat packet sending time interval coefficient; The heartbeat packet sending time interval is obtained by the following formula: Where G represents the heartbeat packet sending interval; G0 represents the preset initial heartbeat packet sending interval; m represents the total number of nodes; F i F represents the heartbeat packet sending time interval coefficient corresponding to the i-th node; z F represents the median value of the heartbeat packet sending time interval coefficient corresponding to m nodes; cmax F represents the maximum value of the coefficient difference of the heartbeat packet sending time interval between every two nodes with data transmission interactive connection corresponding to m nodes; b represents the standard deviation of the heartbeat packet sending time interval coefficient corresponding to m nodes; F cb It represents the standard deviation of the coefficient difference of the heartbeat packet sending time interval between every two nodes with data transmission interactive connection; Send a heartbeat packet to each node according to the heartbeat packet sending time interval; The node survival rate and node resurrection rate corresponding to each heartbeat packet sent are monitored in real time, and the heartbeat packet sending time interval is adjusted according to the node survival rate and node resurrection rate.

7. The method for heterogeneous computing power integration and dynamic optimization allocation according to claim 6 is characterized in that: Real-time monitoring of the node survival rate and node resurrection rate corresponding to each heartbeat packet sent, and adjusting the heartbeat packet sending time interval according to the node survival rate and node resurrection rate, including: Extract the node survival rate and node resurrection rate corresponding to each heartbeat packet sent; Comparing the node survival rate with a preset node survival rate threshold; When the node survival rate is lower than a preset node survival rate threshold, the heartbeat packet sending time interval is adjusted using the node survival rate and the node revival rate to obtain the adjusted heartbeat packet sending time interval; The adjusted heartbeat packet sending time interval is obtained by the following formula: Among them, G t represents the adjusted heartbeat packet sending interval; G represents the heartbeat packet sending interval; G0 represents the preset initial heartbeat packet sending interval; k represents the number of heartbeat packets sent before the node survival rate falls below the preset node survival rate threshold; P c Indicates the node survival rate that is lower than the preset node survival rate threshold; P f Indicates the node resurrection rate when the node survival rate is lower than the preset node survival rate threshold; P ci represents the node survival rate corresponding to the sending of the i-th heartbeat packet; P fi represents the node revival rate corresponding to the sending of the i-th heartbeat packet; P cy Indicates the preset node survival rate threshold; A heartbeat packet is sent to each node according to the adjusted heartbeat packet sending time interval.

8. The method for heterogeneous computing power integration and dynamic optimization allocation according to claim 7 is characterized in that: In S5, computing task allocation data and computing node connection data are mapped. After data mapping is completed, network layer fusion is performed, which also includes: The task mapping data is integrated into a network layer. Before the network layer integration, the task mapping data is first organized into a fusion data set. The fusion data set includes extracting the relevant information of each task from the mapped task mapping data, and extracting the latest status and resource utilization of each node from the computing power node connection data. Based on the task execution status in the fused dataset, the node data is updated. The node data includes the number of tasks assigned to each node, the current load, and the available resources. When the node load exceeds the preset range, dynamic adjustments are made. The node data is updated or dynamically adjusted to perform data set integration, and the standard computing power task allocation data is obtained after the data set integration.

9. A heterogeneous computing power fusion and dynamic optimization allocation system, applied to the heterogeneous computing power fusion and dynamic optimization allocation method according to any one of claims 1 to 8, characterized in that: include: Task execution monitoring unit, used to: When the standard computing power task allocation data is being implemented, the standard computing power task allocation data is monitored in real time; Before real-time monitoring of standard computing power task allocation data, the monitoring indicators should be confirmed. The monitoring indicators include task status, execution progress, resource usage, node load, and abnormal events. During the implementation of the standard computing power task allocation data, the monitoring indicators of each node are monitored in real time; Perform real-time data analysis of standard computing power task allocation data based on the monitoring indicators of each node; Issue early warning prompts based on the analyzed real-time data. If the threshold of real-time data exceeds the preset range, the real-time data exceeding the preset range will be marked as early warning data. Adjust the warning data according to the degree of abnormality and generate a report; Finally, the warning data and adjustment report are transmitted to the display terminal for visual display.

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