Cluster node data processing method and system based on edge side

Abstract

The invention relates to the technical field of high-availability clusters, and discloses an edge-side-based cluster node data processing method and system, and the method comprises the steps: obtaining edge-side cluster node resources, querying a hardware performance parameter and an idle time period of each dynamic access cluster node, and constructing a node association information group; extracting network condition prediction information of each dynamic access cluster node, and planning a dynamic leader duration in a target cluster data processing period by considering node dynamic access distribution information corresponding to a node association information group of each dynamic access cluster node and a constraint set constructed by the network condition prediction information; an election strategy is generated and is used for executing free interval election when a target operation service is executed, so that the real-time dynamic adjustment of the master control node of the high-availability cluster considering the influence of multiple factors is realized, and the high-availability cluster is helped to perform free release and node switching of the master control node before a fault occurs; and the operation stability and the processing performance of the high-availability cluster are improved.

Description

Technical Field

[0001] The present invention relates to the field of high-availability cluster technology, and in particular to an edge-based cluster node data processing method and system. Background Art

[0002] A high-availability cluster (HA Cluster) is a system composed of multiple nodes (physical machines or virtual machines) interconnected through a network. Its core goal is to ensure that the business can continue to run and achieve high service availability when a single point failure (such as node hardware failure, software crash, network interruption) occurs through redundant design and failover mechanism. Usually, when the leader or master node in a high-availability cluster fails, the system will implement failover through the following steps to ensure business continuity. When the leader node fails, the cluster completes the switch through the process of heartbeat detection → election of a new leader → resource takeover → data synchronization. The core goal is to restore service in the shortest time while ensuring that data is not lost and business is not interrupted.

[0003] In actual application scenarios, the cluster node network is also affected by factors such as changes in the number of cluster node accesses caused by dynamic access and exit of cluster nodes, network status parameters caused by network environment fluctuations, and hardware differences of currently accessed cluster nodes, which challenges the stability of the high-availability cluster. This scenario requires real-time adjustment of the master node of the high-availability cluster to ensure that the high-availability cluster is always at a high processing performance and operation stability. The existing heartbeat detection-based failover mechanism is not very applicable under the above-mentioned real-time adjustment requirements before fault detection.

[0004] Therefore, how to achieve real-time dynamic adjustment of the master node of a high-availability cluster taking into account the influence of multiple factors, help the high-availability cluster to release the term of the master node and switch nodes before a failure occurs, and improve the operating stability and processing performance of the high-availability cluster is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The main purpose of the present invention is to provide an edge-based cluster node data processing method and system, aiming to solve at least one of the above-mentioned technical problems.

[0006] To achieve the above object, the present invention provides a cluster node data processing method based on the edge side, the method comprising the following steps: Acquire edge-side cluster node resources that execute target running services within the target cluster; wherein the edge-side cluster node resources include node identifiers of several dynamically accessed cluster nodes; According to the node identifier, query the hardware performance parameters of each dynamically accessed cluster node and the idle period in the target cluster data processing cycle, and construct a node association information group of each dynamically accessed cluster node using the hardware performance parameters and the idle period; Based on the node identifier, call the network status prediction information library, extract the network status prediction information of each dynamic access cluster node in the target cluster data processing cycle, consider the node association information group and network status prediction information of each dynamic access cluster node, plan the dynamic leader term in the target cluster data processing cycle, and generate the target cluster-wide election strategy; The election strategy is deployed to each dynamic access cluster node within the target cluster, driving each dynamic access cluster node to perform term interval elections in the target cluster data processing cycle according to the election strategy when executing the target operation business.

[0007] Optionally, the step of obtaining edge-side cluster node resources within the target cluster range includes: Receiving a target operation service transmitted by a service operation terminal, and extracting a service operation type from the target operation service; wherein each service operation type is mapped to a corresponding service operation requirement; The edge cluster node database is called to perform secondary screening of the edge cluster nodes according to the business operation requirements, and obtain edge cluster node resources that execute the target business within the target cluster range.

[0008] Optionally, calling the edge cluster node database, performing secondary screening on the edge cluster nodes according to the business operation requirements, and obtaining the edge cluster node resource steps for executing the target business within the target cluster range specifically include: Calling the edge-side cluster node database, performing a first screening of edge-side cluster nodes within the entire cluster range from the edge-side cluster node database according to the business execution hardware requirements in the business operation requirements, and performing a second screening of edge-side cluster nodes within the entire cluster range according to the business execution area range requirements in the business operation requirements; The edge cluster nodes that have been screened twice are used as edge cluster node resources that execute the target running business within the target cluster range.

[0009] Optionally, according to the node identifier, querying the hardware performance parameters of each dynamically accessed cluster node and the idle period in the target cluster data processing cycle, and using the hardware performance parameters and the idle period to construct a node association information group step for each dynamically accessed cluster node specifically includes: Extracting a comparison table of edge-side cluster nodes and hardware performance parameters and a service operation queue of each edge-side cluster node from an edge-side cluster node database; According to the node identifier, query the hardware performance parameters of each dynamic access cluster node in the comparison table of edge-side cluster nodes and hardware performance parameters; according to the node identifier, extract the service operation queue of each dynamic access cluster node, and determine the idle period of each dynamic access cluster node in the target cluster data processing cycle; The node association information group of each dynamic access cluster node is constructed by using the hardware performance parameters and the idle period.

[0010] Optionally, based on the node identifier, calling a network status prediction information library to extract network status prediction information of each dynamically accessed cluster node in a target cluster data processing cycle specifically includes: Calling a network status prediction information base; wherein the network status prediction information base records the network status prediction information of each dynamically accessed cluster node; Based on the node identifier, the network status prediction information of each dynamically accessed cluster node in the target cluster data processing cycle is matched in the network status prediction information library.

[0011] Optionally, the construction of a network status prediction information database includes: Obtaining network status parameters of each dynamically accessed cluster node in several historical cluster data processing cycles within the target cluster range; wherein the network status parameters include any one of data throughput, packet loss rate or delay time; Calculate the average network status parameters of several dynamically accessed cluster nodes in each minimum data processing period in each historical cluster data processing cycle, and construct training samples and prediction samples for each historical cluster data processing cycle based on the average network status parameters and timestamps in all minimum data processing periods in each historical cluster data processing cycle; The training samples are used to train a pre-built convolutional neural network model, and the prediction samples and the network status prediction model obtained after the training are used to predict the network status prediction information of each dynamically accessed cluster node in the target cluster data processing cycle.

[0012] Optionally, consider the node association information group and network status prediction information of each dynamically accessed cluster node, plan the dynamic leader term in the target cluster data processing cycle, and generate the target cluster-wide election strategy steps, specifically including: Extract the idle time period in the node association information group of each dynamically accessed cluster node, and generate the node dynamic access distribution information of the target cluster data processing cycle according to the distribution of the idle time period in the target cluster data processing cycle; wherein the node dynamic access distribution information includes the number of dynamically accessed cluster nodes in each minimum data processing time period in the target cluster data processing cycle; Extracting hardware performance parameters in the node association information group of each dynamic access cluster node, and dividing each dynamic access cluster node into a plurality of performance levels according to the service operation type in the target operation service and the hardware performance parameters of each dynamic access cluster node; Based on the performance level of each dynamically accessed cluster node, the average performance level of the dynamically accessed cluster nodes in each minimum data processing period of the target cluster data processing cycle in the node dynamic access distribution information is calculated, the average performance level is written into the node dynamic access distribution information, and the node hardware performance distribution information including the number of dynamically accessed cluster nodes and the average performance level in each minimum data processing period in the target cluster data processing cycle is generated; According to the node hardware performance distribution information and the network status prediction information, the dynamic leader term in the target cluster data processing cycle is planned, and the target cluster-wide election strategy is generated.

[0013] Optionally, according to the node hardware performance distribution information and the network status prediction information, planning the dynamic leader term in the target cluster data processing cycle and generating the target cluster-wide election strategy steps specifically include: The number of dynamically accessed cluster nodes and the average performance level in the node hardware performance distribution information and the network status parameters in the network status prediction information are constructed as a constraint set; The query constraint set uses the business operation failure rate of different leader terms in the business operation process of the historical cluster node for each constraint parameter group. After each minimum data processing period in the target cluster data processing cycle is assigned to the dynamic leader term, the business operation failure rate of each minimum data processing period under the dynamic leader term is less than the failure rate allowable threshold as the constraint condition. The optimization goal is to minimize the sum of the number of different dynamic leader terms of each two adjacent minimum data processing periods. The dynamic leader term set to which each minimum data processing period in the target cluster data processing cycle is assigned is optimized. The dynamic leader term set is used as the election term interval in the target cluster data processing cycle to generate a target cluster-wide election strategy.

[0014] Optionally, the election strategy is deployed to each dynamic access cluster node within the target cluster, driving each dynamic access cluster node to perform term interval election steps in the target cluster data processing cycle according to the election strategy when executing the target operation service, specifically including: Merge two adjacent minimum data processing periods with the same dynamic leader term in the target cluster data processing cycle in the election strategy to generate a deployment sequence including a plurality of processing periods and a dynamic leader term for each processing period; Before each processing period, the dynamic leader term corresponding to the processing period is distributed to each dynamic access cluster node, driving each dynamic access cluster node to perform term interval election in the target cluster data processing cycle according to the election strategy when executing the target operation business.

[0015] In addition, in order to achieve the above-mentioned purpose, the present invention also provides an edge-side based cluster node data processing system, comprising: An acquisition module is used to acquire edge-side cluster node resources that execute target running services within the target cluster range; wherein the edge-side cluster node resources include node identifiers of several dynamically accessed cluster nodes; A construction module, used to query the hardware performance parameters of each dynamically accessed cluster node and the idle period in the target cluster data processing cycle according to the node identifier, and construct a node association information group of each dynamically accessed cluster node using the hardware performance parameters and the idle period; A generation module is used to call a network status prediction information library based on the node identifier, extract the network status prediction information of each dynamic access cluster node in the target cluster data processing cycle, consider the node association information group and network status prediction information of each dynamic access cluster node, plan the dynamic leader term in the target cluster data processing cycle, and generate a target cluster-wide election strategy; The execution module is used to deploy the election strategy to each dynamic access cluster node within the target cluster range, driving each dynamic access cluster node to perform term interval election in the target cluster data processing cycle according to the election strategy when executing the target operation business.

[0016] The beneficial effects of the present invention are as follows: a cluster node data processing method and system based on the edge side are proposed, which obtains the edge side cluster node resources, queries the hardware performance parameters and idle time periods of each dynamically accessed cluster node and constructs a node association information group, extracts the network status prediction information of each dynamically accessed cluster node, considers the node dynamic access distribution information and the constraint set constructed by the network status prediction information corresponding to the node association information group of each dynamically accessed cluster node, takes the business operation failure rate of the leader term of each constraint parameter group in the constraint set during the historical cluster node business operation process as a constraint condition, takes the minimum number of dynamic adjustments of the leader task in the target cluster data processing cycle as the optimization goal, plans the dynamic leader term in the target cluster data processing cycle, generates an election strategy within the target cluster range and uses the election strategy to perform term interval elections when executing the target operation business, realizes real-time dynamic adjustment of the master node of a high-availability cluster considering multiple factors, helps the high-availability cluster to release the term of the master node and switch nodes before a failure occurs, and improves the operation stability and processing performance of the high-availability cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the cluster node data processing method based on the edge side of the present invention; Figure 2 This is a structural diagram of the edge-based cluster node data processing system of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] The embodiment of the present invention provides a cluster node data processing method based on the edge side, referring to Figure 1 , Figure 1 The figure is a flow chart of an embodiment of a cluster node data processing method based on the edge side of the present invention.

[0020] In this embodiment, a cluster node data processing method based on the edge side is provided, and the method comprises the following steps: S100: Acquire edge-side cluster node resources that execute target running services within the target cluster range; wherein the edge-side cluster node resources include node identifiers of several dynamically accessed cluster nodes; S200: querying the hardware performance parameters of each dynamically accessed cluster node and the idle period in the target cluster data processing cycle according to the node identifier, and constructing a node association information group of each dynamically accessed cluster node by using the hardware performance parameters and the idle period; S300: Based on the node identifier, call the network status prediction information library, extract the network status prediction information of each dynamically accessed cluster node in the target cluster data processing cycle, consider the node association information group and network status prediction information of each dynamically accessed cluster node, plan the dynamic leader term in the target cluster data processing cycle, and generate the target cluster-wide election strategy; S400: deploying the election strategy to each dynamic access cluster node within the target cluster, driving each dynamic access cluster node to perform term interval election in the target cluster data processing cycle according to the election strategy when executing the target running service.

[0021] It should be noted that in actual application scenarios, the cluster node network is also affected by factors such as changes in the number of cluster node accesses caused by dynamic access and exit of cluster nodes, network status parameters caused by network environment fluctuations, and hardware differences of currently accessed cluster nodes, which challenges the stability of the high-availability cluster. This scenario requires real-time adjustment of the master node of the high-availability cluster to ensure that the high-availability cluster is always at a high processing performance and operation stability. The existing heartbeat detection-based failover mechanism is not very applicable under the above-mentioned real-time adjustment requirements before fault detection.

[0022] In order to solve the above problems, this embodiment obtains edge-side cluster node resources, queries the hardware performance parameters and idle periods of each dynamically accessed cluster node and constructs a node association information group, extracts the network status prediction information of each dynamically accessed cluster node, considers the constraint set constructed by the node dynamic access distribution information and network status prediction information corresponding to the node association information group of each dynamically accessed cluster node, plans the dynamic leader term in the target cluster data processing cycle, generates an election strategy and uses the election strategy to perform term interval elections when executing the target operation business, thereby realizing real-time dynamic adjustment of the master node of the high-availability cluster considering multiple factors, helping the high-availability cluster to release the term of the master node and switch nodes before a failure occurs, and improving the operation stability and processing performance of the high-availability cluster.

[0023] In a preferred embodiment, the step of obtaining edge-side cluster node resources within the target cluster range specifically includes: S110: receiving a target operation service transmitted by a service operation terminal, and extracting a service operation type from the target operation service; wherein each service operation type is mapped to a corresponding service operation requirement; S120: Calling the edge cluster node database, performing secondary screening on the edge cluster nodes according to the business operation requirements, and obtaining edge cluster node resources that execute the target business within the target cluster range.

[0024] On this basis, the edge cluster node database is called to perform secondary screening of the edge cluster nodes according to the business operation requirements, and the edge cluster node resource steps for executing the target business within the target cluster range are obtained, including: S121: Calling the edge-side cluster node database, performing a first screening of edge-side cluster nodes within the entire cluster range from the edge-side cluster node database according to the service execution hardware requirements in the service operation requirements, and performing a second screening of edge-side cluster nodes within the entire cluster range according to the service execution area requirements in the service operation requirements; S122: The edge cluster nodes that have been screened twice are used as edge cluster node resources that execute target running services within the target cluster range.

[0025] In this embodiment, the edge-side cluster node resources within the target cluster range are obtained by receiving the target operating business, extracting the business operating type, performing a first screening according to the business execution hardware requirements in the business operating requirements, and performing a second screening according to the business execution area range, and finally obtaining all edge-side cluster node resources within the target cluster range that meet the target operating business.

[0026] In a preferred embodiment, according to the node identifier, querying the hardware performance parameters of each dynamically accessed cluster node and the idle period in the target cluster data processing cycle, and using the hardware performance parameters and the idle period to construct a node association information group step for each dynamically accessed cluster node specifically includes: S210: extracting a comparison table of edge-side cluster nodes and hardware performance parameters and a service operation queue of each edge-side cluster node from an edge-side cluster node database; S220: According to the node identifier, query the hardware performance parameters of each dynamic access cluster node in the comparison table of edge-side cluster nodes and hardware performance parameters; according to the node identifier, extract the service operation queue of each dynamic access cluster node, and determine the idle period of each dynamic access cluster node in the target cluster data processing cycle; S230: constructing a node association information group of each dynamically accessed cluster node by using the hardware performance parameters and the idle period.

[0027] In this embodiment, after obtaining the node identifier in the previous step, it is also possible to query the hardware performance parameters recorded in the comparison table of the edge side cluster nodes and the hardware performance parameters, and use the business operation queue of each dynamic access cluster node to analyze the idle period of each dynamic access cluster node in the target cluster data processing cycle, so as to construct a node association information group.

[0028] In a preferred embodiment, based on the node identifier, calling a network status prediction information library to extract network status prediction information of each dynamically accessed cluster node in a target cluster data processing cycle specifically includes: S310: calling a network status prediction information base; wherein the network status prediction information base records network status prediction information of each dynamically accessed cluster node; S320: Based on the node identifier, match the network status prediction information of each dynamically accessed cluster node in the target cluster data processing period in the network status prediction information library.

[0029] In practical applications, the construction of the network status prediction information database specifically includes: S311: Obtaining network status parameters of each dynamically accessed cluster node in a number of historical cluster data processing cycles within the target cluster range; wherein the network status parameters include any one of data throughput, packet loss rate or delay time; S312: Calculate the average network status parameters of several dynamically accessed cluster nodes in each minimum data processing period in each historical cluster data processing cycle, and construct training samples and prediction samples for each historical cluster data processing cycle based on the average network status parameters and timestamps in all minimum data processing periods in each historical cluster data processing cycle; S313: Using the training samples, training a pre-built convolutional neural network model, and using the prediction samples and the network status prediction model obtained after the training, predicting the network status prediction information of each dynamically accessed cluster node in the target cluster data processing cycle.

[0030] In this embodiment, after obtaining the node identifier in the previous step, it is also possible to build a network status prediction information database by using the network status prediction information of the target cluster data processing cycle predicted by the network status prediction model, and then query the network status prediction information of the target cluster data processing cycle according to the node identifier, so as to provide data support for planning the dynamic leader term in the target cluster data processing cycle.

[0031] In a preferred embodiment, the node association information group and network status prediction information of each dynamically accessed cluster node are considered, the dynamic leader term in the target cluster data processing cycle is planned, and the target cluster-wide election strategy steps are generated, specifically including: S330: extracting the idle time period in the node association information group of each dynamically accessed cluster node, and generating node dynamic access distribution information of the target cluster data processing cycle according to the distribution of the idle time period in the target cluster data processing cycle; wherein the node dynamic access distribution information includes the number of dynamically accessed cluster nodes in each minimum data processing time period in the target cluster data processing cycle; S340: extracting hardware performance parameters in the node association information group of each dynamic access cluster node, and dividing each dynamic access cluster node into a plurality of performance levels according to the service operation type in the target operation service and the hardware performance parameters of each dynamic access cluster node; S350: Based on the performance level of each dynamically accessed cluster node, calculate the average performance level of the dynamically accessed cluster nodes in each minimum data processing period of the target cluster data processing cycle in the node dynamic access distribution information, write the average performance level into the node dynamic access distribution information, and generate node hardware performance distribution information including the number of dynamically accessed cluster nodes and the average performance level in each minimum data processing period in the target cluster data processing cycle; S360: According to the node hardware performance distribution information and the network status prediction information, a dynamic leader term in a target cluster data processing cycle is planned, and a target cluster-wide election strategy is generated.

[0032] Furthermore, according to the node hardware performance distribution information and the network status prediction information, the dynamic leader term in the target cluster data processing cycle is planned, and the target cluster-wide election strategy steps are generated, which specifically include: S361: constructing the number of dynamically accessed cluster nodes and the average performance level in the node hardware performance distribution information and the network status parameters in the network status prediction information into a constraint set; S362: query the business operation failure rate of each constraint parameter group in the constraint set in the business operation process of the historical cluster node with different leader terms, and take the business operation failure rate of each minimum data processing period in the target cluster data processing cycle as a constraint condition that is less than the failure rate allowable threshold value after each minimum data processing period is assigned to the dynamic leader term, and take the minimum sum of the number of different dynamic leader terms of each two adjacent minimum data processing periods as the optimization goal, and optimize and solve the dynamic leader term set to which each minimum data processing period in the target cluster data processing cycle is assigned; S363: Using the dynamic leader term set as the election term interval in the target cluster data processing cycle, generating a target cluster-wide election strategy.

[0033] In this embodiment, by acquiring edge-side cluster node resources, querying the hardware performance parameters and idle periods of each dynamically accessed cluster node and constructing a node-related information group, extracting the network status prediction information of each dynamically accessed cluster node, considering the node dynamic access distribution information and network status prediction information corresponding to the node-related information group of each dynamically accessed cluster node to construct a constraint set, taking the business operation failure rate of the leader term of each constraint parameter group in the constraint set during the historical cluster node business operation process as a constraint condition, taking the minimum number of dynamic adjustments of the leader task in the target cluster data processing cycle as the optimization goal, planning the dynamic leader term in the target cluster data processing cycle, generating a target cluster-wide election strategy and using the election strategy to perform term interval elections when executing the target operation business, realizing real-time dynamic adjustment of the master node of a high-availability cluster considering multiple factors, helping the high-availability cluster to release the term of the master node and switch nodes before a failure occurs, and improving the operation stability and processing performance of the high-availability cluster.

[0034] In a preferred embodiment, the election strategy is deployed to each dynamic access cluster node within the target cluster, driving each dynamic access cluster node to perform term interval election steps in the target cluster data processing cycle according to the election strategy when executing the target operation service, specifically including: Merge two adjacent minimum data processing periods with the same dynamic leader term in the target cluster data processing cycle in the election strategy to generate a deployment sequence including a plurality of processing periods and a dynamic leader term for each processing period; Before each processing period, the dynamic leader term corresponding to the processing period is distributed to each dynamic access cluster node, driving each dynamic access cluster node to perform term interval election in the target cluster data processing cycle according to the election strategy when executing the target operation business.

[0035] In this embodiment, after the deployment sequence of the dynamic leader term is obtained, the dynamic leader term corresponding to the processing period is distributed to each dynamically accessed cluster node before each processing period, thereby implementing dynamic leader term management of the cluster nodes.

[0036] Reference Figure 2 , Figure 2 It is a structural block diagram of an embodiment of a cluster node data processing system based on the edge side of the present invention.

[0037] like Figure 2 As shown, the edge-side cluster node data processing system proposed in an embodiment of the present invention includes: An acquisition module 10 is used to acquire edge-side cluster node resources that execute target running services within the target cluster range; wherein the edge-side cluster node resources include node identifiers of several dynamically accessed cluster nodes; A construction module 20 is used to query the hardware performance parameters of each dynamically accessed cluster node and the idle period in the target cluster data processing cycle according to the node identifier, and construct a node association information group of each dynamically accessed cluster node using the hardware performance parameters and the idle period; A generation module 30 is used to call a network status prediction information library based on the node identifier, extract the network status prediction information of each dynamic access cluster node in the target cluster data processing cycle, consider the node association information group and network status prediction information of each dynamic access cluster node, plan the dynamic leader term in the target cluster data processing cycle, and generate a target cluster-wide election strategy; The execution module 40 is used to deploy the election strategy to each dynamic access cluster node within the target cluster, driving each dynamic access cluster node to perform term interval election in the target cluster data processing cycle according to the election strategy when executing the target operation business.

[0038] Other embodiments or specific implementations of the edge-side cluster node data processing system of the present invention may refer to the above-mentioned method embodiments and will not be described in detail here.

[0039] It is understood that, in the description of this specification, the description with reference to the terms "one embodiment", "another embodiment", "other embodiments", or "first to Nth embodiments" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0040] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0041] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A cluster node data processing method based on the edge side, characterized in that: The method comprises the following steps: Acquire edge-side cluster node resources that execute target running services within the target cluster; wherein the edge-side cluster node resources include node identifiers of several dynamically accessed cluster nodes; According to the node identifier, query the hardware performance parameters of each dynamically accessed cluster node and the idle period in the target cluster data processing cycle, and construct a node association information group of each dynamically accessed cluster node using the hardware performance parameters and the idle period; Based on the node identifier, call the network status prediction information library, extract the network status prediction information of each dynamic access cluster node in the target cluster data processing cycle, consider the node association information group and network status prediction information of each dynamic access cluster node, plan the dynamic leader term in the target cluster data processing cycle, and generate the target cluster-wide election strategy; The election strategy is deployed to each dynamic access cluster node within the target cluster, driving each dynamic access cluster node to perform term interval elections in the target cluster data processing cycle according to the election strategy when executing the target operation business.

2. The edge-side cluster node data processing method according to claim 1, characterized in that: The steps to obtain edge cluster node resources within the target cluster range include: Receiving a target operation service transmitted by a service operation terminal, and extracting a service operation type from the target operation service; wherein each service operation type is mapped to a corresponding service operation requirement; The edge cluster node database is called to perform secondary screening of the edge cluster nodes according to the business operation requirements, and obtain edge cluster node resources that execute the target business within the target cluster range.

3. The edge-side cluster node data processing method according to claim 2, characterized in that: Call the edge cluster node database, perform secondary screening of edge cluster nodes according to business operation requirements, and obtain edge cluster node resource steps for executing target business within the target cluster range, specifically including: Calling the edge-side cluster node database, performing a first screening of edge-side cluster nodes within the entire cluster range from the edge-side cluster node database according to the business execution hardware requirements in the business operation requirements, and performing a second screening of edge-side cluster nodes within the entire cluster range according to the business execution area range requirements in the business operation requirements; The edge cluster nodes that have been screened twice are used as edge cluster node resources that execute the target running business within the target cluster range.

4. The edge-based cluster node data processing method according to claim 1, characterized in that: The step of querying the hardware performance parameters of each dynamically accessed cluster node and the idle period in the target cluster data processing cycle according to the node identifier, and constructing the node association information group of each dynamically accessed cluster node by using the hardware performance parameters and the idle period, specifically includes: Extracting a comparison table of edge-side cluster nodes and hardware performance parameters and a service operation queue of each edge-side cluster node from an edge-side cluster node database; According to the node identifier, query the hardware performance parameters of each dynamic access cluster node in the comparison table of edge-side cluster nodes and hardware performance parameters; according to the node identifier, extract the service operation queue of each dynamic access cluster node, and determine the idle period of each dynamic access cluster node in the target cluster data processing cycle; The node association information group of each dynamic access cluster node is constructed by using the hardware performance parameters and the idle period.

5. The edge-side cluster node data processing method according to claim 1, characterized in that: Based on the node identifier, calling a network status prediction information library to extract network status prediction information of each dynamically accessed cluster node during a target cluster data processing cycle specifically includes: Calling a network status prediction information base; wherein the network status prediction information base records the network status prediction information of each dynamically accessed cluster node; Based on the node identifier, the network status prediction information of each dynamically accessed cluster node in the target cluster data processing cycle is matched in the network status prediction information library.

6. The edge-side cluster node data processing method according to claim 5, characterized in that: The construction of the network status prediction information database includes: Obtaining network status parameters of each dynamically accessed cluster node in several historical cluster data processing cycles within the target cluster range; wherein the network status parameters include any one of data throughput, packet loss rate or delay time; Calculate the average network status parameters of several dynamically accessed cluster nodes in each minimum data processing period in each historical cluster data processing cycle, and construct training samples and prediction samples for each historical cluster data processing cycle based on the average network status parameters and timestamps in all minimum data processing periods in each historical cluster data processing cycle; The training samples are used to train a pre-built convolutional neural network model, and the prediction samples and the network status prediction model obtained after the training are used to predict the network status prediction information of each dynamically accessed cluster node in the target cluster data processing cycle.

7. The edge-based cluster node data processing method according to claim 1, characterized in that: Considering the node association information group and network status prediction information of each dynamically connected cluster node, planning the dynamic leader term in the target cluster data processing cycle, and generating the target cluster-wide election strategy steps, specifically including: Extract the idle time period in the node association information group of each dynamically accessed cluster node, and generate the node dynamic access distribution information of the target cluster data processing cycle according to the distribution of the idle time period in the target cluster data processing cycle; wherein the node dynamic access distribution information includes the number of dynamically accessed cluster nodes in each minimum data processing time period in the target cluster data processing cycle; Extracting hardware performance parameters in the node association information group of each dynamic access cluster node, and dividing each dynamic access cluster node into a plurality of performance levels according to the service operation type in the target operation service and the hardware performance parameters of each dynamic access cluster node; Based on the performance level of each dynamically accessed cluster node, the average performance level of the dynamically accessed cluster nodes in each minimum data processing period of the target cluster data processing cycle in the node dynamic access distribution information is calculated, the average performance level is written into the node dynamic access distribution information, and the node hardware performance distribution information including the number of dynamically accessed cluster nodes and the average performance level in each minimum data processing period in the target cluster data processing cycle is generated; According to the node hardware performance distribution information and the network status prediction information, the dynamic leader term in the target cluster data processing cycle is planned, and the target cluster-wide election strategy is generated.

8. The edge-side cluster node data processing method according to claim 7, characterized in that: According to the node hardware performance distribution information and the network status prediction information, the dynamic leader term in the target cluster data processing cycle is planned, and the target cluster-wide election strategy steps are generated, specifically including: The number of dynamically accessed cluster nodes and the average performance level in the node hardware performance distribution information and the network status parameters in the network status prediction information are constructed as a constraint set; The query constraint set is used to find the business operation failure rate of each constraint parameter group in the historical cluster node business operation process with different leader terms. After each minimum data processing period in the target cluster data processing cycle is assigned to the dynamic leader term, the business operation failure rate of each minimum data processing period under the dynamic leader term is less than the failure rate allowable threshold as the constraint condition. The optimization goal is to minimize the sum of the number of different dynamic leader terms of each two adjacent minimum data processing periods. The dynamic leader term set to which each minimum data processing period in the target cluster data processing cycle is assigned is optimized. The dynamic leader term set is used as the election term interval in the target cluster data processing cycle to generate a target cluster-wide election strategy.

9. The edge-side cluster node data processing method according to claim 1, characterized in that: The election strategy is deployed to each dynamic access cluster node within the target cluster, driving each dynamic access cluster node to perform term interval election steps in the target cluster data processing cycle according to the election strategy when executing the target operation service, specifically including: Merge two adjacent minimum data processing periods with the same dynamic leader term in the target cluster data processing cycle in the election strategy to generate a deployment sequence including a plurality of processing periods and a dynamic leader term for each processing period; Before each processing period, the dynamic leader term corresponding to the processing period is distributed to each dynamic access cluster node, driving each dynamic access cluster node to perform term interval election in the target cluster data processing cycle according to the election strategy when executing the target operation business.

10. A cluster node data processing system based on the edge side, characterized in that: include: An acquisition module is used to acquire edge-side cluster node resources that execute target running services within the target cluster range; wherein the edge-side cluster node resources include node identifiers of several dynamically accessed cluster nodes; A construction module, used to query the hardware performance parameters of each dynamically accessed cluster node and the idle period in the target cluster data processing cycle according to the node identifier, and construct a node association information group of each dynamically accessed cluster node using the hardware performance parameters and the idle period; A generation module is used to call a network status prediction information library based on the node identifier, extract the network status prediction information of each dynamic access cluster node in the target cluster data processing cycle, consider the node association information group and network status prediction information of each dynamic access cluster node, plan the dynamic leader term in the target cluster data processing cycle, and generate a target cluster-wide election strategy; The execution module is used to deploy the election strategy to each dynamic access cluster node within the target cluster range, driving each dynamic access cluster node to perform term interval election in the target cluster data processing cycle according to the election strategy when executing the target operation business.

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