Cluster scheduling control method for multichannel solenoid valve test resources

Abstract

The invention relates to a clustering scheduling control method for multichannel electromagnetic valve test resources, and belongs to the technical field of detection. The method comprises the following steps: constructing a product value model to output a product value coefficient, and constructing a node load model to output a node load coefficient based on a node task completion rate, an average test duration, a working duration and a task queue length; a node-product adaptation model is constructed through the air pressure fluctuation ratio, the flow demand ratio and the response time ratio, and the node-product adaptation degree is output; and dynamically allocating the node task load through a task allocation model in combination with the product task load, the value coefficient and the node-product adaptation degree. According to the method, the problems of low resource utilization rate and unintelligent scheduling in a traditional test are solved, high-value task priority allocation, load balancing and physical constraint accurate matching are realized, and the test efficiency and reliability are improved.

Description

Technical Field

[0001] This invention belongs to the field of detection technology, and in particular relates to a clustered scheduling and control method for multi-channel solenoid valve testing resources. Background Technology

[0002] Current testing methods for multi-channel solenoid valves generally suffer from the following shortcomings:

[0003] Rigid resource allocation: Fixed task allocation leads to some nodes being overloaded and others being idle, resulting in insufficient resource utilization;

[0004] Missing constraints: Physical parameters such as air pressure stability, flow matching degree, and response time are not included in the scheduling decision, affecting the test accuracy;

[0005] Weak prioritization mechanism: Urgent orders and high-value products cannot be responded to quickly, resulting in average task delays;

[0006] Poor dynamic adaptability: It cannot detect changes in node load (such as air pressure fluctuations and queue accumulation) in real time, and the fault recovery is slow.

[0007] Existing technologies lack collaborative optimization for multi-dimensional constraints (load, physical parameters, value weights), and there is an urgent need for a clustered scheduling method to achieve elastic resource allocation. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a clustered scheduling and control method for multi-channel solenoid valve testing resources, thus solving the aforementioned problems.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a clustered scheduling and control method for multi-channel solenoid valve testing resources, comprising the following steps:

[0010] A product value model is constructed based on the individual value of the tested product, the product urgency (remaining time before delivery), and the total number of product tasks, and the product value coefficient is output.

[0011] A node load model is constructed based on node task completion rate, average node test duration, node working time, and node task queue length, and the node load coefficient of each node is output.

[0012] Based on the pressure fluctuation ratio, flow demand ratio, and response time ratio between products and nodes under the node load coefficient, a node-product adaptation model is constructed to output the node-product adaptation degree between each node and product.

[0013] A node task allocation model is constructed based on the number of product tasks, product value coefficient, and node-product fit, and the target node task quantity is output.

[0014] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0015] Further technical solutions: The pressure fluctuation ratio is the ratio of node pressure fluctuation to the product's allowable pressure fluctuation; the flow demand ratio is the ratio of the product's required air pressure flow rate to the node's allowable air pressure flow rate; and the response time ratio is the ratio of the node solenoid valve response time to the product's required solenoid valve response time.

[0016] A further technical solution: compare the obtained node load coefficient of each node with the node load coefficient threshold, and select the node whose node load coefficient is within the node load coefficient threshold as the task allocation node.

[0017] A further technical solution: compare the obtained node-product compatibility with the node-product compatibility threshold, and select the nodes whose node-product compatibility is within the node-product compatibility threshold as task allocation nodes.

[0018] Further technical solution: The node task allocation model is represented as follows:

[0019]

[0020] in, Indicates the first Task volume per node Indicates the number of product tasks. Indicates the first Node-product compatibility of each node This represents the product value coefficient, the... .

[0021] Further technical solution: Based on the pressure fluctuation ratio, flow demand ratio, and response time ratio between products and nodes under the node load coefficient, the steps to construct a node-product adaptation model and output the node-product adaptation degree between each node and product are as follows:

[0022] The pressure fluctuation ratio, flow demand ratio, and response time ratio are obtained by performing maximum-min normalization on the pressure fluctuation ratio, flow demand ratio, and response time ratio.

[0023] A node-product adaptation model is constructed based on the pressure fluctuation ratio index, flow demand ratio index, and response time ratio index under the node load coefficient. The node-product adaptation model is expressed as follows:

[0024]

[0025] in, Indicates node-product compatibility. Indicates the node load factor. The pressure fluctuation ratio index represents the pressure fluctuation ratio. The index represents the ratio of traffic demand to demand. Indicates the response time ratio, Represents the attenuation coefficient, the Furthermore, the larger the value, the higher the node-product compatibility;

[0026] The pressure fluctuation ratio, flow demand ratio, and response time ratio of each node under the node load coefficient are imported into the node-product adaptation model to obtain the node-product adaptation degree between each node and the product.

[0027] Further technical solution: The steps for constructing a product value model and outputting the product value coefficient based on the individual value of the tested product, the product urgency (remaining time before delivery), and the total number of product tasks are as follows:

[0028] The product value index is obtained by comparing the value of a single product with the maximum value of a single item in the system.

[0029] The product urgency index is obtained by comparing the product urgency level with the maximum allowable product urgency level.

[0030] The product task volume index is obtained by processing the ratio of the product task volume to the system maximum task volume.

[0031] A product value model is constructed based on the product value index, product urgency index, and product task volume index. The product value model is expressed as follows:

[0032]

[0033] in, Indicates the product value coefficient. Indicates the product value index. Indicates the product urgency index. This represents the product task volume index. Represents the weight coefficient and The The higher the value, the higher the overall value of the product;

[0034] Import the current product value index, current product urgency index, and current product task volume index into the product value model to obtain the current product value coefficient.

[0035] Further technical solution: The steps for constructing a node load model based on node task completion rate, average node test duration, node working time, and node task queue length, and outputting the node load coefficient for each node, are as follows:

[0036] The average test duration of a node, the working time of a node, and the length of a node's task queue are each compared with their respective maximum allowable values ​​to obtain the test duration index, the working time index, and the task queue length index.

[0037] A node load model is constructed based on the test duration index, the worked duration index, and the task queue length index. The node load model is expressed as follows:

[0038]

[0039] in, Indicates the node load factor. Node load analysis coefficients This indicates the optimal value for the node load factor. This represents the load sensitivity coefficient. This indicates the task completion rate of the node. This represents the average test duration index. This indicates the index of working hours. Indicates the index of task queue length. Represents the weight coefficient and The A larger value indicates a higher node load;

[0040] Import the test duration index, working duration index, and task queue length index of each node into the node load model to obtain the node load coefficient of each node.

[0041] This invention provides a clustered scheduling and control method for multi-channel solenoid valve testing resources, which has the following advantages compared with the prior art:

[0042] 1. This invention achieves dynamic task allocation by constructing a product value model, a node load model, and a node-product adaptation model, which solves the load imbalance problem caused by static resource allocation. At the same time, by quantifying the product value coefficient and physical parameter adaptability, it ensures that high-value products are given priority and that test parameters are accurately matched. This invention has the advantages of improving resource utilization, reducing test interruption rate, and shortening the processing cycle of emergency orders. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0045] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0046] Please see Figure 1 The present invention provides a clustered scheduling and control method for multi-channel solenoid valve test resources, comprising the following steps:

[0047] A product value model is constructed based on the individual value of the tested product, the product urgency (remaining time before delivery), and the total number of product tasks, and the product value coefficient is output.

[0048] A node load model is constructed based on node task completion rate, average node test duration, node working time, and node task queue length, and the node load coefficient of each node is output.

[0049] The node load coefficient of each node is compared with the node load coefficient threshold, and the node with the node load coefficient within the node load coefficient threshold is selected as the task allocation node.

[0050] Based on the pressure fluctuation ratio, flow demand ratio, and response time ratio between products and nodes under the node load coefficient, a node-product adaptation model is constructed to output the node-product adaptation degree between each node and product.

[0051] The node-product fit between each node and the product is compared with the node-product fit threshold. Nodes whose node-product fit is within the node-product fit threshold are selected as task allocation nodes.

[0052] A node task allocation model is constructed based on the number of product tasks, product value coefficient, and node-product fit, and the target number of node tasks is output.

[0053] The pressure fluctuation ratio is the ratio of node pressure fluctuation to the product's allowable pressure fluctuation; the flow demand ratio is the ratio of the product's required air pressure flow rate to the node's allowable air pressure flow rate; and the response time ratio is the ratio of the node solenoid valve response time to the product's required solenoid valve response time.

[0054] The product value model refers to a calculation model that generates priority coefficients by quantifying the economic and time-sensitive value of a product. Specifically, it can be implemented using a weighted summation algorithm, where the individual value weight coefficient can be set to 0.4, the urgency weight to 0.35, and the task load weight to 0.25. The node load coefficient is an evaluation indicator reflecting the real-time working status of the test node, specifically calculated using a logistic regression model. When a node has been working continuously for more than 8 hours, its load coefficient automatically increases by 0.2. The node-product compatibility is a quantitative indicator of the degree of matching between the physical parameters of the test node and the product under test, specifically calculated using an exponential decay function. When the air pressure fluctuation ratio exceeds 1.2, the compatibility decreases by 50%. The air pressure fluctuation ratio is the ratio of the air pressure fluctuation amplitude during the test to the product's allowable value. When this ratio exceeds 1, a forced sleep mechanism for the node will be triggered. The flow demand ratio is the ratio of the product's required flow rate to the node's supply capacity. The response time ratio is the ratio of the actual response time of the solenoid valve to the product's required time. This parameter is adjusted in real-time by a PID controller to adjust the solenoid valve drive voltage.

[0055] Specifically, when a new test task enters the system, a value coefficient is first calculated based on the product unit price, remaining delivery time, and total order volume. For example, a batch of ABS anti-lock valves has only 24 hours remaining before delivery, resulting in an urgency index of 0.9 and a weighted value coefficient of 0.82. Then, all online nodes are scanned, and a load coefficient including parameters such as task completion rate and average test duration is calculated for each node. Healthy nodes with load coefficients between 0.3 and 0.7 are selected. For the selected nodes, their pressure fluctuation ratio, flow demand ratio, and response time ratio relative to the current product are calculated and normalized to obtain the index for each parameter. These indices are input into the fit model, and a fit value is generated based on the node's current load status, excluding nodes with a fit value below 0.6. Finally, among the qualified nodes, the test task volume is allocated according to the value coefficient weight, with high-value products prioritized for matching to nodes with the optimal fit value.

[0056] Compared to existing technologies, traditional scheduling methods only consider task queue length for allocation, while this solution constructs a three-layer evaluation system encompassing economic value, physical constraints, and node status. Existing technologies employ a simple queue-jumping mechanism when handling urgent orders; this solution dynamically adjusts task weights through a value coefficient, ensuring priority for high-value tasks while maintaining overall system efficiency. Traditional load assessment ignores the physical performance degradation of solenoid valves; this solution incorporates parameters such as air pressure fluctuations into the fit calculation, effectively reducing testing errors caused by equipment fatigue. Existing technologies use static threshold settings; this solution dynamically adjusts the load coefficient and fit threshold through a real-time feedback mechanism, enabling the system to automatically adapt to different operating conditions.

[0057] Through the above technical solutions, this application achieves dynamic load balancing of test node resources, avoiding equipment failures caused by local node overload. Quantitative processing of physical parameter constraints reduces flow matching error rate and minimizes excessive air pressure fluctuations. The value assessment model ensures improved processing time for urgent orders and shortens the average waiting time for high-value products. The real-time node status monitoring mechanism enhances system fault tolerance; when an abnormal air pressure fluctuation occurs at a node, the task can automatically migrate to a backup node within 5 seconds. Adaptability threshold control effectively prevents the execution of mismatched test tasks, significantly reducing product scrap rates.

[0058] Preferably, the steps for constructing a product value model and outputting the product value coefficient based on the individual value of the tested product, the product urgency (remaining time before delivery), and the total number of product tasks are as follows:

[0059] The product value index is obtained by comparing the value of a single product with the maximum value of a single item in the system.

[0060] The product urgency index is obtained by comparing the product urgency level with the maximum allowable product urgency level.

[0061] The product task volume index is obtained by processing the ratio of the product task volume to the system maximum task volume.

[0062] A product value model is constructed based on the product value index, product urgency index, and product task volume index. The product value model is expressed as follows:

[0063]

[0064] in, Indicates the product value coefficient. Indicates the product value index. Indicates the product urgency index. This represents the product task volume index. Represents the weight coefficient and The The higher the value, the higher the overall value of the product;

[0065] Import the current product value index, current product urgency index, and current product task volume index into the product value model to obtain the current product value coefficient.

[0066] The product value index is a standardized parameter obtained by processing the ratio of a single product value to the system's maximum single-item value. Specifically, it can be achieved by real-time collection of product value data and division with a preset system threshold, thus eliminating the influence of different units of measurement on the evaluation results. The product urgency index is a normalized parameter obtained by processing the ratio of remaining time before delivery to the maximum allowable urgency level. Specifically, it can be calculated using timestamp differences and proportionally converted to the system's configured maximum allowable value, quantifying the urgency of product delivery. The product task volume index is a relative scale parameter obtained by processing the ratio of the current number of tasks to the system's maximum task capacity. Specifically, it can be achieved by calculating the ratio of task queue length statistics to the system's preset upper limit, characterizing the impact of task processing scale on resource consumption. The weighting coefficient is a proportional factor used to adjust the contribution of each index to the final value coefficient. Specifically, it can be allocated using preset empirical values ​​or dynamic adjustment algorithms, satisfying the constraint that the sum is 1, and is used to prioritize evaluation dimensions in different scenarios.

[0067] Specifically, this technical solution transforms heterogeneous product attributes into quantifiable and comparable standardized indices through multi-dimensional data normalization and dynamic weighted fusion mechanisms. First, it establishes proportional relationships between individual value, urgency, and task volume and the system's maximum value, eliminating the influence of different dimensions on the evaluation. For example, when the remaining time before product delivery approaches the system's minimum allowable time, the urgency index approaches 1, triggering a high-priority response. Then, it linearly combines the three indices using preset weighting coefficients to generate a comprehensive value coefficient. These weighting coefficients can be dynamically adjusted according to production strategies; for example, the weight of the task volume index can be reduced when capacity is sufficient, while the weight of the urgency index can be increased during peak order periods. The value coefficient output by this model directly reflects the product's comprehensive priority, providing a quantitative basis for subsequent resource scheduling and ensuring that high-value products and urgent orders receive higher resource allocation weights.

[0068] Compared to existing technologies, traditional methods typically prioritize based on a single dimension, such as production time or unit price, leading to ineffective coordination of resource competition between high-value but non-urgent products and urgent but low-value products. This solution achieves synergistic optimization across three dimensions—value, time, and scale—through multi-dimensional normalization and a configurable weighting mechanism, enabling the system to dynamically adjust priority determination rules based on real-time production status. Furthermore, by incorporating task volume into the evaluation system, it avoids the problem of excessive resource consumption by large-scale low-priority tasks, a problem not yet effectively addressed in existing technologies.

[0069] Through the above technical solution, this application can dynamically balance the impact of product value, delivery urgency, and task scale on scheduling decisions, effectively solving the problem of unreasonable resource allocation caused by the single-dimensional evaluation of traditional priority mechanisms. In the solenoid valve testing scenario, when there are both high-value precision instrument component testing tasks and large-volume routine product testing needs, the system can automatically identify and prioritize high-value or urgent orders based on the real-time configured weight coefficients, while avoiding excessive crowding of testing resources by large-scale tasks, thereby significantly reducing the average waiting time of high-priority tasks and improving the overall utilization efficiency of testing resources.

[0070] Preferably, the steps for constructing a node load model based on node task completion rate, average node test duration, node working time, and node task queue length, and outputting the node load coefficient for each node, are as follows:

[0071] The average test duration of a node, the working time of a node, and the length of a node's task queue are each compared with their respective maximum allowable values ​​to obtain the test duration index, the working time index, and the task queue length index.

[0072] A node load model is constructed based on the test duration index, the worked duration index, and the task queue length index. The node load model is expressed as follows:

[0073]

[0074] in, Indicates the node load factor. Node load analysis coefficients This indicates the optimal value for the node load factor. This represents the load sensitivity coefficient. This indicates the task completion rate of the node. This represents the average test duration index. This indicates the index of working hours. Indicates the index of task queue length. Represents the weight coefficient and The A larger value indicates a higher node load;

[0075] Import the test duration index, working duration index, and task queue length index of each node into the node load model to obtain the node load coefficient of each node.

[0076] Among them, the node task completion rate refers to the proportion of the number of test tasks completed by a node to the total number of tasks received, which can be implemented using a task counter to characterize the node's current processing efficiency. The average test duration index is the ratio of the average time a node takes to complete a single test task to the maximum allowed single-task duration by the system, which can be implemented by collecting test time data through a timer to quantify the impact of node processing speed on load. The accumulated working time index is the ratio of the node's cumulative running time to the maximum allowed continuous working time by the system, which can be implemented using a timer to reflect the fatigue caused by continuous node work. The task queue length index is the ratio of the number of tasks currently pending processing by a node to the maximum allowed queue capacity by the system, which can be implemented through the queue monitoring module to assess the backlog of tasks pending processing by the node. The node load analysis coefficient is a comprehensive index obtained by weighted summation of the node task completion rate, test duration index, accumulated working time index, and task queue length index, which can be implemented by allocating the contribution of each index through weight coefficients to comprehensively reflect the multi-dimensional load status of the node. The sigmoid function is a mathematical function that non-linearly maps node load analysis coefficients. It can be implemented using exponential operations and is used to limit load assessment results within a standard range and enhance sensitivity to load fluctuations. The load sensitivity coefficient is a parameter that adjusts the slope of the sigmoid function curve. It can be calibrated through expert experience or experiments and is used to control the rate at which the load coefficient changes with the load analysis coefficient. The optimal load threshold is a preset optimal reference value for node load, which can be determined based on statistical analysis of historical operating data (historical average) and is used to establish a baseline for load assessment.

[0077] Specifically, the average test duration, working time, and task queue length of each node are first converted into standardized indices related to the maximum allowable value of the system, eliminating the influence of parameters with different dimensions on the evaluation. The test duration index reflects the degree to which the node's processing speed deviates from the maximum allowable efficiency of the system; the working time index characterizes the performance degradation risk caused by the continuous operation of the node; and the task queue length index reflects the pressure on the node caused by the backlog of tasks to be processed. By using the node task completion rate as a negative indicator, a weighted sum is generated with the above three indices to produce a node load analysis coefficient. This coefficient integrates the node's real-time processing efficiency, historical workload, and the pressure of tasks to be processed. Furthermore, a sigmoid function is used to perform a non-linear transformation on the load analysis coefficient, and the steepness of the function curve is adjusted in conjunction with the load sensitivity coefficient, so that the load coefficient exhibits high sensitivity near the optimal load threshold, while remaining stable under extreme load conditions, avoiding drastic fluctuations in the evaluation results. By dynamically adjusting the weight coefficients, the evaluation focus of the load model can be optimized for different test scenarios. For example, in a high-pressure test environment, the weight of the task queue length index can be increased to prioritize alleviating task backlog.

[0078] Compared to existing technologies, traditional methods rely solely on fixed thresholds to determine node load status, failing to quantify the comprehensive impact of multi-dimensional node operating parameters on load. This results in delayed load assessments that do not reflect the true processing capacity of nodes. This solution constructs a multi-dimensional assessment model incorporating real-time efficiency, historical load, and task pressure. Combined with a nonlinear function dynamic mapping mechanism, this allows the node load coefficient to track changes in node status in real time. For example, when a node's test duration suddenly increases due to air pressure fluctuations, the exponential increase in test duration triggers a change in the load analysis coefficient. This is then quickly adjusted using a sigmoid function, achieving sensitive feedback on load status. Furthermore, a weighted coefficient allocation mechanism allows for dynamic adjustment of the assessment model parameters according to different test phases. For instance, during peak task periods, the weight of the task queue length exponent is increased to prioritize resource allocation to nodes with low backlog.

[0079] Through the above technical solutions, this application can perceive the changing trends of multi-dimensional operating parameters of nodes in real time and dynamically generate accurate load assessment results, providing a reliable basis for resource allocation. For example, when a node's working time index exceeds a threshold due to prolonged operation, the load factor will increase significantly, triggering the task scheduling system to reduce the allocation of new tasks to that node, avoiding test errors or equipment failures caused by node fatigue. Simultaneously, when the node's task queue length index increases rapidly due to a sudden surge in tasks, the load factor can promptly reflect this change, prompting the system to migrate some tasks to idle nodes in the queue, effectively alleviating task backlog. Furthermore, through the non-linear characteristics of the sigmoid function, the load factor exhibits high sensitivity when approaching the optimal load threshold, helping the system to intervene and adjust in advance when the node load approaches a critical state, preventing resource allocation imbalance.

[0080] Preferably, the steps for constructing a node-product adaptation model based on the pressure fluctuation ratio, flow demand ratio, and response time ratio between the product and the node under the node load coefficient, and outputting the node-product adaptation degree between each node and the product, are as follows:

[0081] The pressure fluctuation ratio, flow demand ratio, and response time ratio are obtained by performing maximum-min normalization on the pressure fluctuation ratio, flow demand ratio, and response time ratio.

[0082] A node-product adaptation model is constructed based on the pressure fluctuation ratio index, flow demand ratio index, and response time ratio index under the node load coefficient. The node-product adaptation model is expressed as follows:

[0083]

[0084] in, Indicates node-product compatibility. Indicates the node load factor. The pressure fluctuation ratio index represents the pressure fluctuation ratio. The index represents the ratio of traffic demand to demand. Indicates the response time ratio, Represents the attenuation coefficient, the Furthermore, the larger the value, the higher the node-product compatibility;

[0085] The pressure fluctuation ratio, flow demand ratio, and response time ratio of each node under the node load coefficient are imported into the node-product adaptation model to obtain the node-product adaptation degree between each node and the product.

[0086] Among them, the maximum-minimum normalization process refers to mapping the original values ​​of the pressure fluctuation ratio, flow demand ratio, and response time ratio to... The interval, specifically implemented using linear transformation, eliminates the dimensional differences between various physical parameters, making them comparable. The node load coefficient is a quantitative indicator reflecting the current load status of a node, calculated using node task completion rate, average test duration, accumulated work time, and task queue length. It incorporates the dynamic impact of node load into the fitness calculation. The attenuation coefficient is a weighting parameter used to adjust the influence of different physical parameters on fitness. It can be determined through preset empirical values ​​or dynamic adjustment algorithms, controlling the degree to which pressure fluctuations, flow deviations, and response delays suppress fitness.

[0087] Specifically, the pressure fluctuation ratio, flow demand ratio, and response time ratio are each subjected to maximum-min normalization and transformed into standardized exponents before being input into the node-product adaptation model. This model couples the normalized physical parameters with the node load coefficient through an exponential function: the node load coefficient acts as a global attenuation factor, directly affecting the adaptation calculation results, causing the adaptation of high-load nodes to decrease exponentially with increasing load; simultaneously, the attenuation coefficient applies a nonlinear weight adjustment to the normalized physical parameters, amplifying the inhibitory effect of key physical parameters on adaptation. For example, when the pressure fluctuation ratio exponent exceeds a preset threshold, the corresponding attenuation coefficient can be set to a larger value, causing the adaptation to decrease rapidly with increasing pressure fluctuations. By jointly modeling the node load state and physical parameter constraints, the adaptation calculation results can reflect both the current carrying capacity of the node and meet the accuracy requirements of the testing task for pressure stability, flow matching, and response time.

[0088] Compared to existing technologies, traditional methods allocate tasks based solely on node load or a single physical parameter, failing to establish a dynamic correlation mechanism between load status and multiple physical parameters. For example, existing technologies might select idle nodes based solely on node queue length, without considering whether the node experiences excessive pressure fluctuations. This solution constructs a composite matching model that integrates four types of parameters—node load coefficient, pressure fluctuation ratio, flow demand ratio, and response time ratio—in the task allocation decision. This ensures that testing tasks for high-value products are preferentially allocated to nodes with moderate loads and high physical parameter matching.

[0089] Through the above technical solution, this application solves the problem of decreased testing accuracy caused by the disconnect between physical parameters and node load during solenoid valve testing. By normalizing the calculation to ensure comparability of multiple physical parameters, and by dynamically balancing node load and physical constraints using an exponential decay model, it ensures that test tasks are assigned to nodes that meet both pressure stability requirements and have reasonable load levels, thereby simultaneously improving resource utilization and testing quality during clustered scheduling.

[0090] Preferably, the node task allocation model is represented as follows:

[0091]

[0092] in, Indicates the first Task volume per node Indicates the number of product tasks. Indicates the first Node-product compatibility of each node This represents the product value coefficient, the... .

[0093] Among them, node task volume This refers to the specific number of tasks assigned to a particular node. This can be achieved by calculating a weighted ratio of node suitability and product value coefficient. Its function is to dynamically allocate tasks to nodes with higher suitability. (Product task quantity) This refers to the total number of tasks required to test a particular product. Specifically, it can be determined by summing the adaptability of all nodes. Its function is to dynamically correlate the total number of tasks with the global node capabilities. Node-Product Adaptability This refers to the degree of matching between a node and a product, which can be calculated using parameters such as air pressure fluctuation ratio, flow demand ratio, and response time ratio. Its function is to quantify the node's testing capability for a specific product. Product Value Coefficient It refers to the overall priority weight of a product, which can be calculated by weighting product value, urgency and task volume index. Its function is to amplify the suitability differences of high-value products.

[0094] Specifically, the model allocates tasks through the following steps: First, the total number of product tasks is determined based on the sum of the adaptability of all nodes, ensuring that the total task allocation matches the overall capability of the nodes. Second, the adaptability of each node is combined with the product value coefficient in an exponential form, thus amplifying the adaptability differences of high-value products. For example, when the product value coefficient is high, nodes with high adaptability will be allocated more tasks, thereby prioritizing the processing of high-value products. Finally, the weighted adaptability is converted into a probability distribution through normalization, ensuring that the task allocation ratio strictly corresponds to the node capability and product value. Therefore, the task allocation process considers both the real-time adaptability of nodes and the priority weight of products, avoiding resource idleness or overload caused by fixed allocation.

[0095] Compared to existing technologies, which employ fixed task allocation strategies and fail to dynamically integrate suitability with product value, leading to response delays for high-value products and uneven node load, this solution introduces a suitability index-weighted and normalized allocation mechanism. This allows task allocation to be adjusted in real-time based on node capabilities and product priority. For example, existing technologies cannot reallocate tasks when node load changes, while this solution automatically migrates tasks to more suitable nodes by dynamically calculating the combined impact of suitability and value coefficients.

[0096] Through the above technical solution, this application achieves dynamic optimization of task allocation, solves the problem of uneven node load caused by rigid resource allocation, and improves the response speed of high-value products. Simultaneously, because the adaptability calculation includes physical parameter constraints such as air pressure fluctuation ratio and flow demand ratio, the task allocation process can automatically avoid nodes that do not meet testing requirements, thereby ensuring testing accuracy. Furthermore, through the exponential amplification effect of the product value coefficient, urgent orders or tasks for high-value products can be preferentially allocated to the optimal nodes, reducing average task latency.

[0097] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0098] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A clustered scheduling and control method for multi-channel solenoid valve testing resources, characterized in that, Includes the following steps: A product value model is constructed based on the individual value of the product, the urgency of the product, and the total number of product tasks, and the product value coefficient is output. A node load model is constructed based on node task completion rate, average node test duration, node working time, and node task queue length, and the node load coefficient of each node is output. Based on the pressure fluctuation ratio, flow demand ratio, and response time ratio between products and nodes under the node load coefficient, a node-product adaptation model is constructed to output the node-product adaptation degree between each node and product. A node task allocation model is constructed based on the number of product tasks, product value coefficient, and node-product fit, and the target number of node tasks is output. The node task allocation model is represented as follows: ; in, Indicates the first Task volume per node Indicates the number of product tasks. Indicates the first Node-product compatibility of each node This represents the product value coefficient, the... .

2. The clustered scheduling and control method for multi-channel solenoid valve test resources according to claim 1, characterized in that, The pressure fluctuation ratio is the ratio of node pressure fluctuation to the product's allowable pressure fluctuation; the flow demand ratio is the ratio of the product's required air pressure flow rate to the node's allowable air pressure flow rate; and the response time ratio is the ratio of the node solenoid valve response time to the product's required solenoid valve response time.

3. The clustered scheduling and control method for multi-channel solenoid valve test resources according to claim 1 or 2, characterized in that, The node load coefficient of each node is compared with the node load coefficient threshold, and the nodes whose node load coefficient is within the node load coefficient threshold are selected as task allocation nodes.

4. The clustered scheduling and control method for multi-channel solenoid valve test resources according to claim 1 or 2, characterized in that, The node-product fit between each node and the product is compared with the node-product fit threshold. Nodes whose node-product fit is within the node-product fit threshold are selected as task allocation nodes.

5. The clustered scheduling and control method for multi-channel solenoid valve test resources according to claim 4, characterized in that, The steps for constructing a node-product adaptation model based on the pressure fluctuation ratio, flow demand ratio, and response time ratio between products and nodes under node load coefficients, and outputting the node-product adaptation degree for each node and product, are as follows: The pressure fluctuation ratio, flow demand ratio, and response time ratio are obtained by performing maximum-min normalization on the pressure fluctuation ratio, flow demand ratio, and response time ratio. A node-product adaptation model is constructed based on the pressure fluctuation ratio index, flow demand ratio index, and response time ratio index under the node load coefficient. The node-product adaptation model is expressed as follows: ; in, Indicates node-product compatibility. Indicates the node load factor. The pressure fluctuation ratio index represents the pressure fluctuation ratio. The index represents the ratio of traffic demand to demand. Indicates the response time ratio, Represents the attenuation coefficient, the Furthermore, the larger the value, the higher the node-product compatibility; The pressure fluctuation ratio, flow demand ratio, and response time ratio of each node under the node load coefficient are imported into the node-product adaptation model to obtain the node-product adaptation degree between each node and the product.

6. The clustered scheduling and control method for multi-channel solenoid valve test resources according to claim 5, characterized in that, The steps to construct a product value model and output the product value coefficient based on the individual value of the product, the urgency of the product, and the total number of product tasks are as follows: The product value index is obtained by comparing the value of a single product with the maximum value of a single item in the system. The product urgency index is obtained by comparing the product urgency level with the maximum allowable product urgency level. The product task volume index is obtained by processing the ratio of the product task volume to the system maximum task volume. A product value model is constructed based on the product value index, product urgency index, and product task volume index. The product value model is expressed as follows: ; in, Indicates the product value coefficient. Indicates the product value index. Indicates the product urgency index. This represents the product task volume index. Represents the weight coefficient and The The higher the value, the higher the overall value of the product; Import the current product value index, current product urgency index, and current product task volume index into the product value model to obtain the current product value coefficient.

7. The clustered scheduling and control method for multi-channel solenoid valve test resources according to claim 3, characterized in that, The steps to construct a node load model based on node task completion rate, average node test duration, node working time, and node task queue length, and output the node load coefficient for each node are as follows: The average test duration of a node, the working time of a node, and the length of a node's task queue are each compared with their respective maximum allowable values ​​to obtain the test duration index, the working time index, and the task queue length index. A node load model is constructed based on the test duration index, the worked duration index, and the task queue length index. The node load model is expressed as follows: ; in, Indicates the node load factor. Node load analysis coefficients This indicates the optimal value for the node load factor. This represents the load sensitivity coefficient. This indicates the task completion rate of the node. This represents the average test duration index. This indicates the index of working hours. Indicates the index of task queue length. Represents the weight coefficient and The A larger value indicates a higher node load; Import the test duration index, working duration index, and task queue length index of each node into the node load model to obtain the node load coefficient of each node.

Images