Multi-port test equipment resource allocation management system and method

Abstract

The invention discloses a multi-port test equipment resource allocation management system and method, particularly relates to the technical field of equipment resource allocation, and is used for solving the problem that port and resource allocation in multi-port test equipment is not clear. According to the method, the bandwidth, the frequency, the voltage demand and the signal type of the task are extracted, the feature vector is constructed and normalized, the task is classified by using a K-means clustering algorithm, the resource demand of the task is accurately identified, the adaptive capability score of each port is dynamically calculated in combination with the task feature and the port resource capability, and the port load is monitored in real time, so that the adaptive capacity of each port is improved. According to the method, the task is reasonably allocated to the most appropriate port, the port resource allocation is optimized and the port allocation task is intelligently selected by continuously updating the adaptive capability score and combining the greedy algorithm and the optimal scheduling algorithm, so that the resource conflict and waste are avoided, the task delay is reduced, and the task processing capability and the resource utilization efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment resource allocation, and more specifically, to a multi-port test equipment resource allocation management system and method. Background Art

[0002] Multi-port test equipment is used to detect multiple ports of network equipment to help identify faults, evaluate performance and ensure stability. Multi-port test equipment can be used for performance evaluation of communication systems (such as 5G, 4G, Wi-Fi, etc.), including bandwidth, frequency response, signal interference and other tests. It can test multiple frequency bands or multiple network paths at the same time to improve test efficiency; With the rapid development of information technology and automated testing, multi-port test equipment has been widely used in various efficient and high-performance test scenarios. These devices usually need to process multiple test tasks at the same time. Each task has different requirements for resources such as bandwidth, frequency, and voltage. Therefore, how to reasonably allocate tasks and port resources has become the key to improving test efficiency and optimizing resource utilization.

[0003] Deficiencies of the existing technology: The current multi-port test equipment resource allocation management system usually relies on static configuration or simple priority scheduling methods to allocate tasks and ports. However, this method cannot effectively cope with the diversity of task requirements and the dynamic changes of port resources, which can easily lead to resource waste or excessive port load, affecting the efficiency of task execution and system stability. In addition, the existing system usually lacks a real-time feedback mechanism and cannot dynamically adjust the resource allocation strategy according to the actual needs and port status during task execution. Therefore, the existing technology faces the problem that task allocation cannot be flexibly adjusted according to real-time needs and port status, resulting in insufficient or unbalanced resource utilization, and cannot effectively solve problems such as excessive load or task delays. It also lacks adaptive optimization capabilities and cannot continuously improve system performance in a dynamic environment. Summary of the invention

[0004] In order to overcome the above defects of the prior art, the present invention provides a multi-port test equipment resource allocation management system and method to solve the problem of unclear port and resource allocation in the multi-port test equipment in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A multi-port test equipment resource allocation management method comprises the following steps: Extract the bandwidth, frequency, voltage requirements and signal type of each port task, construct the task feature vector and perform normalization, and use the K-means clustering algorithm to classify the tasks according to resource requirements; Based on the task characteristics and the resource capacity of the port, the adaptability score of each port is dynamically calculated, and the real-time load of the port is monitored at the same time. The port adaptability score is updated in real time and the task is assigned to the corresponding port. According to the task characteristics and port adaptability scores, the greedy algorithm and the optimization scheduling algorithm are combined to optimize the port resource allocation, and the ports are intelligently selected for the allocation of different tasks.

[0006] In a preferred embodiment, the bandwidth, frequency, voltage requirements and signal type of each port task are extracted, a task feature vector is constructed and normalized, and the tasks are classified according to resource requirements using a K-means clustering algorithm. The specific process is as follows: The bandwidth requirement indicates the amount of data transmission of the task, the frequency requirement indicates the signal response speed requirement of the task, the voltage requirement indicates the working voltage range required by the task, and the signal type requirement indicates the signal type used by the task, which includes analog signals and digital signals. After normalizing the requirements of each task using the maximum and minimum normalization method, the task feature vector is constructed; Segment the text in the user behavior data, remove stop words, and process special characters; Use the K-means clustering algorithm to classify tasks according to their characteristics and assign a category label to each task.

[0007] In a preferred embodiment, based on the task characteristics and the resource capabilities of the port, the adaptation capability score of each port is dynamically calculated. The specific process is as follows: Define the port adaptation capability. The adaptation capability of each port is defined as the resource type that the port can support. The port adaptation capability includes bandwidth support, frequency response, voltage support, signal type, and port load. Bandwidth supports the maximum bandwidth value that a port can carry. Compared with the bandwidth requirement of the task, if the port bandwidth is higher than the task requirement, the adaptability is stronger. Frequency response is the frequency range supported by the port, voltage support is the voltage range supported by the port, signal type is the signal type supported by the port, and port load is the current load of the port; Obtain the task feature vector and port feature vector to calculate the port adaptability score.

[0008] In a preferred embodiment, the real-time load of the port is monitored at the same time, the port adaptation capability score is updated in real time, and the task is assigned to the corresponding port. The specific steps are as follows: Get the task feature vector as and the port feature vector is , then the port adaptability score The calculation formula is: ,in, They are the bandwidth, frequency, and voltage requirements of the task, respectively; They are the bandwidth, frequency and voltage supported by the port respectively; , and Represents the signal type of the task and port respectively; is the current load of the port, is the maximum load of the port; is the weight coefficient of each dimension; The calculated port adaptability score is used as the basis for task and port allocation. For each task, the port with the highest port adaptability score is selected for allocation.

[0009] In a preferred embodiment, according to the task characteristics and port adaptability scores, a greedy algorithm and an optimization scheduling algorithm are combined to optimize port resource allocation, and ports are intelligently selected for allocation of different tasks. The specific process is as follows: In the mapping process between tasks and ports, a greedy algorithm is used to select the local optimal solution to obtain the global optimal solution, and the task-port pair with the highest adaptability is selected for allocation; The task feature vector, port feature vector and port adaptability score are used as input data, and a greedy algorithm is used to map tasks and ports. Initialize, sort all tasks according to their priority, assign tasks with higher priority first, and determine the priority of tasks according to their importance and urgency. Initialize all port states and record the current load of each port. Calculate the fitness. For each task, use the Euclidean distance to calculate the fitness between the task and each port. : ,in, and are the normalized values ​​of the i-th dimension of the task and port feature vectors, respectively; Convert Euclidean distance to fitness: ; Select the best port, select an unassigned port from the ports with the highest fitness, and perform task allocation. If the selected port load exceeds its maximum load, exclude the port and select the port with the second highest fitness. After the task is assigned to the port, the port load information is updated; Repeat the steps of calculating the fitness, selecting the optimal port, and assigning tasks to ports until all tasks are assigned to ports or all port resources are exhausted; When all tasks are successfully assigned to ports, the mapping of tasks to ports is completed; In the case of concurrent tasks, a genetic algorithm is used to further optimize the mapping between tasks and ports.

[0010] In a preferred embodiment, in the case of concurrent tasks, a genetic algorithm is used to further optimize the mapping between tasks and ports. The specific process is as follows: Perform individual encoding and initialization. The individual in the genetic algorithm is a mapping scheme between tasks and ports. Each individual consists of a mapping relationship between tasks and ports, and each task is mapped to a port. The fitness of each individual is calculated based on the fitness between the task and the port. The fitness of each individual is the sum of the fitness between all tasks and ports under the mapping scheme. Select individuals according to fitness to perform crossover operations and generate a new generation of individuals; Perform exchange mutation and insertion mutation on individuals, map the task to a new port, and disrupt the current mapping relationship; Through iterative evolution, it provides the best task and port allocation solution and converges to the global optimal solution; Select ports for task allocation based on the global optimal solution.

[0011] A multi-port test equipment resource allocation management system, used to implement the multi-port test equipment resource allocation management method, comprising: The task classification module is used to extract the bandwidth, frequency, voltage requirements and signal type of each port task, construct the task feature vector and perform normalization processing, and use the K-means clustering algorithm to classify the tasks according to resource requirements; The port adaptation analysis module is used to dynamically calculate the adaptation capability score of each port based on the task characteristics and the resource capacity of the port, and monitor the real-time load of the port at the same time, update the port adaptation capability score in real time, and assign tasks to the corresponding ports; The task port mapping module is used to optimize the port resource allocation based on the task characteristics and port adaptability scores, using a combination of a greedy algorithm and an optimization scheduling algorithm, and intelligently select ports for the allocation of different tasks.

[0012] Technical effects and advantages of the present invention: The present invention extracts the bandwidth, frequency, voltage requirement and signal type of the task, constructs the task feature vector and performs normalization processing, and classifies the tasks according to the resource requirements in combination with the K-means clustering algorithm, thereby realizing accurate identification of the task resource requirements, dynamically calculating the adaptation capability score of each port based on the task characteristics and the port resource capacity, and monitoring the port load in real time to ensure the optimal utilization of the port resources in the task allocation process, and realizing flexible matching of tasks and ports by continuously updating the port adaptation capability score, thereby improving the efficiency and accuracy of resource allocation, and on this basis, the present invention adopts a combination of a greedy algorithm and an optimization scheduling algorithm to further optimize the allocation of port resources, and intelligently selects the most suitable port to provide support for the task, thereby avoiding resource conflicts and waste, reducing task execution delays, and improving the overall processing capacity and resource utilization of the system, effectively solving the problems of dynamic adaptation and task priority conflicts in multi-port resource allocation, and significantly improving the efficiency of task allocation and the stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flowchart of a method for managing resource allocation of multi-port test equipment.

[0014] Figure 2 The present invention is a schematic diagram of the structure of a multi-port test equipment resource allocation and management system. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example 1: Figure 1 As shown, a multi-port test equipment resource allocation management method comprises the following steps: Extract the bandwidth, frequency, voltage requirements and signal type of each port task, construct the task feature vector and perform normalization, and use the K-means clustering algorithm to classify the tasks according to resource requirements; Based on the task characteristics and the resource capacity of the port, the adaptability score of each port is dynamically calculated, and the real-time load of the port is monitored at the same time. The port adaptability score is updated in real time and the task is assigned to the corresponding port. According to the task characteristics and port adaptability scores, the greedy algorithm and the optimization scheduling algorithm are combined to optimize the port resource allocation, and the ports are intelligently selected for the allocation of different tasks.

[0017] In the resource allocation management of multi-port test equipment, there are significant differences in the resource requirements of different test tasks. These differences are mainly reflected in bandwidth requirements, frequency requirements, voltage range, etc. In order to achieve efficient resource allocation, it is first necessary to understand the characteristics of each task and its specific resource requirements. The purpose of task feature analysis and classification is to extract key features from each task in an automated way and classify the tasks so that the most suitable port can be allocated to each task in the future, thereby improving resource utilization efficiency, reducing resource conflicts, and improving the accuracy and speed of task execution.

[0018] Step 1: Analyze and classify the automated task features of each port, extract representative features from each test task to describe the resource requirements of the task, and then classify the tasks and assign the most appropriate test port to the task. The specific steps are as follows: Perform task feature extraction to extract representative features from each test task to describe the resource requirements of the task. The feature extraction process collects relevant data of the task to form a set of structured feature vectors. Define the characteristics of a task. The characteristics of a task are multi-dimensional data that describe the resources required for the task, including the following aspects: Bandwidth requirements ( ): The bandwidth demand of each task reflects the data transmission volume of the task, which is usually expressed in data transmission rate (such as bits per second bps). The bandwidth demand directly affects the bandwidth resource allocation of the port. If the task requires a higher bandwidth, a port with a larger bandwidth needs to be selected; Frequency requirements ( ): The frequency requirement of the test task reflects the requirement of the task for the signal response speed. Different tasks may have different frequency requirements. Some tasks may require high-frequency response, while others only require low frequency. The frequency range of the task is usually determined by the minimum frequency. and maximum frequency Indicates, for example: ; Voltage requirements ( ): The voltage requirement of a test task refers to the operating voltage range required by the task. Test ports are usually designed with specific voltage carrying capacity, so the voltage requirement of the task determines the type of port to which the task can be assigned; Signal Type ( ): The signal type used by the task is divided into analog signals (Analog) and digital signals (Digital). Different types of ports may be suitable for different signal forms. Therefore, it is necessary to identify the signal type of the task in order to map it to the corresponding port. This feature is discrete and can be represented by 0 (analog) or 1 (digital); Extract features and combine the requirements of each task into a feature vector , the feature vector contains all relevant feature information of the task, which facilitates the subsequent classification and matching of tasks. Each feature corresponds to a dimension: ; In order to eliminate the influence of different characteristic units and dimensions, all eigenvalues ​​will be normalized, and each eigenvalue will be converted to a unified dimension range using the maximum and minimum normalization method; Normalized eigenvalues are all in the range [0, 1]. For signal type , since it is binary, it naturally meets the normalization requirements. The normalized task feature vector is: .

[0019] The test tasks are divided into different categories according to their characteristics. The K-means clustering algorithm is used to divide the tasks into multiple categories according to their characteristics. The tasks will be automatically assigned to the cluster with the smallest distance to the center. The specific process is as follows: Select K initial cluster centers (usually by randomly selecting task feature vectors), assign tasks to the closest clusters based on the distance between the task features and the cluster centers, calculate new cluster centers based on the feature vectors of the tasks in the clusters, update the cluster center positions, and repeat the task assignment and cluster update steps until the cluster centers no longer change or the maximum number of iterations is reached; In the clustering process, the distance metric uses Euclidean distance: ,in, is the center of cluster k, is the normalized value of the i-th dimension in the task feature vector, and n is the feature dimension; After completing the task classification, each task will be assigned a category label, indicating which category the task belongs to, for example, ,in, Represents the category of task K, each category It represents a set of tasks with similar resource requirements, which facilitates subsequent port mapping and resource allocation. The result of task classification includes the task category label and the corresponding feature vector.

[0020] In summary, by extracting the bandwidth, frequency, voltage and other features of the task, normalizing the features, and combining the clustering algorithm to classify the tasks, the resource requirements of different tasks can be accurately identified.

[0021] Step 2: Dynamic port adaptability evaluation and scoring. After completing the task feature analysis and classification, each port is dynamically evaluated and scored for adaptability. The purpose of this step is to dynamically evaluate the adaptability of each port based on the task features and the current status of the port, and assign a capability score to each port. Based on these scores, the most suitable port is selected for each task to ensure the optimal use of port resources. The specific steps are as follows: Define the port adaptability. The adaptability of each port is defined as the resource type that the port can support, including but not limited to bandwidth, voltage, frequency, etc. The port adaptability is the basis for evaluating the matching degree between the task and the port. The port capability evaluation is determined based on the following dimensions: Bandwidth support : The maximum bandwidth value that the port can carry. Compared with the bandwidth requirement of the task, if the port bandwidth is higher than the task requirement, the adaptability is stronger; Frequency Response : The frequency range supported by the port. The frequency response capability of the port determines whether it can handle the frequency range required by the task. The matching degree between the frequency requirement of the task and the frequency range supported by the port directly affects the adaptability; Voltage support : The voltage range supported by the port. The voltage requirement of the task must fall within the supported range of the port to ensure the normal execution of the task; Signal Type : The signal type supported by the port, such as analog or digital. The signal type of the task should match the supported type of the port; Port Load : Current load of the port. Even if a port has a high theoretical capacity, it may not be suitable for taking on new tasks if its current load is too high. The port load needs to be monitored in real time and dynamically evaluated when assigning tasks; Comprehensively evaluate the capabilities of the above dimensions and the current load, and calculate the adaptability score of each port. The adaptability score should reflect the port's ability to meet task requirements. The higher the score, the more suitable the port is for undertaking the task. Get the task feature vector as and the port feature vector is , then the port adaptability score The calculation formula is: ,in, are the bandwidth, frequency and voltage requirements of the task respectively. They are the bandwidth, frequency and voltage supported by the port respectively; , and Respectively represent the signal type of the task and port (0 for analog, 1 for digital), is the current load of the port, is the maximum load of the port; It is the weight coefficient of each dimension, which is used to indicate the influence of different features on adaptability. It is usually determined according to specific needs or through training. The bandwidth adaptability of tasks and ports is measured by bandwidth difference. If the bandwidth supported by the port is close to the bandwidth requirement of the task, the adaptability is high; otherwise, the adaptability is low. The adaptability is calculated by the ratio of bandwidth difference to maximum bandwidth. The frequency compatibility between the task and the port is represented by the difference in frequency requirements. If the frequency range supported by the port covers the frequency range of the task, the compatibility is high. The smaller the frequency difference, the higher the compatibility. Voltage adaptability is evaluated based on the difference between the voltage range required by the task and the voltage range supported by the port. The calculation of voltage adaptability is similar to bandwidth and frequency, and the adaptability is inversely proportional to the voltage difference. The signal type adaptability is directly determined by whether the task and port signal types match. If the signal types of the two are consistent, the adaptability is 1; otherwise, the adaptability is 0; The port load adaptability is calculated by the ratio of the current port load to the maximum load. If the current port load is close to the maximum load, the adaptability is low; The port adaptability score is not static, but will be dynamically updated with the execution of tasks and changes in port load. Therefore, each time a task is assigned, the score needs to be updated according to the real-time load of the port. For example, during the execution of the task, the port load (e.g., bandwidth utilization, response time, etc.) needs to be continuously monitored. When the port load changes, the port adaptability score needs to be updated immediately to recalculate its adaptability to the new task; After completing the port adaptability assessment, the calculated port adaptability score is used as the basis for task and port allocation. For each task, the port with the highest adaptability is selected for allocation. The specific steps are as follows: For each task, the system selects the port with the highest adaptability score for allocation based on the matching degree between its characteristics and the port score. The selection of tasks can be optimized based on the score sorting or using a greedy algorithm. Each task is assigned to the port with the highest port adaptability score based on its characteristics (bandwidth, frequency, voltage, etc.). If the port is currently overloaded, the system will select the next port with a higher adaptability score to ensure that the task is not delayed due to insufficient resources; In the case of concurrent execution of multiple tasks, port allocation is further optimized through optimization scheduling algorithms (such as optimization scheduling algorithms or genetic algorithms). The algorithm considers the priority of tasks, port load and adaptability score to ensure that the system completes tasks in the shortest time and maximizes the utilization efficiency of port resources.

[0022] In summary, the dynamic port adaptability assessment and scoring system evaluates the port's capabilities in multiple dimensions, combines real-time load monitoring with task requirements, and dynamically calculates the port adaptability score. The scoring system ensures that the task selects the most suitable port based on its characteristics, and adjusts the adaptability score in real time to avoid assigning the task to an inappropriate port. Through this evaluation mechanism, the system can adaptively optimize the allocation of port resources during task execution, achieve optimal resource utilization, and improve test efficiency and task execution stability.

[0023] Step 3: Intelligent task and port mapping selection. According to the characteristics of the task and the adaptability score of the port, the task is intelligently mapped to the most suitable port to maximize resource utilization and reduce the delay of task execution. This step will select the port through the optimization algorithm so that the task can meet the resource requirements while avoiding resource conflicts and waste. The specific steps are as follows: In the task allocation process, the resource requirements of each task and the resource capacity of the port need to be considered. Since multiple tasks are executed in parallel, the mapping between tasks and ports is a dynamic process, which needs to be comprehensively considered based on information such as task characteristics, port adaptability scores, and the current port load; In the mapping process between tasks and ports, the greedy algorithm obtains the global optimal solution by selecting the local optimal solution each time. Specifically, the greedy algorithm selects the task-port pair with the highest fitness for allocation each time until all tasks are allocated; Take the result of step 2 as input, including the task feature vector , port feature vector And port adaptability score ; In the case of dynamic load, the real-time load situation of the port and the priority of the task will also affect the port selection. The priority of the task can be determined by the urgency or completion time of the task.

[0024] Use the greedy algorithm to map tasks and ports. The process is as follows: Step 3.1, perform initialization, sort all tasks according to their priorities, and assign tasks with higher priorities first. The priority of tasks can be determined based on the importance, urgency or other business rules of the tasks; initialize all ports to be available, and record the current load of each port; Step 3.2, calculate the fitness. For each task, calculate the fitness between the task and each port. , use Euclidean distance to calculate the fitness: ,in, and are the normalized values ​​of the i-th dimension of the task and port feature vectors, respectively, converting the Euclidean distance into fitness: Since the smaller the Euclidean distance, the higher the fitness, the fitness adopts the inverse of the distance, which can ensure that the smaller the distance, the greater the fitness; Step 3.2, select the best port, select an unassigned port from the ports with the highest fitness, and perform task allocation. If the load of the selected port exceeds its maximum load, exclude the port and select the port with the second highest fitness; Step 3.4: After the task is assigned to the port, the load information of the port is updated to indicate that the port has carried a task. If the port load reaches the maximum load, it is marked as unavailable to avoid further task assignment. Step 3.5, repeat steps 3.2 to 3.4 until all tasks are assigned to ports or all port resources are exhausted; In step 3.6, when all tasks are successfully assigned to ports, the mapping between tasks and ports is completed.

[0025] In the case of concurrent tasks, the greedy algorithm cannot obtain the global optimal solution. The optimal scheduling algorithm (such as genetic algorithm) can be used to further optimize the mapping between tasks and ports. Genetic algorithm (GA) is a search optimization algorithm that simulates the principles of natural selection and genetics. It gradually evolves the optimal solution from the existing solution space by simulating the process of biological evolution. In the mapping between tasks and ports, genetic algorithms can effectively handle the resource allocation problem when multiple tasks are concurrent. Especially when the greedy algorithm may fall into the local optimum, the genetic algorithm can find the global optimal or approximate optimal solution through global search and optimization. The specific steps of using genetic algorithms are as follows: Perform individual encoding and initialization. The individual in the genetic algorithm represents a mapping scheme between tasks and ports. Each individual consists of a mapping relationship between tasks and ports. Each task is mapped to a port, and the mapping relationship can be represented by a sequence of integers. For example, suppose there are 3 tasks and 3 ports , the individual code can be: , where the task Mapped to port ,Task Mapped to port ,Task Mapped to port ; The fitness of each individual is calculated based on the fitness of the task and the port (the fitness of each individual is the sum of the fitness of all tasks and ports under its mapping scheme. The higher the total fitness, the more reasonable the resource allocation of the scheme). Individuals with higher fitness mean that the matching degree between tasks and ports is better. According to the fitness, a part of the better individuals are selected for crossover operation to generate a new generation of individuals. The selection operation is based on the fitness of the individuals. Individuals with high fitness are more likely to be selected, but a certain degree of randomness is also allowed to avoid premature convergence to the local optimal solution. Perform a crossover operation to generate new individuals from the selected individuals to explore a better resource allocation solution. You can perform a single-point crossover to select a crossover point of the parent individual, exchange the genes after that point, and generate two offspring, or perform a multi-point crossover. Mutate individuals to increase the diversity of the solution space and prevent them from falling into local optimal solutions. For example, perform exchange mutation to randomly select task pairs and swap port mapping relationships; or perform insertion mutation to randomly select tasks and map them to a new port to disrupt the current mapping relationship. Genetic algorithms gradually find the optimal solution through multiple generations of evolution. In each generation, the parent individuals will produce new offspring through operations such as selection, crossover, and mutation. The fitness of each generation will gradually increase, and eventually converge to the global optimal or approximate optimal solution; Through iterative evolution, genetic algorithms can provide optimal or nearly optimal task and port allocation solutions for multi-task parallel execution scenarios; A port is selected for task allocation based on the determined global optimal solution or approximate optimal solution.

[0026] It should be noted that the mapping process also needs to meet certain constraints, including that the port load cannot exceed its maximum support capacity, each task must be assigned to a port, and the compatibility between the task and the port must meet the minimum requirements (such as bandwidth, frequency, voltage, etc.).

[0027] During the task execution process, the resource consumption of the task is monitored in real time, such as bandwidth usage, voltage requirements, etc. If the port load assigned to a task is too high or no longer meets the task requirements, the task's adaptability will be dynamically re-evaluated and resources will be reallocated. During the task execution process, the system will periodically recalculate the adaptability between the task and the port. If it is found that the port load is too high or the task resource requirements have changed, the system will select a new port for allocation based on the new adaptability.

[0028] After completing the above steps, the final task and port mapping are determined by the following strategy: The highest priority tasks are assigned ports first, ensuring that tasks are executed on demand; The port with the highest fitness is selected to perform the task, but port load and availability also need to be considered; Dynamic adjustment of task execution ensures continuous optimization of the mapping through feedback mechanisms and real-time load monitoring.

[0029] The intelligent task and port mapping algorithm can effectively map tasks to the most suitable ports by combining greedy algorithms and genetic algorithms. The core goal of this process is to maximize resource utilization, reduce task execution delays, and balance port loads. Through this optimized mapping strategy, efficient resource allocation and smooth task execution can be ensured in a complex multi-task concurrent execution environment.

[0030] It should be noted that the thresholds involved in the embodiments can be determined according to specific scenarios and requirements.

[0031] The present invention extracts the bandwidth, frequency, voltage requirement and signal type of the task, constructs the task feature vector and performs normalization processing, and classifies the tasks according to the resource requirements in combination with the K-means clustering algorithm, thereby realizing accurate identification of the task resource requirements, dynamically calculating the adaptation capability score of each port based on the task characteristics and the port resource capacity, and monitoring the port load in real time to ensure the optimal utilization of the port resources in the task allocation process, and realizing flexible matching of tasks and ports by continuously updating the port adaptation capability score, thereby improving the efficiency and accuracy of resource allocation, and on this basis, the present invention adopts a combination of a greedy algorithm and an optimization scheduling algorithm to further optimize the allocation of port resources, and intelligently selects the most suitable port to provide support for the task, thereby avoiding resource conflicts and waste, reducing task execution delays, and improving the overall processing capacity and resource utilization of the system, effectively solving the problems of dynamic adaptation and task priority conflicts in multi-port resource allocation, and significantly improving the efficiency of task allocation and the stability of the system.

[0032] Embodiment 2: A multi-port test equipment resource allocation management system, such as Figure 2 As shown, specifically including: The task classification module is used to extract the bandwidth, frequency, voltage requirements and signal type of each port task, construct the task feature vector and perform normalization processing, and use the K-means clustering algorithm to classify the tasks according to resource requirements; The port adaptation analysis module is used to dynamically calculate the adaptation capability score of each port based on the task characteristics and the resource capacity of the port, and monitor the real-time load of the port at the same time, update the port adaptation capability score in real time, and assign tasks to the corresponding ports; The task port mapping module is used to optimize the port resource allocation based on the task characteristics and port adaptability scores, using a combination of a greedy algorithm and an optimization scheduling algorithm, and intelligently select ports for the allocation of different tasks.

[0033] The above formulas are all dimensionless and calculated numerically. Specific dimension removal can be achieved by various means such as standardization, which will not be elaborated here. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0034] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, an ATA hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state ATA hard disk.

[0035] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0036] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0037] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0038] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0039] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0040] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-port test equipment resource allocation management method, characterized in that: The steps include: Extract the bandwidth, frequency, voltage requirements and signal type of each port task, construct the task feature vector and perform normalization, and use the K-means clustering algorithm to classify the tasks according to resource requirements; Based on the task characteristics and the resource capacity of the port, the adaptability score of each port is dynamically calculated, and the real-time load of the port is monitored at the same time. The port adaptability score is updated in real time and the task is assigned to the corresponding port. According to the task characteristics and port adaptability scores, the greedy algorithm and the optimization scheduling algorithm are combined to optimize the port resource allocation, and the ports are intelligently selected for the allocation of different tasks.

2. A multi-port test equipment resource allocation management method according to claim 1, characterized in that: Extract the bandwidth, frequency, voltage requirements and signal type of each port task, construct the task feature vector and perform normalization, and use the K-means clustering algorithm to classify the tasks according to resource requirements. The specific process is as follows: The bandwidth requirement indicates the amount of data transmission of the task, the frequency requirement indicates the signal response speed requirement of the task, the voltage requirement indicates the working voltage range required by the task, and the signal type requirement indicates the signal type used by the task, which includes analog signals and digital signals. After normalizing the requirements of each task using the maximum and minimum normalization method, the task feature vector is constructed; Segment the text in the user behavior data, remove stop words, and process special characters; Use the K-means clustering algorithm to classify tasks according to their characteristics and assign a category label to each task.

3. A multi-port test equipment resource allocation management method according to claim 2, characterized in that: Based on the task characteristics and the resource capabilities of the port, the adaptability score of each port is dynamically calculated. The specific process is as follows: Define the port adaptation capability. The adaptation capability of each port is defined as the resource type that the port can support. The port adaptation capability includes bandwidth support, frequency response, voltage support, signal type, and port load. Bandwidth supports the maximum bandwidth value that a port can carry. Compared with the bandwidth requirement of the task, if the port bandwidth is higher than the task requirement, the adaptability is stronger. Frequency response is the frequency range supported by the port, voltage support is the voltage range supported by the port, signal type is the signal type supported by the port, and port load is the current load of the port; Obtain the task feature vector and port feature vector to calculate the port adaptability score.

4. A multi-port test equipment resource allocation management method according to claim 3, characterized in that: At the same time, the real-time load of the port is monitored, the port adaptation capability score is updated in real time, and the task is assigned to the corresponding port. The specific steps are as follows: Get the task feature vector as and the port feature vector is , then the port adaptability score The calculation formula is: ,in, They are the bandwidth, frequency, and voltage requirements of the task, respectively; They are the bandwidth, frequency and voltage supported by the port respectively; , and Represents the signal type of the task and port respectively; is the current load of the port, is the maximum load of the port; is the weight coefficient of each dimension; The calculated port adaptability score is used as the basis for task and port allocation. For each task, the port with the highest port adaptability score is selected for allocation.

5. A method for managing resource allocation of a multi-port test device according to claim 4, characterized in that: According to the task characteristics and port adaptability scores, the greedy algorithm and the optimization scheduling algorithm are combined to optimize the port resource allocation, and ports are intelligently selected for the allocation of different tasks. The specific process is as follows: In the mapping process between tasks and ports, a greedy algorithm is used to select the local optimal solution to obtain the global optimal solution, and the task-port pair with the highest adaptability is selected for allocation; The task feature vector, port feature vector and port adaptability score are used as input data, and a greedy algorithm is used to map tasks and ports. Initialize, sort all tasks according to their priority, assign tasks with higher priority first, and determine the priority of tasks according to their importance and urgency. Initialize all port states and record the current load of each port. Calculate the fitness. For each task, use the Euclidean distance to calculate the fitness between the task and each port. : ,in, and are the normalized values ​​of the i-th dimension of the task and port feature vectors, respectively; Convert Euclidean distance to fitness: ; Select the best port, select an unassigned port from the ports with the highest fitness, and perform task allocation. If the selected port load exceeds its maximum load, exclude the port and select the port with the second highest fitness. After the task is assigned to the port, the port load information is updated; Repeat the steps of calculating the fitness, selecting the optimal port, and assigning tasks to ports until all tasks are assigned to ports or all port resources are exhausted; When all tasks are successfully assigned to ports, the mapping of tasks to ports is completed; In the case of concurrent tasks, a genetic algorithm is used to further optimize the mapping between tasks and ports.

6. A method for managing resource allocation of a multi-port test device according to claim 5, characterized in that: In the case of concurrent tasks, a genetic algorithm is used to further optimize the mapping between tasks and ports. The specific process is as follows: Perform individual encoding and initialization. The individual in the genetic algorithm is a mapping scheme between tasks and ports. Each individual consists of a mapping relationship between tasks and ports, and each task is mapped to a port. The fitness of each individual is calculated based on the fitness between the task and the port. The fitness of each individual is the sum of the fitness between all tasks and ports under the mapping scheme. Select individuals according to fitness to perform crossover operations and generate a new generation of individuals; Perform exchange mutation and insertion mutation on individuals, map the task to a new port, and disrupt the current mapping relationship; Through iterative evolution, it provides the best task and port allocation solution and converges to the global optimal solution; Select ports for task allocation based on the global optimal solution.

7. A multi-port test equipment resource allocation management system, used to implement a multi-port test equipment resource allocation management method according to any one of claims 1 to 6, characterized in that: include: The task classification module is used to extract the bandwidth, frequency, voltage requirements and signal type of each port task, construct the task feature vector and perform normalization processing, and use the K-means clustering algorithm to classify the tasks according to resource requirements; The port adaptation analysis module is used to dynamically calculate the adaptation capability score of each port based on the task characteristics and the resource capacity of the port, and monitor the real-time load of the port at the same time, update the port adaptation capability score in real time, and assign tasks to the corresponding ports; The task port mapping module is used to optimize the port resource allocation based on the task characteristics and port adaptability scores, using a combination of a greedy algorithm and an optimization scheduling algorithm, and intelligently select ports for the allocation of different tasks.

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