Interconnection test method and system for wireless communication module

Abstract

The invention relates to the technical field of communication control, and discloses an interconnection test method and system for a wireless communication module, and the method comprises the steps: collecting real-time communication data through a multi-source communication interface, extracting topological features through a graph theory model to generate a matrix, and obtaining a signal optimization parameter through a self-adaptive filtering model; and constructing a multi-target test path planning model and outputting optimal path data, and establishing a layered test execution model to complete the test. The system comprises a communication topology analysis module, a path dynamic planning module and a hierarchical test execution module. The method overcomes the defects of a traditional testing method, can comprehensively collect data, accurately analyze topology, effectively process signals, scientifically plan a testing path and perform layered collaborative testing, has remarkable effects in improving testing accuracy, efficiency and comprehensiveness, can adapt to the development requirements of a wireless communication technology, and ensures stable and reliable work of a wireless communication module.

Description

Technical Field

[0001] The present invention relates to the technical field of communication control, and particularly to an interconnection test method and system for a wireless communication module. Background Art

[0002] With the rapid development of wireless communication technology, wireless communication modules are increasingly widely used in various electronic devices. From smartphones, smart home devices to industrial Internet of Things terminals, their performance and reliability directly affect the operation effect of the entire system. In actual application scenarios, multiple wireless communication modules often need to be interconnected and work together to achieve data transmission and interaction. Therefore, it is particularly important to comprehensively and efficiently test the interconnection performance of wireless communication modules. In the existing technology, there are many limitations in the test methods and systems for wireless communication modules. Traditional test methods often rely on a single interface for data acquisition. For example, only some information such as signal strength is obtained through the radio frequency signal interface, and it is impossible to comprehensively reflect the real-time state of the communication module from multiple dimensions. This one-sided data acquisition method makes it difficult for testers to deeply understand the overall performance of the communication module in a complex network environment, and may miss some key issues, such as compatibility issues at the baseband protocol level, communication stability issues under different channel states, and the impact of network topology changes on communication.

[0003] In terms of communication topology relationship analysis, the existing technical means lack accuracy and dynamic adaptability. Most methods use simple connection relationship recording methods and cannot fully consider the dynamic change factors in the signal transmission process, such as the impact of signal interference and node load changes on the topology relationship. This leads to the inability to accurately grasp the real connection situation between communication modules during actual testing, thereby affecting the evaluation of the performance of the entire communication network. For example, in a multi-node wireless communication network, due to signal interference, the actual communication quality between some nodes may be much lower than the theoretical expectation, but the existing topology analysis methods may not be able to detect this change in time, resulting in a large deviation between the test results and the actual situation.

[0004] For the processing of communication signals, there are also deficiencies in the filtering algorithms in the existing technology. Conventional filtering methods are difficult to effectively cope with complex and changing noise environments and cannot accurately remove noise interference while ensuring signal integrity. In the process of wireless communication, the sources of noise are extensive, including environmental noise, interference from other wireless devices, etc., and the characteristics of noise are often dynamically changing. Traditional filtering algorithms cannot dynamically adjust the filtering parameters according to the real-time changes of noise, easily resulting in signal distortion or loss of key information during the denoising process, affecting subsequent signal analysis and processing.

[0005] In terms of test path planning, most current methods only consider a single objective, such as simply pursuing test coverage or minimizing test time, without comprehensively weighing multiple optimization objectives. This makes the test process either consume a large amount of time to achieve high coverage, affecting test efficiency; or sacrifice coverage to shorten the test time, unable to comprehensively detect the interconnection performance of the communication module. In addition, when existing technologies perform path planning, they rarely consider practical factors such as resource constraints and protocol priorities during the test process, resulting in the test paths planned may not be able to proceed smoothly due to issues such as insufficient resources or protocol conflicts during actual execution. In the test execution link, existing test systems lack hierarchical and collaborative designs. Different functional levels are independent of each other, lacking effective information interaction and collaborative working mechanisms. For example, when the protocol layer verifies protocol compatibility, it cannot obtain the channel state information of the physical layer in a timely manner, resulting in the verification results may be disconnected from the actual communication environment; when the application layer schedules test tasks, it cannot fully consider the requirements and limitations of the protocol layer and the physical layer, making the execution efficiency of test tasks low and unable to meet the actual test requirements.

[0006] With the development of wireless communication technology towards high speed, large capacity, and low latency, the functions and complexity of wireless communication modules are constantly increasing, posing higher requirements for their interconnection testing technology. Existing test methods and systems have been difficult to meet the growing test requirements, and there is an urgent need for an innovative and comprehensive wireless communication module interconnection testing method and system to improve the accuracy, efficiency, and comprehensiveness of testing, and ensure that wireless communication modules can work stably and reliably in various complex environments. Summary of the Invention

[0007] The purpose of the present invention is to provide an interconnection testing method and system for wireless communication modules to solve the problems raised in the above background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solution: An interconnection testing method for wireless communication modules, the method includes: Collect real-time communication data of the target wireless communication module through a multi-source communication interface, the multi-source communication interface includes a radio frequency signal interface, a baseband protocol interface, a channel state interface, and a network topology interface; extract topological features from the real-time communication data based on a graph theory model to generate a communication topology relationship matrix; input the communication topology relationship matrix into a pre-trained adaptive filtering model, the adaptive filtering model adopts a multi-stage cascaded filter structure, and performs iterative denoising processing on the communication signal based on the noise covariance matrix to generate signal optimization parameters; Construct a multi-objective test path planning model according to the signal optimization parameters. The multi-objective test path planning model aims to maximize the test coverage and minimize the test delay, and uses an improved dynamic programming algorithm to globally optimize the test path, where the improved dynamic programming algorithm introduces a time-varying state transition weight and an adaptive backtracking mechanism; output the optimal test path data based on the multi-objective test path planning model; Establish a hierarchical test execution model according to the optimal test path data. The hierarchical test execution model includes a protocol layer, a physical layer, and an application layer. Among them, the protocol layer verifies the communication protocol compatibility based on the signal optimization parameters, the physical layer performs channel state evaluation based on the optimal test path data, and the application layer realizes the dynamic scheduling of test tasks based on a multi-constraint optimization algorithm; output test control instructions through the hierarchical test execution model to complete the interconnection test of the wireless communication module.

[0009] Preferably, input the communication topology relationship matrix into a pre-trained adaptive filtering model. The adaptive filtering model adopts a multi-stage cascaded filter structure and performs iterative denoising processing on communication signals based on the noise covariance matrix. The generated signal optimization parameters include: Obtain real-time communication data, where the real-time communication data includes a signal strength matrix, a bit error rate sequence, a channel impulse response vector, and a protocol handshake timestamp; construct a signal state space based on the real-time communication data, and construct an action space based on the adjustable transmit power, modulation method, and frequency band switching parameters of the communication module; Construct a hybrid cost function based on the signal state space and the action space. The hybrid cost function includes a signal-to-noise ratio cost term, an interference suppression cost term, a protocol consistency cost term, and a switching delay cost term. Among them, the signal-to-noise ratio cost term is calculated by the ratio of the signal strength to the noise power, the interference suppression cost term is calculated by the product of the adjacent channel interference power and the current channel bandwidth, the protocol consistency cost term is calculated by the variance of the protocol handshake timestamp, and the switching delay cost term is calculated by the difference between the frequency band switching time and a preset threshold; Construct a multi-stage cascaded filter structure. The multi-stage cascaded filter includes a pre-filter, a main filter, and a post-filter. The pre-filter and the post-filter adopt a finite impulse response structure, the main filter adopts an infinite impulse response structure, the cut-off frequencies of the pre-filter and the post-filter are dynamically adjusted based on the signal spectrum, and the recursive coefficients of the main filter are updated by the least mean square error algorithm; An error feedback mechanism is constructed based on the multi-stage cascaded filter. The filtering output error is cumulatively statistically analyzed by the sliding window method, and the weight coefficients of the filter are iteratively corrected in combination with the noise covariance matrix. The parameters of the multi-stage cascaded filter are updated using a phased training strategy to generate signal optimization parameters including a filter coefficient matrix, frequency band division parameters, and a noise suppression threshold.

[0010] Preferably, the method for constructing a multi-objective test path planning model according to the signal optimization parameters includes: Construct a multi-objective evaluation function for the test path. The multi-objective evaluation function includes a coverage rate objective function and a time delay objective function. The coverage rate objective function is calculated by the ratio of the number of tested communication interfaces to the total number of interfaces, and the time delay objective function is calculated by accumulating the transmission delays of each test node in the path. Based on the multi-objective evaluation function, construct path constraint conditions. The path constraint conditions include interface connectivity constraints, resource occupancy constraints, and protocol priority constraints. The interface connectivity constraints are used to ensure the physical connection reachability of adjacent test nodes, the resource occupancy constraints are used to limit the concurrent processing capabilities of test nodes, and the protocol priority constraints are used to allocate test order weights according to the communication protocol type. Encode the test path using a state transition graph. Each state node contains interface identification, protocol type, and resource occupancy rate information. Based on the time-varying state transition weights, construct a state transition probability. The time-varying state transition weights are dynamically adjusted by the product of the remaining resource rate of the current node and the protocol priority. Introduce an adaptive backtracking mechanism. The adaptive backtracking mechanism dynamically adjusts the backtracking depth according to the path evaluation result. When the time delay of a local path exceeds a preset threshold, increase the number of backtracking steps to reselect branch nodes. Based on the time-varying state transition weights and the adaptive backtracking mechanism, perform iterative optimization. Perform Pareto front screening of the coverage rate and time delay for the paths generated in each iteration to generate an optimal test path set. Perform topological verification and conflict detection on the optimal test path set, and output the optimal test path data that satisfies multi-objective trade-off.

[0011] Preferably, the improved dynamic programming algorithm further includes: Introduce dynamic programming table compression technology. Map high-dimensional states to low-dimensional spaces through a hash function to reduce memory occupancy. Use a parallel computing architecture to accelerate state transition calculations. Divide state nodes into multiple subsets and allocate them to independent computing units for processing. Optimize the iterative efficiency of the state value function based on an incremental update strategy, and only update the state nodes affected by the current decision.

[0012] Preferably, the protocol layer performs communication protocol compatibility verification based on the signal optimization parameters, including: Construct a protocol consistency verification model, and transform the communication protocol specification into a state machine model, where the state machine model includes a protocol state set, event trigger conditions, and state transition rules; Extract protocol interaction features based on the signal optimization parameters, where the protocol interaction features include handshake success rate, retransmission count statistics, and timeout event frequency; Use a formal verification method to perform equivalence determination on the state machine model and the protocol interaction features, and traverse all protocol state transition paths through a model checking algorithm to identify uncovered transition paths and conflict events; Generate a protocol compatibility report, which includes conflict event types, uncovered path identifiers, and protocol deviation metric values.

[0013] Preferably, the physical layer performs channel state evaluation based on the optimal test path data, including: Use a multi-band scanning technique to obtain channel response data, and convert the time-domain signal into a frequency-domain energy distribution based on the fast Fourier transform; Construct a channel quality index model, which calculates the channel quality score through a weighted combination of signal-to-noise ratio, multipath delay spread, and frequency selective fading parameters; Dynamically predict the channel quality score based on the Kalman filter, and update the channel state prediction error covariance matrix in combination with historical observation data; Generate a channel state evaluation result, which includes a list of available frequency bands, the optimal modulation and coding scheme, and interference avoidance suggestions.

[0014] Preferably, the channel quality index model further includes: Construct an interference source location model based on a Bayesian network, and infer the location and intensity of potential interference sources through a conditional probability table; Use the sparse representation theory to separate multipath signals, and extract the dominant propagation path components through the orthogonal matching pursuit algorithm; Calculate the signal attenuation compensation value based on the path loss model, and dynamically adjust the transmit power to maintain the target signal-to-noise ratio level.

[0015] Preferably, the application layer realizes dynamic scheduling of test tasks based on a multi-constraint optimization algorithm, including: Construct a task scheduling model, abstract the test tasks into a weighted directed acyclic graph, where the nodes represent test steps, and the edges represent task dependencies and data transmission overhead; Construct a mixed integer programming problem based on resource constraints and deadline constraints, where the resource constraints include processor core occupancy and memory usage, and the deadline constraints are defined by the latest completion time of task nodes; The branch and bound algorithm is used to solve the mixed integer programming problem, generating a lower bound estimate by relaxing non-integer variables and combining pruning strategies to reduce the search space; Output the task scheduling sequence, which includes the task execution order, resource allocation scheme, and time window division.

[0016] Preferably, the branch and bound algorithm further includes: Construct a heuristic cost estimation function to dynamically adjust the branch priority through the ratio of task weight to remaining time; Introduce a tabu search mechanism to avoid local optimal solutions, recording the recent search paths through a tabu list and prohibiting repeated access; Adopt an elastic resource reservation strategy to pre-allocate redundant resources for high-priority tasks to cope with sudden load fluctuations.

[0017] Preferably, the maintenance plan generation module further includes a plan editor, which provides an editing function for users to add, modify, or delete keys and values in the hash table according to the actual device status and technology updates.

[0018] Preferably, the present invention further includes an interconnection test system for a wireless communication module to implement the above interconnection test method. The system includes: A communication topology analysis module, which is used to collect real-time communication data of the target wireless communication module through a multi-source communication interface, generate a communication topology relationship matrix based on a graph theory model, and output signal optimization parameters through an adaptive filtering model; A path dynamic programming module, which is used to construct a multi-objective test path planning model according to the signal optimization parameters and generate optimal test path data by using an improved dynamic programming algorithm; A hierarchical test execution module, which is used to establish a hierarchical test execution model based on the optimal test path data and output test control instructions through the cooperation of the protocol layer, physical layer, and application layer.

[0019] Compared with the prior art, the beneficial effects of the present invention are: Collect data through a multi-source communication interface, covering radio frequency signals, baseband protocols, channel status, and network topology interfaces, to achieve a comprehensive acquisition of real-time communication data of the target wireless communication module. This method overcomes the one-sidedness of data collection through traditional single interfaces and can reflect the working status of the communication module from multiple dimensions, providing a rich and accurate data basis for subsequent precise analysis. Based on the graph theory model, topological feature extraction is carried out and a communication topology relationship matrix is generated, which can accurately describe the connection relationship between communication modules, fully considering the impact of dynamic factors such as interference and node load changes on the topology during signal transmission. Compared with the traditional method of simply recording connection relationships, it can more realistically show the actual situation of the communication network, improving the accuracy and dynamic adaptability of communication topology analysis.

[0020] Use a pre-trained adaptive filtering model and a multi-stage cascaded filter structure to process communication signals. This model performs iterative denoising based on the noise covariance matrix. By constructing a mixed cost function and comprehensively considering factors such as signal-to-noise ratio, interference suppression, protocol consistency, and handover delay, it effectively balances the performance requirements in different aspects. In a complex and changing noise environment, it can dynamically adjust the filter parameters, while removing noise interference, ensuring the integrity of the signal to the greatest extent, avoiding signal distortion and loss of key information, providing high-quality signal data for subsequent tests, and significantly improving the effect and reliability of signal processing.

[0021] The constructed multi-objective test path planning model aims to maximize test coverage and minimize test delay. It uses an improved dynamic programming algorithm, introducing time-varying state transition weights and an adaptive backtracking mechanism. This method abandons the limitations of traditional methods that only consider a single objective, comprehensively weighs multiple optimization objectives, and ensures more comprehensive test coverage within a limited time. At the same time, the improved dynamic programming algorithm also reduces memory occupancy, accelerates state transition calculations, and optimizes the iteration efficiency of the state value function through dynamic programming table compression technology, parallel computing architectures, and incremental update strategies, further improving the computational efficiency and accuracy of path planning. In addition, considering actual constraints such as interface connectivity, resource occupancy, and protocol priorities, the planned test paths are more feasible during actual execution, effectively avoiding test interruption problems caused by insufficient resources or protocol conflicts.

[0022] The established hierarchical test execution model includes a protocol layer, a physical layer, and an application layer, which work together. The protocol layer verifies the compatibility of communication protocols based on signal optimization parameters. By constructing a protocol consistency verification model and using formal verification methods, it can comprehensively and accurately detect the protocol state migration paths, identify conflict events and uncovered paths, generate a detailed protocol compatibility report, and ensure that the communication protocol meets the specification requirements. The physical layer performs channel state assessment based on the optimal test path data. Using multi-band scanning technology, a channel quality index model, and a Kalman filter, it can achieve accurate assessment and dynamic prediction of channel quality, provide available frequency bands, the optimal modulation and coding scheme, and interference avoidance suggestions for communication, and ensure the stability of communication under different channel conditions. The application layer realizes the dynamic scheduling of test tasks based on a multi-constraint optimization algorithm. By constructing a task scheduling model, considering resource and deadline constraints, and using the branch and bound algorithm and related optimization strategies, such as a heuristic cost estimation function, a tabu search mechanism, and a flexible resource reservation strategy, it reasonably arranges the task execution order, allocates resources, and divides time windows, improves the test task execution efficiency, ensures the smooth progress of high-priority tasks, and effectively responds to sudden load fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 FIG. is a flowchart of the working process of the interconnection test method of the present invention; Figure 2 FIG. is a flowchart of the working process of constructing a multi-objective test path planning model; Figure 3 FIG. is a flowchart of the test task scheduling of the application layer. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] Please refer to Figures 1-3 , the present invention provides a technical solution: an interconnection test method for a wireless communication module, the method includes: Collect real-time communication data of the target wireless communication module using a multi-source communication interface, and the multi-source communication interface covers a radio frequency signal interface, a baseband protocol interface, a channel state interface, and a network topology interface. These interfaces obtain communication data from different dimensions. The radio frequency signal interface obtains information such as radio frequency signal strength and frequency; the baseband protocol interface collects protocol data related to baseband signal processing; the channel state interface obtains state information such as channel attenuation and noise; the network topology interface obtains connection relationship data of the module in the network.

[0026] Extract topological features from the collected real-time communication data based on the graph theory model. The graph theory model analyzes information such as node connection relationships and signal transmission directions in the communication data, converts it into a mathematical topological structure, and then generates a communication topology relationship matrix. This matrix accurately describes the connection relationships between wireless communication modules in digital form, providing a basis for subsequent analysis.

[0027] Input the communication topology relationship matrix into a pre-trained adaptive filtering model. This adaptive filtering model adopts a multi-stage cascaded filter structure and performs iterative denoising processing on the communication signal based on the noise covariance matrix. By continuously adjusting the filter parameters, noise interference in the communication signal is removed, the signal quality is improved, and finally signal optimization parameters are generated, which are used for subsequent test path planning and test execution.

[0028] Construct a multi-objective test path planning model based on the generated signal optimization parameters. This model aims to maximize the test coverage and minimize the test delay, and uses an improved dynamic programming algorithm to globally optimize the test path. The improved dynamic programming algorithm introduces time-varying state transition weights and an adaptive backtracking mechanism, fully considering the dynamic factors in the test process, avoiding being trapped in local optimal solutions, and finally outputting the optimal test path data.

[0029] Establish a hierarchical test execution model according to the optimal test path data. This model includes a protocol layer, a physical layer, and an application layer. The protocol layer verifies the compatibility of communication protocols based on the signal optimization parameters; the physical layer performs channel state evaluation based on the optimal test path data; the application layer realizes the dynamic scheduling of test tasks based on the multi-constraint optimization algorithm. Through the collaborative work of these three layers, test control instructions are output to complete the interconnection test of wireless communication modules and comprehensively evaluate the interconnection performance of wireless communication modules.

[0030] The present invention will be further described below in conjunction with Embodiments 1 to 5: Embodiment 1: The purpose of this embodiment is to elaborate in detail how the adaptive filtering model generates signal optimization parameters based on real-time communication data, providing more accurate data support for subsequent test path planning and test execution. Specifically, it includes: Obtain real-time communication data, including the signal strength matrix , the bit error rate sequence , the channel impulse response vector , and the protocol handshake timestamp . Based on these real-time communication data, construct a signal state space . The signal state space comprehensively reflects the comprehensive state of the communication signal at a certain moment. At the same time, based on the adjustable transmit power , modulation method , and frequency band switching parameters of the communication module Construct the action space . The action space defines the adjustment operations that the communication module can take during the test.

[0031] Based on the signal state space and the action space Construct the hybrid cost function . The hybrid cost function includes the signal-to-noise ratio cost term , the interference suppression cost term , the protocol consistency cost term and the handover delay cost term .

[0032] The signal-to-noise ratio cost term is calculated by the ratio of the signal strength to the noise power, and the formula is , where represents the th element in the signal strength matrix , and is the noise power. The signal-to-noise ratio cost term is used to measure the quality of the signal in a noisy environment. The higher the signal-to-noise ratio, the smaller the cost term, indicating better signal quality.

[0033] The interference suppression cost term is calculated by the product of the adjacent channel interference power and the current channel bandwidth , and the formula is . The interference suppression cost term reflects the impact of adjacent channel interference on the current channel communication. The greater the interference power and the wider the channel bandwidth, the greater the interference suppression cost term.

[0034] The protocol consistency cost term is calculated by the variance of the protocol handshake timestamps, and the formula is . The variance of the protocol handshake timestamps can measure the stability of the protocol handshake time. The greater the variance, the greater the fluctuation of the protocol handshake time, the greater the protocol consistency cost term, and the worse the protocol consistency.

[0035] The handover delay cost term is calculated by the difference between the frequency band handover time and the preset threshold , and the formula is . When the frequency band handover time exceeds the preset threshold, the handover delay cost term will increase, reflecting the impact of the frequency band handover delay on communication.

[0036] The expression of the hybrid cost function is , where is a weight coefficient, which is adjusted according to different test requirements and scenarios and is used to balance the importance of each cost item in the mixed cost function.

[0037] Construct a multi-stage cascaded filter structure, which includes a pre-filter, a main filter, and a post-filter. The pre-filter and the post-filter adopt a finite impulse response (FIR) structure, and the main filter adopts an infinite impulse response (IIR) structure. The cut-off frequency of the pre-filter and the cut-off frequency of the post-filter are dynamically adjusted based on the signal spectrum, and the formula is , where is the signal sampling frequency, and are dynamic adjustment coefficients, which are adjusted according to the distribution of the signal spectrum. The recursive coefficient of the main filter is updated by the least mean square error algorithm, and the formula is:

[0038] where is the th recursive coefficient of the main filter at the th iteration, is the step size factor, is the filtering error at the th iteration, is the value of the input signal at time.

[0039] Error feedback mechanism and parameter update: Based on the multi-stage cascaded filter, an error feedback mechanism is constructed, and the filtering output error is cumulatively statistically analyzed by the sliding window method. Let the sliding window size be , the cumulative error , combined with the noise covariance matrix to iteratively correct the weight coefficient of the filter, and the formula is:

[0040] where is the weight coefficient of the filter at the th iteration, is the current input signal. A phased training strategy is adopted to update the parameters of the multi-stage cascaded filter, and different step size factors and the number of training times are set in different training stages, and finally, signal optimization parameters including the filter coefficient matrix , the frequency band division parameter and the noise suppression threshold are generated.

[0041] Example 2: This example elaborates in detail the construction process of the multi-objective test path planning model to ensure that the optimal test path data that can meet the maximum test coverage and the minimum test delay can be generated. The specific process includes: ① Construction of the multi-objective evaluation function: Construct the multi-objective evaluation function of the test path, including the coverage objective function and the delay objective function .

[0042] Coverage objective function is calculated by the ratio of the number of tested communication interfaces to the total number of interfaces . The formula is . The coverage objective function is used to measure the coverage degree of the test path for the communication interfaces. The closer its value is to 1, the more interfaces the test path covers and the better the comprehensiveness of the test.

[0043] Delay objective function is obtained by cumulative calculation of the transmission delays of each test node in the path . The formula is , where is the number of test nodes in the path. The delay objective function reflects the execution time of the test path. The smaller its value, the higher the execution efficiency of the test path.

[0044] ② Construction of path constraint conditions: Based on the multi-objective evaluation function, construct path constraint conditions, including interface connectivity constraints, resource occupancy constraints, and protocol priority constraints.

[0045] Interface connectivity constraint: Let the connection status between test nodes and be . When , it means that the physical connection between nodes and is reachable. The interface connectivity constraint requires that the of adjacent test nodes in the test path to ensure physical connection reachability.

[0046] Resource occupancy constraint: Let the resource occupancy situation of test node be , including the processor core occupancy rate, memory usage, etc. The resource occupancy constraint limits the concurrent processing ability of the test node, that is , where is the set of nodes currently executing test tasks in the current test path, is the total system resource volume.

[0047] Protocol priority constraint: According to the communication protocol type Allocate test sequence weights For protocols with higher importance, set higher weights. For example, for protocols with high real-time requirements, are larger and are preferentially tested during test path planning.

[0048] ③ Test path encoding and construction of state transition probability: Use a state transition diagram to encode the test path. Each state node contains an interface identifier , protocol type and resource occupancy rate information . Based on the time-varying state transition weight construct the state transition probability . The time-varying state transition weight is dynamically adjusted by the product of the remaining resource rate of the current node and the protocol priority . The formula is . The calculation formula for the state transition probability is , where is the set of neighbor nodes of node , and is the time-varying state transition weight from node to node .

[0049] ④ Adaptive backtracking mechanism: Introduce an adaptive backtracking mechanism to dynamically adjust the backtracking depth according to the path evaluation results. Let the delay of the local path be , and the preset threshold be . When , increase the number of backtracking steps to reselect the branch node. The adjustment formula for the number of backtracking steps is , where is the initial number of backtracking steps, and is the adjustment coefficient, which is set according to the actual test situation.

[0050] Iterative optimization and output of optimal test path data: Based on the time-varying state transition weight and the adaptive backtracking mechanism, perform iterative optimization, and perform Pareto front screening of coverage and delay for the paths generated in each iteration. The Pareto front refers to a set of non-dominated solutions in a multi-objective optimization problem, and these solutions achieve a balance between different objectives. By screening the paths on the Pareto front, obtain the optimal test path set. Perform topological verification and conflict detection on the optimal test path set to check whether the paths conform to the network topology structure and whether there are resource conflicts, etc., and finally output the optimal test path data that satisfies multi-objective trade-off.

[0051] Example 3: This example details the specific operations for improving the dynamic programming algorithm to enhance the computational efficiency and accuracy of the multi-objective test path planning model. The specific operations include: Dynamic programming table compression technology: Introduce dynamic programming table compression technology. Map high-dimensional states to a low-dimensional space through a hash function to reduce memory occupancy. Let the high-dimensional state be , and the hash function maps it to a value y in the low-dimensional space, that is, . When storing the dynamic programming table, only store the values in the low-dimensional space and their corresponding state values, thus greatly reducing memory occupancy. For example, for a high-dimensional state containing information such as the states of multiple test nodes and resource occupancy states, it is mapped to a smaller integer through a hash function, and relevant data is stored in the dynamic programming table using this integer as an index.

[0052] Parallel computing architecture: Adopt a parallel computing architecture to accelerate state transition calculations. Divide state nodes into multiple subsets , and allocate them to independent computing units for processing. Each computing unit independently calculates the state transition values of the state nodes in the subset, and finally combines the calculation results of each computing unit. For example, in a multi-core processor system, each core serves as an independent computing unit, respectively processing the state nodes of different subsets to improve computational efficiency.

[0053] Incremental update strategy: Optimize the iterative efficiency of the state value function based on the incremental update strategy, and only update the state nodes affected by the current decision. Let the state value function be , the current decision be , and the set of state nodes affected by the current decision be . When iteratively updating the state value function, only update the state nodes in , and the formula is , where , is the increment of the state value function calculated according to the current decision . In this way, unnecessary updates to all state nodes are avoided, and the iterative efficiency is improved. Example

[0054] This example details how the protocol layer verifies the communication protocol compatibility based on signal optimization parameters to ensure that the communication protocol of the wireless communication module complies with the specifications.

[0055] Build a protocol consistency verification model and transform the communication protocol specification into a state machine model. The state machine model includes the protocol state set event trigger conditions and state transition rules For example, for the TCP protocol, the set of protocol states includes states such as "LISTEN", "SYN_SENT", "SYN_RCVD", etc. The event trigger conditions E include events such as receiving a SYN packet and receiving an ACK packet. The state transition rules T define the ways of state transition under different event triggers. For example, when a SYN packet is received in the "LISTEN" state, it transitions to the "SYN_RCVD" state.

[0056] Extract protocol interaction features based on signal optimization parameters. The protocol interaction features include handshake success rate , retransmission count statistics and timeout event frequency . The handshake success rate is calculated by the ratio of the number of successful handshakes to the total number of handshakes . The formula is . The retransmission count statistics records the number of data retransmissions during the test. The timeout event frequency is calculated by the ratio of the number of timeout events to the total test duration . The formula is .

[0057] Use formal verification methods to determine the equivalence between the state machine model and protocol interaction features, and traverse all protocol state transition paths through model checking algorithms. Model checking algorithms can use methods based on state space search, such as depth-first search (DFS) or breadth-first search (BFS). During the traversal, identify uncovered transition paths and conflict events. For example, in the state machine model, there is a transition path from state A to state B, but it never appears in the actual protocol interaction. This is an uncovered transition path; if two mutually exclusive event trigger conditions are satisfied simultaneously in a certain state, resulting in a state transition conflict, this is a conflict event.

[0058] Generate a protocol compatibility report, which includes the types of conflict events , identifiers of uncovered paths and protocol deviation metric values . The types of conflict events describe the specific situations of conflict events in detail, such as "receiving two different types of control packets simultaneously in state , resulting in a state transition conflict". The identifiers of uncovered paths are used to uniquely identify the uncovered transition paths for subsequent analysis. The protocol deviation metric values are obtained by calculating the differences between the actual protocol interaction features and the standard protocol specifications. The formula is , where is the actual protocol interaction eigenvalue, is the eigenvalue in the standard protocol specification, is the weight coefficient, which is set according to the importance of different features.

[0059] Example 5: The physical layer performs channel state evaluation based on the optimal test path data, and the application layer realizes the dynamic scheduling of test tasks based on the multi-constraint optimization algorithm to improve the function of the entire wireless communication module interconnection test system.

[0060] The physical layer channel state evaluation includes: Multi-band scanning and signal conversion: At the physical layer, the multi-band scanning technology is used to comprehensively detect the channel. The test device sequentially sends specific detection signals on different frequency bands according to the preset frequency band range. For example, from the low frequency band to the high frequency band, scanning is performed at a certain frequency interval. After sending the detection signal on each frequency band, the receiving device collects the returned signals, which contain various characteristic information of the channel in this frequency band.

[0061] The received signal in the time domain is converted into the frequency domain energy distribution through the fast Fourier transform (FFT), and its formula is . Through the frequency domain energy distribution, the attenuation and interference conditions of the channel at different frequencies can be analyzed. Through the frequency domain energy distribution, the energy distribution of the channel at different frequencies can be clearly analyzed, and then the attenuation characteristics of the channel and the potential interference frequency bands can be understood.

[0062] Channel quality index model construction and calculation: Construct a channel quality index model, which comprehensively considers the signal-to-noise ratio , multi-path delay spread and frequency selective fading parameters to calculate the channel quality score . The signal-to-noise ratio is calculated by the ratio of the signal power to the noise power , that is , which reflects the intensity contrast of the signal in the noise environment. The multi-path delay spread is caused by the signal arriving at the receiving end through multiple paths during propagation, and the propagation delays of different paths are different. By analyzing the received signal, the value of the multi-path delay spread can be measured. The frequency selective fading parameter is used to describe the difference in the fading degree of the channel at different frequencies. The channel quality score is calculated by weighted combination, and the formula is:

[0063] where are weight coefficients. These weight coefficients are adjusted according to the characteristics of the actual test environment and the degree of attention to different parameters.

[0064] Dynamic prediction based on the Kalman filter: The Kalman filter is used to dynamically predict the channel quality score. The state equation of the channel quality score is set as where represents the state of the channel quality score at time is the state transition matrix, which describes the variation law of the channel state over time, is the process noise, representing the random factors in the system that cannot be accurately modeled. The observation equation is is the observation value at time , that is, the data related to the channel quality score obtained through actual measurement, is the observation matrix, which is used to convert the channel state into observable quantities, is the observation noise, reflecting the error in the measurement process. The Kalman filter works through two key steps: prediction and update. In the prediction stage, the state at the current time is predicted based on the state and state transition matrix at the previous time; in the update stage, the prediction result is corrected by combining the current observation value, and the channel state prediction error covariance matrix is updated. By continuously iterating this process, the changing trend of the channel quality score can be predicted more accurately.

[0065] Generating the channel state evaluation result: Based on the above calculations and predictions, a detailed channel state evaluation result is generated. Among them, the available frequency band list is a set of frequency bands suitable for communication selected according to the channel quality score and frequency characteristics. For example, those frequency bands with too low channel quality scores or severe interference are excluded, and the remaining frequency bands are listed in the available frequency band list. The optimal modulation and coding scheme is determined based on the channel quality score. A higher channel quality score can usually support more efficient modulation and coding methods to improve the data transmission rate, while a lower channel quality score requires a more robust modulation and coding scheme to ensure the reliability of data transmission. The interference avoidance suggestion is a specific measure proposed for the detected interference sources and interference situations. For example, if a strong narrowband interference is found in a certain frequency band, it can be suggested to switch to other frequency bands with less interference; or according to the characteristics of the interference, adjust the transmit power, change the modulation method, etc., to reduce the impact of interference on communication.

[0066] The dynamic scheduling of the application layer test tasks described above includes: At the application layer, the test tasks are abstracted into a weighted directed acyclic graph . The nodes in the graph represent each test step, and each node is assigned a weight , which reflects the importance or priority of this test step in the entire test task.

[0067] A mixed-integer programming problem is constructed based on resource constraints and deadline constraints. Resource constraints mainly consider the processor core occupancy and the memory usage . For the set of tasks that are being executed or planned to be executed , it is necessary to satisfy and , where and are the total processor core resources and memory resources of the system respectively. This ensures that at the same time, the resource requirements of all executing tasks do not exceed the carrying capacity of the system. The deadline constraint is defined by the latest completion time of the task node. Each task must be completed before its latest completion time, that is , where is the start time of task , is the execution duration of task . This ensures that the entire test task can be completed on time and avoids affecting the overall test progress due to the delay of a certain task.

[0068] The branch and bound algorithm is used to solve the mixed-integer programming problem. First, a lower bound estimate is generated by relaxing the non-integer variables. Suppose the relaxed problem is , and its optimal solution is , then is a lower bound of the optimal solution of the original problem. During the search process, pruning strategies are combined to reduce the search space. When the lower bound of a certain branch is greater than the currently found optimal solution, it means that this branch cannot produce a better solution, so this branch can be pruned and no further search is performed on it. This can greatly improve the search efficiency of the algorithm and reduce the calculation time.

[0069] After the solution by the branch and bound algorithm, the task scheduling sequence is finally output. This sequence includes the task execution order O, the resource allocation plan and the time window division . The task execution order clarifies the sequential execution order of each test task, ensuring that the dependencies between tasks are satisfied. The resource allocation plan Specific processor cores and memory resources are allocated for each task to ensure sufficient resource support during task execution. Time window partitioning A specific execution time interval is allocated for each task, enabling the task to execute efficiently while meeting resource constraints and deadline constraints. For example, task executes within the time interval and is allocated processor cores and amount of memory.

[0070] Construct a heuristic cost estimation function , and dynamically adjust the branch priority through the ratio of the task weight to the remaining time . The formula is . During the search process, preferentially select the branches with larger heuristic cost estimation function values for expansion. This can make the algorithm more inclined to explore those task branches with high importance and urgent remaining time, improve the search efficiency, and find better solutions faster.

[0071] Introduce a tabu search mechanism to avoid local optimal solutions. Record the recent search paths through the tabu list . When a certain node or path is included in the tabu list, it is prohibited from being repeatedly visited within a certain number of iterations. However, if the objective function value of the currently expanded node is significantly better than the current optimal solution, that is, when it meets specific "release" conditions, then this node can be released for access. This mechanism helps the algorithm jump out of local optimal solutions and explore a wider solution space, thus potentially finding the global optimal solution.

[0072] Adopt a flexible resource reservation strategy to pre-allocate redundant resources for high-priority tasks to cope with sudden load fluctuations. Determine the set of high-priority tasks . For each high-priority task in the set, pre-allocate additional processor core resources and memory resources , that is . . During task execution, if load fluctuations are detected, give priority to ensuring the resource requirements of high-priority tasks. For example, when system resources are scarce, preferentially allocate the reserved resources to high-priority tasks to ensure the smooth execution of these critical tasks and avoid test failures or inaccurate test results due to insufficient resources.

[0073] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0074] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for testing interconnection of wireless communication modules, characterized in that: The method comprises: The real-time communication data of the target wireless communication module is collected through a multi-source communication interface, wherein the multi-source communication interface includes a radio frequency signal interface, a baseband protocol interface, a channel state interface, and a network topology interface; the topological features of the real-time communication data are extracted based on a graph theory model to generate a communication topology relationship matrix; the communication topology relationship matrix is ​​input into a pre-trained adaptive filtering model, wherein the adaptive filtering model adopts a multi-stage cascade filter structure, performs iterative denoising processing on the communication signal based on a noise covariance matrix, and generates signal optimization parameters; Constructing a multi-objective test path planning model according to the signal optimization parameters, wherein the multi-objective test path planning model takes maximum test coverage and minimum test delay as optimization goals, and adopts an improved dynamic programming algorithm to globally optimize the test path, wherein the improved dynamic programming algorithm introduces time-varying state transfer weights and an adaptive backtracking mechanism; outputting optimal test path data based on the multi-objective test path planning model; A hierarchical test execution model is established according to the optimal test path data, the hierarchical test execution model includes a protocol layer, a physical layer and an application layer, wherein the protocol layer performs communication protocol compatibility verification based on the signal optimization parameters, the physical layer performs channel state evaluation based on the optimal test path data, and the application layer implements dynamic scheduling of test tasks based on a multi-constraint optimization algorithm; and test control instructions are output through the hierarchical test execution model to complete the interconnection test of the wireless communication module.

2. The interconnection testing method according to claim 1, characterized in that: The communication topology relationship matrix is ​​input into a pre-trained adaptive filtering model, the adaptive filtering model adopts a multi-stage cascade filter structure, and performs iterative denoising on the communication signal based on the noise covariance matrix, and generates signal optimization parameters including: Acquire real-time communication data, the real-time communication data including a signal strength matrix, a bit error rate sequence, a channel impulse response vector, and a protocol handshake timestamp; construct a signal state space based on the real-time communication data, and construct an action space based on adjustable transmit power, modulation mode, and frequency band switching parameters of the communication module; A hybrid cost function is constructed based on the signal state space and the action space, wherein the hybrid cost function includes a signal-to-noise ratio cost item, an interference suppression cost item, a protocol consistency cost item, and a switching delay cost item, wherein the signal-to-noise ratio cost item is calculated by the ratio of signal strength to noise power, the interference suppression cost item is calculated by multiplying the adjacent channel interference power by the current channel bandwidth, the protocol consistency cost item is calculated by the variance of the protocol handshake timestamp, and the switching delay cost item is calculated by the difference between the frequency band switching time and a preset threshold; Constructing a multi-stage cascade filter structure, the multi-stage cascade filter includes a pre-filter, a main filter and a post-filter, the pre-filter and the post-filter adopt a finite impulse response structure, the main filter adopts an infinite impulse response structure, the cutoff frequencies of the pre-filter and the post-filter are dynamically adjusted based on the signal spectrum, and the recursive coefficient of the main filter is updated by a minimum mean square error algorithm; An error feedback mechanism is constructed based on the multi-stage cascade filter, and the filtering output error is accumulated and counted by the sliding window method, and the weight coefficient of the filter is iteratively corrected in combination with the noise covariance matrix; a phased training strategy is adopted to update the parameters of the multi-stage cascade filter, and signal optimization parameters including the filter coefficient matrix, frequency band division parameters and noise suppression threshold are generated.

3. The interconnection testing method according to claim 1, characterized in that: The method for constructing a multi-objective test path planning model according to the signal optimization parameters includes: Construct a multi-objective evaluation function for the test path, the multi-objective evaluation function includes a coverage objective function and a delay objective function, wherein the coverage objective function is calculated by the ratio of the number of tested communication interfaces to the total number of interfaces, and the delay objective function is calculated by accumulating the transmission delay of each test node in the path; Constructing path constraints based on the multi-objective evaluation function, the path constraints include interface connectivity constraints, resource occupancy constraints and protocol priority constraints, the interface connectivity constraints are used to ensure the physical connection accessibility of adjacent test nodes, the resource occupancy constraints are used to limit the concurrent processing capability of the test nodes, and the protocol priority constraints are used to assign test sequence weights according to the communication protocol type; The test path is encoded using a state transition diagram, each state node contains an interface identifier, a protocol type, and resource occupancy information, and a state transition probability is constructed based on the time-varying state transition weight, and the time-varying state transition weight is dynamically adjusted by the product of the current node resource surplus rate and the protocol priority; An adaptive backtracking mechanism is introduced, which dynamically adjusts the backtracking depth according to the path evaluation result. When the delay of the local path exceeds the preset threshold, the number of backtracking steps is increased to reselect the branch node; Iterative optimization is performed based on the time-varying state transfer weight and adaptive backtracking mechanism, and the paths generated in each iteration are screened for Pareto fronts of coverage and delay to generate an optimal test path set; topology verification and conflict detection are performed on the optimal test path set, and optimal test path data that meets multi-objective trade-offs is output.

4. The interconnection testing method according to claim 3, characterized in that: The improved dynamic programming algorithm further comprises: Introducing dynamic programming table compression technology, mapping high-dimensional states to low-dimensional space through hash functions to reduce memory usage; A parallel computing architecture is used to accelerate state transfer calculations, dividing state nodes into multiple subsets and assigning them to independent computing units for processing; The iteration efficiency of the state value function is optimized based on the incremental update strategy, and only the state nodes affected by the current decision are updated.

5. The interconnection testing method according to claim 1, characterized in that: The protocol layer performs communication protocol compatibility verification based on the signal optimization parameters, including: Construct a protocol consistency verification model to convert the communication protocol specification into a state machine model, which includes a protocol state set, event trigger conditions, and state transition rules; Extracting protocol interaction features based on the signal optimization parameters, wherein the protocol interaction features include handshake success rate, retransmission count statistics, and timeout event frequency; A formal verification method is used to determine the equivalence of the state machine model and the protocol interaction characteristics, and a model detection algorithm is used to traverse all protocol state migration paths to identify uncovered migration paths and conflict events; A protocol compatibility report is generated, the report including the conflict event type, uncovered path identifiers, and protocol deviation metrics.

6. The interconnection testing method according to claim 1, characterized in that: The physical layer performs channel state evaluation based on the optimal test path data, including: Multi-band scanning technology is used to obtain channel response data, and the time domain signal is converted into frequency domain energy distribution based on fast Fourier transform; Constructing a channel quality index model, the model calculates the channel quality score through a weighted combination of signal-to-noise ratio, multipath delay spread, and frequency selective fading parameters; Dynamically predict the channel quality score based on the Kalman filter and update the channel state prediction error covariance matrix based on historical observation data; A channel status assessment result is generated, wherein the result includes a list of available frequency bands, an optimal modulation and coding scheme, and interference avoidance suggestions.

7. The interconnection testing method according to claim 6, characterized in that: The channel quality index model further includes: Construct an interference source location model based on a Bayesian network, and infer the location and strength of potential interference sources through a conditional probability table; The sparse representation theory is used to separate multipath signals, and the dominant propagation path components are extracted through the orthogonal matching pursuit algorithm. The signal attenuation compensation value is calculated based on the path loss model, and the transmit power is dynamically adjusted to maintain the target signal-to-noise ratio level.

8. The interconnection testing method according to claim 1, characterized in that: The application layer implements dynamic scheduling of test tasks based on multi-constraint optimization algorithms, including: Build a task scheduling model and abstract the test tasks into a weighted directed acyclic graph, where nodes represent test steps and edges represent task dependencies and data transmission overhead. Constructing a mixed integer programming problem based on resource constraints and deadline constraints, wherein the resource constraints include processor core occupancy and memory usage, and the deadline constraints are defined by the latest completion time of the task nodes; A branch and bound algorithm is used to solve the mixed integer programming problem, a lower bound estimate is generated by relaxing non-integer variables, and a pruning strategy is combined to reduce the search space; Output task scheduling sequence, which includes task execution order, resource allocation scheme and time window division.

9. The interconnection testing method according to claim 8, characterized in that: The branch and bound algorithm further comprises: Construct a heuristic cost estimation function to dynamically adjust branch priorities based on the ratio of task weight to remaining time; Introduce a taboo search mechanism to avoid local optimal solutions, record recent search paths through a taboo table and prohibit repeated access; An elastic resource reservation strategy is adopted to pre-allocate redundant resources for high-priority tasks to cope with sudden load fluctuations.

10. An interconnection test system for wireless communication modules, used to implement the method according to any one of claims 1 to 9, characterized in that: include: The communication topology analysis module is used to collect the real-time communication data of the target wireless communication module through the multi-source communication interface, generate the communication topology relationship matrix based on the graph theory model, and output the signal optimization parameters through the adaptive filtering model; A path dynamic planning module, used to construct a multi-objective test path planning model according to the signal optimization parameters, and generate optimal test path data using an improved dynamic planning algorithm; The layered test execution module is used to establish a layered test execution model based on the optimal test path data, and collaboratively output test control instructions through the protocol layer, physical layer and application layer.

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