An antenna radio frequency and impedance testing system

Abstract

The application relates to the technical field of antenna testing, and discloses an antenna radio frequency and impedance testing system. The system comprises an antenna testing template library, a flowchart integration unit and a testing requirement mapping unit. The antenna testing template library stores various antenna type templates, each of which is associated with a radio frequency testing flowchart comprising a radio frequency parameter processing node; the flowchart integration unit combines the radio frequency testing flowcharts of the same type of antenna into an integrated radio frequency testing flowchart; and the testing requirement mapping unit obtains radio frequency and impedance testing requirements, selects a corresponding integrated radio frequency testing flowchart based on the radio frequency performance requirements, and extracts a basic radio frequency testing flowchart from the integrated radio frequency testing flowchart based on the impedance testing requirements. The system realizes standardized management and flexible calling of the antenna testing process, promotes the collaborative linkage of radio frequency and impedance testing, adapts to different testing requirements and antenna types, and improves the testing efficiency and completeness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of antenna testing, in particular to an antenna radio frequency and impedance testing system. BACKGROUND

[0002] In the current rapid development of wireless communication technology, the radio frequency performance and impedance characteristics of antennas, as key components for signal transmission and reception, directly affect the overall efficiency of communication systems. Different types of antennas, such as millimeter wave antennas, array antennas, and wearable device integrated antennas, have significant differences in testing procedures due to differences in application scenarios, structural design, and performance indicators.

[0003] Currently, antenna testing relies heavily on manual configuration of testing procedures, and testers need to set radio frequency parameter testing nodes such as frequency range, gain, and VSWR for each antenna type. In addition, separate steps for impedance testing are also planned. This approach not only consumes a lot of time, but also leads to inconsistent testing standards due to human operational differences. For example, when testing 5G base station antennas, the focus is on beamforming-related radio frequency parameters, while for small antennas used in Internet of Things devices, the focus is on impedance matching performance under low power consumption. Switching between different testing focuses often requires rebuilding the testing framework, resulting in low efficiency.

[0004] Existing testing systems lack standardized management of testing procedures for different antenna types, and the reusability of testing data is poor. When testing multiple batches of the same type of antenna, existing testing logic cannot be directly invoked, and the testing process needs to be configured repeatedly, increasing testing costs. Meanwhile, radio frequency testing and impedance testing are usually independent of each other, making it difficult to achieve coordinated linkage between the two, resulting in potential parameter omissions or conflicts during testing, which affects the accuracy and integrity of the test results. With the continuous innovation of antenna technology, new antenna types and performance indicators are constantly emerging, and traditional testing methods have become difficult to meet the efficient and flexible testing needs. SUMMARY

[0005] The present application aims to provide an antenna radio frequency and impedance testing system to solve the problems raised in the background.

[0006] To achieve the above-mentioned purpose, the present application provides an antenna radio frequency and impedance testing system, which comprises:

[0007] An antenna test template library, which stores multiple antenna type templates, each antenna type template being associated with a radio frequency test flowchart, and the radio frequency test flowchart including radio frequency parameter processing nodes;

[0008] A flowchart integration unit, which is used to merge the radio frequency test flowcharts of the same type of antenna type templates into an integrated radio frequency test flowchart;

[0009] The test requirement mapping unit is configured to obtain radio frequency performance requirements and impedance test requirements, select a corresponding integrated radio frequency test flowchart based on the radio frequency performance requirements, and extract a basic radio frequency test flowchart in the integrated radio frequency test flowchart based on the impedance test requirements.

[0010] Preferably, the flowchart integration unit comprises:

[0011] The node level division subunit is configured to number each radio frequency parameter processing node according to a radio frequency parameter flow direction mark in the radio frequency test flowchart, and divide the radio frequency test flowchart into levels based on the numbering.

[0012] The main target fusion subunit is configured to set the radio frequency test flowchart with the most levels in the same type of antenna type template as a main target, set the radio frequency parameter processing nodes contained in the main target as main nodes, and locate reference nodes in the remaining radio frequency test flowcharts.

[0013] The node merging execution subunit is configured to merge the radio frequency parameter processing nodes of each level of the radio frequency test flowchart to the main target starting from the reference node, and execute node merging when the main nodes and the radio frequency parameter processing nodes satisfy radio frequency feature fusion conditions, or set the radio frequency parameter processing nodes as branch node chains to the main target.

[0014] Preferably, the main target fusion subunit comprises:

[0015] The reference node locating subunit is configured to locate a first node at the minimum level of the radio frequency test flowchart, set the first node as a reference node when the main target has a main node that satisfies radio frequency feature fusion conditions with the first node, or continue to extract nodes at other levels and repeatedly locate the nodes.

[0016] Preferably, the test requirement mapping unit comprises:

[0017] The requirement flowchart generation subunit is configured to generate a requirement flowchart based on the impedance test requirements.

[0018] The endpoint matching subunit is configured to locate a starting endpoint and a terminating endpoint at two ends of the requirement flowchart, match radio frequency parameter processing nodes that satisfy radio frequency feature fusion conditions with the starting endpoint and the terminating endpoint in the integrated radio frequency test flowchart as top end nodes and bottom end nodes.

[0019] The flowchart extraction subunit is configured to generate a set of candidate flowcharts with a top node and a bottom node as start and end points, calculate a radio frequency feature similarity between a demand flowchart and each candidate flowchart, and select a candidate flowchart with the highest similarity as a basic radio frequency test flowchart.

[0020] Preferably, the endpoint matching subunit comprises:

[0021] The target node identification subunit is configured to traverse a flowchart node of a demand flowchart, and mark the flowchart node as a target node when an integrated radio frequency test flowchart has a radio frequency parameter processing node that meets a radio frequency feature fusion condition of the flowchart node.

[0022] The endpoint definition subunit is configured to define the target nodes first identified at two ends of the demand flowchart as a start endpoint and an end endpoint.

[0023] Preferably, the system further comprises:

[0024] The radio frequency feature verification unit is configured to configure a radio frequency attribute data set for each radio frequency parameter processing node, and the radio frequency attribute data set comprises a radio frequency function label, a radio frequency input source node label, and a radio frequency output node label.

[0025] The fusion condition judgment subunit is configured to determine that a radio frequency feature fusion condition is met when a main node and a radio frequency parameter processing node have the same radio frequency function label, and at least one of a radio frequency input source node label and a radio frequency output node label is the same.

[0026] Preferably, the flowchart extraction subunit comprises:

[0027] The topological feature analysis subunit is configured to generate a radio frequency feature topology graph based on a radio frequency function label of a radio frequency parameter processing node, and analyze a radio frequency feature correlation strength of adjacent nodes layer by layer.

[0028] The similarity calculation subunit is configured to generate a first feature set and a second feature set of a demand flowchart and a candidate flowchart according to a radio frequency feature topology graph, and calculate a radio frequency feature similarity based on a number of the same radio frequency feature correlation strengths.

[0029] Preferably, the system further comprises:

[0030] The test exception detection unit is configured to mark a radio frequency parameter processing node to be modified in the basic radio frequency test flowchart.

[0031] The dynamic migration unit is configured to generate a complete radio frequency test flowchart after completing the adjustment of the radio frequency parameter processing node to be modified, identify a radio frequency parameter processing node with a radio frequency test risk, and calculate a data migration priority and a migration path of the radio frequency parameter processing node.

[0032] Preferably, the test exception detection unit comprises:

[0033] The radio frequency node verification subunit verifies the radio frequency parameter processing node of the complete radio frequency test flowchart based on a neural network model, and marks the radio frequency parameter processing node as a risk node when an error probability of the radio frequency parameter processing node exceeds a preset threshold.

[0034] The risk warning subunit is configured to generate a radio frequency test risk prompt pointing to the risk node.

[0035] Preferably, the dynamic migration unit comprises:

[0036] The radio frequency data distribution analysis subunit is configured to analyze radio frequency data distribution characteristics and radio frequency data interaction frequency of the risk node.

[0037] The migration path planning subunit is configured to calculate a migration priority index according to the radio frequency data interaction frequency, and plan a radio frequency data migration path.

[0038] The radio frequency storage optimization subunit is configured to migrate the radio frequency data to a low-risk storage node and update access permissions of the radio frequency parameter processing node.

[0039] Compared with the prior art, the present application has the following beneficial effects:

[0040] By constructing an antenna test template library, the test flowcharts of various antenna types are standardized and stored, so that the test logic of different antennas can be uniformly managed. Each antenna type template is associated with a corresponding radio frequency test flowchart, which contains specific radio frequency parameter processing nodes, thereby providing a basis for fast calling of the test flowchart. When facing the test requirements of the same type of antenna, it is not necessary to redesign the test steps, and the corresponding template can be directly called from the template library, thereby reducing repetitive labor.

[0041] The flowchart integration unit combines the radio frequency test flowcharts of the same type of antenna into an integrated radio frequency test flowchart, thereby enhancing the integrity and continuity of the test flowchart. The integrated flowchart can cover the differences between the same type of antennas and form a unified test framework, which not only retains the particularity of each antenna test, but also realizes centralized management and control of the test flowchart, thereby facilitating unified scheduling and monitoring of the test process of the same type of antenna.

[0042] The test requirement mapping unit realizes the precise docking of test requirements and test processes. By obtaining the radio frequency performance requirements and impedance test requirements, this unit can select matching content from the integrated radio frequency test flowchart and extract the basic radio frequency test flowchart to meet the impedance test requirements, so that the radio frequency test and the impedance test form an organic whole. This linkage mechanism avoids the isolated performance of the two tests, ensures the consistency and relevance of the test parameters, and reduces problems caused by the disconnection of test links.

[0043] At the same time, the system has good adaptability and expansibility. When new antenna types or test requirements appear, new test processes can be included by updating the antenna test template library, without the need for substantial adjustment of the overall system architecture. The test requirement mapping unit can flexibly adjust the selection and extraction of test processes according to new requirements, so that the system can cope with changing test scenarios. In addition, standardized templates and integrated processes help to improve the standardization and reusability of test data, providing convenience for subsequent data analysis and test optimization.

[0044] The system integrates radio frequency test and impedance test into the same framework for collaborative processing, simplifying the complexity of test operations. Test personnel do not need to frequently switch between different test modules, and can complete multiple test tasks through a unified system, improving the smoothness of the test process. For batch testing of the same type of antenna, the integrated test flowchart can realize batch application of test processes, shorten the test cycle, and is suitable for testing requirements in large-scale production environment. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 a timing diagram of the antenna radio frequency and impedance test system described in the present application;

[0046] Figure 2 a flowchart of the process integration unit;

[0047] Figure 3 a flowchart of the requirement flowchart generation and matching;

[0048] Figure 4 a flowchart of the radio frequency feature fusion condition determination;

[0049] Figure 5 a flowchart of test exception detection and dynamic migration. DETAILED DESCRIPTION

[0050] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application.

[0051] Referring to Figure 1 The present application provides an antenna RF and impedance test system, which comprises:

[0052] An antenna test template library, a flowchart integration unit and a test requirement mapping unit. The antenna test template library stores a plurality of antenna type templates, each template being associated with an RF test flowchart, and the RF test flowchart being composed of a plurality of RF parameter processing nodes. The flowchart integration unit integrates the RF test flowcharts of the same type of antenna templates into an integrated RF test flowchart, and the test requirement mapping unit selects the integrated flowchart according to the RF performance requirement and extracts a basic RF test flowchart based on the impedance test requirement.

[0053] The RF parameter processing node contains an RF attribute data set, which records the RF function label, the input source node label and the output node label. When integrating the flowcharts, the hierarchical structure of each flowchart is determined by node level division, and the flowchart with the most levels is taken as the main target. The nodes of the remaining flowcharts are judged by RF feature fusion conditions whether to be merged or as branch chains into the main target. When mapping the test requirements, the start and end nodes in the integrated flowchart are located by endpoint matching, the alternative flowchart set is generated and the RF feature similarity is calculated, and finally the basic flowchart with the highest matching degree is output.

[0054] Embodiment 1: Referring to Figure 2 When the flowchart integration unit performs the integration of the RF test flowcharts of the same type of antenna templates, a node level division subunit is first started. The subunit analyzes the RF parameter flow direction markers of the RF test flowcharts, and assigns a unique number to each RF parameter processing node. The numbering rule follows the RF parameter transmission direction, starting from the initial input source node and numbering as N1, and the subsequent nodes are numbered as N2, N3, etc. in the order of parameter flow direction. The hierarchical division is based on the continuity characteristics of the node numbers, and the nodes with the same number interval are classified into the same level. For example, the numbers N1-N5 are the first level, N6-N10 are the second level, and so on. The nodes in each level perform the same stage of RF parameter processing task.

[0055] The main target fusion subunit selects the radio frequency test flowchart with the largest number of levels in the same type of antenna template as the main target. All radio frequency parameter processing nodes of the main target automatically become the main node, and the hierarchical structure and numbering sequence are used as the merging reference. For the remaining radio frequency test flowcharts to be merged, the reference node positioning subunit starts scanning from the smallest level (i.e., the starting level). The subunit extracts the first node (referred to as the first node) in the smallest level, and compares the radio frequency function tags of the first node with the main nodes in the same level of the main target one by one. If the radio frequency function tags of the main node and the first node are completely consistent, and at least one of the radio frequency input source node tags or the radio frequency output node tags of the two is the same, the first node is marked as the reference node. If there is no matching node in the smallest level, the scanning process is repeated to the next level until the reference node is located or all levels are traversed.

[0056] The node merging execution subunit processes the remaining nodes of the current flowchart in order of levels starting from the reference node. For each node after the level of the reference node, the subunit performs the following operations: search for a main node in the corresponding level of the main target that has the same radio frequency function tag as the current node. If such a main node exists, further check the radio frequency input source node tags and the radio frequency output node tags of the two. If at least one of the input tags or the output tags is the same, perform the node merging operation: incorporate the non-conflicting fields in the radio frequency attribute data set of the current node into the main node, and preferentially retain the original data of the main node for the conflicting fields. After merging, the current node is removed from the flowchart to be merged.

[0057] If there is no matching main node in the current level, or there is a main node that matches the function tag but does not match the input / output tag, no merging is performed. At this time, the current node becomes a branch node and needs to be linked into the main target. The access position of the branch node is dynamically determined according to the difference between its number and the number of the main target node: calculate the average difference between the number of the current node and the number of the nodes in the same level of the main target, if the difference is less than a preset tolerance value, insert the branch node at the end of the current level of the main target; if the difference exceeds the tolerance value, create a new slot between the adjacent levels of the main target according to the number size. When accessing, a bidirectional data link is established, and the input / output tags of the branch node are automatically aligned with the adjacent nodes of the main target.

[0058] For the nodes before the level of the reference node (i.e., upstream nodes), a reverse merging strategy is adopted. Starting from the level of the reference node to the starting level, the nodes are compared layer by layer. If the upstream node and the main node of the corresponding level of the main target meet the radio frequency feature fusion conditions (the function tags are the same and at least one of the input / output tags matches), merging is performed; otherwise, the node becomes a branch node and is inserted into the starting level of the main target or a new level is created to link in according to the number difference rule.

[0059] After the node merging of a single flowchart is completed, other radio frequency test flowcharts of the same type are processed repeatedly according to the above process. When all branch nodes are connected, the input and output labels in the radio frequency attribute data set are automatically associated with the adjacent node numbers of the main target. The finally generated integrated radio frequency test flowchart contains a complete main node network and branch node chain, and the radio frequency parameter flow direction between the nodes is realized through label association to achieve logical consistency. The structural depth of the flowchart is determined by the original hierarchical number of the main target, and the width is dynamically expanded with the increase of the branch nodes.

[0060] In the merging process, if multiple branch nodes need to be connected to the same main target position, the node merging execution subunit starts the conflict arbitration mechanism. According to the priority order of the branch nodes from the source template, the high-priority branch nodes are directly connected, and the low-priority nodes are converted into secondary branch chains into the high-priority nodes. The priority is determined by the version number or creation timestamp of the antenna template. All node merging operations are logged, including the merging node number, fusion condition judgment result and branch node access coordinates, for use in flowchart structure backtracking verification.

[0061] The radio frequency attribute data set of the radio frequency parameter processing node is updated synchronously during the merging process. When the nodes are merged, the historical test data of the merged nodes are migrated to the main node data set storage area; the branch nodes retain independent data set storage areas, but the input and output labels point to the main target node. The final output of the integrated flowchart contains a global node number mapping table, which records the correspondence between the original flowchart nodes and the nodes in the integrated graph, as well as the link path table of the branch nodes.

[0062] Embodiment 2: see Figure 3 When the test requirement mapping unit is started, the requirement flowchart generation subunit receives the impedance test requirement text input. The subunit uses natural language processing technology to analyze the text and identify core terms related to radio frequency testing. Term identification is based on a pre-defined radio frequency function keyword library, which contains a standard term set in the field of antenna impedance testing. During the analysis process, the subunit filters non-key modifiers and extracts valid phrases of verb-noun combinations. Each valid phrase is mapped to a radio frequency function label, and the label naming rule is consistent with the radio frequency parameter processing node function label of the antenna test template library. The subunit arranges the radio frequency function labels according to the order of their appearance in the text to form an ordered sequence. The adjacent labels in the sequence have an implicit execution dependency relationship, and the subunit automatically builds the flow direction association between the labels to generate a requirement flowchart containing nodes and directed edges. The number of nodes in the flowchart is consistent with the number of radio frequency function labels, and the node attributes only contain the radio frequency function label field.

[0063] When the endpoint matching subunit processes the generated requirement flowchart, it first locates the boundary nodes at both ends of the flowchart. The starting node is the node corresponding to the radio function tag at the beginning of the sequence, and the terminal node is the node corresponding to the tag at the end of the sequence. The target node identification subunit performs bidirectional scanning in the integrated radio test flowchart: forward scanning starts from the first level of the integrated flowchart and searches for radio parameter processing nodes with the same radio function tag as the starting node of the requirement flowchart layer by layer; reverse scanning starts from the last level of the integrated flowchart and searches upwards layer by layer for nodes with the same label as the terminal node. During the scanning process, the subunit compares the radio function tag strings between nodes, and records the node number when a complete match is found. If there are multiple matching nodes at the same level, the node with the smallest number is selected as the candidate. When the forward scanning first finds a matching node, it is marked as the starting endpoint; when the reverse scanning first finds a matching node, it is marked as the terminal endpoint. If the bidirectional scanning does not locate the starting endpoint and the terminal endpoint at the same time, the subunit relaxes the matching condition and allows partial matching (such as the same prefix of the label) and calculates the similarity score. When the score exceeds the threshold, it is considered as a valid endpoint.

[0064] The flowchart extraction subunit takes the located starting endpoint and terminal endpoint as the reference to search for the connection path in the integrated radio test flowchart. The path search uses a depth-first search algorithm, starting from the starting endpoint and traversing all possible downstream node paths until reaching the terminal endpoint. Each valid path generates a candidate flowchart, and the nodes in the path retain the original radio attribute data set. If there are multiple paths connecting the starting and terminal endpoints, a set of candidate flowcharts is formed. The topological feature analysis subunit constructs a radio feature topology graph for each candidate flowchart: the radio function tag of each node in the flowchart is converted into a vertex of the topology graph, and the radio parameter flow between nodes is converted into a directed edge. The radio feature correlation strength between adjacent vertices is reflected by the edge attribute, and the correlation strength value is calculated according to the node radio attribute data set. The specific calculation depends on the matching degree of the radio input source node label and the radio output node label: if the two nodes are directly connected in the flowchart and the input and output labels point to each other, the correlation strength is set to the highest value; if they are indirectly connected through intermediate nodes, the correlation strength value is attenuated according to the path length.

[0065] The similarity calculation subunit converts the demand flowchart into a radio frequency characteristic topology chart of the same specification. The subunit aligns the vertex sequence of the demand topology chart and each alternative topology chart: taking the vertex order of the demand chart as the benchmark, the vertex of the same radio frequency function label in the alternative chart is searched, and the missing vertex is processed as a null value. After the vertex alignment, the subunit compares the edge structure of the two charts: the number of directed edges of the same flow direction (i.e., the edges with matching starting point and ending point labels) is counted, which is recorded as the number of completely matched edges; the number of edges with the same starting point but different ending points is counted, which is recorded as the number of partially matched edges. The weight of each edge is weighted by its associated strength value. The radio frequency feature similarity is composed of three parts: the proportion of the number of completely matched edges to the total number of edges, the discount proportion of the number of partially matched edges, and the supplementary weight of the vertex matching degree. The three parts are combined into a comprehensive similarity score according to the preset coefficients. The subunit performs the above calculation on all alternative flowcharts, and selects the alternative flowchart with the highest score as the basic radio frequency test flowchart.

[0066] In the topology chart processing process, if the size difference between the demand flowchart and the alternative flowchart is large, the similarity calculation subunit starts the scale normalization process. The topology chart with more vertices is sampled in layers, and the key path nodes are retained; the topology chart with fewer vertices is supplemented with virtual nodes to complete the level. The radio frequency function label of the virtual node is set to "empty operation", and the associated strength value is taken as the lowest constant. The finally output basic radio frequency test flowchart inherits all the radio frequency attribute data sets of the alternative flowchart, and the input and output labels between the nodes remain the original pointing relationship. The flowchart structure is automatically optimized: the unselected alternative path branches are removed, the same type of nodes performing in parallel are merged, and the redundant levels are compressed. The system generates a flowchart difference report, which records the topology matching details of the demand flowchart and the basic flowchart, including the unmatched vertex list and the associated strength deviation value.

[0067] Embodiment 3: see Figure 4 The radio frequency feature verification unit configures the radio frequency attribute data set for the radio frequency parameter processing node in the system. The data set adopts a structured storage format and includes three core fields: the radio frequency function label field records the test function type executed by the node, which adopts standardized string encoding, and the encoding rule is synchronized with the global dictionary of the antenna test template library; the radio frequency input source node label field stores the unique identifier of the upstream node, which has a three-part combination of "template number-layer index-node sequence"; the radio frequency output node label field records the downstream node information with the same structure. The data set is managed by a distributed database, and the attribute data of each node is independently stored but supports cross-node associated query.

[0068] The fusion condition determination subunit performs node merging judgment, and establishes a three-dimensional verification matrix. The first dimension of the matrix scans the radio frequency function tags of the main node and the nodes to be merged, requiring that the character strings be completely matched and the character encoding value difference be zero. The second dimension compares the input source node tags, and uses a similarity algorithm to calculate the proportion of common subsequences in the tag string:

[0069]

[0070] Among them: represents the input label similarity, the function calculates the length of the longest common subsequence of the two label strings, and respectively represent the input source node tag strings of the main node and the nodes to be merged. The third dimension performs the same calculation on the output node label to obtain When or any value exceeds the fusion threshold , it is determined that the radio frequency feature fusion condition is met. The threshold is dynamically adjusted according to the antenna type, and the default value is 0.8 for wideband antennas and 0.6 for narrowband antennas.

[0071] When constructing the radio frequency feature topology graph, the radio frequency function tag of each node is mapped to the topology vertex , and the vertex attribute set inherits the radio frequency attribute data of the original node. The generation of the directed edge follows strict constraints: only when there is a parameter transmission link from node to in the actual radio frequency test process, the corresponding edge is established. The weight of the edge is determined by the composite factor:

[0072]

[0073] Among them: represents the semantic correlation of the function tags of nodes and , which is calculated by the cosine similarity of the pre-trained radio frequency term vector model; reflects the parameter transmission success rate between the two nodes in the historical test data, and takes the average of the success rate of the last 100 tests; is the co-occurrence frequency statistic of the node pair, which calculates the probability of the two nodes being in the same path in the historical integration process graph. The coefficients , , constitute a normalized weight vector, which is dynamically configured according to the characteristics of the antenna operating frequency band.

[0074] When processing the requirement flowchart and alternative flowcharts, the similarity calculation subunit first aligns the topology graphs of both to the same dimension. For each vertex of the requirement graph... Find the matching graph in the candidate graph. vertex ,in It is the vertex matching threshold. The alignment process allows for one-to-many mappings, each... Multiple candidate vertices can be matched. Edge similarity calculation employs a hierarchical matching strategy: first comparing directly connected edges (paths with a step size of 1), then gradually expanding to multi-hop paths. Path similarity integral. Calculate the harmonic mean weight of all matching paths, and reduce the contribution of long paths by an exponential decay factor.

[0075] The final calculation of radio frequency feature similarity integrates vertex matching degree. Path similarity . It is the proportion of successfully matched vertices out of the total number of vertices, taking into account the weighted correction of the semantic similarity of the tags; Based on the path matching results at each level, the contribution of shorter paths is given priority. The system maintains a configurable similarity calculation strategy library, supporting the selection of weighting modes according to different antenna testing scenarios. For example, impedance testing focuses on precise vertex matching, while RF performance testing pays more attention to path continuity.

[0076] Each node merging or flowchart modification generates a new dataset version, while retaining snapshots of historical versions. Version tracking allows the system to roll back to any verification point, and automatically triggers version comparison analysis when an anomaly in RF feature correlation is detected. A real-time update module for the topology graph edge weights monitors the test data flow and dynamically adjusts it. Factors, ensuring weights This reflects the latest test status. For edges that have not been activated for a long time, the system initiates an aging test. If no data flows through them for several consecutive test cycles, their weight is reduced until they are temporarily removed.

[0077] New node combinations generated during integration are fed back to the verification unit in real time, triggering the expansion and update of the RF attribute dataset. The verification unit periodically scans the dataset for conflicting items, such as duplicated input / output labels and isolated nodes, and generates data cleaning suggestions. This collaborative mechanism ensures that the integrated RF test flowchart maintains consistency constraints on RF characteristics while its structure expands.

[0078] When multiple candidate vertices have close matching scores with the demand vertex, the system records the conflict path and initiates secondary verification: checking the context environment of the conflict vertex in historical tests, prioritizing the vertex that is more compatible with the current demand flowchart topology. For serious conflicts that cannot be resolved, a to-be-confirmed matter is generated for manual decision, while the intermediate state of automated processing is preserved. All conflict processing results are recorded in the knowledge base for optimizing subsequent matching strategies.

[0079] For partially matched vertex pairs, the matching score is adjusted according to the hierarchical relationship of their labels. The parent-child relationship of RF function labels is defined in the standard terminology library, and a partial matching score is obtained when a child label matches a parent label. This mechanism enhances the system's ability to handle non-standard test requirements. When the demand flowchart contains abstract function descriptions, it can still effectively associate with the specific implementation of the candidate flowchart nodes.

[0080] Embodiment 4: refer to Figure 5 When the test anomaly detection unit performs scanning in the basic RF test flowchart, it uses a hierarchical traversal strategy to check the integrity of the RF parameter processing nodes. Taking the test flow of a certain type of multi-band antenna as an example, after the system loads a flowchart containing 32 nodes, the detection unit starts checking from the starting node "N1-1-1" (representing template 1, layer 1, node 1). The RF attribute data set of each node is parsed into a structured record, and the detection logic focuses on three key fields: whether the RF function label is empty, whether the input source node label points to a valid node, and whether the output node label is referenced by other nodes.

[0081] In the example flowchart, the detection log of node "N3-2-5" shows an anomaly: its input source node label points to "N2-1-3", but the verification found that template 2 does not have this numbered node. The anomaly detection unit marks this node as a to-be-modified node and records the anomaly type "node reference invalid" in the audit log. At the same time, the output node label of node "N4-3-8" is blank, but its RF function label indicates that this node should output impedance matching parameters, and the system determines it as a "data outlet missing" type anomaly. The following table shows part of the anomaly detection results of the flowchart of this type of antenna:

[0082] Table 1: Abnormal node record table of multi-band antenna test flowchart.

[0083] Node number Exception type Associated field Amendment measure N3-2-5 Node reference invalidation Input source node label Update input source to valid node N2-1-2 N4-3-8 Data outlet missing Output node label Add output link pointing to N5-4-1 N5-4-6 Function label conflict Radio frequency function label Change "standing wave ratio detection" to "impedance detection" N6-5-2 Circular reference warning Input / output label Check pointing relationship from N6-5-3 to N6-5-1

[0084] Taking the multi-band antenna as an example, the modified flowchart contains 34 nodes (2 new compensation nodes are added). The radio frequency node verification subunit loads a convolutional neural network model, and the model input layer receives three sets of feature data: the current radio frequency function tag code value of the node, the hash check value of the input and output tags, and the parameter fluctuation variance in the last 10 test records. The hidden layer uses a three-dimensional convolution kernel to extract features, and the output layer calculates the error probability value of the node. When verifying node “N4-3-8”, the model detects that there are 3 impedance parameter jumps (more than ±5Ω) in its historical test records, and outputs an error probability of 0.87, which exceeds the preset threshold value of 0.8. The node is marked as a risk node.

[0085] For node “N4-3-8”, the system outputs a structured warning message: the risk level is “high risk”, the error type is “insufficient parameter stability”, and the associated affected nodes include downstream nodes “N5-4-1” and “N5-4-3”. The warning message is attached with repair suggestions: check the impedance matching network connector of the node and recalibrate the test equipment. At the same time, the system automatically locks the test permission of the risk node and prohibits new test tasks from being assigned to the node until manual confirmation of the repair is completed.

[0086] When the dynamic migration unit analyzes the data distribution characteristics of the risk node, a data heat distribution map is established. In the example, the historical data of node “N4-3-8” is stored in Node-12 of the server cluster, with an access frequency of 142 times in the past 24 hours and a total data amount of 3.7GB. The radio frequency data distribution analysis subunit calculates the migration priority index of the node as 0.91 (the highest is 1.0), triggering an emergency migration task. The migration path planning module scans the storage topology network and selects Node-07 as the target node with a load rate lower than 30% and an error rate less than 5%, and the migration path is Node-12→Node-05→Node-07, avoiding the current high-load relay node Node-09.

[0087] The 3.7GB data is divided into 256MB block units, and each block is attached with a CRC32 check code. During the migration process, the transmission rate and error rate are monitored in real time, and when a block of data fails to transmit, it is automatically switched to the backup path Node-12→Node-11→Node-07. After the migration is completed, the system updates the access permission table of node “N4-3-8”: the new data positioning address points to Node-07, the access key is replaced with a 256-bit AES new key, and the data mirror on Node-12 is retained for 48 hours as a disaster recovery copy.

[0088] The test anomaly detection unit and the dynamic migration unit work together to form a closed-loop management. In the example, after completing the risk node data migration, the system re-evaluates the flowchart state: the error probability of node "N4-3-8" is reduced to 0.45, and the risk lock state is released; the newly added compensation nodes "N3-2-9" and "N4-3-10" are verified and integrated into the main test flow path. The system generates a final version of the complete radio frequency test flowchart, marking the data storage location and access permission of all nodes for the test execution engine to call.

[0089] The knowledge accumulation mechanism in the abnormal handling process continuously optimizes the system performance. The node reference failure case in the example is added to the rule library, and subsequent detection will prioritize checking cross-template node references; the threshold for parameter stability risk is dynamically adjusted according to the repair effect of "N4-3-8", and the threshold for the same type of antenna is reduced from 0.8 to 0.7.

[0090] For medium-risk nodes with an error probability of 0.7-0.8, the system uses a background silent migration mode, which does not affect the current test task execution; high-risk nodes with an error probability of 0.8 or higher immediately trigger an interrupt migration, pausing the related test thread until the migration is complete. The migration path selection algorithm considers network topology distance, storage node health, historical migration success rate, and other parameters, and automatically updates the path score data after each migration task is completed, optimizing subsequent decisions.

[0091] Embodiment 5: Data collection covers three core dimensions: historical data storage location information records physical storage node number and logical partition address; access timestamp sequence statistics the time distribution density of the latest access; data volume change trend tracking storage space occupancy rate fluctuation. The analysis engine projects these parameters into a time-space coordinate system to generate a three-dimensional data distribution heat map. The coordinate axes of the heat map represent the storage node topology location, time window index, and data magnitude interval, and the heat value is calculated by normalizing the product of access frequency and data volume. High-frequency hotspots appear as highlighted color blocks in the heat map, and the system automatically marks these areas as candidate areas for migration.

[0092] The index value is generated by a dynamic composite algorithm, and the basic factors include data interaction frequency and node error probability. The interaction frequency takes the peak and average values within the last 72 hours, and the error probability uses the sliding window average value. Additional factors consider data sensitivity level and real-time network load, with higher sensitivity data weight increased and network congestion period index correspondingly decayed. The index calculation result is sorted to generate a migration task queue, and the head task of the queue gets immediate execution permission.

[0093] The storage network is modeled as a weighted directed graph, with vertices corresponding to physical storage nodes and edge weights incorporating physical link delays, current load rates, and historical failure rates. The planning algorithm starts from the source node and ends at the target node, searching for the optimal path under constraints. Constraints include maximum hop count limits, single-hop delay upper limits, and failure node avoidance lists. The algorithm monitors the health status of path nodes in real time, and when it detects a sudden increase in target node load or an excessive response delay, it automatically switches to a backup path. The path optimization process implements an incremental update mechanism, and after each migration task is completed, the edge weight parameters of the network model are corrected based on the actual transmission performance.

[0094] The first stage of pre-migration verification checks the digital signature of the source data, verifies storage node access permissions, and reserves target storage space. The second stage of data transmission divides the data stream into fixed-size block units, each with metadata tags recording verification information. The transmission process implements dual-channel monitoring, with the main channel transmitting data blocks and the auxiliary channel synchronously sending verification codes. The receiving end compares the verification results in real time, and triggers an immediate retransmission mechanism for abnormal blocks. The third stage of integrity confirmation reconstructs the complete data set at the target node and compares it with the source data hash value. If the difference exceeds the tolerance, a differential compensation transmission is initiated.

[0095] Permission updates include three levels: physical layer updates the addressing mapping table of the storage node, pointing the logical address to the new physical location; logical layer resets the access control list, including user authorization policies and API call credentials; security layer rotates encryption keys, generating new symmetric encryption key pairs. Permission change information is broadcast to all related system components, and the radio frequency parameter processing node in the flowchart automatically synchronizes the new access path. The permission update log records the complete operation trajectory, supporting bidirectional change tracing.

[0096] The monitoring period is dynamically adjusted according to the risk level of the nodes, with high-risk nodes implementing minute-level inspection and low-risk nodes scanning hourly. Monitoring indicators include storage capacity fluctuations, read / write error rates, response delay curves, etc. When the target node's capacity is found to be insufficient or the error rate is rising, the early warning system triggers a secondary migration assessment. The assessment algorithm calculates the stability score of the current node, and if the score is below the threshold, a new migration task is generated, but the migration priority is lower than the first migration task.

[0097] Each migration task records the source node performance baseline, migration path selection logic, transmission interruption events, and final time consumption parameters. The knowledge base uses a case-based reasoning mechanism, and when a new migration task is triggered, the system retrieves similar historical cases and preferentially uses verified efficient path patterns. For new storage architectures without historical references, the system starts an exploratory migration mode: it first tries standard path templates and dynamically adjusts path weights based on real-time performance feedback.

[0098] The original data retains a cache copy for a set duration after migration, and access requests within the cache period can still be traced back to the source node. Before the cache expires, the system performs data cleaning verification, and automatically erases the source data after confirming that the target data is complete and available. The erasure process uses a multi-round overwrite secure deletion protocol to ensure that sensitive radio frequency test data cannot be recovered. All lifecycle operations generate audit reports to record data location transition tracks and permission change history.

[0099] When the radio frequency node verification subunit detects a new risk node, it automatically notifies the storage optimization unit to pre-judge data migration needs. Conversely, abnormal information of storage nodes found during the migration process is synchronized in real time to the risk warning database. This cooperative mechanism enables the system to have forward-looking optimization capabilities, actively adjusting data distribution status before risks become explicit. The migration task scheduler supports parallel processing queues and ensures that high-priority migration tasks are not disturbed by low-priority tasks through resource isolation technology.

[0100] It should be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0101] While embodiments of the present application have been shown and described with reference to particular embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application as defined by the appended claims and their equivalents.

Claims

1. An antenna RF and impedance testing system, characterized in that, The system includes: An antenna test template library stores templates for various antenna types. Each antenna type template is associated with an RF test flowchart, which includes RF parameter processing nodes. A flowchart integration unit is used to merge the RF test flowcharts of the same type of antenna template into an integrated RF test flowchart. The test requirement mapping unit is used to obtain RF performance requirements and impedance test requirements, select the corresponding integrated RF test flowchart based on the RF performance requirements, and extract the basic RF test flowchart from the integrated RF test flowchart based on the impedance test requirements.

2. The antenna RF and impedance testing system according to claim 1, characterized in that, The flowchart integration unit includes: The node-level subunit is used to number the node for each RF parameter processing node according to the RF parameter flow direction mark in the RF test flowchart, and to divide the RF test flowchart layer based on the number. The main target fusion subunit is used to set the RF test flowchart with the most levels in the same type of antenna template as the main target, set the RF parameter processing nodes contained in the main target as the main nodes, and locate reference nodes in the other RF test flowcharts. The node merging execution subunit is used to merge the radio frequency parameter processing nodes of each level of the radio frequency test flowchart into the main target, starting from the reference node. When the main node and the radio frequency parameter processing node meet the radio frequency feature fusion conditions, the node merging is executed; otherwise, it is set as a branch node and linked into the main target.

3. The antenna RF and impedance testing system according to claim 2, characterized in that, The main target fusion subunit includes: The reference node positioning subunit is used to locate the first node at the lowest level of the RF test flowchart. When there is a main node in the main target that meets the RF feature fusion condition with the first node, it is set as a reference node; otherwise, other level nodes are extracted and the positioning is repeated.

4. The antenna RF and impedance testing system according to claim 2, characterized in that, The test requirement mapping unit includes: A requirement flowchart generation subunit is used to generate a requirement flowchart based on impedance testing requirements. Endpoint matching subunit, which is used to locate the start endpoint and the end endpoint at both ends of the requirement flowchart, and match the RF parameter processing node that meets the RF feature fusion condition with the start endpoint and the end endpoint in the integrated RF test flowchart, as the top node and the bottom node. The flowchart extraction subunit is used to generate a set of candidate flowcharts with the top node and bottom node as the start and end points, calculate the RF feature similarity between the required flowchart and each candidate flowchart, and select the candidate flowchart with the highest similarity as the basic RF test flowchart.

5. The antenna RF and impedance testing system according to claim 4, characterized in that, The endpoint matching subunit includes: The target node identification subunit is used to traverse the process nodes of the requirement flowchart. When there is an RF parameter processing node in the integrated RF test flowchart that meets the RF feature fusion condition with the process node, the process node is marked as the target node. The endpoint definition subunit defines the target nodes first identified at both ends of the requirement flow diagram as the start endpoint and the end endpoint.

6. The antenna RF and impedance testing system according to claim 2, characterized in that, The system also includes: The radio frequency feature verification unit is used to configure a radio frequency attribute dataset for each radio frequency parameter processing node. The radio frequency attribute dataset includes its own radio frequency function label, radio frequency input source node label, and radio frequency output node label. The fusion condition determination subunit is used to determine that the radio frequency feature fusion condition is met when the radio frequency function labels of the master node and the radio frequency parameter processing node are the same, and at least one of the radio frequency input source node labels and the radio frequency output node labels are the same.

7. The antenna RF and impedance testing system according to claim 4, characterized in that, The flowchart extraction subunit include: The topology feature analysis subunit is used to generate a radio frequency feature topology map based on the radio frequency function tags of the radio frequency parameter processing nodes, and to analyze the radio frequency feature association strength of adjacent nodes layer by layer. The similarity calculation subunit is used to generate a first feature set and a second feature set of a requirement flowchart and an alternative flowchart based on the radio frequency feature topology map, and to calculate the radio frequency feature similarity based on the number of the same radio frequency feature association strength.

8. The antenna RF and impedance testing system according to claim 1, characterized in that, The system also includes: A test anomaly detection unit is used to mark the RF parameter processing nodes to be modified in the basic RF test flowchart. The dynamic migration unit is used to generate a complete RF test flowchart after the adjustment of the RF parameter processing node to be modified is completed, identify RF parameter processing nodes with RF test risks, and calculate the data migration priority and migration path of the RF parameter processing node.

9. The antenna RF and impedance testing system according to claim 8, characterized in that, The test anomaly detection unit includes: The radio frequency node verification subunit verifies the radio frequency parameter processing node of the complete radio frequency test flowchart based on a neural network model. When the error probability of the radio frequency parameter processing node exceeds a preset threshold, it is marked as a risk node. A risk warning subunit is used to generate radio frequency test risk warnings pointing to the risk node.

10. The antenna RF and impedance testing system according to claim 8, characterized in that, The dynamic migration unit includes: The radio frequency data distribution analysis subunit is used to analyze the radio frequency data distribution characteristics and radio frequency data interaction frequency of risk nodes. A migration path planning subunit is used to calculate a migration priority index based on the radio frequency data interaction frequency and plan the radio frequency data migration path. The radio frequency (RF) storage optimization subunit is used to migrate RF data to low-risk storage nodes and update the access permissions of RF parameter processing nodes.

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