Antenna radio frequency and impedance test system

Abstract

The invention relates to the technical field of antenna testing, and discloses an antenna radio frequency and impedance testing system. The system comprises an antenna test template library, a flow chart integration unit and a test demand mapping unit. The antenna test template library stores a plurality of antenna type templates, and each template is associated with a radio frequency test flow chart containing radio frequency parameter processing nodes; the flow chart integration unit combines the radio frequency test flow charts of the antennas of the same type into an integrated radio frequency test flow chart; the test demand mapping unit obtains radio frequency and impedance test demands, selects a corresponding integrated radio frequency test flow chart based on radio frequency performance demands, and extracts a basic radio frequency test flow chart from the integrated radio frequency test flow chart based on impedance test demands. The system realizes standardized management and flexible calling of an antenna test process, promotes cooperative linkage of radio frequency and impedance tests, adapts to different test requirements and antenna types, and improves test efficiency and integrity.

Description

Technical Field

[0001] This invention relates to the field of antenna testing technology, specifically to an antenna radio frequency and impedance testing system. Background Technology

[0002] In today's rapidly developing wireless communication technology, antennas, as key components for signal transmission and reception, directly impact the overall performance of communication systems through their radio frequency performance and impedance characteristics. Different types of antennas, such as millimeter-wave antennas, array antennas, and antennas integrated into wearable devices, have significantly different requirements for testing procedures due to differences in application scenarios, structural designs, and performance indicators.

[0003] Currently, antenna testing largely relies on manual configuration of test procedures. Testers must set up RF parameter test nodes one by one according to the antenna type, such as frequency range, gain, and VSWR, while also planning impedance testing steps separately. This approach is not only time-consuming but also prone to inconsistencies in test standards due to differences in human operation. For example, when testing 5G base station antennas, the focus should be on beamforming-related RF parameters, while for small antennas used in IoT devices, the emphasis is more on impedance matching performance under low power consumption. Switching between different test priorities often requires rebuilding the test framework, resulting in low efficiency.

[0004] Existing testing systems lack standardized management of testing procedures for different antenna types, resulting in poor reusability of test data. When testing multiple batches of antennas of the same type, existing test logic cannot be directly invoked, requiring repeated process configuration, which increases testing costs. Furthermore, RF testing and impedance testing are typically independent, making coordinated operation difficult. This can lead to parameter omissions or conflicts during testing, affecting the accuracy and completeness of test results. With continuous innovation in antenna technology, new antenna types and performance indicators are constantly emerging, making traditional testing methods insufficient to meet the demands for efficient and flexible testing. Summary of the Invention

[0005] The purpose of this invention is to provide an antenna radio frequency and impedance testing system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an antenna radio frequency and impedance testing system, the system comprising:

[0007] 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.

[0008] 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.

[0009] 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 based on the impedance test requirements from the integrated RF test flowchart.

[0010] Preferably, the flowchart integration unit includes:

[0011] 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.

[0012] 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.

[0013] 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.

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

[0015] 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.

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

[0017] A requirement flowchart generation subunit is used to generate a requirement flowchart based on impedance testing requirements.

[0018] 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.

[0019] 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.

[0020] Preferably, the endpoint matching subunit includes:

[0021] 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.

[0022] 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.

[0023] Preferably, the system further includes:

[0024] 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.

[0025] 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.

[0026] Preferably, the flowchart extraction subunit includes:

[0027] 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.

[0028] 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.

[0029] Preferably, the system further includes:

[0030] A test anomaly detection unit is used to mark the RF parameter processing nodes to be modified in the basic RF test flowchart.

[0031] 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.

[0032] Preferably, the test anomaly detection unit includes:

[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. When the error probability of the radio frequency parameter processing node exceeds a preset threshold, it is marked as a risk node.

[0034] A risk warning subunit is used to generate radio frequency test risk warnings pointing to the risk node.

[0035] Preferably, the dynamic migration unit includes:

[0036] 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.

[0037] 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.

[0038] 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.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] By constructing an antenna test template library, the test procedures for various antenna types are standardized and stored, enabling unified management of the test logic for different antennas. Each antenna type template is associated with a corresponding RF test flowchart, containing specific RF parameter processing nodes, providing a foundation for rapid retrieval of test procedures. When faced with test requirements for the same type of antenna, there is no need to redesign the test steps; the corresponding template can be directly retrieved from the template library, reducing repetitive work.

[0041] The flowchart integration unit merges the RF test flowcharts for similar antennas into an integrated RF test flowchart, enhancing the integrity and coherence of the test process. While there may be subtle testing differences between antennas of the same type, the integrated flowchart covers these differences and forms a unified test framework. This preserves the specificities of each antenna's test while achieving centralized control of the test process, facilitating unified scheduling and monitoring of the testing process for antennas of the same type.

[0042] The test requirement mapping unit achieves precise alignment between test requirements and test procedures. By acquiring RF performance requirements and impedance test requirements, this unit can select matching content from the integrated RF test flowchart and extract the basic RF test flowchart to meet the impedance test requirements, thus forming an organic whole between RF testing and impedance testing. This linkage mechanism avoids the isolated execution of the two tests, ensures the consistency and correlation of test parameters, and reduces problems caused by disconnections in test procedures.

[0043] Meanwhile, the system possesses excellent adaptability and scalability. When new antenna types or testing requirements emerge, the antenna test template library can be updated to incorporate new testing procedures without requiring significant adjustments to the overall system architecture. The test requirement mapping unit can flexibly adjust the selection and extraction of test procedures based on new needs, enabling the system to cope with constantly changing testing scenarios. Furthermore, standardized templates and integrated processes help improve the standardization and reusability of test data, facilitating subsequent data analysis and test optimization.

[0044] This system integrates RF testing and impedance testing within a unified framework, simplifying the complexity of testing operations. Testers no longer need to frequently switch between different test modules; multiple test tasks can be completed through a unified system, improving the smoothness of the testing process. For batch testing of similar antennas, the integrated test flowchart enables the batch application of the test process, shortening the testing cycle and making it suitable for testing needs in large-scale production environments. Attached Figure Description

[0045] Figure 1 This is a timing diagram of the antenna RF and impedance testing system described in this invention;

[0046] Figure 2 A flowchart for the work of the flowchart integration unit;

[0047] Figure 3 Generate and match flowcharts for the requirements flowchart;

[0048] Figure 4 The flowchart for determining the conditions for radio frequency feature fusion;

[0049] Figure 5 This is a flowchart for testing anomaly detection and dynamic migration. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1 This invention provides an antenna radio frequency and impedance testing system, the system comprising:

[0052] The system includes an antenna test template library, a flowchart integration unit, and a test requirement mapping unit. The antenna test template library stores templates for various antenna types, each associated with an RF test flowchart. Each RF test flowchart consists of several RF parameter processing nodes. The flowchart integration unit merges RF test flowcharts for antenna templates of the same type into an integrated RF test flowchart. The test requirement mapping unit selects an integrated flowchart based on RF performance requirements and extracts a basic RF test flowchart based on impedance test requirements.

[0053] The RF parameter processing node contains an RF attribute dataset, recording RF function labels, input source node labels, and output node labels. During flowchart integration, the hierarchical structure of each flowchart is determined by node hierarchy, with the flowchart with the highest hierarchy serving as the primary target. Nodes in other flowcharts are merged or added as branches to the primary target based on RF feature fusion conditions. During test requirement mapping, endpoint matching is used to locate the start and end nodes in the integrated flowchart, generating a set of candidate flowcharts and calculating RF feature similarity. Finally, the base flowchart with the highest matching degree is output.

[0054] Example 1: See Figure 2 When the flowchart integration unit merges RF test flowcharts for antenna templates of the same type, it first initiates the node hierarchy division subunit. This subunit parses the RF parameter flow direction markers in the RF test flowchart and assigns a unique number to each RF parameter processing node. The numbering rule follows the RF parameter transmission direction, starting with the initial input source node numbered N1, and subsequent nodes are numbered sequentially according to the parameter flow order as N2, N3, etc. The hierarchy division is based on the continuity of node numbers, grouping nodes with numbers in the same range into the same level. For example, numbers N1-N5 belong to the first level, N6-N10 to the second level, and so on. Nodes in each level perform the same stage of RF parameter processing tasks.

[0055] The main target fusion subunit selects the RF test flowchart with the most layers among antenna templates of the same type as the main target. All RF parameter processing nodes of the main target automatically become main nodes, and their hierarchical structure and numbering sequence serve as the merging benchmark. For the remaining RF test flowcharts to be merged, the reference node positioning subunit starts scanning from the smallest layer (i.e., the starting layer). This subunit extracts the first node in the smallest layer (called the first node) and compares its RF function label with the main nodes of the same layer in the main target one by one. If the RF function labels of the main node and the first node are found to be completely consistent, and at least one of their RF input source node label or RF output node label is the same, then the first node is marked as the reference node. If there is no matching node in the smallest layer, the scanning process is repeated to the next layer until the reference node is located or all layers are traversed.

[0056] The node merging execution subunit starts from the reference node and processes the remaining nodes in the current flowchart in hierarchical order. For each node after the level of the reference node, the subunit performs the following operations: It searches for a master node in the corresponding level of the main target that has the same RF function label as the current node. If such a master node exists, it further verifies the RF input source node label and RF output node label of both. If at least one of the input or output labels is the same, the node merging operation is performed: non-conflicting fields in the RF attribute data set of the current node are merged into the master node, while conflicting fields retain the original data of the master node. After merging, the current node is removed from the flowchart to be merged.

[0057] If there is no matching master node at the current level, or if there is a master node with matching function labels but mismatched input / output labels, then merging will not be performed. In this case, the current node becomes a branch node and needs to be linked into the master target. The access position of the branch node is dynamically determined based on the difference between its number and the master target node number: the average difference between the current node number and the number of nodes at the same level of the master target is calculated. If the difference is less than a preset tolerance value, the branch node is inserted at the end of the current level of the master target; if the difference exceeds the tolerance value, a new slot is created between adjacent levels of the master target based on the number size. A bidirectional data link is established during access, and the input / output labels of the branch node are automatically aligned with the adjacent nodes of the master target.

[0058] For nodes at levels preceding the reference node (i.e., upstream nodes), a reverse merging strategy is employed. The process involves backtracking from the reference node's level to the starting level, comparing nodes layer by layer. If an upstream node meets the radio frequency feature fusion conditions (identical functional labels and at least one matching input / output label) with the main node at the corresponding level of the main target, merging is performed; otherwise, it becomes a branch node and is inserted into the main target's starting level or linked into a new level according to the number difference rule.

[0059] After merging nodes in a single flowchart, the above process is repeated for other RF test flowcharts of the same type. When all branch nodes are connected, their input / output labels in their RF attribute datasets are automatically associated with the adjacent node numbers of the main target. The final generated integrated RF test flowchart contains a complete main node network and branch node chain, with logical consistency achieved through label association of RF parameter flows between nodes. The structural depth of the flowchart is determined by the original number of levels in the main target, while its width dynamically expands as the number of branch nodes increases.

[0060] During the merging process, if multiple branch nodes need to connect to the same primary target location, the node merging execution subunit initiates a conflict arbitration mechanism. Branch nodes are sorted according to their source template priority; high-priority branch nodes connect directly, while low-priority nodes become secondary branches linked to high-priority nodes. Priority is determined by the antenna template version number or creation timestamp. All node merging operations are logged, including the merged node number, fusion condition determination results, and branch node connection coordinates, for use in flowchart structure backtracking and verification.

[0061] The RF attribute datasets of the RF parameter processing nodes are kept synchronized during the merging process. When nodes are merged, the historical test data of the merged nodes are migrated to the master node's dataset storage area; branch nodes retain their independent dataset storage areas, but their input and output labels point to the master target node. The final output of the integration flowchart includes a global node number mapping table, recording the correspondence between nodes in the original flowchart and nodes in the integration flowchart, as well as a link path table for branch nodes.

[0062] Example 2: See Figure 3 When the test requirement mapping unit starts, the requirement flowchart generation subunit receives the impedance test requirement text input. This subunit uses natural language processing technology to parse the text and identify core terms related to RF testing. Terminology recognition is based on a predefined RF function keyword library, which contains a standard terminology set in the antenna impedance testing field. During parsing, the subunit filters out non-key modifiers and extracts valid phrases with verb-noun combinations. Each valid phrase is mapped to an RF function tag, and the tag naming rules are consistent with the RF parameter processing node function tags in the antenna test template library. The subunit arranges the RF function tags according to the order of their appearance in the text, forming an ordered sequence. Implicit execution dependencies exist between adjacent tags in the sequence, and the subunit automatically constructs the flow associations between tags, generating a requirement flowchart containing nodes and directed edges. The number of nodes in the flowchart is the same as the number of RF function tags, and the node attributes only contain RF function tag fields.

[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 first RF function label in the sequence, and the ending node is the node corresponding to the last label in the sequence. The target node identification subunit performs a bidirectional scan in the integrated RF test flowchart: the forward scan starts from the first level of the integrated flowchart and searches layer by layer for RF parameter processing nodes whose RF function labels are the same as the starting node of the requirement flowchart; the reverse scan starts from the last level of the integrated flowchart and searches layer by layer for nodes whose labels are the same as the ending node. During the scan, the subunit compares the RF function label strings between nodes, and records the node number when there is a complete match. If there are multiple matching nodes at the same level, the node with the smallest number is selected as the candidate. When a matching node is found for the first time in the forward scan, it is marked as the starting endpoint; when a matching node is found for the first time in the reverse scan, it is marked as the ending endpoint. If the bidirectional scan does not locate the starting endpoint and the ending endpoint at the same time, the subunit relaxes the matching conditions, allowing partial matching (such as the same label prefix) and calculates a similarity score. When the score exceeds the threshold, it is considered a valid endpoint.

[0064] The flowchart extraction subunit uses the located start and end endpoints as a reference to retrieve connection paths within the integrated RF test flowchart. Path retrieval employs a depth-first search algorithm, traversing all possible downstream node paths from the start endpoint until the end endpoint is reached. Each valid path generates a candidate flowchart, with nodes in the path retaining their original RF attribute datasets. If multiple paths connect the start and end endpoints, a set of candidate flowcharts is formed. The topology feature analysis subunit constructs an RF feature topology graph for each candidate flowchart: the RF function label of each node in the flowchart is transformed into a vertex of the topology graph, and the RF parameter flow between nodes is transformed into directed edges. The RF feature association strength between adjacent vertices is reflected through edge attributes, and the association strength value is calculated based on the node's RF attribute dataset. The specific calculation depends on the matching degree between the RF input source node label and the RF output node label: if two nodes are directly connected in the flowchart and their input and output labels refer to each other, the association strength is set to the highest value; if they are indirectly connected through intermediate nodes, the association strength value is attenuated according to the path length.

[0065] The similarity calculation subunit transforms the requirement flowchart into a standardized RF feature topology diagram. This subunit aligns the vertex sequence of the requirement topology diagram with each candidate topology diagram: using the vertex order of the requirement diagram as a baseline, it searches for vertices with the same RF function labels in the candidate diagrams, treating missing vertices as null values. After vertex alignment, the subunit compares the edge structure of the two diagrams: it counts the number of directed edges with the same flow direction (i.e., edges where both start and end labels match), recorded as the number of fully matched edges; it counts the number of edges with the same start but different end points, recorded as the number of partially matched edges. The weight of each edge is weighted by its association strength value. The RF feature similarity consists of three parts: the proportion of fully matched edges to the total number of edges, the reduction ratio of partially matched edges, and the supplementary weight of vertex matching degree. These three parts are combined according to preset coefficients to form a comprehensive similarity score. The subunit performs the above calculation on all candidate flowcharts and selects the candidate flowchart with the highest score as the basic RF test flowchart.

[0066] During topology graph processing, if the size difference between the requirement flowchart and the candidate flowchart is significant, the similarity calculation subunit initiates scale normalization. Topology graphs with a large number of vertices undergo hierarchical sampling, retaining critical path nodes; topology graphs with a small number of vertices add virtual nodes to complete the hierarchy. The RF function label of the virtual nodes is set to "no operation," and the association strength value is taken as the lowest constant. The final output basic RF test flowchart inherits all RF attribute datasets from the candidate flowcharts, and the input / output labels between nodes maintain their original pointing relationships. The flowchart structure is automatically optimized: unselected candidate path branches are removed, parallel execution of similar nodes is merged, and redundant levels are compressed. The system generates a flowchart difference report, recording the topology matching details between the requirement flowchart and the basic flowchart, including the list of unmatched vertices and the association strength deviation value.

[0067] Example 3: See Figure 4 The RF feature verification unit configures RF attribute datasets for the RF parameter processing nodes in the system. This dataset uses a structured storage format and contains three core fields: the RF function label field records the type of test function performed by the node, using standardized string encoding with encoding rules synchronized with the global dictionary of the antenna test template library; the RF input source node label field stores the unique identifier of the upstream node, in a three-segment combination of "template number-hierarchy index-node sequence number"; and the RF output node label field uses the same structure to record downstream node information. The dataset is managed through a distributed database, with each node's attribute data stored independently but supporting cross-node relational queries.

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

[0069]

[0070] in: Indicates the similarity of input tags. The function calculates the length of the longest common subsequence of two label strings. and These represent the input source node label strings for the master node and the node to be merged, respectively. The third dimension is calculated using the same method on the output node labels. .when or Any value exceeding the fusion threshold At that time, the radio frequency feature fusion condition is determined to be met. Threshold The value is dynamically adjusted according to the antenna type. The default value for broadband antennas is 0.8, and for narrowband antennas it is 0.6.

[0071] When constructing the RF feature topology graph, the topology feature analysis subunit maps the RF function label of each node to a topology vertex. Vertex attribute sets inherit the radio frequency attribute data of the original node. Directed edges. The generation follows strict constraints: it only occurs when there is a slave node in the actual RF test procedure. arrive The corresponding edge is established only when the parameters are passed to the link. The edge weight... Determined by composite factors:

[0072]

[0073] in: Represents a node and The semantic relevance of functional labels is calculated using a pre-trained radio frequency term vector model to determine cosine similarity. The success rate of parameter transfer between two nodes in historical test data is reflected by taking the average success rate of the most recent 100 tests. This is the co-occurrence frequency statistic of node pairs, calculating the probability that two nodes are on the same path in the historical integration flowchart. (Coefficient) , , The normalized weight vector is constructed and dynamically configured according to the characteristics of the antenna's 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 a close match with the required vertex, the system records the conflict path and initiates secondary verification: it checks the context of the conflicting vertex in historical tests and prioritizes vertices more compatible with the current requirement flowchart topology. For severe conflicts that cannot be resolved, a pending confirmation item is generated for manual decision-making, while the intermediate state of automated processing is retained. All conflict resolution results are recorded in a knowledge base for optimizing subsequent matching strategies.

[0079] For partially matching vertex pairs, the matching score is adjusted based on the terminology hierarchy of their labels. The standard terminology library defines the parent-child relationships of RF function labels; a partial match score is obtained when a child label matches a parent label. This mechanism enhances the system's ability to handle non-standard testing requirements, effectively linking alternative flowchart nodes to concrete implementations even when the requirement flowchart contains abstract functional descriptions.

[0080] Example 4: See Figure 5 When the test anomaly detection unit performs a scan in the basic RF test flowchart, it uses a layered traversal strategy to check the integrity of the RF parameter processing nodes. Taking the test flowchart of a certain type of multi-band antenna as an example, after the system loads the flowchart containing 32 nodes, the detection unit checks layer by layer starting from the starting node "N1-1-1" (representing the first node of the first layer of template 1). The RF attribute dataset of each node is parsed into a structured record, and the detection logic focuses on three key fields: whether the RF function label has a null value, 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 for node "N3-2-5" shows an anomaly: its input source node label points to "N2-1-3," but verification reveals that this node number does not exist in template 2. The anomaly detection unit marks this node as a node to be modified and records the anomaly type "node reference invalid" in the audit log. Meanwhile, the output node label for node "N4-3-8" is blank, but its RF function label indicates that this node should output impedance matching parameters; the system classifies this as an anomaly of the "missing data output" type. The table below shows some of the anomaly detection results for this antenna model's flowchart:

[0082] Table 1: Flowchart of Multi-Band Antenna Testing - Record of Abnormal Nodes

[0083] Node number Exception types Related fields Proposed amendments N3-2-5 Node reference invalid Input source node label Update the input source to the valid node N2-1-2 N4-3-8 Data export missing Output node labels Add an output link to N5-4-1 N5-4-6 Function tag conflict Radio frequency function label Change "VSWR testing" to "impedance testing" N6-5-2 Circular reference warning Input / output labels Check the pointing relationship between N6-5-3 and N6-5-1

[0084] Taking the aforementioned multi-band antenna as an example, the revised flowchart contains 34 nodes (with 2 newly added compensation nodes). The RF node verification subunit loads a convolutional neural network model. The model's input layer receives three sets of feature data: the node's current RF function label encoding value, the hash checksum of the input and output labels, and the parameter fluctuation variance in the last 10 test records. The hidden layer uses a 3D convolutional kernel to extract features, and the output layer calculates the node's error probability value. When verifying node "N4-3-8", the model detects 3 impedance parameter jumps (exceeding ±5Ω) in its historical test records, outputting an error probability of 0.87, exceeding the preset threshold of 0.8. This node is marked as a risk node.

[0085] For node "N4-3-8", the system outputs a structured early warning message: risk level "high risk", error type "insufficient parameter stability", and related affected nodes include downstream nodes "N5-4-1" and "N5-4-3". The early warning message includes remedial suggestions: check the impedance matching network connectors of this node and recalibrate the test equipment. Simultaneously, the system automatically locks the testing permissions for the risk node, prohibiting new test tasks from being assigned to this node until manual confirmation of completion of the repair.

[0086] When analyzing the data distribution characteristics of risk nodes, the dynamic migration unit establishes a data heatmap. In the example, the historical data of node "N4-3-8" is stored on Node-12 of the server cluster. It was accessed 142 times in the past 24 hours, with a total data volume of 3.7GB. The radio frequency data distribution analysis subunit calculates the migration priority index of this node as 0.91 (maximum 1.0), triggering an emergency migration task. The migration path planning module scans the storage topology network and selects Node-07, with a load rate below 30% and an error rate below 5%, as the target node. The migration path is Node-12 → Node-05 → Node-07, avoiding the currently high-load Node-09 relay node.

[0087] The 3.7GB data was divided into 256MB blocks, each with a CRC32 checksum. During the migration, the transmission rate and error rate were monitored in real time. If a block of data transmission failed, the system automatically switched to the backup path Node-12→Node-11→Node-07. After the migration was complete, the system updated the access permission table for node "N4-3-8": the new data location address pointed to Node-07, the access key was rotated to a new 256-bit AES key, and the data mirror on Node-12 was retained for 48 hours as a disaster recovery copy.

[0088] The collaborative work of the anomaly detection unit and the dynamic migration unit forms a closed-loop management system. In the example, after completing the data migration of risk nodes, the system re-evaluates the flowchart status: the error probability of node "N4-3-8" drops to 0.45, and the risk lock status is lifted; the newly added compensation nodes "N3-2-9" and "N4-3-10" pass verification and are integrated into the main test flow path. The system generates the final complete RF test flowchart, marking the data storage location and access permissions of all nodes for the test execution engine to call.

[0089] The knowledge accumulation mechanism during anomaly handling continuously optimizes system performance. Node reference failure cases in the example are added to the rule base, and subsequent detection will prioritize checking cross-template node references; the threshold for judging parameter stability risks is dynamically adjusted based on the repair effect of "N4-3-8," with the threshold for the same type of antenna decreasing from 0.8 to 0.7.

[0090] For medium-risk nodes with an error probability of 0.7-0.8, the system adopts a silent background migration mode, which does not affect the execution of the current test task. For high-risk nodes with an error probability of 0.8 or higher, the migration is immediately interrupted, and the relevant test threads are paused until the migration is completed. The migration path selection algorithm comprehensively considers parameters such as network topology distance, storage node health, and historical migration success rate. After each migration task is completed, the path score data is automatically updated to optimize subsequent decisions.

[0091] Example 5: Data acquisition covers three core dimensions: historical data storage location information (physical storage node number and logical partition address); access timestamp sequence statistics (recent access time distribution density); and data volume change trend tracking (storage space occupancy fluctuation). The analysis engine projects these parameters onto a spatiotemporal coordinate system to generate a three-dimensional data distribution heatmap. The axes of the heatmap represent the storage node topology location, time window index, and data volume range, respectively. The heatmap value is calculated by normalizing the product of access frequency and data volume. High-frequency hotspots are highlighted in the heatmap, and the system automatically marks these areas as candidate areas for migration.

[0092] The index values ​​are generated by a dynamic composite algorithm, with basic factors including data interaction frequency and node error probability. Interaction frequency is calculated by weighting the peak and mean values ​​over the past 72 hours, while error probability is calculated using a sliding window average. Additional factors consider data sensitivity level and real-time network load; data with higher sensitivity levels receives increased weight, while the index decays during periods of network congestion. The index calculation results are sorted to generate a migration task queue, with the task at the head of the queue receiving immediate execution privileges.

[0093] The storage network is modeled as a weighted directed graph, with vertices corresponding to physical storage nodes. Edge weights are determined by a combination of physical link latency, current load rate, and historical failure rate. The planning algorithm starts from the source node and ends at the target node, searching for the optimal path under constraints. These constraints include a maximum hop count limit, a single-hop latency cap, and a fault node avoidance list. The algorithm monitors the health status of path nodes in real time, automatically switching to a backup path when a sudden increase in load or excessive response latency is detected at the target node. The path optimization process employs an incremental update mechanism; after each migration task, the edge weight parameters of the network model are adjusted based on actual transmission performance.

[0094] The first stage, pre-migration verification, checks the digital signature of the source data, verifies storage node access permissions, and reserves target storage space. The second stage, data transmission, divides the data stream into fixed-size blocks, attaching metadata tags to each block to record verification information. The transmission process is monitored via dual channels: the main channel transmits data blocks, while the auxiliary channel synchronously sends verification codes. The receiving end compares the verification results in real time, and abnormal blocks trigger an immediate retransmission mechanism. The third stage, integrity confirmation, reconstructs the complete dataset on the target node and compares it with the hash value of the source data. If the difference exceeds the tolerance limit, difference compensation transmission is initiated.

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

[0096] The monitoring cycle is dynamically adjusted based on the node's risk level. High-risk nodes are inspected every minute, while low-risk nodes are scanned hourly. Monitoring indicators include storage capacity fluctuations, read / write error rates, and response latency curves. When insufficient storage capacity or an increase in the error rate is detected on a target node, the early warning system triggers a secondary migration assessment. The assessment algorithm calculates the stability score of the current node. If the score is below a threshold, a new migration task is generated, but its migration priority is lower than the initial migration task.

[0097] Each migration task records parameters such as the source node's performance baseline, migration path selection logic, transmission interruption events, and final latency. The knowledge base employs a case-based reasoning mechanism; when a new migration task is triggered, the system retrieves similar historical cases and prioritizes validated, efficient path patterns. For new storage architectures without historical references, the system initiates an exploratory migration mode: first, it attempts standard path templates, dynamically adjusting path weights based on real-time performance feedback.

[0098] After migration, a cached copy of the original data is retained for a set period, during which access requests can still be traced back to the source node. Before the cache expires, the system performs data cleanup verification, and automatically erases the source data after confirming that the target data is complete and usable. The erasure process uses a multi-round overwrite secure deletion protocol to ensure that sensitive RF test data is unrecoverable. All lifecycle operations generate audit reports, recording the data location change trajectory and permission change history.

[0099] When the RF node verification subunit detects a new risk node, it automatically notifies the storage optimization unit to predict data migration needs. Conversely, anomalies in storage nodes discovered during the migration process are synchronized to the risk warning database in real time. This collaborative mechanism enables the system to proactively optimize, adjusting the data distribution before risks become apparent. The migration task scheduler supports parallel processing queues and uses resource isolation technology to ensure that high-priority migration tasks are not interfered with by low-priority tasks.

[0100] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. 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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