A passive intermodulation test method and a passive intermodulation test system

Abstract

The present application relates to the technical field of wireless communication test, and discloses a passive intermodulation test method and a passive intermodulation test system.The method comprises the following steps: a spatial correlation model corresponding to a test space is established, and when a detection device moves for testing, real-time position data of the detection device is synchronized to dynamically verify the execution integrity of a preset test node.In the verification process, a measured signal is extracted, a target signal component is separated out, and the target signal component is subjected to spatial attribution positioning and time marking in combination with the spatial model.According to the time, the positioning point in the model is traced back and verified, and then the bound environmental parameter benchmark is called.The evaluation result is generated by comprehensively combining the environmental benchmark, the node record and the target signal.The method realizes real-time dynamic verification and closed-loop correlation of the test process and signal analysis, and improves the reliability and traceability of the test result.

Description

Technical Field

[0001] This invention relates to the field of wireless communication testing technology, specifically to a passive intermodulation testing method and a passive intermodulation testing system. Background Technology

[0002] Passive intermodulation testing is a key method for evaluating the nonlinear characteristics of passive components in wireless communication systems. Existing conventional testing methods mainly rely on preset fixed test points or predetermined scanning paths. During the test, the detection device moves and collects signals according to predetermined steps, and the execution status of the test steps is usually confirmed afterward through manual recording or device status codes. In the signal processing stage, potential passive intermodulation signal components are screened from the collected data set based on frequency characteristics.

[0003] Existing technologies suffer from a disconnect between the verification of test procedure integrity and the actual spatial movement trajectory of the equipment. This makes it impossible to objectively determine in real-time whether all preset test nodes have been traversed correctly. Any path deviations or node omissions can only be discovered later in the data analysis process, or may not even be detected at all, leading to uncertainty in the spatial coverage of the test and compromising the reliability of the data foundation. Furthermore, there is no mandatory correlation between the identified target signal and its specific spatial location, as well as the corresponding test operation history. The signal analysis result becomes an isolated data point, making it impossible to retrospectively verify whether the test procedure was executed completely or whether the environment was controlled at the time of acquisition. This disconnect makes it difficult to guarantee the reliability of the test results, making it impossible to distinguish between genuine intermodulation signals and abnormal responses introduced by flaws in the test process, and also weakening the traceability of the results.

[0004] For the reasons mentioned above, a method is needed to combine the spatial movement of the test equipment with the real-time verification of the test process, and to establish a reliable correlation between the target signal and the test process state corresponding to its acquisition position and acquisition time, so as to solve the problems of insufficient controllability of the test process and reliability of the results. Summary of the Invention

[0005] The main objective of this invention is to provide a passive intermodulation testing method and a passive intermodulation testing system, so as to at least partially solve the shortcomings of the prior art.

[0006] To achieve the above-mentioned main objectives, the present invention provides a passive intermodulation testing method, the method comprising:

[0007] S1: Establish a spatial association model corresponding to the preset physical test space;

[0008] S2: In the spatial association model, real-time location data generated by the detection device during the test movement is synchronized;

[0009] S3: Based on the synchronized real-time location data, dynamically verify the node integrity of the test steps. During the dynamic verification of node integrity, extract the actual signal dataset captured by the detection device.

[0010] S4: Separate the target signal component that conforms to the preset frequency characteristics from the measured signal dataset;

[0011] S5: Combining the spatial association model, spatially assign the target signal component to a location and mark the location time at which the assigned position is generated;

[0012] S6: Based on the positioning time, trace and verify the node verification records associated in the spatial association model;

[0013] S7: Retrieve the environmental parameter baselines bound to the node verification record and the stated location;

[0014] S8: Based on the environmental parameter benchmark, node verification records, and target signal components, derive the evaluation results of the passive intermodulation test.

[0015] Preferably, step S1 includes:

[0016] Construct a virtual three-dimensional coordinate grid covering the preset physical test space;

[0017] Identify the geometric center position of all interfaces of the device under test in the preset physical test space;

[0018] Based on the geometric center location, a core associated region is defined in the virtual three-dimensional coordinate grid;

[0019] Based on the preset test scanning logic, multiple theoretical detection paths extend outward from the core associated region;

[0020] Each theoretical detection path is discretized into a series of ordered coordinate points;

[0021] Each coordinate point sequence is assigned a unique path identifier and step sequence identifier. The spatial association model is composed of the virtual three-dimensional coordinate grid, the core association region, all theoretical detection paths and their coordinate point sequences and identifiers.

[0022] Preferably, step S2 includes:

[0023] Receive raw coordinate data transmitted back at fixed intervals by the positioning device attached to the detection equipment;

[0024] The original coordinate data is matched and calibrated with the virtual three-dimensional coordinate grid in the spatial association model to eliminate coordinate offset;

[0025] Based on the matched and calibrated coordinate data, the actual movement trajectory of the detection device is drawn in the virtual three-dimensional coordinate grid;

[0026] The actual movement trajectory is compared with all theoretical detection paths to determine the target theoretical detection path that matches the actual movement trajectory.

[0027] Each sampling point on the actual movement trajectory is mapped to the coordinate point sequence of the theoretical detection path of the target according to the temporal relationship, thereby synchronizing the real-time location data with the spatial association model.

[0028] Preferably, step S3 includes:

[0029] During the mapping process, it is monitored whether the sampling points of the actual movement trajectory continuously cover the coordinate point sequence of the theoretical detection path of the target;

[0030] If the sampling point of the actual movement trajectory is detected to skip a specific coordinate point in the coordinate point sequence, then a step jump event is recorded.

[0031] Create an event log for each recorded step jump event, the event log containing at least the jump start coordinates, the jump end coordinates, and the time when the jump occurred;

[0032] All step jump events that occur during a single test are cumulatively counted, and a node integrity verification report is generated, which serves as part of the node verification record.

[0033] Preferably, step S4 includes:

[0034] Perform a bandpass filter operation on the measured signal dataset to filter out background noise outside the frequency domain and obtain the initial screening signal;

[0035] Perform time-frequency transformation on the initial screening signal to obtain its corresponding time-frequency distribution spectrum;

[0036] In the time-frequency distribution spectrum, identify signal regions where the energy intensity exceeds a preset energy threshold;

[0037] Extract the frequency values ​​corresponding to the signal region and compare them one by one with a preset frequency characteristic library;

[0038] The original signal segments corresponding to signal regions with consistent frequency matching are marked as target signal components.

[0039] Preferably, step S5 includes:

[0040] Obtain the precise time stamp of the target signal component when it is captured;

[0041] Based on the precise time stamp, query the corresponding coordinate position of the detection device in the spatial association model from the synchronized real-time location data;

[0042] Using the corresponding coordinate position as the center, and combining the boundary information of the core association region defined in the spatial association model, the spatial affiliation probability is calculated;

[0043] The core associated region with the highest spatial assignment probability is determined as the location to which the target signal component belongs.

[0044] Preferably, step S6 includes:

[0045] Based on the positioning time, determine the theoretical node position corresponding to it on the coordinate point sequence of the theoretical detection path of the target;

[0046] Query the node integrity verification report to check whether there are any step jump events within the preset time window before and after the theoretical node position;

[0047] If a step skip event exists, extract all event logs related to the step skip event;

[0048] The event log is associated and bound with the theoretical node location to form node context verification information for the location time.

[0049] Preferably, step S7 includes:

[0050] A set of standard environmental parameters associated with each core associated region in the spatial association model is pre-stored, and the set of standard environmental parameters includes standard temperature, standard humidity and standard background noise spectrum;

[0051] Based on the determined location, the corresponding set of standard environmental parameters is indexed.

[0052] Simultaneously, the sequence of environmental parameters actually collected by environmental sensors within the time period covered by the node verification record is obtained;

[0053] Calculate the deviation metric between the environmental parameter sequence and the standard environmental parameter set, wherein the environmental parameter benchmark is jointly characterized by the standard environmental parameter set and the deviation metric.

[0054] Preferably, step S8 includes:

[0055] Based on the signal strength and signal purity of the target signal components, calculate the original signal quality score;

[0056] Based on the aforementioned deviation metric, the impact of environmental parameters is quantitatively corrected to generate environmental correction coefficients.

[0057] Calculate the node compliance deduction score based on the severity and frequency of step skipping events in the node context verification information;

[0058] Substituting the original signal quality score, the environmental correction coefficient, and the node compliance deduction score into the preset evaluation formula, the final passive intermodulation test evaluation result is obtained.

[0059] Preferably, the present invention also includes a passive intermodulation testing system, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the steps of the source intermodulation testing method described above.

[0060] Compared with the prior art, the beneficial effects of the present invention are:

[0061] By synchronizing real-time location data of the probe during its movement to a spatial correlation model, and dynamically verifying the integrity of test nodes based on this model, the method achieves synchronization between test execution and process verification. During the probe's movement, the method continuously compares and verifies the actual spatial coordinates with the test nodes in the preset model. This allows for timely detection and alerts to any deviations from the preset plan or omissions in the test path, ensuring that every preset spatial node is effectively detected. This eliminates blind spots caused by human error or equipment control malfunctions, guarantees the spatial integrity of the collected data and the reliability of the process, and enables subsequent analysis to be based on a complete and controlled test process.

[0062] By spatially affixing and locating the separated target signal components, and then tracing back and verifying the node verification records in the model based on the location time, a closed-loop correlation of "signal-space-process" is established. This technique endows the target signal with a clear spatiotemporal background and process context. When a suspected intermodulation signal is identified, not only is its location known, but it is also possible to immediately verify whether the test steps performed at that location have been correctly and completely verified, eliminating the possibility of abnormal signals generated due to improper test step execution, thereby reliably distinguishing real intermodulation signals from process noise or errors. Simultaneously, this correlation allows the source of any analysis result to be precisely traced to its spatial location and the corresponding verified test operation records, enhancing the credibility and auditability of the test conclusions. Attached Figure Description

[0063] Figure 1 This is a schematic diagram illustrating the working principle of the passive intermodulation testing method described in this invention.

[0064] Figure 2 A flowchart for establishing a spatial association model;

[0065] Figure 3A flowchart for separating the target signal components;

[0066] Figure 4 A spatial distribution diagram of the theoretical detection path in passive intermodulation testing;

[0067] Figure 5 This is a normalized comparison chart of the core indicators of the original signal quality in passive intermodulation testing. Detailed Implementation

[0068] To better understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Many specific details are set forth in the following description with reference to embodiments in order to provide a thorough understanding of the present invention; however, it should be understood that the following embodiments and detailed descriptions are for illustrative purposes only and do not limit the scope of protection of the present invention.

[0069] like Figure 1 As shown, the present invention provides a passive intermodulation testing method, the method comprising the following steps:

[0070] S1: Establish a spatial association model corresponding to the preset physical test space;

[0071] S2: In the spatial association model, real-time location data generated by the detection device during the test movement is synchronized;

[0072] S3: Based on the synchronized real-time location data, dynamically verify the node integrity of the test steps. During the dynamic verification of node integrity, extract the actual signal dataset captured by the detection device.

[0073] S4: Separate the target signal component that conforms to the preset frequency characteristics from the measured signal dataset;

[0074] S5: Combining the spatial association model, spatially assign the target signal component to a location and mark the location time at which the assigned position is generated;

[0075] S6: Based on the positioning time, trace and verify the node verification records associated in the spatial association model;

[0076] S7: Retrieve the environmental parameter baselines bound to the node verification record and the stated location;

[0077] S8: Based on the environmental parameter benchmark, node verification records, and target signal components, derive the evaluation results of the passive intermodulation test.

[0078] In steps S1-S2, a spatial association model corresponding to the preset physical test space is established; in the spatial association model, real-time position data generated by the detection device during the test movement is synchronized.

[0079] In one embodiment of the present invention, see [reference] Figure 2 A virtual three-dimensional coordinate grid covering the preset physical test space is constructed. The geometric center positions of all interfaces of the devices under test in the preset physical test space are identified. Based on the geometric center positions, a core association region is defined in the virtual three-dimensional coordinate grid. According to the preset test scanning logic, multiple theoretical detection paths are extended outward from the core association region. Each theoretical detection path is discretized into a series of ordered coordinate point sequences. A unique path identifier and step sequence identifier are assigned to each coordinate point sequence. The spatial association model is composed of the virtual three-dimensional coordinate grid, the core association region, all theoretical detection paths and their coordinate point sequences and identifiers. The system receives raw coordinate data from a positioning device attached to the detection equipment at fixed intervals. This raw coordinate data is then matched and calibrated against a virtual 3D coordinate grid in the spatial association model to eliminate coordinate offsets. Based on the calibrated coordinate data, the actual movement trajectory of the detection equipment is plotted in the virtual 3D coordinate grid. The actual movement trajectory is compared with all theoretical detection paths to determine the target theoretical detection path that matches the actual movement trajectory. Each sampling point on the actual movement trajectory is mapped to the coordinate point sequence of the target theoretical detection path according to a temporal relationship, thus synchronizing the real-time position data with the spatial association model. When defining the core association area and extending multiple theoretical detection paths, for scenarios involving multiple frequency band intermodulation combinations that may exist within the preset physical test space, the coordinate point sequence of each theoretical detection path reserves sampling configurations corresponding to different intermodulation frequency characteristics. This allows the detection equipment to simultaneously adapt to the signal acquisition requirements of multiple preset frequencies during its movement along the path, eliminating the need to repeatedly plan paths for different intermodulation combinations and improving the continuity and efficiency of the testing process.

[0080] In practical implementation, establishing a spatial correlation model and synchronizing real-time position data are fundamental steps when implementing the passive intermodulation testing method. Taking a pre-defined physical test space with dimensions of 10 meters (length), 10 meters (width), and 5 meters (height) as an example, a virtual three-dimensional coordinate grid is constructed with the southwest corner as the origin and the axis parallel to the edge of the room. The resolution of the grid in the X, Y, and Z directions is set to 0.1 meters, 0.1 meters, and 0.05 meters, respectively. Two interfaces of the devices under test within the pre-defined physical test space are identified, and their geometric center positions are measured to be coordinates (2,2,1) and (8,8,1), respectively. A spherical region with a radius of 0.5 meters is defined as the core correlation region, centered on these coordinate points. According to the pre-defined test scanning logic of raster scanning along the device surface, multiple theoretical detection paths extend outward from the surface of the core correlation region, such as a horizontal straight path along the X-axis and a spiral upward path around the core correlation region. The horizontal straight path is discretized into a sequence of 100 ordered coordinate points from point (1.5,2,1) to point (2.5,2,1), and the spiral upward path is discretized into a sequence of 150 ordered coordinate points. The horizontal straight path is assigned a path identifier "PATH_01" and each coordinate point is assigned a step sequence identifier from 1 to 100. The spiral upward path is assigned a path identifier "PATH_02" and each coordinate point is assigned a step sequence identifier from 1 to 150. The spatial association model is thus composed of a virtual three-dimensional coordinate grid, two core association regions, two theoretical detection paths and their coordinate point sequences and identifiers.

[0081] In some embodiments, the positioning device on the detection equipment transmits raw coordinate data containing X, Y, and Z coordinates at a frequency of 100 Hz. After receiving this raw coordinate data, it performs matching calibration with a virtual three-dimensional coordinate grid to eliminate coordinate offsets caused by installation deviations of the positioning device or inconsistencies in the initial coordinate system. Matching calibration is achieved by calculating a coordinate transformation matrix, which describes the rotation and translation relationship between the raw coordinate system and the virtual three-dimensional coordinate grid coordinate system. After matching calibration is completed, the calibrated coordinate data is connected in chronological order in the virtual three-dimensional coordinate grid to draw the actual movement trajectory of the detection equipment starting from point (0,0,0). The actual movement trajectory is compared with the theoretical detection paths represented by path identifiers "PATH_01" and "PATH_02" in terms of similarity. The similarity comparison is performed by calculating the Friesian distance between the set of points on the actual movement trajectory and the coordinate point sequence of each theoretical detection path. The theoretical detection path with the smallest Friesian distance is determined as the target theoretical detection path matched by the actual movement trajectory. Each sampling point on the actual movement trajectory is mapped to the theoretical coordinate point that is closest in time to the coordinate point sequence of the target's theoretical detection path, based on its timestamp, thus synchronizing the real-time location data with the spatial correlation model.

[0082] The application of coordinate transformation matrices is a specific method of matching calibration; these matrices are obtained through the calibration process. In practice, the matching calibration process involves solving the following relationships:

[0083]

[0084] in: This represents a vector composed of the original coordinate data returned by the positioning device. This represents the coordinate vector corresponding to the virtual 3D coordinate grid after transformation. It is a 3x3 rotation matrix used to correct for deviations in the coordinate axis orientation. It is a 3x1 translation vector used to correct the offset of the coordinate origin. By collecting the original coordinate data of multiple reference points with known precise grid coordinates in a preset physical test space, the rotation matrix can be solved by fitting. Translation vector The specific parameters.

[0085] Optionally, the similarity comparison function can be a metric based on the average distance of the point set, with the following function form:

[0086]

[0087] in: Represents the actual movement trajectory With a theoretical detection path Similarity score, Represents the actual movement trajectory The number of sampling points in the sample. Represents the first in the actual movement trajectory coordinate points, The first point in the sequence of coordinate points representing the theoretical detection path A coordinate point, symbol The function represents the calculation of the Euclidean distance between two points. This indicates that for points in the actual movement trajectory Calculate its path to the theoretical detection path Find the minimum Euclidean distance to all points. Score The lower the value, the greater the actual movement trajectory. Compared with theoretical detection path The higher the similarity.

[0088] In some embodiments, the mapping process employs a nearest neighbor matching rule based on minimum Euclidean distance. For a sampling point on the actual movement trajectory, the Euclidean distance between it and all points in the target theoretical detection path coordinate point sequence is calculated. The theoretical coordinate point with the smallest Euclidean distance is taken as the mapping target of the sampling point. This process is executed sequentially for all sampling points to ensure that the spatiotemporal information of the actual movement trajectory is accurately associated with the predefined theoretical detection path nodes in the spatial association model.

[0089] In step S3, the node integrity of the test step is dynamically verified based on the synchronized real-time location data; during the dynamic verification of node integrity, the measured signal dataset captured by the detection device is extracted.

[0090] In one embodiment of the present invention, during the mapping process, it is monitored whether the sampling points of the actual movement trajectory continuously cover the coordinate point sequence of the target theoretical detection path. If it is detected that the sampling points of the actual movement trajectory skip a specific coordinate point in the coordinate point sequence, a step jump event is recorded. An event log is created for each recorded step jump event. The event log includes at least the jump start coordinate point, the jump end coordinate point, and the time when the jump occurs. All step jump events that occur in a single test are accumulated and statistically analyzed to generate a node integrity verification report. The node integrity verification report is part of the node verification record.

[0091] In practice, the node integrity of the dynamic verification test steps is performed based on synchronized real-time location data. The mapping process associates the sampling points of the actual movement trajectory with the coordinate point sequence of the target theoretical detection path. The monitoring process checks whether the sampling points of the actual movement trajectory continuously cover the coordinate point sequence of the target theoretical detection path in theoretical order. For example, the coordinate point sequence of the target theoretical detection path identifier "PATH_01" contains 100 ordered points from step sequence identifier 1 to step sequence identifier 100. After mapping, the corresponding step sequence identifiers of the sampling points of the actual movement trajectory are 1, 2, 3, 10, 13, 14, 100. The monitoring found that the next mapping identifier after step sequence identifier 10 is step sequence identifier 13, and step sequence identifiers 11 and 12 are skipped, recording a step skip event.

[0092] In some embodiments, the condition for recording a step jump event is achieved by determining whether the difference between the step sequence identifiers of two consecutive mapping points is greater than 1, specifically based on the following relationship:

[0093]

[0094] in: This represents the step sequence identifier in the target theoretical detection path coordinate point sequence mapped to the nth actual movement trajectory sampling point. This indicates the step sequence identifier mapped to the (n+1)th sampling point, when the calculated difference... When the value is greater than 1, it is determined that a step sequence identifier has been generated. Step sequence identifier The step jump event. For the jump from step sequence identifier 10 to step sequence identifier 13, the jump start coordinate point is the coordinate point (2.05, 2.00, 1.00) corresponding to step sequence identifier 10, and the jump end coordinate point is the coordinate point (2.16, 2.00, 1.00) corresponding to step sequence identifier 13. The time when the jump occurs is the timestamp of the moment after the sampling point mapped to step sequence identifier 10 is collected and before the sampling point mapped to step sequence identifier 13 is collected, recorded as "2023-10-27 10:15:30.450".

[0095] Optionally, the event log created for each recorded step jump event adopts a structured data recording format. The event log includes "Event_ID", "Path_ID", "Skip_From", "Skip_To", and "Timestamp" fields. The event log record for one step jump event is as follows:

[0096] Event_ID:E001,Path_ID:PATH_01,Skip_From:10,Skip_To:13,Timestamp:2023-10-2710:15:30.450.

[0097] During a single test, multiple step jump events may occur. For example, a subsequent jump from step sequence identifier 50 to step sequence identifier 55 may occur. The corresponding event log record is as follows:

[0098] Event_ID:E002,Path_ID:PATH_01,Skip_From:50,Skip_To:55,Timestamp:2023-10-2710:16:15.720.

[0099] In some embodiments, all step jump events occurring during a single test are cumulatively counted to generate a node integrity verification report. This report summarizes all event logs and includes statistical information, presented in tabular and summary form. An example of a node integrity verification report is as follows: Report ID "NIVR_20231027_001", associated theoretical detection path "PATH_01", total theoretical nodes 100, total number of detected jump events 2, detailed event list (event logs E001 and E002), and report generation timestamp "2023-10-27 10:20:00.000". This node integrity verification report, as part of the node verification record, is used for subsequent traceability and verification.

[0100] It is understandable that the recording of step jump events includes not only jumps in sequence identifiers but also unconventional changes in direction. In practice, if the sequence of step identifiers mapped from the sampling points of the actual movement trajectory is reversed, for example, from step sequence identifier 15 to step sequence identifier 12, it is also recorded as a step jump event. The jump start coordinate point is the coordinate point corresponding to step sequence identifier 15, and the jump end coordinate point is the coordinate point corresponding to step sequence identifier 12. The cumulative statistical process counts and classifies step jump events of all types and directions, and the final node integrity verification report objectively reflects the deviation of the actual test movement from the predetermined theoretical detection path.

[0101] In steps S4-S5, target signal components that conform to preset frequency characteristics are separated from the measured signal dataset; combined with the spatial correlation model, the target signal components are spatially assigned and located, and the location time at which the assigned position is generated is marked.

[0102] In one embodiment of the present invention, see [reference] Figure 3 A bandpass filter is performed on the measured signal dataset to remove background noise outside the frequency domain, resulting in a preliminary screening signal. A time-frequency transformation is then performed on the preliminary screening signal to obtain its corresponding time-frequency distribution map. Signal regions with energy intensities exceeding a preset energy threshold are identified within the time-frequency distribution map. The frequency values ​​corresponding to these signal regions are extracted and compared one by one with a preset frequency characteristic library. The original signal segments corresponding to signal regions with consistent frequency comparisons are marked as target signal components. The precise time stamp of the target signal component at the time of capture is obtained. Based on the precise time stamp, the corresponding coordinate position of the detection device in the spatial association model is queried from the synchronized real-time location data. Using this corresponding coordinate position as the center, and combining the boundary information of the core association region defined in the spatial association model, the spatial attribution probability is calculated. The core association region with the highest spatial attribution probability is determined as the location of the target signal component.

[0103] In practice, separating the target signal component conforming to preset frequency characteristics from the measured signal dataset involves a series of signal processing operations. The measured signal dataset is captured by the detection device during the dynamic verification of node integrity and includes timestamps and corresponding signal amplitude sequences, such as a discrete signal sequence lasting 10 seconds with a sampling rate of 100 MHz. Bandpass filtering is performed on the measured signal dataset, with the passband frequency set to a preset intermodulation product frequency range, such as from 900 MHz to 910 MHz, filtering out background noise outside this frequency range to obtain a preliminary screening signal containing only frequency components within the passband. A short-time Fourier transform is performed on the preliminary screening signal to obtain its corresponding time-frequency distribution spectrum. The horizontal axis of the time-frequency distribution spectrum represents time, the vertical axis represents frequency, and the color depth represents signal energy intensity. In the time-frequency distribution spectrum, signal regions with energy intensities exceeding a preset energy threshold are identified. The preset energy threshold is set to -50 dBm; for example, a cluster region with an energy intensity of -45 dBm appears near time 3.2 seconds and frequency 905 MHz. The center frequency value of 905 MHz corresponding to this signal region is extracted and compared one by one with multiple expected intermodulation frequency points pre-stored in a preset frequency characteristic library, which includes entries such as 905.0 MHz and 1805.0 MHz. The original signal segment corresponding to the signal region with consistent frequency comparison, that is, the original signal data segment that covers this signal region in time from the measured signal dataset, is marked as the target signal component. The preset frequency characteristic library integrates frequency parameters of multiple typical intermodulation combinations. When comparing the frequency values ​​of the signal region, multiple sets of target intermodulation frequencies can be matched simultaneously without the need for individual screening of each set. This achieves synchronous separation of multiple intermodulation combination target signal components in the same measured signal dataset, reducing repetitive operations in signal processing.

[0104] In some embodiments, the frequency comparison of target signal components is determined using an absolute error limit, and the following relationship is considered to indicate a matching agreement:

[0105]

[0106] in: This represents the frequency value extracted from the signal region of the time-frequency distribution map. This represents a pre-stored frequency entry in the preset frequency characteristic library. This indicates the preset frequency tolerance, for example, set to 0.1 MHz. When extracting the original signal segment corresponding to the signal region, the starting time of the signal region in the time-frequency distribution spectrum is used. and end time The time segment extracted from the original measured signal dataset is... arrive All sampling points within the interval constitute the time-domain data of the target signal component.

[0107] It is understandable that spatial attribution positioning of target signal components using a spatial correlation model requires time synchronization information. This involves obtaining the precise timestamp of the target signal component at the time of capture. This timestamp originates from the timestamp of the starting sampling point or center point of the original signal segment corresponding to the target signal component, for example, the timestamp "2023-10-27 10:15:33.200". Based on this precise timestamp, the corresponding coordinates of the detection device in the spatial correlation model are queried from the synchronized real-time location data. The real-time location data is a list of timestamps and spatial coordinates. By matching the timestamp closest to the precise timestamp, the corresponding coordinates are found, for example, coordinates (2.10, 2.00, 1.05). Using this corresponding coordinates as the center, the spatial attribution probability is calculated by combining the boundary information of the core correlation region defined in the spatial correlation model. The core correlation region is a spherical region with a radius of 0.5 meters centered at point (2.0, 2.0, 1.0). The Euclidean distance from the corresponding coordinates (2.10, 2.00, 1.05) to the center of this spherical region is calculated.

[0108] Optional, spatial affiliation probability The calculation can be performed using a distance-based attenuation function, and a specific calculation relationship is as follows:

[0109]

[0110] in: This represents the probability that a target signal component belongs to a certain core correlation region. This represents the Euclidean distance from the corresponding coordinate position of the detection device when the target signal component is captured to the geometric center of the core associated region. It is a preset attenuation coefficient used to control the rate at which the probability decreases with increasing distance, for example, setting... Calculate the Euclidean distance from the corresponding coordinate position (2.10, 2.00, 1.05) to the center of the core associated region (2.0, 2.0, 1.0). Meters, substitute into the formula to calculate the probability of belonging. This probability calculation is performed on all core association regions in the spatial association model, and the core association region with the highest spatial assignment probability, such as a spherical region with a probability of 0.975, is determined as the location to which the target signal component belongs.

[0111] In steps S6-S7, based on the positioning time, the node verification records associated with the spatial association model are traced and verified; the environmental parameter benchmarks bound to the node verification records and their home locations are retrieved.

[0112] In one embodiment of the present invention, based on the positioning time, the theoretical node position corresponding to the coordinate point sequence of the theoretical detection path of the target is determined. The node integrity verification report is queried, and it is checked whether there is a step jump event within a preset time window before and after the theoretical node position. If a step jump event exists, all event logs related to the step jump event are extracted, and the event logs are associated and bound with the theoretical node position to form node context verification information for the positioning time. A set of standard environmental parameters associated with each core association region in the spatial association model is pre-stored. The set of standard environmental parameters includes standard temperature, standard humidity, and standard background noise spectrum. Based on the determined location, the corresponding set of standard environmental parameters is indexed. At the same time, the environmental parameter sequence actually collected by the environmental sensor within the time period covered by the node verification record is obtained, and the deviation metric between the environmental parameter sequence and the set of standard environmental parameters is calculated. The environmental parameter benchmark is jointly characterized by the set of standard environmental parameters and the deviation metric.

[0113] In the specific implementation, based on the positioning time, the verification record of the associated nodes in the spatial association model needs to be clearly defined to trace and verify the theoretical node position. According to the positioning time of the target signal component "2023-10-27 10:15:33.200", the theoretical node position corresponding to it on the coordinate point sequence of the target theoretical detection path identifier "PATH_01" is determined. The timestamp corresponding to step sequence identifier 22 in the coordinate point sequence is calculated to be "2023-10-27 10:15:33.200". Therefore, the theoretical node position is the coordinate point (2.24, 2.00, 1.00) corresponding to step sequence identifier 22. Query the previously generated node integrity verification report identifier "NIVR_20231027_001" to check whether there are any step jump events within the preset time window before and after the theoretical node location. The preset time window is set to 0.3 seconds before and after the positioning time, that is, check the event logs within the time range from "2023-10-27 10:15:32.900" to "2023-10-27 10:15:33.500". If a step-skip event exists, all event logs related to the step-skip event are extracted. The jump occurrence time recorded in event log "Event_ID:E001" as "2023-10-27 10:15:30.450" is before the preset time window, while the jump occurrence time recorded in event log "Event_ID:E002" as "2023-10-27 10:16:15.720" is after the preset time window. Therefore, no step-skip event was found within this time window. The event logs are associated and bound with the theoretical node positions to form node context verification information for the positioning time. This information indicates that near the critical moment of capturing the target signal component, the movement of the detection device followed the node sequence of the theoretical detection path.

[0114] In some embodiments, retrieving the environmental parameter benchmarks bound to the node verification record and the attribution location requires pre-storing a set of standard environmental parameters. This pre-storing set of standard environmental parameters is associated with each core association region in the spatial association model. For example, the set of standard environmental parameters associated with the coordinate point (2.0, 2.0, 1.0) of the core association region for attribution location determination includes a standard temperature of 23.0 degrees Celsius, a standard relative humidity of 45%, and a standard background noise spectrum. Based on the determined attribution location, the corresponding set of standard environmental parameters is indexed. Simultaneously, the sequence of environmental parameters actually collected by environmental sensors within the time period covered by the node verification record is obtained. The time period covered by the node verification record is the entire test process from "2023-10-27 10:15:00.000" to "10:20:00.000". The environmental sensors collect temperature, humidity, and background noise spectrum data at a frequency of 1 Hz. A deviation metric is calculated between the environmental parameter sequence and the set of standard environmental parameters. This deviation metric is used to quantify the difference between the actual environment and the standard environment. It is understandable that the comparison between the environmental parameter sequence and the standard environmental parameter set can be presented in tabular form. See Table 1, which shows an environmental parameter comparison table.

[0115] Table 1: Comparison Table of Environmental Parameters

[0116] Parameter type Standard value Measured average absolute deviation temperature 23.0°C 23.4°C +0.4°C humidity 45%RH 43%RH -2%RH Background noise (1 GHz) -110dBm -108dBm +2dBm

[0117] In some embodiments, the standard background noise spectrum is stored as a list of noise power values ​​at discrete frequency points, for example, -110 dBm at 1 GHz and -109 dBm at 1.5 GHz. The actual collected background noise spectrum sequence requires first calculating the average power value of each frequency point over a time period, and then performing a norm calculation of the vector difference with the standard value list. The environmental parameter benchmark not only includes a comprehensive deviation metric but also retains a specific deviation comparison table to analyze the impact of specific environmental factors in subsequent assessments.

[0118] See Figure 4 This is a spatial distribution chart of the theoretical detection path in passive intermodulation testing, with the core being the coordinate trajectory of the target's theoretical detection path (PATH_01). The Y-coordinate remains relatively stable at around 2.000 meters, while the X-coordinate extends linearly from 1.0 meter to 3.5 meters, indicating that the theoretical detection path is a straight-line scanning path along the X-axis, consistent with the scanning logic of "extending outward from the core associated region" in passive intermodulation testing. The discrete nodes on the path correspond to the preset sampling points of the detection device, ensuring uniform coverage of the physical space during the test. This type of chart is used in the path planning stage of passive intermodulation testing to clarify the theoretical movement trajectory of the detection device, providing a benchmark for subsequent "similarity comparison between actual movement trajectory and theoretical path" and "node integrity verification," and serving as a fundamental basis for ensuring the standardization of test procedures.

[0119] In step S8, the evaluation results of the passive intermodulation test are derived based on environmental parameter benchmarks, node verification records, and target signal components.

[0120] In one embodiment of the present invention, an original signal quality score is calculated based on the signal strength and signal purity of the target signal component. The influence of environmental parameters is quantified and corrected according to the deviation metric value to generate an environmental correction coefficient. The node compliance deduction score is calculated based on the severity and frequency of step jump events in the node context verification information. The original signal quality score, the environmental correction coefficient, and the node compliance deduction score are substituted into a preset evaluation formula to obtain the final passive intermodulation test evaluation result.

[0121] In practical implementation, deriving the evaluation results of passive intermodulation testing requires integrating intermediate data from multiple sources. The original signal quality score is calculated based on the signal strength and purity of the target signal component. The target signal component is a 905 MHz intermodulation product signal segment with a peak power of -45 dBm. Signal purity is obtained by calculating the ratio of the power of this signal segment at the 905 MHz center frequency to the average noise power within the adjacent 10 kHz frequency band. The signal-to-noise ratio is 15 dB. The original signal quality score is... The calculation uses a weighted summation model, and the calculation relationship is as follows:

[0122]

[0123] in: This indicates the original signal quality score. This represents the signal strength value after normalization. This represents the signal-to-noise ratio value after normalization, for example, mapping 15dB to 0.9. and These are preset weighting coefficients used to balance the contributions of signal strength and signal purity in the scoring. , The original signal quality score was calculated. During the calculation of the original signal quality score, the signal strength and signal purity data of multiple intermodulation combination target signal components under the same location can be simultaneously included. Combined with a unified environmental correction coefficient and node compliance deduction value, a comprehensive evaluation of multiple intermodulation combinations can be completed in one go, avoiding the process redundancy caused by individual group evaluation and further improving testing efficiency.

[0124] In some embodiments, the impact of the deviation metric on environmental parameters is quantified and corrected to generate environmental correction coefficients. The deviation metric... The calculated value, derived from environmental parameter benchmarks, is 1.5, representing an environmental correction factor. The calculation uses an inverse proportional decay function, and the specific relationship is as follows:

[0125]

[0126] in: Represents the environmental correction factor. This represents the deviation measurement value. It is a sensitive factor used to control the degree of influence of environmental deviations on the correction coefficient, and is set... The environmental correction factor was calculated. .

[0127] It is understandable that the node compliance deduction score is calculated based on the severity and frequency of step jump events in the node context verification information. The node context verification information indicates that no step jump event occurred near the positioning time of the target signal component. However, the node integrity verification report shows that two step jump events occurred during the entire test. The severity of the jump event is defined by the number of nodes traversed. The first event jumped 3 nodes (from step sequence identifier 10 to step sequence identifier 13), and the second event jumped 5 nodes (from step sequence identifier 50 to step sequence identifier 55). The deduction base for each jump event is set at 0.5 points per node jumped. The node compliance deduction score is... The calculation formula is cumulative deduction, that is... .

[0128] Optionally, the original signal quality score, environmental correction coefficient, and node compliance deduction score can be substituted into a preset evaluation formula, which is defined as a linear combination: ,in: This indicates the final passive intermodulation test evaluation result. The original signal quality score is 0.84. The environmental correction factor is 0.769. The node compliance score is reduced by 4.0, and the final evaluation result is obtained. In some embodiments, the evaluation formula may include different weights or nonlinear terms, but its core lies in mathematically combining signal quality, environmental deviation quantification correction, and step execution standardization deduction to obtain a comprehensive quantitative evaluation result, which serves as the numerical basis for evaluating the passive intermodulation performance of the interface of the device under test.

[0129] See Figure 5This is a normalized comparison chart of key indicators of raw signal quality in passive intermodulation (PIM) testing, focusing on the quantitative performance of signal strength and signal-to-noise ratio (SNR). A significantly higher normalized SNR value than signal strength indicates superior purity of the target signal, while its strength is at a moderate level. These two indicators are the core inputs for calculating the "raw signal quality score," which will be weighted and summed to obtain a comprehensive score, providing a foundation for the final evaluation of PIM testing. This type of chart is used in the signal quality assessment phase of PIM testing, visually displaying the key dimensions of the target signal's performance. It helps testers quickly identify the signal's core strengths and weaknesses, serving as preliminary data support for subsequent environmental correction and node deduction steps.

[0130] Although the present invention has been described above by way of embodiments, the above embodiments are only used to exemplify possible implementations of the present invention and are not intended to limit the scope of protection of the present invention. Any equivalent substitutions or changes made by those skilled in the art in accordance with the present invention should also be covered by the scope of protection defined by the claims of the present invention.

Claims

1. A passive intermodulation testing method, characterized in that, The passive intermodulation test method includes: S1: Establish a spatial association model corresponding to the preset physical test space; Step S1 includes: constructing a virtual three-dimensional coordinate grid covering the preset physical test space; Identify the geometric center position of all interfaces of the device under test in the preset physical test space; Based on the geometric center location, a core associated region is defined in the virtual three-dimensional coordinate grid; Based on the preset test scanning logic, multiple theoretical detection paths extend outward from the core associated region; Each theoretical detection path is discretized into a series of ordered coordinate points; Each coordinate point sequence is assigned a unique path identifier and step sequence identifier. The spatial association model is composed of the virtual three-dimensional coordinate grid, the core association region, all theoretical detection paths and their coordinate point sequences and identifiers. S2: In the spatial association model, real-time location data generated by the detection device during the test movement is synchronized; S3: Based on the synchronized real-time location data, dynamically verify the node integrity of the test steps. During the dynamic verification of node integrity, extract the measured signal dataset captured by the detection device. S4: Separate the target signal component that conforms to the preset frequency characteristics from the measured signal dataset; S5: Combining the spatial association model, spatially assign the target signal component to a location and mark the location time at which the assigned position is generated; S6: Based on the positioning time, trace and verify the node verification records associated in the spatial association model; S7: Retrieve the environmental parameter baselines bound to the node verification record and the stated location; Step S7 includes: pre-storing a set of standard environmental parameters associated with each core associated region in the spatial association model, wherein the set of standard environmental parameters includes standard temperature, standard humidity and standard background noise spectrum; Based on the determined location, the corresponding set of standard environmental parameters is indexed. Simultaneously, the sequence of environmental parameters actually collected by environmental sensors within the time period covered by the node verification record is obtained; Calculate the deviation metric between the environmental parameter sequence and the standard environmental parameter set, wherein the environmental parameter benchmark is jointly characterized by the standard environmental parameter set and the deviation metric. S8: Based on the environmental parameter benchmark, node verification records, and target signal components, derive the evaluation results of the passive intermodulation test.

2. The passive intermodulation testing method according to claim 1, characterized in that, Step S2 includes: Receive raw coordinate data transmitted back at fixed intervals by the positioning device attached to the detection equipment; The original coordinate data is matched and calibrated with the virtual three-dimensional coordinate grid in the spatial association model to eliminate coordinate offset; Based on the matched and calibrated coordinate data, the actual movement trajectory of the detection device is drawn in the virtual three-dimensional coordinate grid; The actual movement trajectory is compared with all theoretical detection paths to determine the target theoretical detection path that matches the actual movement trajectory. Each sampling point on the actual movement trajectory is mapped to the coordinate point sequence of the theoretical detection path of the target according to the temporal relationship, thereby synchronizing the real-time location data with the spatial association model.

3. The passive intermodulation testing method according to claim 2, characterized in that, Step S3 includes: During the mapping process, it is monitored whether the sampling points of the actual movement trajectory continuously cover the coordinate point sequence of the theoretical detection path of the target; If the sampling point of the actual movement trajectory is detected to skip a specific coordinate point in the coordinate point sequence, then a step jump event is recorded. Create an event log for each recorded step jump event, the event log containing at least the jump start coordinates, the jump end coordinates, and the time when the jump occurred; All step jump events that occur during a single test are cumulatively counted, and a node integrity verification report is generated, which serves as part of the node verification record.

4. The passive intermodulation testing method according to claim 3, characterized in that, Step S4 includes: Perform a bandpass filter operation on the measured signal dataset to filter out background noise outside the frequency domain and obtain the initial screening signal; Perform time-frequency transformation on the initial screening signal to obtain its corresponding time-frequency distribution spectrum; In the time-frequency distribution spectrum, identify signal regions where the energy intensity exceeds a preset energy threshold; Extract the frequency values ​​corresponding to the signal region and compare them one by one with a preset frequency characteristic library; The original signal segments corresponding to signal regions with consistent frequency matching are marked as target signal components.

5. The passive intermodulation testing method according to claim 4, characterized in that, Step S5 includes: Obtain the precise time stamp of the target signal component when it is captured; Based on the precise time stamp, query the corresponding coordinate position of the detection device in the spatial association model from the synchronized real-time location data; Using the corresponding coordinate position as the center, and combining the boundary information of the core association region defined in the spatial association model, the spatial affiliation probability is calculated; The core associated region with the highest spatial assignment probability is determined as the location to which the target signal component belongs.

6. The passive intermodulation testing method according to claim 5, characterized in that, Step S6 includes: Based on the positioning time, determine the theoretical node position corresponding to it on the coordinate point sequence of the theoretical detection path of the target; Query the node integrity verification report to check whether there are any step jump events within the preset time window before and after the theoretical node position; If a step skip event exists, extract all event logs related to the step skip event; The event log is associated and bound with the theoretical node location to form node context verification information for the location time.

7. The passive intermodulation testing method according to claim 6, characterized in that, Step S8 includes: Based on the signal strength and signal purity of the target signal components, calculate the original signal quality score; Based on the aforementioned deviation metric, the impact of environmental parameters is quantitatively corrected to generate environmental correction coefficients. Calculate the node compliance deduction score based on the severity and frequency of step skipping events in the node context verification information; Substituting the original signal quality score, the environmental correction coefficient, and the node compliance deduction score into the preset evaluation formula, the final passive intermodulation test evaluation result is obtained.

8. A passive intermodulation testing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the source intermodulation test method as described in any one of claims 1 to 7.

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