Industrial production line wireless propagation characteristic test method and device and computer equipment
Patent Information
- Application Number
- CN202611138140.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-29
- Publication Date
- 2026-11-03
AI Technical Summary
In existing technologies, wireless propagation characteristic testing of industrial production lines cannot accurately reflect the signal mutation characteristics under dynamic operating conditions, resulting in inaccurate test results and failing to cover the wireless propagation characteristics of equipment movement trajectories and the entire spatial area.
The optimal test path is generated using the A* path planning algorithm. Combined with adaptive grid partitioning and Kalman filtering, the test vehicle samples signals on the production line to generate a three-dimensional wireless propagation characteristic distribution model. This model is then compared with a standard threshold library to identify abnormal spatial points.
It has achieved full automation of the wireless propagation characteristics test in industrial production lines, with full coverage of the test path and no blind spots, improved data accuracy, and significantly improved test efficiency. The generated 3D model provides a quantitative basis for wireless network optimization and equipment layout adjustment.
Smart Images
Figure CN123035502A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of automated testing technology, and in particular to a method, apparatus and computer equipment for testing the wireless propagation characteristics of industrial production lines. Background Technology
[0002] With the rapid development of Industry 4.0 and intelligent manufacturing, wireless communication devices such as 5G industrial private networks, WiFi 6, ZigBee, and Bluetooth are widely deployed in industrial production lines. Wireless transmission has become the core carrier for data interaction and remote monitoring of industrial equipment. Industrial production lines are characterized by dense equipment, numerous metal obstructions, complex electromagnetic interference, and dynamic equipment movement, which differs significantly from civilian indoor scenarios. Wireless signals are prone to attenuation, multipath effects, and latency jitter, directly affecting communication stability and production reliability. Therefore, testing the wireless propagation characteristics of production lines is crucial.
[0003] Currently, the testing of wireless propagation characteristics in industrial production lines mainly employs manual, fixed-point testing. Testers use handheld testing equipment to sample data at various points along the production line, recording parameters such as signal strength and signal-to-noise ratio. However, this method only enables static testing at discrete points and cannot cover the wireless propagation characteristics of the entire production line, including the movement trajectory of equipment. During the dynamic operation of conveyor lines, robotic arms, and other equipment, the constantly changing obstruction and electromagnetic interference environment cause instantaneous signal attenuation and latency jitter. Manual fixed-point testing cannot capture these signal abrupt changes under dynamic conditions, and the test results cannot accurately reflect the wireless propagation characteristics under actual production line operation. Summary of the Invention
[0004] This application provides a method, apparatus, and computer equipment for testing the wireless propagation characteristics of industrial production lines, in order to solve the technical problem that the test results of manual fixed-point testing methods cannot truly reflect the wireless propagation characteristics under the actual operating conditions of the production line.
[0005] To address the aforementioned technical problems, in a first aspect, this application provides a method for testing the wireless propagation characteristics of an industrial production line, comprising: acquiring production line layout information of the industrial production line; generating an optimal test path using an A* path planning algorithm based on the production line layout information; determining the operating condition type of the industrial production line based on the operating status parameters of the production line equipment; controlling the movement of a test vehicle according to the optimal test path; obtaining sampled data by sampling signals using a target sampling algorithm during the movement of the test vehicle, wherein the target sampling algorithm is determined based on the operating condition type; performing data correction and spatial interpolation fitting on the sampled data to generate a three-dimensional wireless propagation characteristic distribution model continuously covering the entire production line, wherein the three-dimensional wireless propagation characteristic distribution model includes the signal loss value, signal-to-noise ratio, and interference intensity corresponding to each spatial coordinate point; and comparing the three-dimensional wireless propagation characteristic distribution model point-by-point with a preset standard threshold library to obtain the test results of the industrial production line, wherein the test results include abnormal spatial points and the abnormality types corresponding to the abnormal spatial points.
[0006] This method for testing the wireless propagation characteristics of industrial production lines ensures full coverage and no blind spots through adaptive grid partitioning and A* path planning, solving the problems of sparse manual test points and inability to cover dynamic operating conditions. Kalman filtering and multi-source data fusion correction effectively eliminate sudden electromagnetic interference and noise introduced by environmental fluctuations in the industrial environment, significantly improving the accuracy of test data. The resulting 3D visualization model intuitively presents the signal loss, signal-to-noise ratio, and interference intensity distribution at various locations throughout the production line, providing a quantitative basis for wireless network optimization and equipment layout adjustments. This method achieves full automation of the wireless propagation characteristic testing process for industrial production lines, completely eliminating the dependence on personnel for traditional manual fixed-point testing. The testing time for a complete production line across all dimensions is reduced from several days to several hours, significantly improving testing efficiency.
[0007] In some embodiments, generating the optimal test path using the A* path planning algorithm based on the production line layout information includes: identifying densely obstructed equipment areas, high-incidence electromagnetic interference areas, and open transmission areas in the industrial production line based on the production line layout information; performing grid subdivision on the densely obstructed equipment areas and the high-incidence electromagnetic interference areas at a first grid granularity, and performing grid subdivision on the open transmission areas at a second grid granularity to generate a test grid map with differentiated granularity, wherein the first grid granularity is smaller than the second grid granularity; identifying impassable and traversable grids in the test grid map, and marking the impassable grids as obstacles; using the center point of the traversable grid as the test sampling point, and the impassable grid as obstacle constraints, using the A* path planning algorithm to generate an optimal test path that sequentially traverses all the traversable grids and avoids the impassable grids.
[0008] In this embodiment, a fine-grained grid is used in areas with dense equipment and high interference to ensure that regions with sudden signal changes are sampled precisely; a coarse-grained grid is used in open areas to improve overall testing efficiency. Impassable grids are marked as obstacles and used as obstacle avoidance constraints for the A* algorithm to ensure the safe movement of the test vehicle and avoid collisions with production line equipment.
[0009] In some embodiments, identifying densely obstructed equipment areas, high-incidence electromagnetic interference areas, and open transmission areas in the industrial production line based on the production line layout information includes: dividing the test area of the industrial production line into multiple evaluation units according to a preset initial granularity; calculating the metal obstruction density value and electromagnetic interference intensity value of each evaluation unit based on the production line layout information; marking evaluation units with metal obstruction density values greater than a first threshold as densely obstructed equipment areas, marking evaluation units with electromagnetic interference intensity values greater than a second threshold as high-incidence electromagnetic interference areas, and marking evaluation units with metal obstruction density values not greater than the first threshold and electromagnetic interference intensity values not greater than the second threshold as open transmission areas.
[0010] In this embodiment, by quantitatively calculating the metal obstruction density and electromagnetic interference intensity values, an objective and accurate division of production line areas is achieved, avoiding the subjectivity of manual division. Based on this, differentiated grid subdivision is performed to implement a sampling strategy that densifies key areas and efficiently samples open areas, achieving an optimal balance between testing accuracy and efficiency.
[0011] In some embodiments, the operating condition type includes static shutdown operating condition and dynamic production operating condition. Determining the operating condition type of the industrial production line based on the operating status parameters of the production line equipment includes: obtaining the status register bit identifier and real-time operating speed of the target device in the industrial production line, wherein the target device is a movable device in the industrial production line; if the status register bit identifier is a shutdown identifier and the real-time operating speed is 0, then the industrial production line is determined to be in a static shutdown operating condition; if the status register bit identifier is a running identifier or the real-time operating speed is not 0, then the industrial production line is determined to be in a dynamic production operating condition.
[0012] In this embodiment, the operating condition type is automatically determined through real-time communication with the PLC / MES system, eliminating the need for manual intervention. Dual verification using status register bits and real-time operating speed ensures that if one data point is abnormal, the other can still be used independently for determination, improving the system's fault tolerance and the reliability of operating condition identification.
[0013] In some embodiments, the step of obtaining sampling data by sampling signals according to a target sampling algorithm during the movement of the test vehicle includes: if the operating condition is a static shutdown condition, calculating the variance of signal received power fluctuation corresponding to each target sampling point in the optimal test path, and collecting static characteristic parameters of the wireless signal when the variance of fluctuation is less than a preset steady-state threshold; if the operating condition is a dynamic production condition, obtaining the real-time operating speed of the target equipment in the industrial production line, controlling the test vehicle to move along the optimal test path at a speed matching the real-time operating speed, and collecting dynamic characteristic parameters of the wireless signal during the movement.
[0014] In this embodiment, in static mode, a steady-state determination mechanism replaces the fixed-duration waiting time. When the signal is unstable, the acquisition is automatically delayed, and the data is recorded promptly after stabilization, balancing data accuracy and sampling efficiency. In dynamic mode, the speed of the test vehicle is precisely matched with the equipment, accurately reflecting the signal change patterns during equipment operation and filling the blind spots of static testing.
[0015] In some embodiments, the step of performing data correction and spatial interpolation fitting on the sampled data to generate a three-dimensional wireless propagation characteristic distribution model continuously covering the entire production line includes: filtering the sampled data to remove noise outliers and instantaneous fluctuation anomalies, obtaining a denoised signal loss value sequence; associating and fusing the signal loss value sequence with corresponding spatial location data, environmental temperature and humidity data, electromagnetic interference data, and operating condition data, and performing weighted correction on the fused data using preset compensation coefficients and preset weight coefficients to obtain a corrected effective dataset; and performing spatial interpolation fitting on the unsampled positions between discrete sampled points in the corrected effective dataset to generate a three-dimensional wireless propagation characteristic distribution model continuously covering the entire production line.
[0016] In this embodiment, filtering removes sudden electromagnetic noise and environmental interference from the industrial site, improving the signal-to-noise ratio. Multi-source data fusion and weighted correction eliminate systematic errors such as temperature drift and humidity attenuation. Spatial interpolation fitting upgrades the discrete sampled data into a continuous, fully covered three-dimensional model, enabling accurate estimation of signal characteristics at unsampled locations.
[0017] In some embodiments, the step of comparing the three-dimensional wireless propagation characteristic distribution model with a preset standard threshold library point by point to obtain the test results of the industrial production line includes: comparing the signal loss value, signal-to-noise ratio, and interference intensity of each spatial coordinate point in the three-dimensional wireless propagation characteristic distribution model with the corresponding standard threshold in the standard threshold library item by item; if the signal loss value of the spatial coordinate point is greater than the first standard threshold, or the signal-to-noise ratio of the spatial coordinate point is less than the second standard threshold, or the interference intensity of the spatial coordinate point is greater than the third standard threshold, then the spatial coordinate point is marked as an abnormal spatial point.
[0018] In this embodiment, each spatial coordinate point and each type of signal parameter in the 3D model is compared with a standard threshold to achieve comprehensive coverage and blind-spot-free detection of signal quality across the entire production line. Identified anomalies are automatically marked and their parameter types are recorded, providing an accurate data foundation for subsequent anomaly type determination.
[0019] In some embodiments, after marking the spatial coordinate points as abnormal spatial points, the method further includes: acquiring location feature information, electromagnetic interference information, and the time of occurrence of the abnormality corresponding to each abnormal spatial point. The location feature information is used to characterize whether the abnormal spatial point is located within the obstruction area of a metal device, whether it is located in an open area, or whether there is obstruction. The electromagnetic interference information includes the peak time of electromagnetic interference. If the abnormal spatial point is located within the obstruction area of a metal device, the abnormality type of the abnormal spatial point is determined to be obstruction attenuation abnormality. If the time deviation between the time of occurrence of the abnormality and the peak time of electromagnetic interference is within a preset range, and the abnormal spatial point is not obstructed, the abnormality type of the abnormal spatial point is determined to be electromagnetic interference abnormality. If the abnormal spatial point is located in an open area and the signal loss value corresponding to the abnormal spatial point is greater than the first standard threshold, the abnormality type of the abnormal spatial point is determined to be network configuration abnormality.
[0020] In this embodiment, location features, electromagnetic interference features, and the time of anomaly occurrence are correlated and analyzed to achieve automated and accurate source tracing of anomaly types. Maintenance personnel can directly take targeted measures based on the three types of judgment results: obstruction attenuation, electromagnetic interference, and network configuration, significantly shortening fault diagnosis time and reducing operation and maintenance costs.
[0021] Secondly, this application provides a wireless propagation characteristic testing device for an industrial production line, comprising: a first generation module, used to acquire production line layout information of the industrial production line and generate an optimal test path using an A* path planning algorithm based on the production line layout information; a determination module, used to determine the operating condition type of the industrial production line based on the operating status parameters of the production line equipment; a sampling module, used to control the movement of a test vehicle according to the optimal test path and to sample data by performing signal sampling according to a target sampling algorithm during the movement of the test vehicle, wherein the target sampling algorithm is determined based on the operating condition type; a second generation module, used to perform data correction and spatial interpolation fitting on the sampled data to generate a three-dimensional wireless propagation characteristic distribution model that continuously covers the entire production line, wherein the three-dimensional wireless propagation characteristic distribution model includes the signal loss value, signal-to-noise ratio, and interference intensity corresponding to each spatial coordinate point; and a comparison module, used to compare the three-dimensional wireless propagation characteristic distribution model with a preset standard threshold library point by point to obtain the test results of the industrial production line, wherein the test results include abnormal spatial points and the abnormal types corresponding to the abnormal spatial points.
[0022] Thirdly, this application provides a computer device, including a processor and a memory, wherein the memory is used to store a computer program, and the computer program, when executed by the processor, implements the above-described method for testing the wireless propagation characteristics of industrial production lines. Attached Figure Description
[0023] Figure 1 This is a flowchart illustrating a method for testing the wireless propagation characteristics of an industrial production line according to an embodiment of this application. Figure 2 This is a schematic diagram of the structure of an industrial production line wireless propagation characteristic testing device shown in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device shown in an embodiment of this application. Detailed Implementation
[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0025] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for testing the wireless propagation characteristics of an industrial production line, provided as an embodiment of this application. Figure 1 As shown, the industrial production line wireless propagation characteristic testing method of this embodiment includes steps S11 to S15, which are detailed below: Step S11: Obtain the production line layout information of the industrial production line, and generate the optimal test path using the A* path planning algorithm based on the production line layout information.
[0026] In this step, the production line layout information includes the 3D model of the production line, the equipment planar layout, and the location and dimensions of each piece of equipment. The A* path planning algorithm is a classic heuristic search algorithm widely used in robot path planning. Its core idea is to introduce a heuristic function based on Dijkstra's algorithm, using an evaluation function f(n) = g(n) + h(n) to guide the search direction. Here, g(n) is the actual cost from the starting point to the current node, and h(n) is the estimated cost from the current node to the target node. During the search process, the algorithm prioritizes exploring the node with the smallest evaluation function value, enabling it to quickly find the shortest path from the starting point to the target point.
[0027] In some embodiments, when performing step S11, firstly, based on the production line layout information, densely obstructed areas, high-incidence areas of electromagnetic interference, and open transmission areas in the industrial production line are identified; then, densely obstructed areas and high-incidence areas of electromagnetic interference are divided into grids at a first grid granularity, and open transmission areas are divided into grids at a second grid granularity to generate test grid maps with differentiated granularities, where the first grid granularity is smaller than the second grid granularity; inaccessible and accessible grids in the test grid map are identified, and inaccessible grids are marked as obstacles; using the center point of the accessible grid as the test sampling point and the inaccessible grid as obstacle constraints, the A* path planning algorithm is used to generate the optimal test path that sequentially traverses all accessible grids and avoids inaccessible grids.
[0028] For example, based on the equipment location, dimensions, and material information in the production line layout, three types of areas are first identified: densely packed equipment obstruction areas (numerous metal equipment, severe obstruction), high-incidence electromagnetic interference areas (areas with concentrated inverters and high-power motors), and open transmission areas (unobstructed, interference-free passageways). Then, the densely packed equipment obstruction areas and high-incidence electromagnetic interference areas are divided into a first grid granularity (e.g., 10cm × 10cm) to obtain a high-density, fine-grained grid; the open transmission areas are divided into a second grid granularity (e.g., 50cm × 50cm) to obtain a low-density, coarse-grained grid, forming a test grid map with differentiated granularity. Next, the grid map is identified by distinguishing impassable grids occupied by equipment from passable grids accessible to the test vehicle, marking the impassable grids as obstacles. Finally, using the center points of all passable grids as test sampling points and all impassable grids as obstacle avoidance constraints, the optimal test path is calculated using the A* path planning algorithm, starting from the initial point, sequentially traversing all sampling points, and avoiding all obstacles. By employing fine-grained grid subdivision in densely populated equipment areas and areas prone to electromagnetic interference, refined sampling of key areas is achieved, ensuring accurate capture of signal abrupt changes near obstruction boundaries and interference sources. Coarse-grained grid subdivision is used in open areas, significantly reducing the number of sampling points and test time. Simultaneously, by marking impassable grids as obstacles and using them as obstacle avoidance constraints for the A* algorithm, the planned test path is ensured to be safe and feasible, preventing collisions between the test vehicle and production line equipment during travel, thus guaranteeing the safety of the testing process.
[0029] In some embodiments, when identifying densely obstructed equipment areas, high-incidence electromagnetic interference areas, and open transmission areas in an industrial production line based on production line layout information, the test area of the industrial production line is first divided into multiple evaluation units according to a preset initial granularity; based on the production line layout information, the metal obstruction density value and electromagnetic interference intensity value of each evaluation unit are calculated; evaluation units with metal obstruction density values greater than a first threshold are marked as densely obstructed equipment areas, evaluation units with electromagnetic interference intensity values greater than a second threshold are marked as high-incidence electromagnetic interference areas, and evaluation units with metal obstruction density values not greater than the first threshold and electromagnetic interference intensity values not greater than the second threshold are marked as open transmission areas.
[0030] For example, the test area of the industrial production line is first divided into multiple evaluation units according to a preset initial granularity (e.g., 1m × 1m). Then, based on the location, outline, and material of each piece of equipment in the production line layout information, the proportion of the projected area of the metal equipment in each evaluation unit to the total area of that unit is calculated to obtain the metal obstruction density value. Based on the power parameters and distances of interference sources such as frequency converters and motors in the production line layout information, the comprehensive electromagnetic interference intensity value of each evaluation unit is calculated. Finally, evaluation units with a metal obstruction density value greater than a first threshold (e.g., 60%) are marked as densely obstructed equipment areas; evaluation units with an electromagnetic interference intensity value greater than a second threshold are marked as high-incidence electromagnetic interference areas; and evaluation units with a metal obstruction density value not greater than the first threshold and an electromagnetic interference intensity value not greater than the second threshold are marked as open transmission areas. By quantitatively calculating the metal obstruction density value and electromagnetic interference intensity value, objective and accurate division of different areas of the production line is achieved, avoiding the subjectivity and arbitrariness of manual division based on experience. Based on the quantitative judgment results, subsequent differentiated grid subdivision and sampling parameter configuration are carried out to ensure that denser test sampling is obtained in densely populated equipment areas and areas with high interference, and more efficient traversal testing is obtained in open areas, thus achieving the optimal balance between test accuracy and test efficiency.
[0031] Step S12: Determine the operating condition type of the industrial production line based on the operating status parameters of the production line equipment.
[0032] In this step, communication with the production line PLC or MES system is conducted via API interface to obtain the status register bit identifier and real-time running speed of movable equipment (such as conveyor lines, robotic arms, and mobile tooling) in the production line. If the status register bit identifier is a stop identifier and the real-time running speed is 0, it is determined to be a static stop condition; if the status register bit identifier is a run identifier or the real-time running speed is not 0, it is determined to be a dynamic production condition.
[0033] In some embodiments, the operating condition types include static shutdown operating conditions and dynamic production operating conditions. When executing step S12, the status register bit identifier and real-time operating speed of the target device in the industrial production line are first obtained. The target device is the movable device in the industrial production line. If the status register bit identifier is a shutdown identifier and the real-time operating speed is 0, the industrial production line is determined to be in a static shutdown operating condition. If the status register bit identifier is a running identifier or the real-time operating speed is not 0, the industrial production line is determined to be in a dynamic production operating condition.
[0034] The host computer establishes a communication connection with the production line PLC or MES system via an API interface, and reads the status register bit identifiers and real-time operating speeds of movable devices in the production line at preset intervals. The status register bit identifiers are binary bits stored internally by the PLC to indicate the device's running / stopping status; 0 indicates stop, and 1 indicates running. The host computer performs a comprehensive judgment on the read data: if all target devices have status register bit identifiers indicating stop and real-time operating speeds of 0, the current condition is determined to be static stop; if any target device has a status register bit identifier indicating run or a real-time operating speed greater than 0, the current condition is determined to be dynamic production. Real-time communication with the PLC / MES system via the API interface enables automated and real-time determination of operating conditions, eliminating the need for manual observation and switching of test modes, thus avoiding human error and operational delays. A dual verification mechanism using status register bit identifiers and real-time operating speeds ensures the accuracy of operating condition determination; even if a communication anomaly occurs in the status register bit identifiers, the speed data can still be used as a redundant judgment basis.
[0035] Step S13: Control the test vehicle to move according to the optimal test path. During the movement of the test vehicle, the signal is sampled according to the target sampling algorithm to obtain sampled data, wherein the target sampling algorithm is determined according to the working condition type.
[0036] In this step, if it is a static shutdown condition, the test vehicle is controlled to travel sequentially to each sampling point along the optimal test path. The variance of signal received power fluctuation is calculated with a time window of 200ms. When the variance of fluctuation is less than ±0.5dB, the signal is considered stable, and data recording is triggered. If it is a dynamic production condition, the real-time operating speed of the target equipment is obtained, and the test vehicle is controlled to travel along the optimal test path at the matching speed, continuously collecting signals during the journey.
[0037] In some embodiments, when performing step S13, if the operating condition is a static shutdown condition, the variance of the signal received power fluctuation corresponding to each target sampling point in the optimal test path is calculated, and the static characteristic parameters of the wireless signal are collected when the variance of the fluctuation is less than a preset steady-state threshold; if the operating condition is a dynamic production condition, the real-time operating speed of the target equipment in the industrial production line is obtained, the test vehicle is controlled to travel along the optimal test path at a speed that matches the real-time operating speed, and the dynamic characteristic parameters of the wireless signal are collected during the travel.
[0038] If the operating condition is a static shutdown condition, the test vehicle travels to each sampling point along the optimal test path, collects signal received power data at a sampling frequency of 100Hz, and calculates the power fluctuation variance with a time window of 200ms. When the fluctuation variance is less than ±0.5dB, the signal is considered to have reached a steady state, and the static characteristic parameters of the wireless signal at that point (including signal power, path loss, and signal-to-noise ratio) are immediately collected and recorded. If the fluctuation variance continues to exceed the standard, the system continues to wait until the signal stabilizes or a timeout alarm is triggered. If the operating condition is a dynamic production condition, the host computer obtains the current operating speed of the target equipment in real time, controls the travel speed of the test vehicle to match this speed, and keeps the test vehicle and the equipment in a relatively stationary following state, traveling synchronously along the optimal test path. During the journey, the dynamic characteristic parameters of the signal (including delay jitter, instantaneous attenuation, and packet loss rate) are continuously collected at a high-frequency sampling rate of 100Hz, and the position coordinates and timestamps at each moment are bound.
[0039] In static mode, a steady-state determination mechanism replaces the traditional fixed-duration waiting time. When the signal is unstable, the waiting period is automatically extended, and data is collected immediately after stabilization. This ensures data accuracy while avoiding unnecessary waiting time waste, significantly improving static sampling efficiency. In dynamic mode, the test vehicle and equipment speeds are precisely matched to ensure a stable relative position. This allows the collected dynamic characteristic parameters to accurately reflect the signal change patterns under equipment operating conditions, overcoming the limitation of traditional static testing in acquiring dynamic transient data.
[0040] Step S14: Perform data correction and spatial interpolation fitting on the sampled data to generate a three-dimensional wireless propagation characteristic distribution model that continuously covers the entire production line. The three-dimensional wireless propagation characteristic distribution model includes the signal loss value, signal-to-noise ratio, and interference intensity corresponding to each spatial coordinate point.
[0041] In this step, the sampled data is first denoised using Kalman filtering to remove noise outliers. Then, the denoised data is correlated and fused with five types of data: spatial location, temperature and humidity, electromagnetic interference, and operating conditions. Weighted correction is performed using preset compensation coefficients and weighting coefficients. The ordinary Kriging interpolation algorithm is used to spatially interpolate and fit the unsampled locations between discrete sampling points to generate a three-dimensional wireless propagation characteristic distribution model.
[0042] In some embodiments, when performing step S14, the sampled data is first filtered to remove noise outliers and transient fluctuation anomalies, resulting in a denoised signal loss value sequence. The signal loss value sequence is then correlated and fused with corresponding spatial location data, environmental temperature and humidity data, electromagnetic interference data, and operating condition data. The fused data is then weighted and corrected using preset compensation coefficients and preset weight coefficients to obtain a corrected effective dataset. Finally, spatial interpolation fitting is performed on the unsampled locations between discrete sampling points in the corrected effective dataset to generate a three-dimensional wireless propagation characteristic distribution model that continuously covers the entire production line.
[0043] For example, firstly, Kalman filtering is used to filter and denoise the sampled data. The state variables are set as the true signal loss values, and the observed values are the noisy loss values and interference intensity. Through iterative calculations of the state equation and observation equation, noise outliers and transient fluctuation anomalies are gradually eliminated, outputting a denoised sequence of true signal loss values. Then, the denoised signal loss value sequence is uniformly calibrated to microsecond-level timestamps using the host computer's synchronous clock as a reference, and associated with the spatial three-dimensional coordinates as the unique primary key. Weighted correction is then applied to the fused data using preset compensation coefficients (temperature compensation coefficient 0.02dB / ℃, humidity compensation coefficient 0.01dB / %RH) and weighting coefficients (electromagnetic interference weight 0.3, operating condition weight 0.7), eliminating system errors introduced by temperature and humidity fluctuations and electromagnetic interference, outputting a corrected effective dataset. Finally, the ordinary Kriging interpolation algorithm is used to perform spatial interpolation fitting on the unsampled positions between discrete sampling points in the calibrated effective dataset. The spatial autocorrelation between sample points is calculated through the variogram function. The interpolation results of signal loss, signal-to-noise ratio and interference intensity are calculated one by one for the unsampled positions in the whole domain to generate a continuous and smooth three-dimensional wireless propagation characteristic distribution model.
[0044] Kalman filtering effectively eliminated abnormal data caused by sudden electromagnetic interference and environmental noise in industrial settings, improving the signal-to-noise ratio. Five-element data fusion and weighted correction eliminated the systematic effects of temperature drift, humidity attenuation, and electromagnetic interference in the final data, significantly improving data accuracy. Ordinary Kriging interpolation generated a continuous, full-coverage 3D model, achieving data dimensionality enhancement from discrete sampling points to a continuous spatial distribution, enabling accurate estimation of signal characteristics even at unsampled locations.
[0045] Step S15: Compare the three-dimensional wireless propagation characteristic distribution model with a preset standard threshold library point by point to obtain the test results of the industrial production line, wherein the test results include abnormal spatial points and the abnormal types corresponding to the abnormal spatial points.
[0046] In this step, the signal loss value, signal-to-noise ratio, and interference intensity of each spatial coordinate point in the 3D model are compared with the corresponding thresholds in the standard threshold library. If the loss value is greater than the corresponding threshold, the signal-to-noise ratio is less than the corresponding threshold, or the interference intensity is greater than the corresponding threshold, it is marked as an abnormal spatial point, and the abnormal spatial point and the corresponding abnormal type are output.
[0047] In some embodiments, when performing step S15, the signal loss value, signal-to-noise ratio, and interference intensity of each spatial coordinate point in the three-dimensional wireless propagation characteristic distribution model are first compared with the corresponding standard thresholds in the standard threshold library. If the signal loss value of the spatial coordinate point is greater than the first standard threshold, or the signal-to-noise ratio of the spatial coordinate point is less than the second standard threshold, or the interference intensity of the spatial coordinate point is greater than the third standard threshold, then the spatial coordinate point is marked as an abnormal spatial point.
[0048] In this step, the host computer extracts the signal loss value, signal-to-noise ratio (SNR), and interference intensity of each spatial coordinate point from the three-dimensional wireless propagation characteristic distribution model, and compares them item by item with the standard thresholds of the corresponding signal parameters in the standard threshold library. The standard threshold library is a set of industrial wireless communication standard parameter thresholds pre-stored in the host computer's storage medium, including the upper limit threshold for signal loss (first standard threshold), the lower limit threshold for SNR (second standard threshold), and the upper limit threshold for interference intensity (third standard threshold). When the signal loss value of a spatial coordinate point is greater than the first standard threshold, or the SNR is less than the second standard threshold, or the interference intensity is greater than the third standard threshold, the system determines that there is a communication anomaly at that point, marks the spatial coordinate point as an abnormal spatial point, and records the type and degree of abnormality of the abnormal parameters at that point.
[0049] By employing a dual comparison method combining point-by-point and item-by-item comparison, every spatial coordinate point and every type of signal parameter in the 3D model is checked against its corresponding standard threshold, ensuring that signal quality is covered at every location across the entire production line without omissions or blind spots. Once an anomaly is detected, the system automatically marks the anomaly location and records the type of anomaly parameter, achieving comprehensive coverage and precise location of anomaly detection, and providing an accurate data foundation for subsequent anomaly type determination.
[0050] In some embodiments, after marking spatial coordinate points as abnormal spatial points, location feature information, electromagnetic interference information, and the time of occurrence of the abnormality can be obtained for each abnormal spatial point. The location feature information is used to characterize whether the abnormal spatial point is located within the obstruction area of a metal device, whether it is located in an open area, or whether there is any obstruction. The electromagnetic interference information includes the peak time of electromagnetic interference. If the abnormal spatial point is located within the obstruction area of a metal device, the abnormality type of the abnormal spatial point is determined to be obstruction attenuation abnormality. If the time deviation between the time of occurrence of the abnormality and the peak time of electromagnetic interference is within a preset range, and the abnormal spatial point is not obstructed, the abnormality type of the abnormal spatial point is determined to be electromagnetic interference abnormality. If the abnormal spatial point is located in an open area and the signal loss value corresponding to the abnormal spatial point is greater than a first standard threshold, the abnormality type of the abnormal spatial point is determined to be network configuration abnormality.
[0051] In this step, after marking the abnormal spatial points, the location feature information, electromagnetic interference information, and the time of occurrence of the anomaly are obtained for each abnormal spatial point. The location feature information indicates whether the point is located within a metal equipment obstruction area, whether it is located in an open area, or whether there is any obstruction. It is derived from the area identification results and obstacle sensor data during the testing process. The electromagnetic interference information includes the time of occurrence of the electromagnetic interference peak, which is derived from the timestamp of the interference peak detected by the electromagnetic interference sensor in the environmental perception module. The time of occurrence of the anomaly is the timestamp recorded when the signal parameter value of the point exceeds the standard threshold.
[0052] The system then categorizes anomalies according to three rules: If the anomaly is located within the obstruction area of metal equipment (e.g., directly behind a large machine tool), it is determined to be an anomaly caused by static signal attenuation due to metal equipment obstruction. If the time deviation between the occurrence of the anomaly and the peak time of electromagnetic interference is within a preset range (e.g., ±200ms), and there is no spatial obstruction at that point, it is determined to be an anomaly of co-channel / adjacent-channel electromagnetic interference. This determination identifies the interference source by excluding obstruction factors and locking the consistency of electromagnetic interference time. If the anomaly is located in an open area and the signal loss value is still greater than the first standard threshold, it is determined to be an anomaly in network configuration caused by unreasonable wireless node layout or improper transmission power / channel parameter configuration. These three anomaly types correspond to the three most common wireless communication problems in industrial production lines, providing comprehensive coverage without overlap. The system summarizes the spatial coordinates, anomaly parameter values, and anomaly types of each anomaly point, generating a standardized test report that includes a 3D model, anomaly point distribution, and cause analysis.
[0053] By constructing a correlation analysis between location characteristics, electromagnetic interference characteristics, and the time of anomaly occurrence, automated and accurate source tracing of anomaly types is achieved, eliminating the need for manual post-event data analysis and guesswork based on experience. Maintenance personnel can directly take targeted measures based on the anomaly type in the report: for anomalies caused by obstruction and attenuation, equipment layout adjustments are needed; for anomalies caused by electromagnetic interference, shielding should be added or the operating frequency adjusted; for anomalies caused by network configuration, AP locations or transmission power should be adjusted. This significantly shortens troubleshooting time and reduces operation and maintenance costs.
[0054] This method for testing the wireless propagation characteristics of industrial production lines ensures full coverage and no blind spots through adaptive grid partitioning and A* path planning, solving the problems of sparse manual test points and inability to cover dynamic operating conditions. Kalman filtering and multi-source data fusion correction effectively eliminate sudden electromagnetic interference and noise introduced by environmental fluctuations in the industrial environment, significantly improving the accuracy of test data. The resulting 3D visualization model intuitively presents the signal loss, signal-to-noise ratio, and interference intensity distribution at various locations throughout the production line, providing a quantitative basis for wireless network optimization and equipment layout adjustments. This method achieves full automation of the wireless propagation characteristic testing process for industrial production lines, completely eliminating the dependence on personnel for traditional manual fixed-point testing. The testing time for a complete production line across all dimensions is reduced from several days to several hours, significantly improving testing efficiency.
[0055] To implement the industrial production line wireless propagation characteristic testing method corresponding to the above method embodiments, in order to achieve the corresponding functions and technical effects. See also Figure 2 , Figure 2 This diagram illustrates the structure of an industrial production line wireless propagation characteristic testing device according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The industrial production line wireless propagation characteristic testing device 20 provided in this embodiment includes a first generation module 21, a determination module 22, a sampling module 23, a second generation module 24, and a comparison module 25, as detailed below: The first generation module 21 is used to acquire the production line layout information of the industrial production line and generate the optimal test path using the A* path planning algorithm based on the production line layout information; the determination module 22 is used to determine the operating condition type of the industrial production line based on the operating status parameters of the production line equipment; the sampling module 23 is used to control the movement of the test vehicle according to the optimal test path and to sample the signal during the movement of the test vehicle according to the target sampling algorithm to obtain sampled data, wherein the target sampling algorithm is determined according to the operating condition type; the second generation module 24 is used to perform data correction and spatial interpolation fitting on the sampled data to generate a three-dimensional wireless propagation characteristic distribution model that continuously covers the entire production line, wherein the three-dimensional wireless propagation characteristic distribution model includes the signal loss value, signal-to-noise ratio and interference intensity corresponding to each spatial coordinate point; the comparison module 25 is used to compare the three-dimensional wireless propagation characteristic distribution model with a preset standard threshold library point by point to obtain the test results of the industrial production line, wherein the test results include abnormal spatial points and the abnormal types corresponding to the abnormal spatial points.
[0056] The aforementioned industrial production line wireless propagation characteristic testing device can implement the industrial production line wireless propagation characteristic testing method of the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.
[0057] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 3 As shown, the computer device 30 of this embodiment includes: at least one processor 31 ( Figure 3 (Only one is shown in the diagram), memory 32, and computer program 33 stored in the memory 32 and executable on the at least one processor 31, wherein the processor 31 executes the computer program 33 to implement the steps in any of the above method embodiments.
[0058] The computer device 30 may be a desktop computer or a cloud server, etc. The computer device 30 may include, but is not limited to, a processor 31 and a memory 32. Those skilled in the art will understand that... Figure 3 The computer device 30 is merely an example and does not constitute a limitation on the computer device 30. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.
[0059] The processor 31 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.
[0060] In some embodiments, the memory 32 may be an internal storage unit of the computer device 30, such as a hard disk or memory of the computer device 30. In other embodiments, the memory 32 may be an external storage device of the computer device 30, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 30. Furthermore, the memory 32 may include both internal and external storage units of the computer device 30. The memory 32 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program 33. The memory 32 may also be used to temporarily store data that has been output or will be output.
[0061] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above method embodiments.
[0062] This application provides a computer program product that, when run on a computer device, enables the computer device to execute the steps described in the various method embodiments above.
[0063] In the several embodiments provided in this application, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.
[0064] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0065] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.
Claims
1. A method for testing the wireless propagation characteristics of an industrial production line, characterized in that, include: Obtain the production line layout information of the industrial production line, and generate the optimal test path based on the production line layout information using the A* path planning algorithm; The operating condition type of the industrial production line is determined based on the operating status parameters of the production line equipment. The test vehicle is controlled to move according to the optimal test path. During the movement of the test vehicle, the signal is sampled according to the target sampling algorithm to obtain sampled data, wherein the target sampling algorithm is determined according to the working condition type. The sampled data is corrected and spatially interpolated to generate a three-dimensional wireless propagation characteristic distribution model that continuously covers the entire production line. The three-dimensional wireless propagation characteristic distribution model includes the signal loss value, signal-to-noise ratio and interference intensity corresponding to each spatial coordinate point. The three-dimensional wireless propagation characteristic distribution model is compared point by point with a preset standard threshold library to obtain the test results of the industrial production line. The test results include abnormal spatial points and the abnormal types corresponding to the abnormal spatial points.
2. The method for testing the wireless propagation characteristics of an industrial production line as described in claim 1, characterized in that, The step of generating the optimal test path using the A* path planning algorithm based on the production line layout information includes: Based on the production line layout information, identify densely obstructed equipment areas, high-incidence electromagnetic interference areas, and open transmission areas in the industrial production line; The densely obstructed equipment area and the high electromagnetic interference area are divided into grids with a first grid granularity, and the open transmission area is divided into grids with a second grid granularity to generate a test grid map with differentiated granularity, wherein the first grid granularity is smaller than the second grid granularity. Identify impassable and passable grids in the test grid map, and mark the impassable grids as obstacles; Using the center point of the passable grid as the test sampling point and the impassable grid as the obstacle constraint, the A* path planning algorithm is used to generate the optimal test path that traverses all the passable grids in sequence and avoids the impassable grids.
3. The method for testing the wireless propagation characteristics of an industrial production line as described in claim 2, characterized in that, The step of identifying densely packed equipment areas, high-incidence electromagnetic interference areas, and open transmission areas in the industrial production line based on the production line layout information includes: The test area of the industrial production line is divided into multiple evaluation units according to a preset initial granularity. Based on the production line layout information, calculate the metal shielding density value and electromagnetic interference intensity value of each evaluation unit; Evaluation units with metal obstruction density values greater than a first threshold are marked as densely obstructed equipment areas; evaluation units with electromagnetic interference intensity values greater than a second threshold are marked as high-incidence electromagnetic interference areas; and evaluation units with metal obstruction density values not greater than the first threshold and electromagnetic interference intensity values not greater than the second threshold are marked as open transmission areas.
4. The method for testing the wireless propagation characteristics of an industrial production line as described in claim 1, characterized in that, The operating condition types include static shutdown operating conditions and dynamic production operating conditions. Determining the operating condition type of the industrial production line based on the operating status parameters of the production line equipment includes: Obtain the status register bit identifier and real-time operating speed of the target device in the industrial production line, wherein the target device is a movable device in the industrial production line; If the status register bit is marked as a shutdown flag and the real-time operating speed is 0, then the industrial production line is determined to be in a static shutdown condition. If the status register bit is a running identifier or the real-time running speed is not 0, then the industrial production line is determined to be in dynamic production condition.
5. The method for testing the wireless propagation characteristics of an industrial production line as described in claim 1, characterized in that, The sampling data obtained by sampling signals according to the target sampling algorithm during the movement of the test vehicle includes: If the operating condition is a static shutdown condition, calculate the variance of the signal received power fluctuation corresponding to each target sampling point in the optimal test path. When the variance of fluctuation is less than a preset steady-state threshold, collect the static characteristic parameters of the wireless signal. If the operating condition is a dynamic production condition, the real-time operating speed of the target equipment in the industrial production line is obtained, the test vehicle is controlled to travel along the optimal test path at a speed matching the real-time operating speed, and the dynamic characteristic parameters of the wireless signal are collected during the travel.
6. The method for testing the wireless propagation characteristics of an industrial production line as described in claim 1, characterized in that, The step of performing data correction and spatial interpolation fitting on the sampled data to generate a three-dimensional wireless propagation characteristic distribution model that continuously covers the entire production line includes: The sampled data is filtered to remove noise outliers and transient fluctuation anomalies, resulting in a denoised signal loss value sequence. The signal loss value sequence is associated and fused with the corresponding spatial location data, environmental temperature and humidity data, electromagnetic interference data and operating condition data, and the fused data is weighted and corrected by preset compensation coefficient and preset weight coefficient to obtain a corrected effective dataset. Spatial interpolation fitting is performed on the unsampled positions between discrete sampling points in the corrected effective dataset to generate a three-dimensional wireless propagation characteristic distribution model that continuously covers the entire production line.
7. The method for testing the wireless propagation characteristics of an industrial production line as described in claim 1, characterized in that, The step of comparing the three-dimensional wireless propagation characteristic distribution model with a preset standard threshold library point by point to obtain the test results of the industrial production line includes: The signal loss value, signal-to-noise ratio and interference intensity of each spatial coordinate point in the three-dimensional wireless propagation characteristic distribution model are compared with the corresponding standard thresholds in the standard threshold library item by item. If the signal loss value of the spatial coordinate point is greater than the first standard threshold, or the signal-to-noise ratio of the spatial coordinate point is less than the second standard threshold, or the interference intensity of the spatial coordinate point is greater than the third standard threshold, then the spatial coordinate point is marked as an abnormal spatial point.
8. The method for testing the wireless propagation characteristics of an industrial production line as described in claim 7, characterized in that, After marking the spatial coordinate point as an anomalous spatial point, the method further includes: Acquire the location feature information, electromagnetic interference information and the time of occurrence of each abnormal spatial point. The location feature information is used to characterize whether the abnormal spatial point is located in the shielding area of the metal equipment, whether it is located in an open area or whether there is any shielding. The electromagnetic interference information includes the peak time of electromagnetic interference. If the abnormal spatial point is located within the obstruction area of the metal equipment, then the abnormality type of the abnormal spatial point is determined to be obstruction attenuation abnormality; If the time deviation between the time of the anomaly occurrence and the time of the peak electromagnetic interference is within a preset range, and the anomaly spatial point is not obstructed, then the anomaly type of the anomaly spatial point is determined to be an electromagnetic interference anomaly. If the abnormal spatial point is located in an open area and the signal loss value corresponding to the abnormal spatial point is greater than the first standard threshold, then the abnormal type of the abnormal spatial point is determined to be a network configuration abnormality.
9. A wireless propagation characteristic testing device for industrial production lines, characterized in that, include: The first generation module is used to obtain the production line layout information of the industrial production line and generate the optimal test path based on the production line layout information using the A* path planning algorithm. The determination module is used to determine the operating condition type of the industrial production line based on the operating status parameters of the production line equipment. The sampling module is used to control the movement of the test vehicle according to the optimal test path, and to sample the signal to obtain sampling data according to the target sampling algorithm during the movement of the test vehicle, wherein the target sampling algorithm is determined according to the working condition type; The second generation module is used to perform data correction and spatial interpolation fitting on the sampled data to generate a three-dimensional wireless propagation characteristic distribution model that continuously covers the entire production line. The three-dimensional wireless propagation characteristic distribution model includes the signal loss value, signal-to-noise ratio and interference intensity corresponding to each spatial coordinate point. The comparison module is used to compare the three-dimensional wireless propagation characteristic distribution model with a preset standard threshold library point by point to obtain the test results of the industrial production line. The test results include abnormal spatial points and the abnormal types corresponding to the abnormal spatial points.
10. A computer device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program, which, when executed by the processor, implements the industrial production line wireless propagation characteristic testing method as described in any one of claims 1 to 8.