Signal testing method and device and electronic equipment

Abstract

The invention discloses a signal testing method and device and electronic equipment, and relates to the technical field of testing. The method comprises the steps that a preset route is set, and the park cruising device runs according to the preset route; an acquisition terminal is arranged on the park cruise device, and the acquisition terminal is used for acquiring signal data of the park in real time in the driving process; acquiring signal data uploaded by the acquisition terminal in the driving process; and performing data cleaning processing on the signal data to obtain a test result of the park. Compared with the prior art, the acquisition terminal is arranged on the cruise device, the cruise device automatically runs according to the planned route, and the acquisition terminal periodically acquires the signal data and uploads the signal data to the server in real time for processing, so that the test efficiency is greatly improved, and the real-time performance and accuracy of the test can be ensured; the technical problems that an existing park signal testing method is low in efficiency and poor in real-time performance are solved.

Description

Technical Field

[0001] The present application relates to the field of testing technology, and in particular to a signal testing method, device and electronic equipment. Background Art

[0002] With the rapid development of technology, 5G networks have gradually become an indispensable part of modern society. For example, in the park, the application of 5G private networks is constantly deepening, providing a high-speed and stable network environment for various services. However, due to the complexity of factors such as business volume, user scale, and building terrain, 5G signal coverage often faces many challenges. Problems such as weak signal areas and blind spots not only affect the user's network experience, but may also affect the normal operation of key services. Therefore, how to locate the signal coverage of 5G private networks in the park and ensure the quality and stability of signal coverage has become a technical problem that has attracted much attention.

[0003] Currently, signal testing in a park is usually done through a monitoring system or through manual measurement. The monitoring system cannot obtain and process real-time signaling data in a timely manner, and there is a lag. Manual testing is done by technicians carrying test equipment, such as test phones and test computers, to conduct field tests in the park and record signal coverage conditions. However, this method of testing is inefficient. Summary of the invention

[0004] In view of this, the present application provides a signal testing method, device and electronic equipment, the main purpose of which is to improve the technical problems of low efficiency and poor real-time performance of the current campus signal testing method.

[0005] In a first aspect, the present application provides a signal testing method, comprising:

[0006] A preset route is set so that the park cruise device travels along the preset route; a collection terminal is provided on the park cruise device, and the collection terminal is used to collect signal data of the park in real time during the driving process;

[0007] Acquiring the signal data uploaded by the acquisition terminal during driving;

[0008] The signal data is cleaned to obtain a test result of the park.

[0009] Optionally, the signal data includes an upload timestamp, a wireless signal strength at the current location, and corresponding location information; the data cleaning processing of the signal data to obtain the test result of the park includes: performing data verification on the wireless signal strength and the corresponding location information, and eliminating extreme value data; converting the data type of the wireless signal strength and the corresponding location information into a preset format, and associating them with the upload timestamp to obtain the test result of the park.

[0010] Optionally, after obtaining the test results of the park, the method further includes: presenting the test results via GIS.

[0011] Optionally, presenting the test results via GIS includes: generating a grid map; the grid map includes a number of grid areas; performing cluster analysis on the test results via a K-means clustering algorithm to generate image clusters and data clusters; optimizing the data clusters via a K-nearest neighbor algorithm; adaptively dividing the image clusters and the optimized data clusters on the grid map to obtain a GIS map; the GIS map can characterize the signal strength of each grid area in the park.

[0012] Optionally, the image clusters and the optimized data clusters are adaptively divided on the grid map, including: rendering and presenting the image clusters and the optimized data clusters on the grid map, and determining an initial frame number; judging whether the initial frame number meets a preset clarity requirement; if the initial frame number does not meet the preset clarity requirement, processing the rendering process by a threshold reduction algorithm so that the rendered image meets the clarity requirement.

[0013] In a second aspect, the present application provides a signal testing method, comprising:

[0014] When receiving the running signal, the collection terminal is turned on; the collection terminal is used to collect the signal data of the park in real time during the driving process;

[0015] Determine a preset route and drive along the preset route;

[0016] The signal data collected during driving is uploaded in real time; the signal data is used to obtain the test results of the park.

[0017] In a third aspect, the present application provides a signal testing device, comprising:

[0018] A setting unit is configured to set a preset route so that the park cruise device travels along the preset route; the park cruise device is provided with a collection terminal, and the collection terminal is used to collect signal data of the park in real time during the driving process;

[0019] An acquisition unit, configured to acquire the signal data uploaded by the acquisition terminal during driving;

[0020] The processing unit is configured to perform data cleaning processing on the signal data to obtain the test result of the park.

[0021] In a fourth aspect, the present application provides a signal testing device, comprising:

[0022] The receiving unit is configured to start a collection terminal when receiving a running signal; the collection terminal is used to collect signal data of the park in real time during driving;

[0023] A driving unit, configured to determine a preset route and drive along the preset route;

[0024] The uploading unit is configured to upload the signal data collected during driving in real time; the signal data is used to obtain the test results of the park.

[0025] In a fifth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the signal testing method described in the first aspect or the second aspect is implemented.

[0026] In a sixth aspect, the present application provides an electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the signal testing method described in the first aspect or the second aspect when executing the computer program.

[0027] In a seventh aspect, the present application provides a computer program product having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the signal testing method described in the first aspect or the second aspect is implemented.

[0028] By means of the above technical scheme, the present application provides a signal testing method, device and electronic device. First, a preset route is set so that the park cruise device travels along the preset route. At the same time, a collection terminal is set on the park cruise device, and the collection terminal is used to collect the signal data of the park in real time during the driving process. Then, the signal data uploaded by the collection terminal during the driving process is obtained, and the signal data is cleaned and processed to obtain the test results of the park. Compared with the related art, by setting the collection terminal on the cruise device and allowing the cruise device to automatically travel along the planned route, the collection terminal periodically collects signal data and uploads it to the server for processing in real time, which not only greatly improves the test efficiency, but also ensures the real-time and accuracy of the test, and improves the current park signal testing method. The technical problems of low efficiency and poor real-time performance.

[0029] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0032] Figure 1 A schematic diagram of a signal testing method provided in an embodiment of the present application is shown;

[0033] Figure 2 A schematic diagram of the architecture of a signal testing method provided in an embodiment of the present application is shown;

[0034] Figure 3 A schematic diagram showing the steps of a signal testing method provided in an embodiment of the present application is shown;

[0035] Figure 4 A schematic diagram showing the rendering effect of a signal testing method provided in an embodiment of the present application is shown;

[0036] Figure 5 A schematic structural diagram of a signal testing device provided in an embodiment of the present application is shown.

[0037] Figure 6 A schematic structural diagram of a signal testing device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0038] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0039] A signal testing method proposed in this embodiment is applied to a signal testing device or electronic device. The device or electronic device can be installed or integrated in an electronic device, or installed on the cruise device proposed in this embodiment, and can execute any signal testing method mentioned below during operation.

[0040] In order to improve the technical problems of low efficiency and poor real-time performance of the current campus signal testing method, this embodiment proposes a signal testing method, such as Figure 1 As shown, the method includes:

[0041] S101, setting a preset route so that the park cruise device travels along the preset route.

[0042] Specifically, the park cruising device can be a cruising vehicle or a cruising drone, on which a collection terminal is provided, and the collection terminal is used to collect signal data of the park in real time during driving.

[0043] The preset route can be set on the server side or the remote control device. In addition to setting the route, you can also set only the starting point and the end point, and let the cruise device calculate the optimal path by itself. In a feasible embodiment, an AGV cart is deployed in the park and is provided with a collection terminal. The collection terminal is a soft probe terminal, which outputs the test path using an algorithm by receiving information such as the park map, the starting point and the end point. Its path planning work is replaced by a machine algorithm to perform 5G signal testing. For example, using the A-star search algorithm, in the road test planning scenario, an optimal path from the starting point to the end point is calculated by inputting information such as the park map, the starting point, and the end point. In order for the algorithm to be applied, it is first necessary to model the map and divide it into grids, and define the coordinates for each grid point so that the algorithm can be implemented.

[0044] In addition, AGVs also need to use key technologies such as positioning and risk avoidance. During driving, the AGV monitors the surrounding environment and its own status in real time, and makes dynamic adjustments based on actual conditions to ensure safe, accurate and efficient arrival at the target point. Use GPS satellite positioning technology and path correction algorithms to achieve positioning and risk avoidance. GPS satellite positioning technology determines the location of the AGV by receiving signals sent by GPS satellites. The AGV first calculates the propagation time of the satellite signal, then calculates the distance between the receiver and each space satellite, and then calculates the specific location of the AGV in the park based on the ephemeris information and spatial geometric relationship of each satellite. In this way, the preset route can be obtained, so that the park cruise device can travel along the preset route.

[0045] S102, obtaining signal data uploaded by a collection terminal during driving.

[0046] The soft probe terminal is bound to the AGV together with the UPS power supply. The UPS power supply provides power for the soft probe terminal. The soft probe terminal is used to collect and report the park's 5G signal and latitude and longitude and other key information to the server. Among them, the data signal mainly includes: upload timestamp, wireless signal strength of the current location and corresponding location information. Among them, the cellular status information is the coverage signal information: collection time, terminal identification, reference signal received power (RSRP), cellular network technology (4G / 5G), received signal strength (RSSI), etc. Cellular cell information is the cell status information to which the terminal is connected: collection time, terminal identification, access cell ID (GCI information), access cell PCI, adjacent cell frequency and other information. GPS satellite positioning system information is the latitude and longitude information of the collection reporting point: collection time, terminal identification, GPS positioning information, etc.

[0047] S103, performing data cleaning processing on the signal data to obtain the test results of the park.

[0048] After the AGV and soft probe terminals are deployed, the soft probe terminals regularly collect data and transmit it to the server. The collected data needs to be cleaned and associated due to terminal false alarms, data loss, data anomalies, etc. Data cleaning is an important step in data preprocessing, which aims to improve data quality and make it more suitable for subsequent tasks. In this process, automatic data verification, automatic removal of extreme value data, and automatic data type conversion will be performed to ensure the accuracy, completeness and consistency of the data. The cleaned data is used as a soft probe data sample. Subsequent clients can also use these sample data for GIS rendering to analyze and evaluate the signal coverage of the park.

[0049] In this embodiment, a preset route is first set so that the park cruise device travels along the preset route. At the same time, a collection terminal is provided on the park cruise device, and the collection terminal is used to collect the signal data of the park in real time during the driving process. Then, the signal data uploaded by the collection terminal during the driving process is obtained, and the signal data is cleaned and processed to obtain the test results of the park. Compared with the related art, by setting the collection terminal on the cruise device and allowing the cruise device to automatically travel along the planned route, the collection terminal periodically collects signal data and uploads it to the server for processing in real time, which not only greatly improves the test efficiency, but also ensures the real-time and accuracy of the test, and improves the technical problems of low efficiency and poor real-time performance of the current park signal testing method.

[0050] Optionally, the signal data includes an upload timestamp, the wireless signal strength of the current location, and the corresponding location information; the signal data is cleaned to obtain the test results of the park, including: data verification of the wireless signal strength and the corresponding location information, and elimination of extreme value data; the data type of the wireless signal strength and the corresponding location information is converted into a preset format, and associated with the upload timestamp to obtain the test results of the park.

[0051] In this embodiment, the soft probe terminal is bound to the AGV, and is used to collect and report key information such as the upload timestamp of the park, the wireless signal strength of the current location, and the corresponding location information. The upload timestamp is a mark that records the specific time point when data is uploaded to the server or other storage media, and the corresponding location information is collected through the GPS satellite positioning system.

[0052] Automatic data verification includes automatically checking whether each record contains necessary fields, such as: collection time, positioning information, etc.; automatically eliminating records with missing key fields, such as: collection time, positioning information, etc.; automatically checking whether it contains non-numeric characters or abnormally large / small values. Automatic elimination of extreme value data includes automatically checking the threshold range of indicators such as wireless signal strength; automatically identifying data points that exceed the threshold range. These points are caused by terminal false alarms or anomalies and need to be eliminated; automatically checking GPS positioning information, if the data exceeds the geographical scope of the park, it will be eliminated. Convert the data type of wireless signal strength and corresponding location information into a preset format and associate it with the upload timestamp, that is, convert the collection time into a unified date and time format. The park client uses GIS rendering to present these data to analyze and evaluate the park signal coverage.

[0053] Optionally, after obtaining the test results of the park, the method further includes: presenting the test results via GIS.

[0054] In this embodiment, the display is performed through a Geographic Information System (GIS). GIS is a computer system used to capture, store, check, analyze, manage and present all types of geographic data. Presenting the test results through GIS means combining the test results with geographic location information and presenting them in a map, chart or other visual form.

[0055] Optionally, the test results are presented through GIS, including: generating a grid map; the grid map includes several grid areas; clustering the test results through the K-means clustering algorithm to generate image clusters and data clusters; optimizing the data clusters through the K-nearest neighbor algorithm; adaptively dividing the image clusters and the optimized data clusters on the grid map to obtain a GIS map; the GIS map can characterize the signal strength of each grid area in the park.

[0056] In this embodiment, first, the geographic space of the park is divided into several small grid areas. These grid areas form the basis of the grid map, and each grid represents a specific area in the park. The test results are clustered using the K-means clustering algorithm. K-means clustering is an algorithm that divides data points into K clusters, and the center of each cluster is represented by the mean of all points in the cluster. The algorithm divides the grid area into several image clusters based on the similarity of signal strength, and generates corresponding data clusters at the same time. The data clusters are further optimized, and the K-Nearest Neighbors (KNN) algorithm is used here. The KNN algorithm is used to further adjust or smooth the signal strength values ​​in the data clusters to reduce the impact of outliers and make the data more accurate. The image clusters and data clusters that have been clustered and optimized are adaptively divided on the grid map. The display mode of the grid map is automatically adjusted according to the characteristics of the data clusters to better characterize the signal strength of each grid area in the park. The GIS map finally generated can intuitively display the distribution of signal strength in the park. The GIS map can clearly display the signal strength of each grid area in the park, analyze and evaluate the park's signal coverage, optimize areas with weak coverage, and realize visualization of the park's operation and maintenance.

[0057] Optionally, the image clusters and the optimized data clusters are adaptively divided on the grid map, including: rendering the image clusters and the optimized data clusters on the grid map, and determining the initial frame number; judging whether the initial frame number meets the preset clarity requirement; if the initial frame number does not meet the preset clarity requirement, processing the rendering process by reducing the threshold algorithm so that the rendered image meets the clarity requirement.

[0058] In this embodiment, first, the image clusters and optimized data clusters are located and presented on the raster map. Rendering refers to displaying these data in a visual form on the map, so that the user can intuitively see the distribution and relationship of the image and data in the geographic space. During the rendering process, an initial frame number needs to be determined. Next, the system will evaluate whether the image quality of the initial frame number meets the standard based on the preset clarity requirements. If the rendering frame number meets the requirements of smooth browsing scenes, the data is simplified according to the current reduction threshold; if the rendering frame number does not meet the requirements of smooth browsing scenes, the reduction threshold of each cluster is reset through linear interpolation. Through this adaptive adjustment process, the system can gradually optimize the rendering results until the preset clarity requirements are met.

[0059] Furthermore, this embodiment proposes a signal testing method, which includes: when an operating signal is received, turning on a collection terminal; determining a preset route, and driving along the preset route; and uploading signal data collected during driving in real time.

[0060] After receiving the running signal sent from the server, the AGV turns on the collection terminal on the vehicle. The collection terminal is used to collect signal data of the park in real time during driving. In this embodiment, it is a soft probe terminal. The soft probe terminal mainly includes the following modules: data detection, event reporting, northbound interface, etc. The soft probe terminal has a data detection function, including detection of: terminal equipment, network coverage, and service quality measurement data.

[0061] If the server only sets the starting point and the end point, the car receives the campus map, the starting point, the end point and other information and uses the algorithm to output the test path. The path planning work is replaced by the machine algorithm. Using the A-star search algorithm, in the road test planning scenario, the optimal path from the starting point to the end point is calculated by inputting the campus map, the starting point, the end point and other information. The car drives along this preset route and collects data. If the server sets the motion route, it drives along the motion route.

[0062] The car uploads the collected signal data in real time during driving. The signal data is used to obtain the test results of the park, including the upload timestamp, the wireless signal strength of the current location and the corresponding location information.

[0063] Furthermore, this embodiment realizes the full automation from signal acquisition, data processing to GIS presentation through technologies such as AGV autonomous navigation, soft probe automatic dialing, and data automatic correlation analysis, solving the problem of low efficiency of manual testing. In addition, through the 5G park client adaptive rendering algorithm, the 5G test data is clustered based on the K-means algorithm, the generated clusters are adaptively divided into grids, and the adaptive rendering of 5G park customers is realized through the threshold reduction algorithm combined with user rendering requirements. The problem of single GIS rendering in the prior art is solved.

[0064] like Figure 2 As shown, a schematic diagram of the architecture of a signal testing method is shown. The soft probe terminal is tied to the AGV car (or an external probe box is installed on the 5G module of the AGV car), and the AGV car automatically drives along the predetermined route. During the driving process of the AGV car, the soft probe terminal periodically collects 5G coverage signals and longitude and latitude data, and the data is transmitted to the server for processing. The park client adaptively performs GIS rendering and presentation. This automatic testing method not only greatly improves the test efficiency, but also ensures the real-time and accuracy of the test, providing strong support for subsequent TOB operation and maintenance.

[0065] like Figure 3 As shown, a schematic diagram of the steps of a signal testing method is shown, and the main steps of the method include: deploying an AGV car, deploying a soft probe terminal, data association processing, and GIS rendering presentation.

[0066] (I) Deployment of AGV Car

[0067] AGVs are deployed in the park, which automatically plan the test path and carry soft probe terminals to test 5G signals. The workload of path planning is replaced by machine algorithms, which can reduce the workload by more than 80%. In this embodiment, AGVs need to use key technologies such as path planning, positioning and risk avoidance.

[0068] 1. Path planning

[0069] Path planning refers to the AGV car receiving information such as the park map, starting point and end point and outputting the test path using an algorithm. The steps are as follows:

[0070] (1) Input

[0071] ① Initial environmental data: environmental map data scanned in advance or manually input, including: buildings, roads, target locations, etc.

[0072] ② Positioning information: Get the real-time location of the AGV through positioning technologies such as GPS and lidar.

[0073] ③Sensor data: data collected in real time by lidar, ultrasonic sensors, infrared sensors, and cameras.

[0074] ④Task requirements: Describe the tasks that the AGV needs to complete, such as the starting location, end location, transportation time requirements, etc.

[0075] (2) Algorithm

[0076] This embodiment uses the A-star search algorithm. The specific application of the algorithm in the road test planning scenario calculates an optimal path from the starting point to the end point through the input of the campus map, starting point, end point and other information. In order to apply the algorithm, it is necessary to first model the map and divide it into grids, and define the coordinates of each grid point so that the algorithm can be implemented. The specific steps include the following steps.

[0077] ① Map modeling: Model the operating environment and divide the map into grids. Each grid represents a node and marks key information such as the starting point (S), end point (D), and obstacles.

[0078] ② Heuristic function analysis: Design a heuristic function f(n)=g(n)+h(n), where g(n) uses Euclidean distance to calculate the cost of node n from the starting point (S), and h(n) uses Manhattan distance to evaluate the estimated cost of node n from the end point (D). Select the node (N) with the smallest (optimal) f(n) value as the next node to be traversed.

[0079] ③ Path search: Starting from the starting point, use the A-star search algorithm to calculate the f(n) value of the surrounding neighboring points, and select the node with the smallest value (N) for expansion.

[0080] ④ Move the node (N) into the list to be checked, repeat the above steps to calculate the f(n) values ​​of the surrounding neighboring points, and select the node N with the smallest value to expand until the end point (D) is found.

[0081] (3) Output

[0082] ① Path: The driving path from the starting point of the park to the target location.

[0083] 2. Positioning and hedging

[0084] Positioning and risk avoidance refers to the AGV car monitoring the surrounding environment and its own status in real time during driving, and making dynamic adjustments according to the actual situation to ensure safe, accurate and efficient arrival at the target point. This embodiment uses GPS satellite positioning technology and path correction algorithm to achieve positioning and risk avoidance.

[0085] GPS satellite positioning technology determines the location of the AGV by receiving signals sent by GPS satellites. The AGV first calculates the propagation time of the satellite signal, then calculates the distance between the receiver and each space satellite, and then calculates the specific location of the AGV in the park based on the ephemeris information and spatial geometric relationship of each satellite.

[0086] The main steps of the path correction algorithm are as follows:

[0087] (1) Input

[0088] ① AGV real-time position and direction: obtain the current accurate position and direction information of AGV through sensors (such as lidar, camera, etc.);

[0089] ② Path information: including the coordinate points, direction, curvature, etc. of the path, which is used to compare with the real-time position of the AGV;

[0090] ③Sensor data: such as distance, speed and other data.

[0091] (2) Algorithm

[0092] Assume that the target direction of AGV is θ t , the current direction is θ c , then the deviation angle is δ = θ t- θ c . The calculation formula of the angular velocity of the correction is:

[0093]

[0094] Where ω is the angular velocity of the correction, K p , K i , K d They are the deviation angle proportion, integral and differential coefficients respectively, and δ is the deviation angle, etc.

[0095] (3) Output

[0096] ① Correction instruction: According to the results calculated by the algorithm, it is output to the AGV control system to guide the AGV to adjust the direction or speed to return to the predetermined path;

[0097] ② Adjusted AGV path: The algorithm predicts and outputs the new path or path segment that the AGV should follow after the correction.

[0098] The AGV's path correction algorithm automatically detects and corrects the AGV's deviation from the path, ensuring that the AGV can travel stably and accurately along the predetermined path.

[0099] (II) Deployment of soft probe terminals

[0100] The soft probe terminal is bound to the AGV together with the UPS power supply. The UPS power supply provides power for the soft probe terminal. The soft probe terminal is used to collect and report key information such as the 5G signal and longitude and latitude of the park. The soft probe terminal mainly includes the following modules: data detection, event reporting, northbound interface, etc.

[0101] The soft probe terminal has data detection functions, including detection of terminal equipment, network coverage, service quality measurement and other data. The probe data involved in this proposal mainly includes: cellular status information, cellular cell information and GPS satellite positioning system information.

[0102] Cellular status information covers signal information: collection time, terminal identification, reference signal received power (RSRP), cellular network technology (4G / 5G), received signal strength (RSSI), etc.

[0103] Cellular cell information collects the cell status information to which the terminal is connected: collection time, terminal identification, access cell ID (GCI information), access cell PCI, neighboring cell frequency, and other information.

[0104] The GPS satellite positioning system information collects the latitude and longitude information of the reporting point: collection time, terminal identification, GPS positioning information, etc.

[0105] The soft probe terminal has the function of reporting events. When the monitored terminal status changes or the data is abnormal, the soft probe terminal reports the event data to the server. The reported events mainly include: 5G network coverage events, gateway status events, 5G network events, etc. 5G network coverage events include: RSRP exceeding threshold, SINR exceeding threshold, RSSI exceeding threshold, etc. Gateway status events include: abnormal temperature, CPU utilization, memory utilization, etc. 5G network events include: disconnection, registration failure, card removal, etc.

[0106] The soft probe terminal has a northbound interface to realize data interaction between itself and the server. The terminal uses the WebSocket full-duplex communication protocol and performs handshakes through the HTTP / 1.1 protocol. After completing a handshake, a persistent connection is established between the soft probe terminal and the server, and two-way data transmission is performed.

[0107] (III) Data association processing

[0108] After deploying the AGV and soft probe terminals, the soft probe terminals regularly collect data and transmit it to the server. The collected data needs to be cleaned and associated due to terminal false alarms, data loss, data anomalies, etc., which mainly includes the following steps: automatic data verification, automatic removal of extreme value data, and automatic conversion of data types.

[0109] 1. Automatic data verification

[0110] ① Automatically check whether each record contains necessary fields, such as: collection time, RSRP, positioning information, etc.;

[0111] ② Automatically remove records with missing key fields, such as collection time, RSRP, positioning information, etc.

[0112] ③Automatically check whether it contains non-numeric characters or abnormally large / small values.

[0113] ④ Automatically check whether identification fields such as cell ID and PCI follow naming rules and expected formats.

[0114] 2. Automatic elimination of extreme value data

[0115] ① Automatically check the threshold range of indicators such as RSRP and RSSI.

[0116] ② Automatically identify data points that exceed the threshold range. These points may be caused by terminal false alarms or anomalies and need to be eliminated.

[0117] ③ Automatically check GPS positioning information and eliminate the data if it exceeds the geographical scope of the park.

[0118] 3. Automatic conversion of data types

[0119] ①Automatically convert the collection time into a unified date and time format.

[0120] ② Automatically check that text data (such as cell ID, PCI, etc.) uses a unified encoding format to avoid garbled characters.

[0121] ③ Automatically check numerical data to ensure the correct format and remove unnecessary spaces or special characters.

[0122] The cleaned data is used as soft probe data samples. These sample data mainly include: cellular status information data, cellular cell information data, and GPS satellite positioning system information.

[0123] The main data examples of cellular status information are as follows:

[0124]

[0125] Table 1 Main data of cellular status information

[0126]

[0127] Table 2 Main data of cellular cell information

[0128] Lightweight network management platform collection time Terminal identification longitude Dimensions Recommended minimum positioning information 2022 / 12 / 29 17:56 8656****1617 113.23512 23.55122 113.23512,23.55122

[0129] Table 3 Main information of GPS satellite positioning system

[0130] By associating the timestamp and terminal identifier, the location information of the park and the corresponding signal coverage can be obtained, as shown in Table 4 below.

[0131]

[0132] Table 4 Park location information and corresponding signal coverage (IV) GIS rendering

[0133] GIS rendering enables 5G campus customers to adaptively render GIS data based on test results. GIS rendering is divided into three stages: k-means clustering test data, simplified test data, and client-side adaptive rendering test data.

[0134] Stage 1: k-means clustering test data

[0135] The k-means clustering test data realizes the clustering of the park test data, which is mainly divided into two steps: 5G park remote sensing image classification and 5G park test data clustering.

[0136] 1. 5G Park Remote Sensing Image Classification

[0137] (1) Input

[0138] ① The pixel point set D of the 5G park remote sensing image is {x1,x2,...,x m};

[0139] ②The number of clusters K.

[0140] The objects in the remote sensing images of the 5G park generally include buildings, roads, park equipment, etc. The number of objects in the 5G park n is determined, and the number of clusters K = n.

[0141] (2) Process

[0142] ① Randomly select K pixels from the 5G park remote sensing image sample set D as the initial centroid μ i (1≤i≤K);

[0143] ②Traverse each point x in the 5G park remote sensing image sample set D j (j=1,2,...,m), calculate x j With μ i Distance: d ji =||x j -μ i ||2;

[0144] ③ Determine x according to the principle of closest distance j Cluster label: λ j =argmin i∈{1,2,...,k} d ji, and the sample x j Assign to the corresponding clusters:

[0145] ④ Output cluster set C = {c1,c2,...,c k} and recalculate the centroid If μ i ′≠μ i , then the current center of mass μ i Update to μ i ′, repeat the above steps until μ i ′=μ i .

[0146] (3) Output

[0147] ① Output 5G private network remote sensing image cluster C = {c1,c2,...,c k}.

[0148] 2. Clustering of test data in G Park

[0149] (1) Input

[0150] ① Cluster C of 5G campus remote sensing images = {c1,c2,...,c k};

[0151] ②Soft probe test data set E={p1,p2,...,p m}.

[0152] Cluster C of the 5G private network remote sensing image is the output result of the above steps, and the test data E is the soft probe data set after the previous data association processing.

[0153] (2) Process

[0154] ① Calculate the 5G private network remote sensing image for each cluster C i The pixel point p with the minimum coordinates of (i=1,2,...,k) min (x min ,y min ) and the pixel point p with the maximum coordinates max (x max ,y max ), construct a rectangular area and divide the area evenly into N 2 grids;

[0155] ② Calculate the image information entropy of each grid: Calculate the sampling step size: SS = roundup (aH + SS max ). Among them, SS max is the maximum sampling step, SS, SS max≥0 and is an integer; a is the attenuation coefficient (-1≤a≤0), obtained by parameter tuning; roundup() means rounding up the value inside the parentheses.

[0156] ③ Project the test data into the corresponding grid of the remote sensing image through coordinate transformation;

[0157] ④ Cluster the test data set E, and the number of clusters K = bN / Area. Where b is the degradation centroid coefficient (0 < b < 1), N is the quantity, and Area is the area of this grid.

[0158] ⑤ Randomly select K pixel points from the test data set E as the initial centroids μ i (1≤i≤K);

[0159] ⑥ Traverse each point p in the test data set E j (j = 1,2,...,m), calculate the distance between p j and μ i : d ji = ||p j - μ i ||2;

[0160] ⑦ Determine the cluster label of p j according to the nearest distance principle: λ j = argmin i＝{1,2,...,k} d ji , and assign the sample p j to the corresponding cluster:

[0161] ⑧ Output the cluster set A i (i = 1,2,...,k), and recalculate the centroid μ i ′ =, if μ i ′ ≠ μ i , then update the current centroid μ i to μ i ′, repeat the above steps until μ i ′ = μ i .

[0162] (3) Output

[0163] ① Output the cluster set A of the soft probe test data = {a1,a2,...,a k};

[0164] ② Sampling step size SS.

[0165] Stage two: Simplify the test data

[0166] (1) Input

[0167] ① The cluster set A of the soft probe test data output in the previous stage is {a1, a2, ..., a k};

[0168] ② The sampling step length SS of the output of the previous stage;

[0169] (2) Process

[0170] ① According to the cluster A of the test data output in stage 1 and the corresponding sampling step SS, for each cluster a i (i=1,2,...,k), sample the data according to SS to form a new set B=(b1,b2,...,b k ).

[0171] ②For cluster b j Point p in (n=1,2,...,k) i (i=1,2,...), and the k nearest neighbor algorithm is used to output the probability that the point is a non-edge point, a sharp edge point, or a boundary point [p ne ,p se ,p b ]. Compare the sizes of the three probabilities. If p ne Maximum, then p i It is judged as a non-edge point. If p se The maximum, then point p i is judged as a sharp edge point; if p b The maximum, then point p i It is judged as a boundary point;

[0172] ③ According to the clustering result A of stage one, set cluster a j Initial reduction threshold for (j=1,2,...,k): Where: t j represents the reduction threshold of the jth cluster, n ne represents the number of samples classified as non-edge points in the jth cluster, n j represents the number of samples in the jth cluster.

[0173] ④ Initial reduction threshold set T = {t1, t2, ..., t k}, for the non-edge points in the jth cluster, when p ne >t j , the point will be discarded.

[0174] ⑤For each cluster P n (n=1,2,...,k), execute the above steps, and finally output the simplified cluster A={a1,a2,...,a k}.

[0175] (3) Output

[0176] The simplified test data cluster A={a1,a2,...,a k}.

[0177] Phase 3: Client Adaptive Rendering Test Data

[0178] (1) Input

[0179] ① Simplified test data cluster A={a1,a2,...,a k}.

[0180] ②5G campus client rendering scenarios are shown in Table 5.

[0181] Rendering Frames Client-side rendering scene [0,30) Proper basic operation [30,60] Smooth browsing (60,∞) Smooth rendering

[0182] Table 5 5G campus client rendering scenarios

[0183] (2) Process

[0184] ① The soft probe data cluster A is rendered and presented through the rendering engine, and the opengl graphics API interface is used to render the data and obtain the frame number.

[0185] ② If the number of rendered frames meets the requirements of smooth browsing scenes, the data is simplified according to the current reduction threshold; if the number of rendered frames does not meet the requirements of smooth browsing scenes, the reduction threshold of each cluster is reset through linear interpolation. The calculation formula of the reduction threshold is as follows:

[0186]

[0187] Among them, t j ' represents the new reduction threshold of the jth cluster, t j represents the initial reduction threshold of the jth cluster, and x represents the current rendering frame number.

[0188] ③ After calculating the new reduction threshold, simplify the data again and repeat the above steps until the number of rendered frames is in the range [30,60], so that users can browse smoothly.

[0189] (3) Output

[0190] The final automatic test and GIS rendering effect are as follows Figure 4 As shown, the adaptive grid road test rendering effect diagram of the campus client is shown. The effect has achieved the test purpose, the route planning is complete, and the test situation is transmitted back to the server and then rasterized by GIS to intuitively present the 5G signal coverage.

[0191] In summary, in this embodiment, by binding the soft probe terminal to the AGV cart, the signal collection is automated, and the entire test process does not require manual intervention, which greatly improves the test efficiency. The adaptive rendering algorithm enables the 5G park client to adaptively render according to the test data. Combining the signal acquisition of the soft probe terminal with GPS positioning technology, high-precision positioning is achieved, which helps to obtain more accurate coverage information of each longitude and latitude point in the park. The method of automatically collecting the park's 5G private network signal coverage and GIS presentation is conducive to quickly evaluating the park's coverage and improving TOB's intelligent operation and maintenance capabilities.

[0192] Further, as Figures 1 to 4 The specific implementation of the method shown in this embodiment provides a signal testing device, such as Figure 5 As shown, the device includes: a setting unit 501, an acquisition unit 502 and a processing unit 503.

[0193] The setting unit 501 is configured to set a preset route so that the park cruise device travels along the preset route; the park cruise device is provided with a collection terminal, and the collection terminal is used to collect signal data of the park in real time during the driving process;

[0194] An acquisition unit 502 is configured to acquire the signal data uploaded by the acquisition terminal during driving;

[0195] The processing unit 503 is configured to perform data cleaning processing on the signal data to obtain the test result of the park.

[0196] In a specific application scenario, the processing unit 503 is specifically configured to perform data verification on the wireless signal strength and the corresponding location information, and to eliminate extreme value data; convert the data type of the wireless signal strength and the corresponding location information into a preset format, and associate it with the upload timestamp to obtain the test result of the park.

[0197] In a specific application scenario, the processing unit 503 is further configured to present the test result in a GIS manner.

[0198] In a specific application scenario, the processing unit 503 is further configured to generate a grid map; the grid map includes a plurality of grid areas; the test results are clustered and analyzed by a K-means clustering algorithm to generate image clusters and data clusters; the data clusters are optimized by a K-nearest neighbor algorithm; the image clusters and the optimized data clusters are adaptively divided on the grid map to obtain a GIS map; the GIS map can characterize the signal strength of each grid area in the park.

[0199] In a specific application scenario, the processing unit 503 is further configured to render the image cluster and the optimized data cluster on the grid map, and determine the initial frame number; determine whether the initial frame number meets the preset clarity requirement; if the initial frame number does not meet the preset clarity requirement, process the rendering process through a threshold reduction algorithm so that the rendered image meets the clarity requirement.

[0200] Further, as Figures 1 to 4 The specific implementation of the method shown in this embodiment provides a signal testing device, such as Figure 6 As shown, the device includes: a receiving unit 601, a driving unit 602 and an uploading unit 603.

[0201] The receiving unit 601 is configured to start a collection terminal when receiving a running signal; the collection terminal is used to collect signal data of the park in real time during driving;

[0202] The driving unit 602 is configured to determine a preset route and drive according to the preset route;

[0203] The uploading unit 603 is configured to upload the signal data collected during the driving process in real time; the signal data is used to obtain the test results of the park.

[0204] It should be noted that for other corresponding descriptions of the functional units involved in the signal testing device provided in this embodiment, reference can be made to Figures 1 to 4 The corresponding description in will not be repeated here.

[0205] Based on the above Figures 1 to 4 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figures 1 to 4 The method shown.

[0206] Based on the above Figures 1 to 4 The method shown in the embodiment also provides a computer program product, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned Figures 1 to 4 The method shown.

[0207] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0208] Based on the above Figures 1 to 4 The method shown, and Figure 5 and Figure 6 In order to achieve the above-mentioned purpose, the embodiment of the present application also provides an electronic device, which can be configured on a computer terminal, etc. The device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figures 1 to 4 The method shown.

[0209] Based on the above Figures 1 to 4 The method shown, and Figure 5 and Figure 6 In order to achieve the above-mentioned purpose, the embodiment of the virtual device shown in the embodiment of the present application further provides a chip, including one or more interface circuits and one or more processors; the interface circuit is used to receive a signal from the memory of the electronic device and send the signal to the processor, the signal including the computer instruction stored in the memory; when the processor executes the computer instruction, the electronic device executes the above-mentioned Figures 1 to 4 The method shown.

[0210] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0211] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0212] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0213] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware. By applying the solution of this embodiment, compared with the related art, by setting the acquisition terminal on the cruise device, and letting the cruise device automatically drive along the planned route, the acquisition terminal periodically collects signal data and uploads it to the server for processing in real time, which not only greatly improves the test efficiency, but also ensures the real-time and accuracy of the test, and improves the current park signal test method, which has the technical problems of low efficiency and poor real-time performance.

[0214] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0215] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A signal testing method, characterized in that: include: Setting a preset route so that the park cruise device travels along the preset route; The park cruise device is provided with a collection terminal, and the collection terminal is used to collect the signal data of the park in real time during the driving process; Acquiring the signal data uploaded by the acquisition terminal during driving; The signal data is cleaned to obtain a test result of the park.

2. The method according to claim 1, characterized in that: The signal data includes an upload timestamp, a wireless signal strength at the current location, and corresponding location information; The performing data cleaning processing on the signal data to obtain the test result of the park includes: Performing data verification on the wireless signal strength and the corresponding location information, and removing extreme value data; The data types of the wireless signal strength and the corresponding location information are converted into a preset format and associated with the upload timestamp to obtain the test result of the park.

3. The method according to claim 1, characterized in that After obtaining the test results of the park, the method further includes: The test results are presented through GIS.

4. The method according to claim 3, characterized in that Presenting the test results in a GIS manner includes: Generate a grid map; the grid map includes a plurality of grid areas; Performing cluster analysis on the test results by using a K-means clustering algorithm to generate image clusters and data clusters; Optimizing the data clusters using a K-nearest neighbor algorithm; The image clusters and the optimized data clusters are adaptively divided on the grid map to obtain a GIS map; the GIS map can characterize the signal strength of each grid area in the park.

5. The method according to claim 4, characterized in that Adaptively dividing the image cluster and the optimized data cluster on the grid map, comprising: Rendering the image cluster and the optimized data cluster on the grid map, and determining an initial frame number; Determining whether the initial frame number meets a preset definition requirement; When the initial number of frames does not meet the preset definition requirement, the rendering process is processed by using a threshold reduction algorithm so that the rendered image meets the definition requirement.

6. A signal testing method, characterized in that: include: When receiving the operation signal, the acquisition terminal is turned on; The acquisition terminal is used to collect signal data of the park in real time during driving; Determine a preset route and drive along the preset route; Uploading the signal data collected during driving in real time; The signal data is used to obtain the test results of the park.

7. A signal testing device, characterized in that: include: A setting unit is configured to set a preset route so that the park cruise device travels along the preset route; The park cruise device is provided with a collection terminal, and the collection terminal is used to collect the signal data of the park in real time during the driving process; An acquisition unit, configured to acquire the signal data uploaded by the acquisition terminal during driving; The processing unit is configured to perform data cleaning processing on the signal data to obtain the test result of the park.

8. A signal testing device, characterized in that: include: The receiving unit is configured to start the acquisition terminal when receiving the operation signal; The acquisition terminal is used to collect signal data of the park in real time during driving; A driving unit, configured to determine a preset route and drive along the preset route; An uploading unit, configured to upload the signal data collected during driving in real time; The signal data is used to obtain the test results of the park.

9. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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