Method and apparatus for generating drive test data, computing device, and storage medium

Abstract

This invention discloses a method, apparatus, computing device, and storage medium for generating simulated road test data. The method includes: acquiring historical on-site road test data; determining the trend characteristics of signal indicator values ​​for any road segment based on the historical on-site road test data; acquiring MRO (Maintenance, Repair, and Overhaul) data for any user; determining the road segment matched to any user based on the MRO data and trend characteristics; determining the initial position of any MRO sampling point for any user based on the corresponding MRO data; acquiring the location information of the road segment matched to the user; calibrating the initial position of the user's MRO sampling point based on the road segment location information to obtain the calibration position of the MRO sampling point; and generating simulated road test data based on the calibration position of the MRO sampling point and the corresponding MRO data. Using this solution can improve the efficiency of road test data generation, reduce road test costs, and improve the accuracy of road test data.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and specifically to a method, apparatus, computing device, and storage medium for generating simulated drive test data. Background Technology

[0002] With the continuous development of technology and society, wireless networks have become widely used. Currently, in order to obtain the coverage capability of a wireless network, it is usually necessary to use appropriate drive testing techniques to detect the network coverage.

[0003] However, during implementation, the inventors discovered the following drawbacks in the existing technology: When acquiring road test data for evaluating wireless networks, the existing technology typically requires on-site road testing. On-site road testing involves a test vehicle moving along a test road and acquiring relevant signal data at test points during the movement. However, this method of generating road test data is inefficient and costly. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method, apparatus, computing device and storage medium for generating simulated road test data that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for generating simulated road test data is provided, comprising:

[0006] Acquire historical on-site road test data, and determine the trend characteristics of signal indicator values ​​for any road segment based on the historical on-site road test data;

[0007] Retrieve MRO data for any user;

[0008] Based on the MRO data and the trend characteristics, determine the road segment matched for any user;

[0009] For any MRO sampling point of any user, determine the initial position of the MRO sampling point based on the MRO data corresponding to that MRO sampling point;

[0010] For any user, obtain the location information of the road segment matched to the user, and calibrate the initial position of the user's MRO sampling point based on the location information of the road segment to obtain the calibration position of the MRO sampling point;

[0011] Based on the calibration location of the MRO sampling points and the corresponding MRO data, simulated road test data is generated.

[0012] In an optional implementation, determining the trend characteristics of signal indicator values ​​for any road segment based on the historical on-site road test data further includes: generating sample data based on the historical on-site road test data; and training the pre-built trend learning model using the sample data so that the trend learning model can determine the trend characteristics of signal indicator values ​​for any road segment.

[0013] The step of determining the road segment matched by any user based on the MRO data and the trend characteristics further includes: inputting the MRO data of any user into the trained trend learning model, and obtaining the information of the road segment matched by the user output by the trend learning model.

[0014] In one optional implementation, the method further includes:

[0015] Retrieve MDT data for any user;

[0016] For any user, the first type of MDT sampling points and the second type of MDT sampling points are identified based on the user's MDT data; wherein, the MDT data contains the location information of the first type of MDT sampling points, but does not contain the location information of the second type of MDT sampling points;

[0017] The MDT data of the user's first type MDT sampling points are compared with the MRO data of the user's MRO sampling points to determine the first type MRO sampling points; wherein, the MRO data of the first type MRO sampling points matches the MDT data of the first type MDT sampling points.

[0018] Based on the location information of the first type of MRO sampling points matched with the first type of MRO sampling points, the calibration position of the first type of MRO sampling points is generated.

[0019] The step of determining the initial position of the MRO sampling point based on the MRO data corresponding to the MRO sampling point further includes: determining the initial position of the second type of MRO sampling point based on the MRO data corresponding to the second type of MRO sampling point;

[0020] The step of calibrating the initial position of the user's MRO sampling point based on the location information of the road segment to obtain the calibration position of the MRO sampling point further includes: calibrating the initial position of the user's second type of MRO sampling point based on the location information of the road segment to obtain the calibration position of the second type of MRO sampling point.

[0021] In one optional implementation, the method further includes:

[0022] For any second type MDT sampling point of any user, determine the initial position of the second type MDT sampling point based on the MDT data corresponding to the second type MDT sampling point;

[0023] For any user, obtain the location information of the road segment matched to the user, and calibrate the initial position of the user's second type MDT sampling point based on the location information of the road segment to obtain the calibration position of the second type MDT sampling point;

[0024] The step of generating simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data further includes:

[0025] Simulated road test data is generated based on the calibration positions of the MRO sampling points, the calibration positions of the second type of MDT sampling points, the location information of the first type of MDT sampling points, the MRO data, and the MDT data.

[0026] In an optional implementation, determining the initial position of the MRO sampling point based on the MRO data corresponding to the MRO sampling point further includes: determining the initial position of the second type of MRO sampling point based on the signal index value and propagation loss of the second type of MRO sampling point;

[0027] The step of determining the initial position of the second type of MDT sampling point based on the MDT data corresponding to the second type of MDT sampling point further includes: determining the initial position of the second type of MDT sampling point based on the signal index value and propagation loss of the second type of MDT sampling point.

[0028] In one optional implementation, the method further includes:

[0029] Retrieve MRE data for any user;

[0030] Obtain event information for any event location based on the MRE data;

[0031] Identify MRO sampling points that match the event location;

[0032] The step of generating simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data further includes: generating simulated road test data based on the calibration location of the MRO sampling points, the corresponding MRO data, and the event information.

[0033] In an optional implementation, the method further includes: identifying the outdoor MRO sampling point of any user based on the MRO data of that user;

[0034] The step of generating simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data further includes: generating simulated road test data based on the calibration location of the outdoor MRO sampling points and the corresponding MRO data.

[0035] According to a second aspect of the present invention, an apparatus for generating simulated road test data is provided, comprising:

[0036] The acquisition module is used to acquire historical on-site road test data and MRO data for any user.

[0037] The road segment determination module is used to determine the trend characteristics of signal indicator values ​​of any road segment based on the historical on-site road test data; and to determine the road segment matched for any user based on the MRO data and the trend characteristics.

[0038] The initial location determination module is used to determine the initial location of any MRO sampling point for any user based on the MRO data corresponding to that MRO sampling point.

[0039] The calibration module is used to obtain the location information of the road segment matched by any user, and to calibrate the initial position of the user's MRO sampling point based on the location information of the road segment, so as to obtain the calibration position of the MRO sampling point.

[0040] The generation module is used to generate simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data.

[0041] In an optional implementation, the road segment determination module is further configured to: generate sample data based on the historical on-site road test data; and train the pre-built trend learning model using the sample data so that the trend learning model can determine the trend characteristics of the signal indicator values ​​of any road segment.

[0042] Input any user's MRO data into the trained trend learning model, and obtain the information of the road segment matched by the user output by the trend learning model.

[0043] In one alternative implementation, the acquisition module is further configured to: acquire MDT data of any user;

[0044] The device further includes: an identification module, used to identify, for any user, a first type of MDT sampling point and a second type of MDT sampling point based on the user's MDT data; wherein, the MDT data contains location information of the first type of MDT sampling point, and the MDT data does not contain location information of the second type of MDT sampling point;

[0045] The calibration module is further used to: compare the MDT data of the user's first type MDT sampling point with the MRO data of the user's MRO sampling point to determine the first type MRO sampling point; wherein the MRO data of the first type MRO sampling point matches the MDT data of the first type MDT sampling point; and generate the calibration position of the first type MRO sampling point based on the location information of the first type MDT sampling point matched by the first type MRO sampling point.

[0046] The initial location determination module is further used to: determine the initial location of the second type of MRO sampling point based on the MRO data corresponding to the second type of MRO sampling point;

[0047] The calibration module is further used to: calibrate the initial position of the user's second type of MRO sampling point according to the location information of the road segment, so as to obtain the calibration position of the second type of MRO sampling point.

[0048] In one optional implementation, the initial position determination module is further configured to: for any second type MDT sampling point of any user, determine the initial position of the second type MDT sampling point based on the MDT data corresponding to the second type MDT sampling point;

[0049] The calibration module is further configured to: for any user, obtain the location information of the road segment matched by the user, and calibrate the initial position of the second type MDT sampling point of the user according to the location information of the road segment, so as to obtain the calibration position of the second type MDT sampling point;

[0050] The generation module is further used to generate simulated road test data based on the calibration positions of the MRO sampling points, the calibration positions of the second type of MDT sampling points, the location information of the first type of MDT sampling points, the MRO data, and the MDT data.

[0051] In one alternative implementation, the initial position determination module is further configured to: determine the initial position of the second type of MRO sampling point based on the signal index value and propagation loss of the second type of MRO sampling point;

[0052] The initial position of the second type of MDT sampling point is determined based on the signal index value and propagation loss of the sampling point.

[0053] In one optional implementation, the acquisition module is further configured to: acquire MRE data of any user;

[0054] The device also includes: an event module, used to obtain event information at any event location based on the MRE data; and to determine the MRO sampling point that matches the event location;

[0055] The generation module is further used to generate simulated road test data based on the calibration location of the MRO sampling points, the corresponding MRO data, and the event information.

[0056] In one optional embodiment, the device further includes: an identification module for identifying the outdoor MRO sampling point of any user based on the MRO data of any user;

[0057] The generation module is further used to generate simulated road test data based on the calibration location of the outdoor MRO sampling points and the corresponding MRO data.

[0058] According to a third aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0059] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for generating simulated road test data.

[0060] According to a fourth aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform operations corresponding to the above-described method for generating simulated road test data.

[0061] The present invention provides a method, apparatus, computing device, and storage medium for generating simulated road test data: It acquires historical on-site road test data; determines the trend characteristics of signal indicator values ​​for any road segment based on the historical on-site road test data; acquires MRO (Maintenance, Repair, and Overhaul) data for any user; determines the road segment matched for any user based on the MRO data and trend characteristics; for any MRO sampling point of any user, determines the initial position of the MRO sampling point based on the corresponding MRO data; for any user, acquires the location information of the road segment matched for that user, calibrates the initial position of the user's MRO sampling point based on the road segment location information to obtain the calibration position of the MRO sampling point; and generates simulated road test data based on the calibration position of the MRO sampling point and the corresponding MRO data. Using this solution, the efficiency of road test data generation can be improved, road test costs reduced, and the accuracy of road test data enhanced.

[0062] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0063] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0064] Figure 1 A flowchart illustrating a method for generating simulated road test data according to Embodiment 1 of the present invention is shown.

[0065] Figure 2 A flowchart illustrating a method for generating simulated road test data according to Embodiment 2 of the present invention is shown.

[0066] Figure 3 A flowchart illustrating a method for generating simulated road test data according to Embodiment 3 of the present invention is shown.

[0067] Figure 4 This diagram illustrates the structure of a simulated road test data generation device according to Embodiment 4 of the present invention.

[0068] Figure 5 A schematic diagram of the structure of a computing device provided in Embodiment Six of the present invention is shown. Detailed Implementation

[0069] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0070] Existing technologies require on-site drive tests every time network coverage is assessed, resulting in low efficiency and high cost in generating drive test data. Therefore, this invention provides a simulated drive test data generation scheme. This scheme can simulate drive test data based on historical on-site drive test data and MDT data, eliminating the need for real-time on-site drive tests. This achieves real-time drive test data generation, improves efficiency, and reduces costs.

[0071] The following will illustrate in detail the scheme for generating simulated road test data in the embodiments of the present invention with reference to various examples.

[0072] Example 1

[0073] Figure 1A flowchart illustrating a method for generating simulated road test data according to Embodiment 1 of the present invention is shown. The flowchart in this embodiment is not intended to limit the order of execution steps. Steps in the flowchart may be added to or removed as needed.

[0074] like Figure 1 As shown, the method specifically includes the following steps:

[0075] Step S110: Obtain historical on-site road test data, and determine the trend characteristics of signal indicator values ​​for any road segment based on the historical on-site road test data.

[0076] When evaluating the network coverage of a wireless network, it is not necessary to conduct on-site drive tests. Instead, historical on-site drive test data that has already been generated can be obtained. This historical on-site drive test data is obtained through on-site drive tests within a certain historical period. Since the coverage of wireless networks can change, this historical on-site drive test data cannot accurately reflect the current network coverage of the wireless network. Therefore, this embodiment of the invention only uses this historical on-site drive test data as the basic data required to simulate the current drive test data.

[0077] Although wireless network coverage may change, the trend of signal strength indicators on a road segment does not fluctuate significantly. Based on this, this embodiment of the invention determines the trend characteristics of signal strength indicators for any road segment using historical on-site road test data. These trend characteristics reflect the changing trend of signal strength indicators with the location of that road segment.

[0078] In one optional implementation, determining the trend characteristics of signal indicator values ​​for any road segment based on historical on-site road test data includes the following steps:

[0079] For any given road segment, the location information of the road test points and the signal index values ​​of the primary serving cells of those points are obtained from historical on-site road test data. Based on the location information of the road test points and the signal index values ​​of the primary serving cells, the first trend characteristic of the signal index values ​​for that road segment is determined. Therefore, the first trend characteristic is the trend of the signal index values ​​of the primary serving cells in the road segment changing with the location of the road segment.

[0080] And / or, for any road segment, obtain the location information of the road test points in that road segment and the signal index values ​​of the neighboring serving cells from the historical field road test data of that road segment. Based on the location information of the road test points and the signal index values ​​of the neighboring serving cells, determine the second trend feature of the signal index values ​​of that road segment. Thus, the second trend feature is the trend of the signal index values ​​of the neighboring serving cells changing with the location of the road test points.

[0081] The signal indicator value can be RSRP (Reference Signal Receiving Power) and / or RSRQ (Reference Signal Receiving Quality), etc.

[0082] Step S120: Obtain MRO data for any user.

[0083] MRO (Raw Mobility Measurement Report) data contains periodic measurement data from all user terminals within the cell coverage area. Compared to field drive test data, MRO data has a wider coverage scope. For example, field drive test data is usually limited to measurement data on public roads, while MRO data includes signal data from both public and non-public roads. Therefore, this embodiment of the invention can improve the scenario coverage of simulated drive test data generated based on MRO data. Moreover, MRO data is closer to the user's actual perception, thus the obtained simulated drive test data matches the user's actual perception. In addition, MRO data contains a variety of data types, such as SINR (Signal to Interference plus Noise Ratio), AOA (Angle of Arrival), TA (Timing Advance), RSRP, RSRQ, etc. Therefore, the simulated drive test data generated based on MRO data can reflect the network status of wireless signals from multiple perspectives. If the initially obtained RSRP is in interval representation, then the interval is mapped to the corresponding dB value according to the interval step size corresponding to 1dB. The specific mapping method can adopt the mapping method in the existing technology, which will not be elaborated here.

[0084] In one optional implementation, MRO data includes, but is not limited to: TimeStamp, Amfuengapid, MR.NRScEarfcn, MR.NRScPci, MR.NRncssrsrp, MR.NRncssrsrp1 (neighbor cell 1 RSRP), MR.NRncssrsrp2 (neighbor cell 2 RSRP), MR.NRncssrsrp3 (neighbor cell 3 RSRP), MR.UELongitude, MR.UELatitude, primary serving cell RSRP, and neighbor cell PCI. Within the same AMF, the Amfuengapid of a connected user remains unchanged. When the user connection is interrupted, the Amfuengapid is released and reassigned to another user. To avoid data conflicts, the Amfuengapid is typically not reassigned within 20 seconds after release. This time interval is four times the user MRO data reporting frequency. During this time interval, the Amfuengapid will not appear in the same AMF. Based on this, in this embodiment of the invention, the MRO data of different users is distinguished by Amfuengapid, forming MRO data packets for different users.

[0085] Step S130: Based on MRO data and trend characteristics, determine the road segment matched for any user.

[0086] MRO data specifically contains signal indicator values ​​from multiple sampling points. It can then match the signal indicator values ​​of each user's sampling points with the trend characteristics of any road segment. The road segment corresponding to the trend characteristics that have a high degree of matching with the user's sampling point signal indicator values ​​is taken as the matched road segment for that user.

[0087] In one alternative implementation, the road segment matched to any user is determined based on MRO data and a first trend feature and / or a second trend feature.

[0088] Step S140: For any MRO sampling point of any user, determine the initial position of the sampling point based on the MRO data corresponding to the sampling point.

[0089] Since MRO data does not contain location information for MRO sampling points, this embodiment of the invention infers the location of the sampling points based on the MRO data of the sampling points. This location is the initial location of the sampling points. This embodiment of the invention does not limit the specific method for determining the initial location. For example, the initial location of the sampling points can be determined using the following method.

[0090] In one optional implementation, the initial location of the MRO sampling point is determined based on the signal index values ​​and propagation loss of the MRO sampling point. Specifically, the propagation loss is determined based on the difference between the RSRP of the primary serving cell and / or neighboring serving cells of the MRO sampling point and the RSRP of the MRO sampling point. This propagation loss is related to the distance between the base station and the MRO sampling point. Thus, the distance matching this propagation loss can be determined according to a pre-built propagation model. This distance is the distance between the MRO sampling point and the base station. The initial location of the MRO sampling point is then determined based on the location information of the base station and this distance.

[0091] Step S150: For any user, obtain the location information of the road segment matched by the user, and calibrate the initial position of the MRO sampling point of the user according to the location information of the road segment to obtain the calibration position of the MRO sampling point.

[0092] To improve the accuracy of sampling point locations, this step uses the location information of the road segments matched by the user to calibrate the initial location of the corresponding sampling points. The location obtained after calibration is the calibrated location.

[0093] In one optional implementation, the location information of a road segment specifically includes the coordinates of positioning points within the road segment. These positioning points are points that can depict the overall position and shape of the road segment. Positioning points include, but are not limited to, the starting point, ending point, and turning points of the road segment. A position curve of the road segment can then be drawn using these positioning points, and the calibration position of a user's sampling point matching that road segment lies on this position curve. For example, for any user's sampling point, the point on the position curve of the road segment matching that user that is closest to the initial position of that sampling point is determined; the position of this closest point is the calibration position of that sampling point.

[0094] Step S160: Generate simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data.

[0095] Each sampling point has corresponding location information and MRO data, and based on this location information and MRO data, simulated drive test data that can reflect the current wireless network status can be generated.

[0096] Therefore, this invention can simulate drive test data reflecting the current network status of a wireless network by utilizing historical field drive test data and MRO data, thereby improving the efficiency of drive test data generation and reducing drive test costs. Furthermore, this invention determines the trend of signal indicator values ​​for road segments based on historical field drive test data and identifies the road segments matched to users. Then, it uses the location information of the matched road segments to calibrate the initial location of the user's sampling points, thereby improving the accuracy of the sampling point locations and further enhancing the precision of the simulated drive test data.

[0097] Example 2

[0098] Figure 2 A flowchart illustrating a method for generating simulated road test data according to Embodiment 2 of the present invention is shown. The flowchart in this embodiment is not intended to limit the order of execution steps. Steps in the flowchart may be added to or removed as needed.

[0099] like Figure 2 As shown, the method specifically includes the following steps:

[0100] Step S210: Obtain historical on-site road test data, generate sample data based on the historical on-site road test data, and use the sample data to train the pre-built trend learning model so that the trend learning model can determine the trend characteristics of the signal indicator values ​​of any road segment.

[0101] This invention provides a pre-built trend learning model, which is constructed based on a machine learning algorithm. This invention does not limit the specific structure of the model or the machine learning algorithm used. For example, the model can be a multi-class classification model built based on a Softmax regression model.

[0102] When training the trend learning model, historical road test data is used to obtain sample data. For example, the location of each road test user's test point and the road test signal index value can be used to generate corresponding sample data, and sample labels can be generated according to the road segment to which the user belongs. Supervised model training is then performed using the sample data and sample labels. This embodiment of the invention does not limit the specific training process; for example, a corresponding proportion of positive and negative samples can be used, and the gradient descent method can be used to solve the cost function to achieve model training. The trained trend learning model can fully learn the trend characteristics of the signal index values ​​of the road segment.

[0103] Step S220: Obtain the MRO data of any user, input the MRO data of any user into the trained trend change learning model, and obtain the information of the road segment matched by the user output by the trend change learning model.

[0104] The trend learning model can classify users by learning from their MRO data, thereby determining the road segments that match the users.

[0105] Step S230: For any sampling point of any user, determine the initial position of the sampling point based on the MRO data corresponding to the sampling point; for any user, obtain the location information of the road segment matched by the user, and calibrate the initial position of the user's sampling point based on the location information of the road segment to obtain the calibration position of the sampling point.

[0106] Step S240: Generate simulated road test data based on the calibration location of the sampling points and the corresponding MRO data.

[0107] Therefore, the embodiments of the present invention construct a trend learning model based on machine learning algorithms and train the model with historical on-site road test data so that the model can fully learn the trend of the signal indicator values ​​of the road segment. As a result, the model can be used to accurately determine the road segment matched by any user, improve the accuracy of user matching road segments, and thus improve the accuracy of simulated road test data.

[0108] In addition, as an optional alternative implementation of this embodiment, the road segment matched to the user can be determined in the following way: A first change curve of the signal index value for any road segment is generated based on historical on-site road test data. This first change curve is specifically a curve showing the change of the signal index value of the road segment with the location of the road test point. A second change curve of the user's signal index value is generated based on the user's MRO data. This second change curve is a curve showing the change of the signal index value of the sampling point with the sampling time of the sampling point. Then, each first change curve is compared with each second change curve. For any second change curve, the first change curve with the highest similarity is determined as the target first change curve. The road segment corresponding to this target first change curve is determined, and this road segment is designated as the user-matched road segment corresponding to this second change curve.

[0109] Example 3

[0110] Figure 3 A flowchart illustrating a method for generating simulated road test data according to Embodiment 3 of the present invention is shown. The flowchart in this embodiment is not intended to limit the order of execution steps. Steps in the flowchart may be added to or removed as needed.

[0111] like Figure 3 As shown, the method specifically includes the following steps:

[0112] Step S310: Obtain MRO data, MDT data, and MRE data for any user.

[0113] In addition to acquiring MRO data, this embodiment of the invention also acquires MDT (Minimization of Drive-Tests) data for any user. MDT data may include at least one of the following: location, serving cell signal strength data, interference indicators, traffic indicators, etc. Specifically, for MDT data with IMSI, different IMSIs within consecutive time periods are divided into different users, and corresponding user data is compiled, including RSRP, RSRQ, UELongitude, UELatitude, and related events. For MDT data without IMSI, different Amfuengapids within consecutive time periods are divided into different users, and corresponding user data is compiled, including RSRP, RSRQ, UELongitude, UELatitude, and related events, etc.

[0114] This embodiment of the invention also acquires MRE (MR Event, event-triggered measurement report sample) data for any user. The MRE data contains information about the corresponding event, such as events A1, A2, A3, etc.

[0115] Step S320: Obtain the location information of the sampling points.

[0116] For any given user, the user's first-type and second-type MDT sampling points are identified based on their MDT data. The MDT data includes location information for the first-type MDT sampling points, but does not include location information for the second-type MDT sampling points. Specifically, MDT sampling points are classified into first-type and second-type MDT sampling points based on whether corresponding location information exists in the MDT data.

[0117] For the first type of MDT sampling point, since the MDT data contains the location information of the first type of MDT sampling point, and the location information has high precision, the location information in the MDT data can be used as the final location of the first type of MDT sampling point.

[0118] For the second type of MDT sampling point, since the MDT data does not contain the location information of the first type of MDT sampling point, the calibration position of the first type of MDT sampling point can be determined by referring to the method for determining the calibration position of the MRO sampling point in Embodiment 1 and / or Embodiment 2. Specifically, for any second type of MDT sampling point of any user, the initial position of the second type of MDT sampling point is determined based on the MDT data corresponding to the second type of MDT sampling point; for any user, the location information of the road segment matched by the user is obtained, and the initial position of the user's second type of MDT sampling point is calibrated based on the location information of the road segment to obtain the calibration position of the second type of MDT.

[0119] For any given user, their MRO sampling points can be further divided into first MRO sampling points and second MRO sampling points. Specifically, the MDT data of the user's first-type MDT sampling points is compared with the MRO data of the user's MRO sampling points to determine the first-type MRO sampling points; where the MRO data of the first-type MRO sampling points matches the MDT data of the first-type MDT sampling points. MRO sampling points other than the first-type MRO sampling points are designated as second-type MRO sampling points. Specifically, if the TimeStamp and IMSI of the MDT data of the first-type MDT sampling points of the same user match the TimeStamp and IMSI of a certain MRO sampling point of the same user, then that MRO sampling point is determined to be a first-type MRO sampling point. This indicates that the first-type MDT sampling point and the first-type MRO sampling point correspond to the same sampling point, thus allowing the sampled MDT and MRO data to be merged.

[0120] For the first MRO sampling point, the calibration position of the first type of MRO sampling point is generated based on the location information of the first type of MDT sampling point matched with the first type of MRO sampling point.

[0121] For the second type of MRO sampling point, the initial position of the second type of MRO sampling point is determined based on the MRO data corresponding to the second type of MRO sampling point; the initial position of the user's second type of MRO sampling point is calibrated based on the location information of the road segment to obtain the calibrated position of the second type of MRO sampling point.

[0122] In one optional implementation, the initial position of the second type of MRO sampling point is determined specifically based on the signal index value and propagation loss of the second type of MRO sampling point. Similarly, the initial position of the second type of MDT sampling point is determined based on the signal index value and propagation loss of the second type of MDT sampling point.

[0123] Further, optionally, to improve the accuracy of initial position determination, this embodiment of the invention specifically constructs a corresponding propagation model and determines the initial position based on the propagation model. This embodiment of the invention does not limit the specific propagation model; for example, the SPM model shown in Formula 1 can be used.

[0124] P RX =P TX +K1+K2logd+K3logH eff +K4Diffraction+K5logH eff logd+K6logH meff +K CLUTTER

[0125] (Formula 1)

[0126] In Formula 1, P TX With P RX The difference is the path loss; K1 is the reference point loss constant; K2 is the ground slope correction factor; K3 is the effective antenna height gain, usually taken as 5.83; K4 is the diffraction correction factor, usually taken as 0.4; K5 is the Okumura Hata multiplicative correction factor, usually taken as -6.55; K6 is the mobile station antenna height correction factor, usually taken as 0; K CLUTTER The ground loss at the location of the mobile station is typically taken as 1; d is the distance (m) between the base station and the mobile terminal; H meff H represents the height (m) of the mobile terminal. eff The effective antenna height (m) of the base station above the ground; Diffraction is the diffraction loss.

[0127] The propagation model can be used to obtain the relationship between the distance between the sampling point and the base station and the path loss. This path loss is related to the base station's reference signal transmit power and the RSRP of the sampling point. From this, the distance between the sampling point and the base station can be obtained, and then the initial position of the sampling point can be determined by combining other data such as azimuth angle.

[0128] Furthermore, before obtaining the path loss using the propagation model, the propagation model is calibrated beforehand. The calibration process includes the following steps:

[0129] First, the locations of sampling points at known locations are matched with those of road test points to identify road test points and sampling points with consistent locations. The sampling points at known locations are designated as Type I MDT sampling points and Type I MRO sampling points. Since different receiving antennas with varying gains are used during road testing, the RSRP of road test points is typically higher than that of sampling points. Therefore, to ensure data consistency, this embodiment calculates the difference in RSRP between the identified road test points and sampling points after determining their consistent locations. This difference is then used to adjust the RSRP of the sampling points; the adjusted RSRP is the sum of the original RSRP and the difference. Furthermore, the propagation model is calibrated using the road test data and the adjusted RSRP of the sampling points to accurately determine the values ​​of the corresponding coefficients.

[0130] Step S330: Generate simulated road test data based on the location information of the sampling points, MRO data, MDT data, and MRE data.

[0131] Specifically, in this embodiment of the invention, MDT data is obtained, and thus this step can generate simulated road test data based on the calibration position of the MRO sampling point, the calibration position of the second type of MDT sampling point, the position information of the first type of MDT sampling point, the MRO data, and the MDT data.

[0132] Furthermore, in this embodiment of the invention, if MRE data is acquired, event information for any event location is obtained based on the MRE data. An MRO sampling point matching the event location is determined, and simulated drive test data is generated based on the calibration location of the MRO sampling point, the corresponding MRO data, and the event information. And / or, an MDT sampling point matching the event location is determined, and simulated drive test data is generated based on the location of the MDT sampling point, the corresponding MDT data, and the event information. Specifically, the event can be a dropped call event and / or a handover event, etc.

[0133] Specifically, the MRE data records relevant information about events A1, A2, and A3. Therefore, this embodiment of the invention determines the event information of dropped calls and / or handover events based on the MRE and MRO data. More specifically, by utilizing the relationship between adjacent cells, the MRO data of the two cells at the time of event A3 are correlated. By combining the TimeStamp, Amfuengapid, and the sampling point information corresponding to the TimeStamps of the two cells before and after event A3, it can be determined whether a handover or a dropped call occurred.

[0134] Taking the determination of a handover event as an example, if an A3 event is determined through MRE, the primary serving cell and the strongest neighbor cell recorded in the corresponding TimeStamp are identified. An Amfuengapid query is performed on the primary serving cell and the strongest neighbor cell. If the same Amfuengapid can be found in the original serving cell at the next TimeStamp sampling point, it indicates that no handover event has occurred; if the Amfuengapid appears in the sampling point of the strongest neighbor cell of the original serving cell, it indicates that a handover event has occurred, and the relevant information of the handover event is recorded.

[0135] Taking the identification of dropped call events as an example, dropped call event statistics are performed using MRO data. If Amfuengapid no longer reports, and the corresponding RSRP value is low (usually below -110dB) or SINR value is poor (usually <-3dB), and statistics based on this TimeStamp show that Amfuengapid continuously reports abnormal data (usually abnormal data is manifested as RSRP, neighbor cell, etc., all being "NIL"), and after reporting abnormal data, it does not appear in the MRO data of this base station for 20 consecutive seconds, then it proves that a dropped call has occurred at this sampling point, and the relevant information of the dropped call event is recorded.

[0136] In one optional implementation, simulated road test data for the same road segment is determined based on MRO data from multiple users, thereby improving the accuracy of the simulated road test data. Specifically, if different users have different RSRPs at the same location on the same road segment, the RSRP at that location is determined using appropriate statistical methods (such as removing RSRPs with large deviations), and the relevant information of overlapping sampling points is merged.

[0137] Therefore, the embodiments of the present invention generate simulated road test data based on MRO data, MDT data, MRE data and historical road test data, thereby improving the comprehensiveness of simulated road test data and facilitating a comprehensive and accurate evaluation of wireless networks through simulated road test data.

[0138] In some optional embodiments of the present invention, outdoor and indoor sampling points corresponding to a user are identified, and simulated road test data is generated based on the information of the outdoor sampling points. This avoids interference from indoor sampling points on the road test data, simplifies the road test data generation process, saves computing resources, and improves the efficiency of road test data generation. Specifically, based on any user's MRO data, the user's outdoor MRO sampling points are identified, and simulated road test data is generated based on the calibration location of the outdoor MRO sampling points and the corresponding MRO data; and / or, based on any user's MDT data, the user's outdoor MDT sampling points are identified, and simulated road test data is generated based on the location of the outdoor MDT sampling points and the corresponding MDT data.

[0139] Further optionally, the outdoor sampling point and indoor sampling point corresponding to the user can be identified based on at least one of the following information: whether the primary serving cell is an indoor distributed system cell, the RSRP of the primary serving cell, the sampling time, the RSRP of the neighboring serving cells, and the number of neighboring serving cells.

[0140] Specifically, when the main service cell is an indoor distributed antenna system cell, the sampling point can be determined as an indoor sampling point or an outdoor sampling point through the following scenarios.

[0141] Scenario 1: If the RSRP of the primary serving cell is greater than X1 (e.g., -90dBm), then the sampling point is determined to be an indoor sampling point. Since outdoor users typically have difficulty accessing indoor stations, and the RSRP value is generally greater than the corresponding value (e.g., -90dBm), the signal received by the user from the indoor distribution system cell does not penetrate through walls. Based on this, sampling points that fit this scenario are considered indoor sampling points.

[0142] Scenario 2: If the RSRP of the primary serving cell is greater than X2 (e.g., -100dBm) and the sampling time falls within a preset time period (e.g., 22:00-06:00), then the sampling point is determined to be an indoor user sampling point. This type of sampling point is located at the edge of the primary serving cell, occupies the indoor distributed antenna system (DAS) area, and the sampling time is mostly during evening rest hours. These users typically spend their time indoors, hence this type of sampling point is determined to be an indoor sampling point.

[0143] Scenario 3: If no relevant data is collected from indoor neighboring cells whose RSRP of the primary serving cell is less than X3 (e.g., -85dBm) and differs from the primary serving cell's RSRP by X4 (e.g., 6dBm), then this sampling point is an outdoor user sampling point. These sampling points often occur in indoor distributed systems where signal control is poor and leakage occurs outdoors. However, because the antennas of the relevant indoor neighboring cells are far from this area, their signal loss is significant, resulting in these sampling points. Therefore, they can be identified as outdoor user sampling points.

[0144] Scenario 4: If the RSRP of the neighboring serving cell is greater than X5 (e.g., -100dBm) and the number of outdoor signals is no greater than X6 (e.g., 3), it is identified as an outdoor user sampling point. Specifically, if the neighboring serving cell has a strong signal and there are many outdoor signals in the neighboring cell, it indicates that the outdoor signal has not been lost through the wall, and therefore it is identified as an outdoor sampling point.

[0145] Scenario 5: If the RSRP of the neighboring outdoor cell is less than X7 (e.g., -100dBm), the sampling point is determined to be an indoor sampling point. The low RSRP of the neighboring outdoor signal received at this sampling point is due to wall loss, therefore it is determined to be an indoor user sampling point.

[0146] When the primary service cell is an outdoor system cell, the sampling point can be determined as an indoor sampling point or an outdoor sampling point through the following scenarios.

[0147] Scenario 6: If the RSRP of the primary serving cell is less than X8 (e.g., -90dBm) and the number of neighboring cells is less than X9 (e.g., 3), it is identified as an indoor sampling point. In outdoor coverage, many areas have strong RSRP values ​​and a large number of neighboring cells. However, indoors, due to the different distances between cells and the varying angles of incidence from the window, the loss varies significantly. Many other cells have RSRP differences greater than the corresponding value compared to the primary serving cell, thus failing to meet the MR sampling neighboring cell relationship requirement. Furthermore, after wall loss, the RSRP is less than the corresponding value. Therefore, this type of sampling point is identified as an indoor sampling point.

[0148] Scenario 7: If the RSRP of the primary serving cell is less than X10 (e.g., -85dBm) and TA is less than X11 (e.g., 1), it is determined to be an indoor sampling point. For this type of sampling point, the outdoor cell signal, after passing through wall losses, has an RSRP less than -85dBm, indicating strong signal strength. However, the sampling time is mostly during evening rest periods. Based on user behavior, it can be known that users are mostly indoors at this time, therefore, it can be identified as an indoor user sampling point.

[0149] Scenario 8: If the neighboring cell has an indoor signal and RSRP is greater than X12 (e.g., 90dBm), it is determined to be an indoor sampling point. Such sampling points can only receive signals from the neighboring cell when indoors.

[0150] Scenario 9: If the RSRP of a neighboring cell is greater than X13 (e.g., -105dBm) and the number of neighboring cells is not less than X14 (e.g., 3), it is determined to be an outdoor sampling point. The RSRP of such sampling points is poor, mainly due to weak outdoor coverage, but the large number of neighboring cells indicates that the signal has not been lost through walls, thus confirming it as an outdoor sampling point.

[0151] Example 4

[0152] Figure 4 A schematic diagram of a device for generating simulated road test data according to Embodiment 4 of the present invention is shown. Figure 4 As shown, the device 400 includes:

[0153] The acquisition module 410 is used to acquire historical on-site road test data and MRO data of any user.

[0154] The road segment determination module 420 is used to determine the trend characteristics of signal indicator values ​​of any road segment based on the historical on-site road test data; and to determine the road segment matched for any user based on the MRO data and the trend characteristics.

[0155] The initial position determination module 430 is used to determine the initial position of any MRO sampling point for any user based on the MRO data corresponding to that MRO sampling point.

[0156] The calibration module 440 is used to obtain the location information of the road segment matched by any user, and to calibrate the initial position of the MRO sampling point of the user according to the location information of the road segment, so as to obtain the calibration position of the MRO sampling point.

[0157] The generation module 450 is used to generate simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data.

[0158] In an optional implementation, the road segment determination module is further configured to: generate sample data based on the historical on-site road test data; and train the pre-built trend learning model using the sample data so that the trend learning model can determine the trend characteristics of the signal indicator values ​​of any road segment.

[0159] Input any user's MRO data into the trained trend learning model, and obtain the information of the road segment matched by the user output by the trend learning model.

[0160] In one alternative implementation, the acquisition module is further configured to: acquire MDT data of any user;

[0161] The device further includes: an identification module, used to identify, for any user, a first type of MDT sampling point and a second type of MDT sampling point based on the user's MDT data; wherein, the MDT data contains location information of the first type of MDT sampling point, and the MDT data does not contain location information of the second type of MDT sampling point;

[0162] The calibration module is further used to: compare the MDT data of the user's first type MDT sampling point with the MRO data of the user's MRO sampling point to determine the first type MRO sampling point; wherein the MRO data of the first type MRO sampling point matches the MDT data of the first type MDT sampling point; and generate the calibration position of the first type MRO sampling point based on the location information of the first type MDT sampling point matched by the first type MRO sampling point.

[0163] The initial location determination module is further used to: determine the initial location of the second type of MRO sampling point based on the MRO data corresponding to the second type of MRO sampling point;

[0164] The calibration module is further used to: calibrate the initial position of the user's second type of MRO sampling point according to the location information of the road segment, so as to obtain the calibration position of the second type of MRO sampling point.

[0165] In one optional implementation, the initial position determination module is further configured to: for any second type MDT sampling point of any user, determine the initial position of the second type MDT sampling point based on the MDT data corresponding to the second type MDT sampling point;

[0166] The calibration module is further used to: for any user, obtain the location information of the road segment matched by the user, and calibrate the initial position of the second type MDT sampling point of the user according to the location information of the road segment, so as to obtain the calibration position of the second type MDT;

[0167] The generation module is further used to generate simulated road test data based on the calibration positions of the MRO sampling points, the calibration positions of the second type of MDT sampling points, the location information of the first type of MDT sampling points, the MRO data, and the MDT data.

[0168] In one alternative implementation, the initial position determination module is further configured to: determine the initial position of the second type of MRO sampling point based on the signal index value and propagation loss of the second type of MRO sampling point;

[0169] The initial position of the second type of MDT sampling point is determined based on the signal index value and propagation loss of the sampling point.

[0170] In one optional implementation, the acquisition module is further configured to: acquire MRE data of any user;

[0171] The device also includes: an event module, used to obtain event information at any event location based on the MRE data; and to determine the MRO sampling point that matches the event location;

[0172] The generation module is further used to generate simulated road test data based on the calibration location of the MRO sampling points, the corresponding MRO data, and the event information.

[0173] In one optional embodiment, the device further includes: an identification module for identifying the outdoor MRO sampling point of any user based on the MRO data of any user;

[0174] The generation module is further used to generate simulated road test data based on the calibration location of the outdoor MRO sampling points and the corresponding MRO data.

[0175] The specific implementation process of each module in this device can be referred to the description in the method embodiment, and will not be repeated here.

[0176] Therefore, this invention can simulate drive test data reflecting the current network status of a wireless network by utilizing historical field drive test data and MRO data, thereby improving the efficiency of drive test data generation and reducing drive test costs. Furthermore, this invention determines the trend of signal indicator values ​​for road segments based on historical field drive test data and identifies the road segments matched to users. Then, it uses the location information of the matched road segments to calibrate the initial location of the user's sampling points, thereby improving the accuracy of the sampling point locations and further enhancing the precision of the simulated drive test data.

[0177] Example 5

[0178] Embodiment 5 of the present invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the method for generating simulated road test data in any of the above method embodiments.

[0179] Example 6

[0180] Figure 5 A schematic diagram of a computing device according to Embodiment Six of the present invention is shown. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0181] like Figure 5 As shown, the computing device may include: a processor 502, a communications interface 504, a memory 506, and a communications bus 508.

[0182] The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508. Communication interface 504 is used to communicate with other network elements such as clients or other servers. The processor 502 executes program 510, specifically performing the relevant steps in the above method embodiments.

[0183] Specifically, program 510 may include program code that includes computer operation instructions.

[0184] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0185] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device. Program 510 can specifically be used to cause processor 502 to execute the steps in any of the above method embodiments.

[0186] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0187] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0188] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0189] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0190] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0191] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0192] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for generating drive test data, the method comprising: receiving a plurality of drive test data; and generating a plurality of simulated drive test data based on the plurality of drive test data. include: Acquire historical on-site road test data, and determine the trend characteristics of signal indicator values ​​for any road segment based on the historical on-site road test data; Retrieve MRO data for any user; Based on the MRO data and the trend characteristics, determine the road segment matched for any user; For any MRO sampling point of any user, determine the initial position of the MRO sampling point based on the MRO data corresponding to that MRO sampling point; For any user, obtain the location information of the road segment matched to the user, and calibrate the initial position of the user's MRO sampling point based on the location information of the road segment to obtain the calibration position of the MRO sampling point; Based on the calibration location of the MRO sampling points and the corresponding MRO data, simulated road test data is generated.

2. The method according to claim 1, characterized in that, The step of determining the trend characteristics of signal indicator values ​​for any road segment based on the historical on-site road test data further includes: generating sample data based on the historical on-site road test data; and using the sample data to train a pre-constructed trend learning model so that the trend learning model can determine the trend characteristics of signal indicator values ​​for any road segment. The step of determining the road segment matched by any user based on the MRO data and the trend characteristics further includes: inputting the MRO data of any user into the trained trend learning model, and obtaining the information of the road segment matched by the user output by the trend learning model.

3. The method according to claim 1, characterized in that, The method further includes: Retrieve MDT data for any user; For any user, the first type of MDT sampling points and the second type of MDT sampling points are identified based on the user's MDT data; wherein, the MDT data contains the location information of the first type of MDT sampling points, but does not contain the location information of the second type of MDT sampling points; The MDT data of the user's first type MDT sampling points are compared with the MRO data of the user's MRO sampling points to determine the first type MRO sampling points; wherein, the MRO data of the first type MRO sampling points matches the MDT data of the first type MDT sampling points. Based on the location information of the first type of MRO sampling points matched with the first type of MRO sampling points, the calibration position of the first type of MRO sampling points is generated. The step of determining the initial position of the MRO sampling point based on the MRO data corresponding to the MRO sampling point further includes: determining the initial position of the second type of MRO sampling point based on the MRO data corresponding to the second type of MRO sampling point; wherein, the second type of MRO sampling point is an MRO sampling point other than the first type of MRO sampling point; The step of calibrating the initial position of the user's MRO sampling point based on the location information of the road segment to obtain the calibration position of the MRO sampling point further includes: calibrating the initial position of the user's second type of MRO sampling point based on the location information of the road segment to obtain the calibration position of the second type of MRO sampling point.

4. The method according to claim 3, characterized in that, The method further includes: For any second type MDT sampling point of any user, determine the initial position of the second type MDT sampling point based on the MDT data corresponding to the second type MDT sampling point; For any user, obtain the location information of the road segment matched to the user, and calibrate the initial position of the user's second type MDT sampling point based on the location information of the road segment to obtain the calibration position of the second type MDT sampling point; The step of generating simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data further includes: generating simulated road test data based on the calibration location of the MRO sampling points, the calibration location of the second type of MDT sampling points, the location information of the first type of MDT sampling points, the MRO data, and the MDT data.

5. The method according to claim 4, characterized in that, The step of determining the initial position of the MRO sampling point based on the MRO data corresponding to the MRO sampling point further includes: determining the initial position of the second type of MRO sampling point based on the signal index value and propagation loss of the second type of MRO sampling point; The step of determining the initial position of the second type of MDT sampling point based on the MDT data corresponding to the second type of MDT sampling point further includes: determining the initial position of the second type of MDT sampling point based on the signal index value and propagation loss of the second type of MDT sampling point.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Retrieve MRE data for any user; Obtain event information for any event location based on the MRE data; Identify MRO sampling points that match the event location; The step of generating simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data further includes: generating simulated road test data based on the calibration location of the MRO sampling points, the corresponding MRO data, and the event information.

7. The method according to any one of claims 1-5, characterized in that, The method further includes: identifying the outdoor MRO sampling point of any user based on the MRO data of any user; The step of generating simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data further includes: generating simulated road test data based on the calibration location of the outdoor MRO sampling points and the corresponding MRO data.

8. A device for generating simulated road test data, characterized in that, include: The acquisition module is used to acquire historical on-site road test data and MRO data for any user. The road segment determination module is used to determine the trend characteristics of signal indicator values ​​for any road segment based on the historical on-site road test data. Based on the MRO data and the trend characteristics, determine the road segment matched for any user; The initial location determination module is used to determine the initial location of any MRO sampling point for any user based on the MRO data corresponding to that MRO sampling point. The calibration module is used to obtain the location information of the road segment matched by any user, and to calibrate the initial position of the user's MRO sampling point based on the location information of the road segment, so as to obtain the calibration position of the MRO sampling point. The generation module is used to generate simulated road test data based on the calibration location of the MRO sampling points and the corresponding MRO data.

9. A computing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the method for generating simulated road test data as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the method for generating simulated road test data as described in any one of claims 1-7.

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