Expressway network analysis method, device and equipment and storage medium

Abstract

The invention discloses an expressway network analysis method, device and equipment and a storage medium, and relates to the technical field of wireless networks. The method comprises the following steps: acquiring historical drive test data and user network use data, and screening out first candidate data from the user network use data; determining a newly-added cell from the second cells and a highway section to which the newly-added cell belongs, and adding the occupation condition data of the newly-added cell and the highway section to which the newly-added cell belongs to the historical road test data to obtain updated road test data; determining a screening index according to the updated drive test data, and screening second candidate data from the user network use data by using the screening index; and determining full-quantity drive test data according to a matching result of the third cell in the second candidate data and the fourth cell in the updated drive test data, and analyzing the expressway network quality by using the full-quantity drive test data. According to the technical scheme provided by the embodiment of the invention, the expressway network user can be more comprehensively identified, and the network can be accurately analyzed.

Description

Technical Field

[0001] This invention relates to the field of wireless network technology, and in particular to a method, apparatus, device, and storage medium for analyzing highway networks. Background Technology

[0002] To understand the network coverage on the road surface, periodic road tests of the communication network are needed to obtain various network indicators.

[0003] Road testing typically employs a manual method, using test equipment and vehicles to collect wireless data along test routes. Its drawbacks are twofold: firstly, a single test setup generates very little data, failing to reflect the actual perceptions of all road users; secondly, periodic road testing incurs high labor and equipment costs. Therefore, virtual road testing has emerged as a technological solution. Virtual road testing technology primarily extracts user data from network data based on user characteristics, replacing manual road testing.

[0004] However, existing virtual road testing methods still require manual delineation of the endpoints of the road test segments or the sub-regions of the segments. The accuracy of the identified users on the highway network is poor, and there are omissions or misidentifications. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for highway network analysis to address the problem of unsatisfactory results from traditional road testing methods.

[0006] In a first aspect, the present invention provides a method for analyzing highway networks, comprising:

[0007] Historical road test data and user network usage data are obtained, and first candidate data is selected from the user network usage data. The historical road test data includes the occupancy data of the first cell and the highway section to which the first cell belongs. The first candidate data includes the second cell suspected of being occupied by highway network users.

[0008] The newly added cell and the highway section to which the newly added cell belong are determined from the second cell, and the occupancy data of the newly added cell and the highway section to which the newly added cell belong are added to the historical road test data to obtain updated road test data;

[0009] The filtering criteria are determined based on the updated drive test data, and the second candidate data is selected from the user network usage data using the filtering criteria.

[0010] The full amount of road test data is determined based on the matching result between the third cell in the second candidate data and the fourth cell in the updated road test data, and the quality of the highway network is analyzed using the full amount of road test data.

[0011] In a second aspect, the present invention provides a highway network analysis device, comprising:

[0012] The first filtering module is used to acquire historical road test data and user network usage data, and to filter out first candidate data from the user network usage data. The historical road test data includes the occupancy data of the first cell and the highway section to which the first cell belongs. The first candidate data includes a second cell suspected of being occupied by highway network users.

[0013] The road test data update module is used to determine the newly added cell from the second cell and the highway section to which the newly added cell belongs, and to add the occupancy data of the newly added cell and the highway section to which the newly added cell belongs to the historical road test data to obtain updated road test data;

[0014] The second filtering module is used to determine filtering indicators based on the updated drive test data, and to use the filtering indicators to filter out second candidate data from the user network usage data.

[0015] The network quality analysis module is used to determine the full amount of road test data based on the matching result of the third cell in the second candidate data and the fourth cell in the updated road test data, and to analyze the quality of the highway network using the full amount of road test data.

[0016] Thirdly, the present invention provides an electronic device comprising:

[0017] At least one processor;

[0018] and memory that is communicatively connected to at least one processor;

[0019] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform the highway network analysis method of the first aspect described above.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to execute the highway network analysis method of the first aspect described above.

[0021] The highway network analysis scheme provided by this invention, tailored to the characteristics of highway network users, eliminates the need for manually defining road segments, setting sub-regions, or reference points at both ends of road segments. This scheme can filter out user data that matches the characteristics of highway network users and dynamically correct historical road test data, thereby achieving a more comprehensive identification of highway network users, improving the accuracy of user identification, and ensuring accurate analysis of the highway network.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a highway network analysis method provided in Embodiment 1 of the present invention;

[0025] Figure 2 This is a flowchart of a highway network analysis method provided in Embodiment 2 of the present invention;

[0026] Figure 3 This is a schematic diagram of the structure of a highway network analysis device according to Embodiment 3 of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0030] Example 1

[0031] Figure 1 The flowchart of a highway network analysis method is provided in Embodiment 1 of the present invention. This embodiment is applicable to the analysis of highway networks. The method can be executed by a highway network analysis device, which can be implemented in hardware and / or software. The highway network analysis device can be configured in an electronic device, which can be composed of two or more physical entities or a single physical entity.

[0032] like Figure 1 As shown, the highway network analysis method provided in Embodiment 1 of the present invention specifically includes the following steps:

[0033] S101. Obtain historical road test data and user network usage data, and filter out first candidate data from the user network usage data. The historical road test data includes the occupancy data of the first cell and the highway section to which the first cell belongs. The first candidate data includes a second cell suspected of being occupied by a highway network user.

[0034] In this embodiment, historical drive test data can be network drive test data obtained through test terminals in historical periods, and user network usage data can be DPI (Deep Packet Inspection) data. Historical drive test data may include occupancy data of a (first) cell and the highway segment to which that (first) cell belongs. From the user network usage data within the first time period, (initial) suspected highway network user-occupied (second) cells are selected, i.e., the first candidate data. For example, the first candidate data can be selected based on information such as the user's cell occupancy status.

[0035] In this context, "suspected highway network users" can be understood as users who are suspected of using wireless networks covering highway areas. In a wireless network, base stations and cells are the core components of a mobile communication system. A cell is a specific area covered by a base station within a mobile communication network.

[0036] S102. Determine the newly added cell and the highway section to which the newly added cell belongs from the second cell, and add the occupancy data of the newly added cell and the highway section to which the newly added cell belongs to the historical road test data to obtain updated road test data.

[0037] In this embodiment, the second cell can be further filtered to determine the new cell and the highway segment to which the new cell belongs. For example, the new cell can be determined based on information such as the occupancy duration of the second cell. The new cell is not in the historical road test data. The occupancy data of the new cell and the highway segment to which the new cell belongs can be updated to the historical road test data to obtain updated road test data.

[0038] S103. Determine the screening criteria based on the updated drive test data, and use the screening criteria to filter out the second candidate data from the user network usage data.

[0039] In this embodiment, a threshold value (i.e., a screening index) can be determined based on the data in the updated drive test data, and this screening index can be used to perform a second screening of user network usage data to select second candidate data. The first candidate data includes third cells suspected to be occupied by (target) highway network users. The screening index for selecting the first candidate data is different from the screening index for selecting the second candidate data.

[0040] S104. Determine the full amount of road test data based on the matching result between the third cell in the second candidate data and the fourth cell in the updated road test data, and use the full amount of road test data to analyze the quality of the highway network.

[0041] In this embodiment, if all third cells exist within the fourth cell, the current updated drive test data can be determined as the full drive test data. If there are third cells that do not belong to the fourth cell, new user network usage data can be used to determine new second candidate data, until all third cells exist within the fourth cell. The specific steps include:

[0042] Obtain user network usage data for the second time period, and filter out new first candidate data from this data;

[0043] Newly added cells and highway sections to which the newly added cells belong are identified from the new second cells. The occupancy data of the newly added cells and the highway sections to which the newly added cells belong are added to the updated road test data to obtain new updated road test data.

[0044] Based on the new updated road test data, new screening indicators are determined, and new second candidate data are selected from the user network usage data in the second time period using the new screening indicators.

[0045] New full-volume drive test data is determined based on the matching results between the new third cell and the new fourth cell.

[0046] By utilizing this full set of road test data, user perception metrics such as upload or download speeds, TCP establishment latency, RTT latency, RTP packet loss rate, and uplink or downlink traffic can be obtained when corresponding users use the highway network. Further big data analysis, including clustering across time and space dimensions, allows for statistical analysis of user distribution and perception metrics, providing insights into highway network user perceptions and helping to understand the operational quality of the highway wireless network, thus enabling quality analysis of the highway network.

[0047] The technical solution of this invention, tailored to the characteristics of highway network users, eliminates the need for manually defining road segments, setting sub-regions, or reference points at both ends of road segments. By filtering out user data that matches the characteristics of highway network users and dynamically correcting historical road test data, it achieves a more comprehensive identification of highway network users, improves the accuracy of user identification, and ensures accurate analysis of the highway network.

[0048] Optionally, determining the new cell from the second cell includes: for each designated highway segment, determining the total number of first cells in the current designated highway segment and the first overlap number between the first cells and the second cells, wherein each designated highway segment is associated with multiple second cells, and the second cells associated with different designated highway segments are not duplicated; for each designated highway segment, if the quotient of the first overlap number and the total number of the current designated highway segment is greater than a preset ratio, then a new cell is determined from the second cells that do not overlap with the first cells.

[0049] Specifically, we can first determine the designated highway segments, and then, for each designated highway segment, determine the total number of first cells N_cell in the current designated highway segment and the first overlap number N_cell_highway of the first cells and the second cells associated with the current designated highway segment. If the quotient R_highway of the first overlap number N_cell_highway of the current designated highway segment and the total number N_cell is greater than a preset ratio Htl (e.g., 30%), then new cells are determined from the second cells that do not match the first cells. For example, second cells that do not match the first cells can be determined as new cells.

[0050] Optionally, determining the full drive test data based on the matching result of the third cell in the second candidate data and the fourth cell in the updated drive test data includes: determining a second overlap between the third cell in the second candidate data and the fourth cell in the updated drive test data, and determining a first target proportion of the second overlap to the total number of the fourth cells; if the first target proportion is greater than a preset value, then the updated drive test data is determined as the full drive test data.

[0051] For example, the first target ratio can be 80%.

[0052] Optionally, before analyzing the highway network quality using the full road test data, the method further includes: determining a target user from the full road test data, wherein the number of target highway segments to which the fifth cell occupied by the target user belongs in the full road test data is greater than a preset number; determining the number of sixth cells occupied by the target user from the user network usage data, and determining the third overlap between the sixth cell and the fifth cell; if the proportion of the third overlap to the number of sixth cells is greater than a first target proportion, then updating the full road test data using the occupancy data of the non-overlapping sixth cells to obtain updated full road test data, wherein the first target proportion is the proportion of the second overlap between the third cell in the second candidate data and the fourth cell in the updated road test data to the total number of the fourth cells.

[0053] Specifically, in the full road test data, for target users whose occupied (fifth) cell belongs to a number of target highway segments greater than a preset number (e.g., 2), the cell occupied by the target user can be determined based on the DPI data between these target highway segments. When the proportion of cells occupied by a target user belonging to the fifth cell is greater than the first target proportion, it indicates that the target user is still within the highway range, but the single cell occupancy time is high, indicating that the target user is driving at low speed. Then, the occupancy data of the non-overlapping sixth cell can be added to the full road test data to obtain updated full road test data.

[0054] Example 2

[0055] Figure 2 This is a flowchart of a highway network analysis method provided in Embodiment 2 of the present invention. The technical solution of the present invention is further optimized based on the above optional technical solutions, and a specific method for analyzing highway networks is given.

[0056] Optionally, the step of filtering the first candidate data from the user network usage data includes: determining the maximum value of the occupancy time of a single first cell in the historical road test data as a first single cell occupancy time threshold, and determining a first consecutive cell occupancy number threshold based on the average base station spacing of the base stations of the first cell and the shortest distance between two adjacent entrances and exits of the highway to which it belongs; filtering the first candidate data that meets the first condition from the user network usage data, wherein the first condition is that the occupancy time of a single first cell is less than the first single cell occupancy time threshold, the number of consecutive occupancy of a user in a first cell is greater than the first consecutive cell occupancy number threshold, and the proportion of the number of times the same user repeatedly occupies the same first cell to the total number of times is less than a preset proportion, wherein the total number of times is the total number of consecutive occupancy of a first cell by the same user.

[0057] Optionally, determining the new cell from the second cells that do not overlap with the first cell includes: selecting new cells from the second cells that do not overlap with the first cell, where the target distance is less than a preset distance and the number of user connections is greater than a preset number, wherein the target distance is the shortest distance between the location of the base station of the second cell and the location of the highway segment in the historical drive test data.

[0058] Optionally, determining the highway segment to which the newly added cell belongs includes: identifying the highway segment in the historical road test data that is closest to the base station of the newly added cell as the highway segment to which the newly added cell belongs.

[0059] like Figure 2 As shown in Embodiment 2 of the present invention, a highway network analysis method specifically includes the following steps:

[0060] S201. Obtain historical road test data and user network usage data, and determine the maximum value of the occupation time of a single first cell in the historical road test data as the first single cell occupation time threshold value, and determine the first consecutive occupation cell number threshold value based on the average base station spacing of the base station of the first cell and the shortest distance between two adjacent entrances and exits of the highway to which it belongs.

[0061] Specifically, the threshold value Ttl for the first single cell occupancy duration is equal to the maximum value max(T) of the first cell occupancy duration. The method for determining the threshold value Ntl for the number of consecutively occupied cells includes:

[0062] Ntl=(min_distance / AverageSiteDistance)*α

[0063] Where α is a preset coefficient, which can be taken as 2. min_distance is the shortest distance between two adjacent entrances / exits of the highway to which the first community belongs, and AverageSiteDistance is the average distance between stations, which can be expressed as:

[0064] AverageSiteDistance=∑(SiteDistance_i) / Number_of_Sites

[0065] Where SiteDistance_i is the distance between base station i and the nearest neighboring base station in the highway to which the first cell belongs, and Number_of_Sites is the total number of base stations in the highway to which the first cell belongs.

[0066] S202. Select first candidate data that meets the first condition from the user network usage data.

[0067] The first candidate data includes a second cell suspected to be occupied by a highway network user. The first condition is that the occupation time of a single first cell is less than the first single cell occupation time threshold, the number of consecutive occupations of a user in a first cell is greater than the first consecutive occupation cell number threshold, and the proportion of the number of times the same user repeatedly occupies the same first cell to the total number of times is less than a preset proportion. The total number of times is the total number of consecutive occupations of the same user in a first cell.

[0068] Specifically, a sliding window with a sliding step of 1 and a window size equal to the threshold value of the number of consecutively occupied cells can be used to filter out the first candidate data of users who meet the first condition from the user network usage data, that is, to filter out the data of users who meet the following three conditions:

[0069] 1) The number of consecutively occupied cells in the first cell exceeds the threshold value for the number of consecutively occupied cells in the first cell;

[0070] 2) The occupancy time of a single first cell is less than the threshold value for the occupancy time of a single first cell;

[0071] 3) The proportion of times the same user repeatedly occupies the same first cell is less than the preset proportion (e.g., 10%).

[0072] If adjacent windows overlap, they are merged into a continuous time period.

[0073] The first candidate data selected can include user number, road segment number, occupied cell number, start time and end time.

[0074] S203. For each set highway segment, determine the total number of first cells in the current set highway segment and the first overlap number between the first cell and the second cell; for each set highway segment, if the quotient of the first overlap number and the total number of the current set highway segment is greater than a preset ratio, then select new cells from the second cells that do not overlap with the first cells, where the target distance is less than a preset distance and the number of user connections is greater than a preset number.

[0075] The target distance is the shortest distance between the location of the base station of the second cell and the location of the highway section in the historical road test data.

[0076] Specifically, if the quotient of the first overlapping number of highway segments to the total number is greater than a preset ratio (e.g., 30%), it can be determined whether a new cell is being added based on the distance between the base station address latitude and longitude of the second cell that does not overlap with the first cell and the highway, as well as the number of times the second cell that does not overlap with the first cell appears in the first candidate data. Specific steps include:

[0077] 1) Determine the set of latitude and longitude points for the highway section:

[0078] Highway=[highway_loc1,…,highway_locn]

[0079] 2) Determine the distance between the base station address latitude and longitude of the second cell that does not overlap with the first cell and all points in the above set, and take the minimum distance to obtain minDist;

[0080] 3) Determine the number of times the second cell, which does not overlap with the first cell, appears in the first candidate data (i.e., the number of user connections) in Times_cell.

[0081] For example, when minDist < 2km and Times_cell > 5, the corresponding cell should be classified as a highway cell, which is a newly added cell.

[0082] S204. The highway segment closest to the base station of the newly added cell in the historical road test data is determined as the highway segment to which the newly added cell belongs.

[0083] S205. Add the occupancy data of the newly added cell and the highway section to which the newly added cell belongs to the historical road test data to obtain updated road test data.

[0084] S206. Determine the screening criteria based on the updated drive test data, and use the screening criteria to select the second candidate data from the user network usage data.

[0085] Optionally, a selection criterion is determined based on the updated drive test data, and the selection criterion is used to select a second candidate data from the user network usage data, including:

[0086] The maximum occupancy duration of a single fourth cell in the updated road test data is determined as the second single cell occupancy duration threshold. The second consecutive occupancy cell number threshold is determined based on the average base station spacing of the base stations of the fourth cell and the shortest distance between two adjacent entrances and exits of the highway to which it belongs. The screening indicators include the second single cell occupancy duration threshold and the second consecutive occupancy cell number threshold.

[0087] Second candidate data that meets the second condition is selected from the user network usage data. The second condition is that the duration of a single first cell occupancy is less than the second threshold value for the duration of a single cell occupancy, the number of consecutive first cell occupancy by a user is greater than the second threshold value for the number of consecutive cell occupancy, and the proportion of the number of times the same user repeatedly occupies the same first cell to the total number of times is less than a preset proportion.

[0088] S207. Determine the second overlap number between the third cell in the second candidate data and the fourth cell in the updated drive test data, and determine the first target ratio of the second overlap number to the total number of the fourth cells; if the first target ratio is greater than a preset value, then determine the updated drive test data as the full drive test data.

[0089] S208. Determine the target user from the full drive test data; determine the number of sixth cells occupied by the target user from the user network usage data, and determine the third overlap between the sixth cell and the fifth cell; if the proportion of the third overlap to the number of sixth cells is greater than the first target proportion, then update the full drive test data using the occupancy data of the non-overlapping sixth cells to obtain the updated full drive test data.

[0090] Wherein, in the full amount of road test data, the number of target highway segments to which the fifth cell occupied by the target user belongs is greater than a preset number, and the first target ratio is the ratio of the second overlap between the third cell in the second candidate data and the fourth cell in the updated road test data to the total number of the fourth cell.

[0091] S209. Analyze the quality of the highway network using the full amount of road test data.

[0092] This solution effectively identifies highway network users, particularly mobile users, eliminating the need for manual road testing and significantly reducing network optimization and maintenance costs. Furthermore, it allows for the tracking of wireless network users on highways, providing a better understanding of user distribution. By analyzing full road test data, the actual network coverage on highways can be assessed, offering valuable data for network optimization and improving network quality and user experience. For example, during holidays, it allows for timely monitoring of highway network usage and the impact of traffic congestion on the network, enabling prompt optimization and adjustments. Simultaneously, it replaces manual testing, effectively reducing labor costs.

[0093] The highway network analysis method provided in this invention uses historical test data to extract features of highway network users. First candidate data for verifying these features is selected from user network usage data. The historical road test data and selection criteria are then corrected, and a more accurate second candidate data is determined through further filtering. By matching the cells in the second candidate data with the cells in the corrected historical road test data, the full road test data is determined. Finally, user network usage data of users traveling at low speeds on the highway, such as those in traffic jams and service areas, are identified to generate the final full road test data. This achieves the identification of all highway network users, further improving the accuracy of highway network user identification, ensuring accurate analysis of the highway network, and replacing periodic manual road testing work, saving manpower and resources.

[0094] Example 3

[0095] Figure 3 This is a schematic diagram of the structure of a highway network analysis device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a first screening module 301, a road test data update module 302, a second screening module 303, and a network quality analysis module 304, wherein:

[0096] The first filtering module is used to acquire historical road test data and user network usage data, and to filter out first candidate data from the user network usage data. The historical road test data includes the occupancy data of the first cell and the highway section to which the first cell belongs. The first candidate data includes a second cell suspected of being occupied by highway network users.

[0097] The road test data update module is used to determine the newly added cell from the second cell and the highway section to which the newly added cell belongs, and to add the occupancy data of the newly added cell and the highway section to which the newly added cell belongs to the historical road test data to obtain updated road test data;

[0098] The second filtering module is used to determine filtering indicators based on the updated drive test data, and to use the filtering indicators to filter out second candidate data from the user network usage data.

[0099] The network quality analysis module is used to determine the full amount of road test data based on the matching result of the third cell in the second candidate data and the fourth cell in the updated road test data, and to analyze the quality of the highway network using the full amount of road test data.

[0100] The highway network analysis device provided in this embodiment of the invention, tailored to the characteristics of highway network users, eliminates the need for manual definition of road segments, setting of sub-regions, or reference points at both ends of road segments. Through this device, user data that conforms to the characteristics of highway network users is filtered out, and historical road test data is dynamically corrected, thereby achieving a more comprehensive identification of highway network users, improving the accuracy of user identification, and ensuring accurate analysis of the highway network.

[0101] Optionally, the first filtering module includes:

[0102] The threshold value determination unit is used to determine the maximum value of the occupation time of a single first cell in the historical road test data as the first single cell occupation time threshold value, and to determine the first consecutive occupation cell number threshold value based on the average base station spacing of the base station of the first cell and the shortest distance between two adjacent entrances and exits of the highway to which it belongs.

[0103] The first filtering unit is used to filter out first candidate data that meets the first condition from the user network usage data. The first condition is that the duration of a single first cell occupancy is less than the first threshold value for the duration of a single cell occupancy, the number of times a user occupies a first cell consecutively is greater than the first threshold value for the number of consecutively occupied cells, and the proportion of the number of times the same user repeatedly occupies the same first cell to the total number of times is less than a preset proportion. The total number of times is the total number of times the same user occupies a first cell consecutively.

[0104] Optionally, the road test data update module includes:

[0105] The first overlapping quantity determination unit is used to determine the total number of first cells in the current set highway segment and the first overlapping quantity between the first cells and the second cells for each set highway segment, wherein each set highway segment is associated with multiple second cells, and the second cells associated with different set highway segments are not repeated;

[0106] The newly added cell determination unit is used to determine a new cell from the second cell that does not overlap with the first cell for each set highway segment. If the quotient of the first overlapping number of the current set highway segment and the total number is greater than a preset ratio, the new cell is determined.

[0107] Optionally, determining the new cell from the second cells that do not overlap with the first cell includes: selecting new cells from the second cells that do not overlap with the first cell, where the target distance is less than a preset distance and the number of user connections is greater than a preset number, wherein the target distance is the shortest distance between the location of the base station of the second cell and the location of the highway segment in the historical drive test data.

[0108] Optionally, the road test data update module includes:

[0109] The road segment affiliation determination unit is used to determine the highway segment that is closest to the base station of the newly added cell in the historical road test data as the highway segment to which the newly added cell belongs.

[0110] Optionally, the network quality analysis module includes:

[0111] The proportion determination unit is used to determine the second overlap number between the third cell in the second candidate data and the fourth cell in the updated drive test data, and to determine the first target proportion of the second overlap number to the total number of the fourth cells;

[0112] The road test data determination unit is used to determine the updated road test data as the full road test data if the first target ratio is greater than a preset value.

[0113] Optionally, the device may also include:

[0114] The target user determination module is used to determine the target user from the full road test data before analyzing the quality of the highway network using the full road test data, wherein the number of target highway segments in the fifth cell occupied by the target user in the full road test data is greater than a preset number.

[0115] The second overlap quantity determination module is used to determine the number of sixth cells occupied by the target user from the user network usage data, and to determine the third overlap quantity between the sixth cell and the fifth cell;

[0116] The update module is used to update the full drive test data with the occupancy data of the non-overlapping sixth cells if the proportion of the third overlapping number to the number of the sixth cells is greater than the first target proportion, so as to obtain the updated full drive test data. The first target proportion is the proportion of the second overlapping number of the third cells in the second candidate data and the fourth cells in the updated drive test data to the total number of the fourth cells.

[0117] The highway network analysis device provided in this embodiment of the invention can execute the highway network analysis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0118] Example 4

[0119] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0120] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0121] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0122] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as highway network analysis methods.

[0123] In some embodiments, the highway network analysis method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the highway network analysis method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the highway network analysis method by any other suitable means (e.g., by means of firmware).

[0124] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoC) systems, complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0126] The computer equipment provided above can be used to execute the highway network analysis method provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0127] Example 5

[0128] In the context of this invention, the computer-readable storage medium may be a tangible medium, and the computer-executable instructions, when executed by a computer processor, are used to perform a highway network analysis method, the method comprising:

[0129] Historical road test data and user network usage data are obtained, and first candidate data is selected from the user network usage data. The historical road test data includes the occupancy data of the first cell and the highway section to which the first cell belongs. The first candidate data includes the second cell suspected of being occupied by highway network users.

[0130] The newly added cell and the highway section to which the newly added cell belong are determined from the second cell, and the occupancy data of the newly added cell and the highway section to which the newly added cell belong are added to the historical road test data to obtain updated road test data;

[0131] The filtering criteria are determined based on the updated drive test data, and the second candidate data is selected from the user network usage data using the filtering criteria.

[0132] The full amount of road test data is determined based on the matching result between the third cell in the second candidate data and the fourth cell in the updated road test data, and the quality of the highway network is analyzed using the full amount of road test data.

[0133] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by, or in conjunction with, an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0134] The computer equipment provided above can be used to execute the highway network analysis method provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0135] It is worth noting that in the embodiments of the above-mentioned highway network analysis device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0136] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for analyzing highway networks, characterized in that, include: Historical road test data and user network usage data are obtained, and first candidate data is selected from the user network usage data. The historical road test data includes the occupancy data of the first cell and the highway section to which the first cell belongs. The first candidate data includes the second cell suspected of being occupied by highway network users. The newly added cell and the highway section to which the newly added cell belong are determined from the second cell, and the occupancy data of the newly added cell and the highway section to which the newly added cell belong are added to the historical road test data to obtain updated road test data; The filtering criteria are determined based on the updated drive test data, and the second candidate data is selected from the user network usage data using the filtering criteria. The full amount of road test data is determined based on the matching result between the third cell in the second candidate data and the fourth cell in the updated road test data, and the quality of the highway network is analyzed using the full amount of road test data.

2. The method according to claim 1, characterized in that, The step of filtering the first candidate data from the user network usage data includes: The maximum value of the occupation time of a single first cell in the historical road test data is determined as the first single cell occupation time threshold, and the first consecutive occupation cell number threshold is determined according to the average base station spacing of the base station of the first cell and the shortest distance between two adjacent entrances and exits of the highway to which it belongs. First candidate data that meets the first condition is selected from the user network usage data. The first condition is that the duration of a single first cell occupancy is less than the threshold value of the duration of a single cell occupancy, the number of times a user occupies a first cell consecutively is greater than the threshold value of the number of consecutively occupied cells, and the proportion of the number of times the same user repeatedly occupies the same first cell to the total number of times is less than a preset proportion. The total number of times is the total number of times the same user occupies a first cell consecutively.

3. The method according to claim 1, characterized in that, The step of determining the new cell from the second cell includes: For each designated highway segment, determine the total number of first cells in the current designated highway segment and the first overlap number between the first cells and the second cells. Each designated highway segment is associated with multiple second cells, and the second cells associated with different designated highway segments are not duplicated. For each designated highway segment, if the quotient of the first overlapping number of the current designated highway segment to the total number is greater than a preset ratio, then a new cell is determined from the second cell that does not overlap with the first cell.

4. The method according to claim 3, characterized in that, The step of determining a new cell from a second cell that does not overlap with the first cell includes: From the second cells that do not overlap with the first cell, select new cells with a target distance less than a preset distance and a user connection count greater than a preset number. The target distance is the shortest distance between the location of the base station of the second cell and the location of the highway segment in the historical drive test data.

5. The method according to claim 1, 3, or 4, characterized in that, Determining the highway section to which the newly added residential area belongs includes: The highway segment closest to the base station of the newly added cell in the historical road test data is determined as the highway segment to which the newly added cell belongs.

6. The method according to claim 1, characterized in that, The step of determining the full drive test data based on the matching result between the third cell in the second candidate data and the fourth cell in the updated drive test data includes: Determine the second overlap number between the third cell in the second candidate data and the fourth cell in the updated drive test data, and determine the first target proportion of the second overlap number to the total number of the fourth cells; If the first target ratio is greater than a preset value, then the updated road test data will be determined as the full road test data.

7. The method according to claim 1 or 6, characterized in that, Before analyzing the highway network quality using the full amount of road test data, the method further includes: The target user is determined from the full amount of road test data, wherein the number of target highway segments occupied by the target user in the fifth cell in the full amount of road test data is greater than a preset number; The number of sixth cells occupied by the target user is determined from the user network usage data, and the third overlap between the sixth cell and the fifth cell is determined. If the proportion of the third overlapping number to the number of the sixth cell is greater than the first target proportion, then the full drive test data is updated using the occupancy data of the non-overlapping sixth cell to obtain the updated full drive test data. The first target proportion is the proportion of the second overlapping number of the third cell in the second candidate data and the fourth cell in the updated drive test data to the total number of the fourth cells.

8. A highway network analysis device, characterized in that, include: The first filtering module is used to acquire historical road test data and user network usage data, and to filter out first candidate data from the user network usage data. The historical road test data includes the occupancy data of the first cell and the highway section to which the first cell belongs. The first candidate data includes a second cell suspected of being occupied by highway network users. The road test data update module is used to determine the newly added cell from the second cell and the highway section to which the newly added cell belongs, and to add the occupancy data of the newly added cell and the highway section to which the newly added cell belongs to the historical road test data to obtain updated road test data; The second filtering module is used to determine filtering indicators based on the updated drive test data, and to use the filtering indicators to filter out second candidate data from the user network usage data. The network quality analysis module is used to determine the full amount of road test data based on the matching result of the third cell in the second candidate data and the fourth cell in the updated road test data, and to analyze the quality of the highway network using the full amount of road test data.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the highway network analysis method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the highway network analysis method according to any one of claims 1-7.

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