Method, device and equipment for optimizing arterial network and storage medium

Abstract

The application relates to the technical field of communication, and provides a trunk network optimization method, device and equipment and a storage medium. The method comprises the following steps: acquiring a trunk list, collecting original signaling data of each target trunk in the trunk list; screening out trunk target users according to the spatial features and the time features of the extracted target trunks; performing summary statistics on target signaling data corresponding to the trunk target users in the original signaling data, generating signaling index data, positioning network problem points according to the signaling index data, and performing network optimization. The trunk network optimization method provided by the application screens out trunk target users from different dimensions of the time domain and the space domain, positions network problem points from the signaling flow level based on the signaling data of the trunk target users, solves the time and space limitations of the data collection and network optimization mode through road testing, reduces the consumption of manpower and material resources required by road testing, and reduces the cost of trunk network optimization.

Description

Technical Field

[0001] This application relates to the field of communication technology, specifically to a trunk network optimization method, apparatus, device, and storage medium. Background Technology

[0002] In trunk network optimization, locating network problem points is a crucial step. Traditional trunk network optimization methods typically require road testing to obtain network information and pinpoint problem areas for optimization. However, this approach has several limitations. First, current wireless networks are multi-frequency networks, meaning test terminals can only monitor one frequency at a time, potentially missing other frequencies. Second, it's constrained by the limited number and variety of test terminals and the fixed system versions of the testing software. In reality, different terminals and system versions exhibit varying performance; a test terminal may function correctly in some scenarios, but specific user terminals may experience issues. Furthermore, road testing requires traversing the entire trunk line to collect data, resulting in a large workload, high costs, and difficulty in achieving long-term, high-frequency monitoring. Therefore, traditional trunk network optimization methods have significant limitations in data collection and problem location. Summary of the Invention

[0003] This application provides a method, apparatus, device, and storage medium for optimizing a trunk road network, which addresses the technical problem that existing trunk road network optimization methods, which rely on road testing to locate problem points, are costly and have limitations.

[0004] In a first aspect, embodiments of this application provide a trunk network optimization method, including: Obtain the list of arterial roads and collect the original signaling data of each target arterial road in the list; Extract the spatial and temporal features of each target arterial road, and filter out the target users of each target arterial road based on the spatial and temporal features; From the original signaling data, the target signaling data corresponding to each trunk road target user is selected; The target signaling data is summarized and statistically analyzed to generate signaling indicator data. Network problem points are located based on the signaling indicator data, and network optimization is performed based on the network problem points.

[0005] In one embodiment, the spatial features include a starting cell group, an ending cell group, and a checkpoint cell group; the temporal features include the longest and shortest travel times within different time periods; and the step of extracting the temporal and spatial features of each target arterial road includes: The starting section, ending section, and key section of each target arterial road are defined, and the operating parameters of the existing network are obtained. The key section is the section that users must pass through on the target arterial road. The coverage cells of each target arterial road are matched according to the engineering parameter data, and the coverage cells corresponding to the starting road segment are assigned to the starting cell group, the coverage cells corresponding to the ending road segment are assigned to the ending cell group, and the coverage cells corresponding to the key road segment are assigned to the checkpoint cell group, so as to obtain the starting cell group, ending cell group and checkpoint cell group of the target arterial road. The mileage distance of each target arterial road is calculated based on the starting road segment and the ending road segment, and the longest and shortest travel times of the target arterial roads in different time periods are statistically analyzed based on the mileage distance.

[0006] In one embodiment, the step of filtering out target users for each target arterial road based on the spatial features and the temporal features includes: Obtain all trunk users attached to the starting cell group and the ending cell group during the monitoring period from the original signaling data, and obtain the signaling records of each trunk user during the monitoring period. Based on the signaling records, users who have attached cells in the starting cell group, the ending cell group, and the checkpoint cell group are selected from each of the trunk users to generate a first user set. Calculate the first attachment timestamp of each user in the first user set in the cell group at the starting point, the second attachment timestamp of the cell group at the ending point, and the third attachment timestamp of the cell group at the checkpoint based on the signaling records. Calculate the time difference between the second attachment timestamp and the first attachment timestamp; Based on the spatial and temporal characteristics, the shortest and longest travel times within the monitoring period are obtained, and target users on the main road are selected from the first user set whose third attached timestamp is between the first and second attached timestamps, and whose time difference is greater than the shortest travel time within the monitoring period and less than the longest travel time within the monitoring period.

[0007] In one embodiment, the first attachment timestamp is the earliest attachment time point in the attachment records of users in the first user set within the starting cell group during the monitoring period, and the second attachment timestamp is the latest attachment time point in the attachment records of users in the first user set within the ending cell group during the monitoring period.

[0008] In one embodiment, the signaling indicator data includes network element-level indicator data and user-level indicator data. The step of summarizing and statistically analyzing the target signaling data to generate signaling indicator data includes: The target signaling data is summarized and statistically analyzed to generate multidimensional indicator data; Based on the multidimensional indicator data, problematic signaling data with degraded signaling process success rate are filtered out from the target signaling data; The signaling data related to the aforementioned issues is aggregated and statistically analyzed from the user perspective to generate user-level indicator data. The signaling data related to the aforementioned issues are aggregated and statistically analyzed at the cell level to generate network element-level indicator data.

[0009] In one embodiment, the step of locating network problem points based on the signaling indicator data and optimizing the network based on the network problem points includes: The user-level indicator data is sorted, and the user problem points that generate the problem signaling data in each of the target trunk lines are determined based on the sorting results. The network element-level indicator data are sorted, and the problem points of the cells that generate the problem signaling data in each of the target trunk lines are determined based on the sorting results. Based on preset troubleshooting rules, network troubleshooting is performed on the target trunk line according to the problem points of the community and / or the problem points of the user, in order to locate the network problem points in the target trunk line; Based on the optimization measures corresponding to the network problem points, network optimization is performed on the target trunk road.

[0010] In one embodiment, the raw signaling data includes signaling data collected from different types of signaling processes, including control plane signaling processes, service plane signaling processes, and voice signaling processes. The metrics data include signaling process metrics, data service metrics, and voice service metrics. The signaling process metrics include connection success rate, hold-up signaling success rate, and handover signaling success rate. The data service metrics include download speed, request access success rate, handshake latency, data retransmission rate, and out-of-order rate. The voice service metrics include connection rate, call drop rate, registration success rate, and average call initiation latency.

[0011] Secondly, embodiments of this application provide a trunk network optimization device, comprising: The data acquisition module is used to obtain the trunk road list and collect the original signaling data of each target trunk road in the trunk road list; The user filtering module is used to extract the spatial and temporal features of each of the target arterial roads, and filter out the target users of each of the target arterial roads based on the spatial and temporal features. The data filtering module is used to filter out the target signaling data corresponding to each trunk target user from the original signaling data; The network optimization module is used to summarize and statistically analyze the target signaling data, generate signaling indicator data, locate network problem points based on the signaling indicator data, and optimize the network based on the network problem points.

[0012] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the trunk network optimization method described in the first aspect.

[0013] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the trunk network optimization method described in the first aspect.

[0014] The arterial road network optimization method, apparatus, device, and storage medium provided in this application collect raw signaling data from each target arterial road and, based on the extracted spatial and temporal characteristics of each arterial road, filter out target users for those arterial roads. They then summarize and statistically analyze the target signaling data corresponding to each target user from different dimensions to generate signaling indicator data. Based on this generated signaling indicator data, network problem points can be located and network optimization can be performed. By filtering target users for arterial roads from different dimensions in the time and spatial domains based on the collected signaling data, and locating network problem points at the signaling process level based on the signaling data of these target users, this method overcomes the time and spatial limitations of data collection and network optimization through road testing. It also reduces the manpower and material resources required for road testing, thereby lowering the cost of arterial road network optimization. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is one of the flowcharts illustrating the trunk network optimization method provided in the embodiments of this application; Figure 2 This is one of the structural schematic diagrams of the trunk network optimization device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Figure 1 This is one of the flowcharts illustrating the trunk network optimization method provided in this application. (Refer to...) Figure 1 The trunk network optimization method provided in this application embodiment may include: Step 100: Obtain the trunk road list and collect the original signaling data of each target trunk road in the trunk road list; When optimizing the arterial road network, the first step is to obtain a list of arterial roads. This list includes all the target arterial roads to be monitored. When network problems are detected in a target arterial road, network optimization is required. This list of arterial roads is compiled from the various arterial roads to be monitored, including major urban traffic arteries such as highways, expressways, high-speed railways, bullet trains, conventional railways, subways, and trams.

[0019] Raw signaling data is collected from each target artery. This raw signaling data can be periodically collected or cumulatively collected. The collected raw signaling data includes signaling data from different types of signaling flows, including control plane signaling flows, service plane signaling flows, and voice signaling flows. Furthermore, the collected raw signaling data includes signaling data from different communication standards such as 4G and 5G. It can be seen that in the network topology of the 4G / 5G coverage cells corresponding to each target trunk, there are different network interfaces such as S1-MME interface, S1-U interface, N1 interface, N2 interface, N3 interface, N11 interface, and IMS interface. By selecting different interfaces as signaling collection points in the network topology, signaling data under different communication standards can be collected. For example, using the S1-MME interface, S1-U interface, and IMS interface as signaling collection points, signaling data of the 4G network can be collected, while using the N1 interface, N2 interface, N3 interface, N11 interface, and IMS interface as signaling collection points, signaling data of the 5G network can be collected. Based on this, the collected raw signaling data includes signaling data of 5G control plane signaling procedures, 5G service plane signaling procedures, and 5G voice signaling procedures, as well as signaling data of 4G control plane signaling procedures, 4G service plane signaling procedures, and 4G voice signaling procedures, which will not be elaborated here.

[0020] Step 200: Extract the spatial and temporal features of each target arterial road, and filter out the target users of each target arterial road based on the spatial and temporal features; Step 300: Filter out the target signaling data corresponding to each trunk road target user from the original signaling data; Step 400: Summarize and statistically analyze the target signaling data to generate signaling indicator data, locate network problem points based on the signaling indicator data, and optimize the network based on the network problem points.

[0021] Spatial and temporal features of each target artery are extracted, and target users for each artery are selected based on these features. The spatial features of the target arteries represent the spatial distribution of network coverage cells and user attachment within those cells, while the temporal features represent the user distribution within network coverage cells at different time intervals. It is understood that the collected raw signaling data represents signaling data generated by different users in different areas and / or at different time intervals on each target artery, based on different business processes. Furthermore, different users may use different terminals, and / or have different system versions and / or different applications installed on their terminals. Therefore, the collection of signaling data is not limited by time and space and is diverse. Compared to artery network optimization methods that rely on road testing and can only collect and detect data on the same frequency point at any given time, this approach overcomes the temporal and spatial limitations of artery network optimization.

[0022] It is known that currently, most network-side monitoring uses cell-level metrics, or network element-level metrics, to monitor backbone network conditions. These network element-level metrics include both high-speed mobile users on the backbone and local low-speed mobile and stationary users. Since the majority are low-speed mobile or stationary users, the performance metrics of high-speed mobile users are diluted by those of low-speed mobile and stationary users, potentially leading to missed detections of issues affecting high-speed mobile users. By combining the temporal and spatial characteristics of each target backbone, target users for each backbone are identified. Then, from the collected raw signaling data, target signaling data corresponding to these target users is extracted. This target signaling data is then aggregated and statistically analyzed from different dimensions, including user and cell levels, to generate signaling indicator data. This multi-dimensional signaling indicator data avoids the dilution of performance metrics for high-speed mobile users, reducing missed detections. Furthermore, the indicator data used to monitor network conditions may differ across different communication standards. When compiling and summarizing indicator data, signaling data under different communication standards such as 4G and 5G can be statistically summarized separately to generate multi-dimensional signaling indicator data for different communication standards, thereby enabling more accurate identification of network problem points.

[0023] A comprehensive analysis of the generated signaling indicator data is performed to accurately pinpoint network problems, and network optimization is then carried out based on these identified problems. Since the raw signaling data collected originates from different signaling processes, the aggregated and statistically analyzed indicator data also includes indicators from different signaling processes. Specifically, the aggregated and statistically analyzed signaling indicator data includes signaling process indicators, data service indicators, and voice service indicators, corresponding to different signaling processes. Signaling process indicators include connection-related signaling failure / success rate, hold-up signaling failure / success rate, and handover signaling failure / success rate (including intra-city and inter-city handovers); data service indicators include download speed, request access failure / success rate, handshake latency, data retransmission rate, and out-of-order rate; voice service indicators include connection rate, call drop rate, registration success rate, and average call initiation latency.

[0024] In this embodiment, raw signaling data from each target arterial road is collected, and target users for each arterial road are selected based on the extracted spatial and temporal characteristics. The target signaling data corresponding to these target users is then aggregated and statistically analyzed from different dimensions to generate signaling indicator data. This generated signaling indicator data allows for the location of network problem points and network optimization. By selecting target users for each arterial road from different dimensions in the time and spatial domains based on the collected signaling data, and locating network problem points at the signaling process level based on the signaling data of these target users, this approach overcomes the time and spatial limitations of data collection and network optimization through road testing. It also reduces the manpower and material resources required for road testing, thereby lowering the cost of arterial road network optimization.

[0025] In one embodiment, the spatial characteristics of each target arterial road include the starting point cell group, the ending point cell group, and the checkpoint cell group of each target arterial road, and the temporal characteristics include the longest and shortest travel times of each target arterial road within different time periods. Step 200, the step of extracting the spatial and temporal characteristics of each target arterial road, specifically includes: Step 201: Delineate the starting section, ending section, and key section of each target arterial road, and obtain the operating parameters data of the current network. The key section is the section that users must pass through in the target arterial road. Step 202: Match the coverage cells of each target arterial road according to the engineering parameter data, and assign the coverage cells corresponding to the starting road segment to the starting cell group, the coverage cells corresponding to the ending road segment to the ending cell group, and the coverage cells corresponding to the key road segment to the checkpoint cell group, so as to obtain the starting cell group, ending cell group and checkpoint cell group of the target arterial road. Step 203: Calculate the mileage distance of each target arterial road based on the starting road segment and the ending road segment, and calculate the longest and shortest travel time of the target arterial road in different time periods based on the mileage distance.

[0026] When extracting the spatial characteristics of each target arterial road, for each target arterial road to be monitored in the arterial road list, the starting route, ending road segment, and checkpoint road segment of the target arterial road are first delineated. The checkpoint road segment is the inevitable route for users on the target arterial road, that is, the road segment that users on the arterial road will definitely occupy, such as tunnels, bridges, entrances and exits along the arterial road. When delineating the starting and ending road segments, the distance between the starting and ending road segments is at least a first distance, and is covered by at least a preset number of wireless network cells. The first distance is, for example, 1 kilometer, and the preset number is, for example, 3. That is, the distance between the starting and ending road segments must be at least 1 kilometer, and at least 3 different wireless network cells must cover the area. This ensures that all users passing through the arterial road will attach to the cells covering that road segment and leave signaling records, avoiding situations where users pass through the starting or ending road segment without leaving signaling records due to over-coverage or untimely handover.

[0027] The process involves acquiring the network's operational parameters and matching coverage cells for each target arterial road. After defining the starting and ending road segments, the process involves matching 4G / 5G coverage cells for the corresponding road segments based on the network's operational parameters and assigning them to the corresponding target cell groups. Specifically, the coverage cells corresponding to the starting road segment are assigned to the starting cell group, the coverage cells corresponding to the ending road segment are assigned to the ending cell group, and the coverage cells for tunnels, bridges, and entrances / exits along the arterial road are included in the checkpoint cell group, resulting in the starting cell group, ending cell group, and checkpoint cell group for each target arterial road. It should be noted that the starting cell group and ending cell group for the same target arterial road can be interchanged depending on the direction of travel. Furthermore, since there are intersecting or overlapping sections between arterial roads, there may be duplicate cells in the starting cell group, ending cell group, and checkpoint cell group for different arterial roads. In other words, the same wireless network cell may exist in one or more of the starting cell group, ending cell group, and checkpoint cell group on the same trunk road, or it may exist in one or more of the starting cell group, ending cell group, and checkpoint cell group on different trunk roads. When extracting the spatial features of each target trunk road, multiple target trunk roads may correspond to the same target cell group, or each target trunk road may correspond to a separate target cell group. Taking the starting cell group as the target cell group as an example, the coverage cells corresponding to the starting segments of different target trunk roads may be assigned to the same starting cell group, or the coverage cells corresponding to the starting segments of each target trunk road may be assigned to their respective starting cell groups. No specific limitation is made here.

[0028] When extracting the temporal characteristics of each target arterial road, the mileage distance of each target arterial road is calculated based on the designated starting and ending road segments. The longest and shortest travel times for each target arterial road within different time periods are then statistically analyzed based on this mileage distance. When calculating the normal travel time for each target arterial road within different time periods, factors such as traffic volume, congestion level, maximum and minimum speed limits, travel speeds of different vehicle types (cars, buses, trucks, and trains), traffic lights, toll collection times at entrances and exits, and platform stop times are considered to comprehensively determine the shortest and longest travel times for each arterial road within different time periods.

[0029] Further, in step 200, based on the extracted spatial and temporal features of each target artery, target users for each target artery are selected, specifically including: Step 204: Obtain all trunk users attached to the starting cell group and the ending cell group during the monitoring period from the original signaling data, and obtain the signaling records of each trunk user during the monitoring period; Step 205: Based on the signaling records, select users from each of the trunk users who have attached cells in the starting cell group, the ending cell group, and the checkpoint cell group, and generate a first user set. Step 206: Calculate the first attachment timestamp of each user in the first user set in the cell group at the starting point, the second attachment timestamp of the cell group at the ending point, and the third attachment timestamp of the cell group at the checkpoint based on the signaling records. Step 207: Calculate the time difference between the second attachment timestamp and the first attachment timestamp; Step 208: Based on the spatial features and the temporal features, obtain the shortest passage time and the longest passage time within the monitoring period, and select from the first user set the main road target users whose third attached timestamp is between the first attached timestamp and the second attached timestamp, and whose time difference is greater than the shortest passage time and less than the longest passage time.

[0030] Based on the spatial and temporal characteristics of the target trunk roads extracted above, when screening target users for each target trunk road, the system first periodically retrieves all trunk road users attached to cells in both the starting cell group and the ending cell group within the monitoring time period from the collected raw signaling data. Then, it retrieves the signaling records of each trunk road user within the monitoring time period and marks users meeting preset conditions as target users for the trunk road. These preset conditions, for example, are as follows within the monitoring time period: 1. Residential areas that were once attached to the starting point residential area group; 2. Residential areas that were previously attached to within the final residential area group; 3. The cell was attached to in the checkpoint cell group, and the attachment timestamp is greater than the attachment timestamp of the cell in the starting point cell group, but less than the attachment timestamp of the cell in the ending point cell group. 4. The difference between the attached timestamp of the cell in the endpoint cell group and the attached timestamp of the cell in the origin cell group is greater than the shortest travel time of the target arterial road and less than the longest travel time.

[0031] Specifically, based on the signaling records of each arterial road user during the monitoring period, users who have attached to cells in the starting cell group, the ending cell group, and the checkpoint cell group of the target arterial road are selected to form a first user set. Then, based on the signaling records of each arterial road user during the monitoring period, the first attachment timestamp in the starting cell group, the second attachment timestamp in the ending cell group, and the third attachment timestamp in the checkpoint cell group of each user in the first user set are calculated, and the time difference between the second attachment timestamp and the first attachment timestamp is calculated. Based on the extracted spatial and temporal characteristics of each target arterial road, the longest and shortest travel times of each target arterial road during the monitoring period are obtained. From the first user set, target users of the arterial road whose third attachment timestamp is between the first and second attachment timestamps, and whose time difference between the first and second attachment timestamps is less than the longest travel time of the target arterial road during the monitoring period and greater than the shortest travel time of the target arterial road during the monitoring period, are selected. It should be noted that when screening target users for the trunk road, the users in the first user set are selected based on the starting cell group, ending cell group, and checkpoint cell group of the same target trunk road. That is, users who have attached to cells in the starting cell group, ending cell group, and checkpoint cell group of the same target trunk road during the monitoring period are marked as users in the first user set. Further, within the interface corresponding to the signaling collection point, the signaling data of each trunk road user during the monitoring period is extracted based on the user number. Based on the signaling records of the extracted signaling data, the attachment timestamps of the trunk road user in the starting cell group and the attachment timestamps of the user in the ending cell group are obtained. Specifically, the first attachment timestamp of the user in the starting cell group is defined as the earliest attachment time point among the attachment records of the user number in each cell of the starting cell group during the monitoring period; the second attachment timestamp of the user in the ending cell group is defined as the latest attachment time point among the attachment records of the user number in each cell of the ending cell group during the monitoring period.

[0032] In this embodiment, by dividing each arterial road into segments and statistically analyzing travel time, the spatial and temporal characteristics of each target arterial road are extracted and used to filter target users on the arterial roads. Target users on the arterial roads are filtered from multiple dimensions such as time domain, spatial domain, and user type. This allows for the selection of key signaling nodes in the existing network topology. Signaling data of arterial road users is collected from different signaling flows such as control plane signaling flow, service plane signaling flow, and voice signaling flow. The signaling data of target users on the arterial roads is statistically summarized into multi-dimensional indicator data containing user-level indicators, which is beneficial for obtaining comprehensive monitoring indicators, thereby accurately locating network problem points. Furthermore, by shortening the monitoring period, high-frequency monitoring can be achieved, improving the monitoring and optimization efficiency of the arterial road network.

[0033] In one embodiment, by summarizing and statistically analyzing the target signaling data corresponding to the target users on the trunk line, the generated signaling indicator data includes network element-level indicator data and user-level indicator data. Step 300, specifically, involves summarizing and statistically analyzing the target signaling data to generate the signaling indicator data, including: Step 301: Summarize and statistically analyze the target signaling data to generate multidimensional indicator data; Step 302: Based on the multi-dimensional indicator data, filter out the problematic signaling data with degraded signaling process success rate from the target signaling data; Step 303: Summarize and statistically analyze the problem signaling data from the user perspective to generate user-level indicator data; Step 304: Summarize and statistically analyze the signaling data of the problem at the cell level to generate network element-level indicator data.

[0034] The target signaling data corresponding to the target users on the trunk line is summarized and statistically analyzed to generate multi-dimensional indicator data. Based on this multi-dimensional indicator data, problematic signaling data with failure rate in the degradation process is filtered out from the target signaling data. Problematic signaling data is summarized from different dimensions such as user dimension and cell dimension to generate corresponding network element-level indicator data and user-level indicator data.

[0035] Based on the generated network element-level and user-level indicator data, step 300 involves locating network problem points according to the signaling indicator data and optimizing the network based on these problem points. Specifically, this includes: Step 305: Sort the user-level indicator data and determine the user problem points that generate the problem signaling data in each of the target trunk lines based on the sorting results; Step 306: Sort the network element-level indicator data, and determine the cell problem points that generate the problem signaling data in each of the target trunk lines based on the sorting results; Step 307: Based on preset troubleshooting rules, conduct network troubleshooting on the target trunk line according to the problem points of the cell and / or the problem points of the user, so as to locate the network problem points in the target trunk line; Step 308: Optimize the target trunk road according to the optimization measures corresponding to the network problem points.

[0036] The user-level indicator data, obtained by summarizing and statistically analyzing problematic signaling data from the user perspective, is sorted. Based on the sorting results, user problem points generating problematic signaling data are identified. Similarly, network element-level indicator data, obtained by summarizing and statistically analyzing problematic signaling data from the cell perspective, is sorted. Based on the sorting results, cell problem points generating problematic signaling data are identified. Specifically, based on the sorting of user-level and network element-level indicator data, users and cells with indicators below a set threshold are identified, and user problem points and cell problem points are determined. Comprehensive analysis based on user problem points and / or cell problem points can accurately locate network problem points. For example, taking the number of problematic signaling in the signaling indicator data as an example, sorting the user-level indicator data can determine the ranking of the number of problematic signaling generated by target users on each trunk line. Based on the sorting results, users whose number of problematic signaling exceeds a set threshold are marked as user problem points, or the top 20% of users with the highest number of problematic signaling are marked as user problem points, i.e., problem users. Furthermore, sorting the network element-level indicator data can determine the ranking of the number of problematic signaling messages generated by each cell. Based on the ranking results, cells that generate more than a set threshold of problematic signaling messages are marked as problematic cells. Alternatively, the top 20% of cells that generate the most problematic signaling messages are marked as problematic cells.

[0037] It is understandable that the reason why problematic signaling data is generated at a user's problem point could be due to the user's terminal, the network conditions of the user's cell, or a combination of both. Similarly, the reason why problematic signaling data is generated at a cell's problem point could be due to the cell's network conditions, the user's terminal, or a combination of both. A comprehensive analysis of the problematic user and the problematic cell is needed to locate the network problem point in the target trunk line. Specifically, this includes comparing the overlap between the cells involved in the signaling records that generated the problematic signaling data and the problematic cell. If the overlap is high, it indicates that the problematic user's signaling data is likely generated by the problematic cell. Problematic signaling data is only generated when the user is within the network coverage area of ​​the problematic cell. In this case, different users are more likely to generate problematic signaling data within the same problematic cell. If the overlap is low, the problematic user's signaling data is likely due to the user's terminal itself, and is not significantly related to the cell's network conditions. In this case, the same user is highly likely to generate problematic signaling data regardless of which cell they are in.

[0038] A comprehensive analysis of problematic users and cells is conducted to pinpoint network problem points on target trunk lines. Specifically, based on pre-defined troubleshooting rules, network troubleshooting is performed on target trunk lines according to cell and / or user problem points to locate network problem points. Then, network optimization is implemented on the target trunk lines based on the corresponding optimization measures for each problem point. For example, the pre-defined troubleshooting rules for cell problem points are as follows: 1. Check the activation status and fault status of the neighboring cells of the problematic cell, and whether the neighboring cell relationships are abnormal; 2. Verify the distance between the problematic cell and neighboring cells to confirm whether it is a case of weak or excessive coverage; 3. Verify whether the power configuration parameters of the neighboring cells of the problematic cell meet the standards.

[0039] 4. Check whether the downtilt angle configuration parameters of the neighboring cells of the problematic community are too large or too small.

[0040] 5. Check whether the interference level of neighboring cells in the problematic cell is too high; 6. Check the handover threshold configuration parameters of the neighboring cells in the problematic cell to see if the handover is too fast or too slow; 7. Regarding the issue of TAC update failure, check whether the TACs of the neighboring cells of the problematic cell are consistent. If they are inconsistent, check whether the problematic signaling data is on the TAC boundary and whether there is any interlacing phenomenon, etc. 8. Verify whether there is weak coverage in the road section corresponding to the problematic community.

[0041] Accordingly, based on the identified network problem points, different optimization measures correspond to different problem points. Optimizing the network according to the corresponding optimization measures for the identified problem points helps achieve the desired optimization effect while saving optimization costs. Taking the above troubleshooting rules as an example, specific optimization measures include: 1. Resolve the fault and reactivate the community.

[0042] 2. Abnormal neighbor cell relationships: Add neighbor cells and delete overwritten neighbor cells; 3. Abnormal neighbor cell threshold configuration: Optimize the switching threshold to avoid switching too fast or too slow; 4. Power configuration anomaly: Calculate and set a reasonable transmission power based on the actual coverage level and required coverage area of ​​the corresponding road segment of the problematic cell; 5. Abnormal downtilt configuration parameters: Calculate based on the actual coverage level and required coverage area of ​​the corresponding road segment of the problematic community, and set reasonable physical downtilt, electronic downtilt, etc.

[0043] 6. Interference level too high: Investigate and eliminate the source of interference; 7. TAC update failure: Optimize the TAC boundary along the target trunk road where the problematic cell is located to reduce the interleaving phenomenon and reduce the number of TAC boundary switching times; 8. Weak coverage: Add new sites to supplement coverage, or replace them with high-performance directional antennas, etc.

[0044] Furthermore, after optimizing the target trunk roads based on the identified network problem points, the network optimization effect is evaluated based on the user-level indicator data summarized and statistically analyzed in the next monitoring period. It is then confirmed whether the identified network problem points have been resolved. If they have been resolved, closed-loop monitoring of the network status of each target trunk road is conducted. If they have not been resolved, optimization continues until the identified network problem points are resolved.

[0045] In this embodiment, target users on the trunk network are selected from multiple dimensions, including time domain, spatial domain, and user type. Key signaling nodes are selected within the existing network topology, and signaling data from these target users is collected from different signaling flows, such as control plane signaling, service plane signaling, and voice signaling. The signaling data is then aggregated and statistically analyzed into user-level and network element-level indicators, enabling precise location of network problems. A closed-loop troubleshooting and optimization process is implemented for the identified network problems, allowing for timely handling and resolution of network faults and improving optimization efficiency.

[0046] The trunk network optimization apparatus provided in the embodiments of this application is described below. The trunk network optimization apparatus described below can be referred to in correspondence with the trunk network optimization method described above.

[0047] Reference Figure 2The trunk network optimization device provided in this application embodiment includes: The data acquisition module 10 is used to obtain the trunk road list and collect the original signaling data of each target trunk road in the trunk road list; User filtering module 20 is used to extract the spatial and temporal features of each of the target trunk roads, and filter out the trunk road target users of each of the target trunk roads based on the spatial and temporal features. The data filtering module 30 is used to filter out the target signaling data corresponding to each trunk target user from the original signaling data; The network optimization module 40 is used to summarize and statistically analyze the target signaling data, generate signaling indicator data, locate network problem points based on the signaling indicator data, and optimize the network based on the network problem points.

[0048] In one embodiment, the spatial features include a starting cell group, an ending cell group, and a checkpoint cell group; the temporal features include the longest and shortest passage times within different time periods; and the user filtering module 20 is further configured to: The starting section, ending section, and key section of each target arterial road are defined, and the operating parameters of the existing network are obtained. The key section is the section that users must pass through on the target arterial road. The coverage cells of each target arterial road are matched according to the engineering parameter data, and the coverage cells corresponding to the starting road segment are assigned to the starting cell group, the coverage cells corresponding to the ending road segment are assigned to the ending cell group, and the coverage cells corresponding to the key road segment are assigned to the checkpoint cell group, so as to obtain the starting cell group, ending cell group and checkpoint cell group of the target arterial road. The mileage distance of each target arterial road is calculated based on the starting road segment and the ending road segment, and the longest and shortest travel times of the target arterial roads in different time periods are statistically analyzed based on the mileage distance.

[0049] In one embodiment, the user filtering module 20 is further configured to: Obtain all trunk users attached to the starting cell group and the ending cell group during the monitoring period from the original signaling data, and obtain the signaling records of each trunk user during the monitoring period. Based on the signaling records, users who have attached cells in the starting cell group, the ending cell group, and the checkpoint cell group are selected from each of the trunk users to generate a first user set. Calculate the first attachment timestamp of each user in the first user set in the cell group at the starting point, the second attachment timestamp of the cell group at the ending point, and the third attachment timestamp of the cell group at the checkpoint based on the signaling records. Calculate the time difference between the second attachment timestamp and the first attachment timestamp; Based on the spatial and temporal characteristics, the shortest and longest travel times within the monitoring period are obtained, and target users on the main road are selected from the first user set whose third attached timestamp is between the first and second attached timestamps, and whose time difference is greater than the shortest travel time within the monitoring period and less than the longest travel time within the monitoring period.

[0050] In one embodiment, the first attachment timestamp is the earliest attachment time point in the attachment records of users in the first user set within the starting cell group during the monitoring period, and the second attachment timestamp is the latest attachment time point in the attachment records of users in the first user set within the ending cell group during the monitoring period.

[0051] In one embodiment, the signaling indicator data includes network element-level indicator data and user-level indicator data, and the network optimization module 40 is further configured to: The target signaling data is summarized and statistically analyzed to generate multidimensional indicator data; Based on the multidimensional indicator data, problematic signaling data with degraded signaling process success rate are filtered out from the target signaling data; The signaling data related to the aforementioned issues is aggregated and statistically analyzed from the user perspective to generate user-level indicator data. The signaling data related to the aforementioned issues are aggregated and statistically analyzed at the cell level to generate network element-level indicator data.

[0052] In one embodiment, the network optimization module 40 is further configured to: The user-level indicator data is sorted, and the user problem points that generate the problem signaling data in each of the target trunk lines are determined based on the sorting results. The network element-level indicator data are sorted, and the problem points of the cells that generate the problem signaling data in each of the target trunk lines are determined based on the sorting results. Based on preset troubleshooting rules, network troubleshooting is performed on the target trunk line according to the problem points of the community and / or the problem points of the user, in order to locate the network problem points in the target trunk line; Based on the optimization measures corresponding to the network problem points, network optimization is performed on the target trunk road.

[0053] In one embodiment, the raw signaling data includes signaling data collected from different types of signaling processes, including control plane signaling processes, service plane signaling processes, and voice signaling processes. The metrics data include signaling process metrics, data service metrics, and voice service metrics. The signaling process metrics include connection success rate, hold-up signaling success rate, and handover signaling success rate. The data service metrics include download speed, request access success rate, handshake latency, data retransmission rate, and out-of-order rate. The voice service metrics include connection rate, call drop rate, registration success rate, and average call initiation latency.

[0054] Figure 3 Example: A schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call a computer program stored in the memory 330 to execute the steps of the trunk network optimization method, such as including: Obtain the list of arterial roads and collect the original signaling data of each target arterial road in the list; Extract the spatial and temporal features of each target arterial road, and filter out the target users of each target arterial road based on the spatial and temporal features; From the original signaling data, the target signaling data corresponding to each trunk road target user is selected; The target signaling data is summarized and statistically analyzed to generate signaling indicator data. Network problem points are located based on the signaling indicator data, and network optimization is performed based on the network problem points.

[0055] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0056] On the other hand, embodiments of this application also provide a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the trunk network optimization method provided in the above embodiments, such as including: Obtain the list of arterial roads and collect the original signaling data of each target arterial road in the list; Extract the spatial and temporal features of each target arterial road, and filter out the target users of each target arterial road based on the spatial and temporal features; From the original signaling data, the target signaling data corresponding to each trunk road target user is selected; The target signaling data is summarized and statistically analyzed to generate signaling indicator data. Network problem points are located based on the signaling indicator data, and network optimization is performed based on the network problem points.

[0057] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the methods provided in the above embodiments, such as including: Obtain the list of arterial roads and collect the original signaling data of each target arterial road in the list; Extract the spatial and temporal features of each target arterial road, and filter out the target users of each target arterial road based on the spatial and temporal features; From the original signaling data, the target signaling data corresponding to each trunk road target user is selected; The target signaling data is summarized and statistically analyzed to generate signaling indicator data. Network problem points are located based on the signaling indicator data, and network optimization is performed based on the network problem points.

[0058] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0059] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0060] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing a trunk road network, characterized in that, The trunk network optimization method includes the following steps: Obtain the list of arterial roads and collect the original signaling data of each target arterial road in the list; Extract the spatial and temporal features of each target arterial road, and filter out the target users of each target arterial road based on the spatial and temporal features; From the original signaling data, the target signaling data corresponding to each trunk road target user is selected; The target signaling data is summarized and statistically analyzed to generate signaling indicator data. Network problem points are located based on the signaling indicator data, and network optimization is performed based on the network problem points. The spatial features include a starting cell group, an ending cell group, and a checkpoint cell group. The temporal features include the longest and shortest travel times within different time periods. The steps for extracting the temporal and spatial features of each target arterial road include: delineating the starting segment, ending segment, and key segment of each target arterial road, and obtaining the network's operational parameters, wherein the key segment is the segment that users must pass through on the target arterial road; matching the coverage cells of each target arterial road according to the operational parameters, and assigning the coverage cells corresponding to the starting segment to the starting cell group, the coverage cells corresponding to the ending segment to the ending cell group, and the coverage cells corresponding to the key segment to the checkpoint cell group, thus obtaining the starting cell group, ending cell group, and checkpoint cell group of the target arterial road; calculating the mileage distance of each target arterial road based on the starting segment and the ending segment, and statistically analyzing the longest and shortest travel times of the target arterial road within different time periods based on the mileage distance; The step of filtering out target users for each target arterial road based on the spatial and temporal features includes: obtaining all arterial road users attached to the starting cell group and the ending cell group during the monitoring period from the original signaling data, and obtaining the signaling records of each arterial road user during the monitoring period; based on the signaling records, filtering out users from each arterial road user who are attached to cells in the starting cell group, cells in the ending cell group, and cells in the checkpoint cell group, generating a first user set; and calculating the number of users in the first user set within the starting cell group based on the signaling records. The system calculates the first attachment timestamp of the cell, the second attachment timestamp of the cell within the endpoint cell group, and the third attachment timestamp of the cell within the checkpoint cell group; it also calculates the time difference between the second attachment timestamp and the first attachment timestamp; based on the spatial and temporal characteristics, it obtains the shortest and longest travel times within the monitoring period, and selects target users on the main road from the first user set whose third attachment timestamp is between the first and second attachment timestamps, and whose time difference is greater than the shortest travel time within the monitoring period and less than the longest travel time within the monitoring period.

2. The trunk road network optimization method according to claim 1, characterized in that, The first attachment timestamp is the earliest attachment time point in the attachment records of users in the first user set within the starting cell group during the monitoring period, and the second attachment timestamp is the latest attachment time point in the attachment records of users in the first user set within the ending cell group during the monitoring period.

3. The trunk road network optimization method according to claim 1, characterized in that, The signaling indicator data includes network element-level indicator data and user-level indicator data. The step of summarizing and statistically analyzing the target signaling data to generate signaling indicator data includes: The target signaling data is summarized and statistically analyzed to generate multidimensional indicator data; Based on the multidimensional indicator data, problematic signaling data with degraded signaling process success rate are filtered out from the target signaling data; The signaling data related to the aforementioned issues is aggregated and statistically analyzed from the user perspective to generate user-level indicator data. The signaling data related to the aforementioned issues are aggregated and statistically analyzed at the cell level to generate network element-level indicator data.

4. The trunk road network optimization method according to claim 3, characterized in that, The step of locating network problem points based on the signaling indicator data and optimizing the network based on the network problem points includes: The user-level indicator data is sorted, and the user problem points that generate the problem signaling data in each of the target trunk lines are determined based on the sorting results. The network element-level indicator data are sorted, and the problem points of the cells that generate the problem signaling data in each of the target trunk lines are determined based on the sorting results. Based on preset troubleshooting rules, network troubleshooting is performed on the target trunk line according to the problem points of the community and / or the problem points of the user, in order to locate the network problem points in the target trunk line; Based on the optimization measures corresponding to the network problem points, network optimization is performed on the target trunk road.

5. The trunk road network optimization method according to claim 1, characterized in that, The raw signaling data includes signaling data collected from different types of signaling processes, including control plane signaling processes, service plane signaling processes, and voice signaling processes. The metrics data include signaling process metrics, data service metrics, and voice service metrics. The signaling process metrics include connection success rate, hold-up signaling success rate, and handover signaling success rate. The data service metrics include download speed, request access success rate, handshake latency, data retransmission rate, and out-of-order rate. The voice service metrics include connection rate, call drop rate, registration success rate, and average call initiation latency.

6. A trunk road network optimization device, characterized in that, include: The data acquisition module is used to obtain the trunk road list and collect the original signaling data of each target trunk road in the trunk road list; The user filtering module is used to extract the spatial and temporal features of each of the target arterial roads, and filter out the target users of each of the target arterial roads based on the spatial and temporal features. The data filtering module is used to filter out the target signaling data corresponding to each trunk target user from the original signaling data; The network optimization module is used to summarize and statistically analyze the target signaling data, generate signaling indicator data, locate network problem points based on the signaling indicator data, and optimize the network based on the network problem points. The spatial features include the starting cell group, the ending cell group, and the checkpoint cell group, and the temporal features include the longest and shortest passage times within different time periods. The user screening module is specifically used for: delineating the starting section, ending section, and key section of each target arterial road, and obtaining the operating parameters data of the existing network, wherein the key section is the section that users must pass through on the target arterial road; matching the coverage cells of each target arterial road according to the operating parameters data, and assigning the coverage cells corresponding to the starting section to the starting cell group, the coverage cells corresponding to the ending section to the ending cell group, and the coverage cells corresponding to the key section to the checkpoint cell group, thereby obtaining the starting cell group, ending cell group, and checkpoint cell group of the target arterial road; calculating the mileage distance of each target arterial road according to the starting section and the ending section, and calculating the longest and shortest travel time of the target arterial road in different time periods according to the mileage distance; The user filtering module is further configured to: obtain all trunk road users attached to the starting cell group and the ending cell group within the monitoring period from the original signaling data, and obtain the signaling records of each trunk road user within the monitoring period; based on the signaling records, filter out users from each trunk road user who are attached to cells in the starting cell group, cells in the ending cell group, and cells in the checkpoint cell group, generating a first user set; calculate the first attachment timestamp, the second attachment timestamp, and the third attachment timestamp of each user in the first user set to cells in the starting cell group, cells in the ending cell group, and cells in the checkpoint cell group based on the signaling records; calculate the time difference between the second attachment timestamp and the first attachment timestamp; obtain the shortest and longest travel times within the monitoring period based on the spatial and temporal characteristics, and filter out trunk road target users from the first user set whose third attachment timestamp is between the first and second attachment timestamps, and whose time difference is greater than the shortest travel time and less than the longest travel time within the monitoring period.

7. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the trunk network optimization method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the trunk network optimization method according to any one of claims 1 to 5.

Images