5G indoor wireless coverage problem positioning analysis method and system based on user relation data

Abstract

The invention relates to the technical field of network identification and positioning, in particular to a 5G indoor wireless coverage problem positioning analysis method and system based on user relation data, and the method comprises the following steps: identifying basic resident users in a building, building a user relation network, calculating a user resident signal propagation space model, and carrying out user relation training control. Determining resident same-address relation users, and positioning the indoor coverage problem of the building; the method has the beneficial effects that intelligent retention and analysis of indoor network signaling and measurement data are realized by analyzing periodic life tracks and round-trip rules of users. According to the intelligent data processing mode, various indoor coverage problems including weak signal areas, rapid signal attenuation areas, frequent user off-network areas and the like can be comprehensively and accurately positioned without field testing.

Description

Technical Field

[0001] The present invention relates to the field of network identification and positioning technology, and specifically to a method and system for locating and analyzing 5G indoor wireless coverage problems based on user relationship data. Background Art

[0002] High-precision positioning based on OTT and MDT only achieves approximately 7% sampling integrity in practical applications, resulting in insufficient data volume. Apple devices' lack of support for MDT technology directly limits its application for building positioning among Apple device users. 5G networks are not yet equipped with MDT functionality, and commonly used wireless fingerprint positioning technology is susceptible to complex and changing environmental interference, significantly reducing its accuracy. These factors combine to result in an extremely low percentage of sampling points that can accurately locate buildings. For example, in urban areas with densely populated high-rise buildings and complex electromagnetic environments, OTT and MDT positioning rely on specific equipment and environmental conditions, making signals highly susceptible to obstruction and interference, making stable and high-precision building positioning difficult. Furthermore, wireless fingerprint positioning in indoor environments suffers from frequent signal changes due to movement of people and the repositioning of furniture, making it difficult to accurately identify building locations.

[0003] The current statistical method of discrete measurement reports is significantly biased when assessing building coverage. This method can only analyze overall coverage and average received signal strength, but cannot sensitively and accurately identify sudden, rapid signal attenuation in indoor areas. Discrete measurement reports are essentially records of signals at discrete time points or locations, making it difficult to capture instantaneous, subtle changes in the signal, and leaving numerous blind spots in data monitoring. For example, in a large hospital building, a discrete measurement report may show that the overall signal coverage and average strength meet standards. However, in certain ward areas, electromagnetic interference from large medical equipment may cause the signal to rapidly attenuate, and discrete measurement methods cannot detect this problem in a timely manner.

[0004] Currently, relying on manual, periodic testing of building network coverage cannot provide real-time, continuous monitoring assurance. Manual testing is severely constrained by time and human resources, making it difficult to conduct uninterrupted, 24 / 7 testing of building networks. For example, if manual testing is arranged in a large office building, only a limited number of tests can be completed each day. During the test intervals, any network failures or signal anomalies are difficult to detect and address in a timely manner, significantly impacting the user experience.

[0005] In most building scenarios, manual testing typically only covers public areas like hallways and elevators. In enclosed rooms, complete testing is often impossible due to signal obstruction by walls and other structures, as well as the difficulty of actually entering the rooms for testing. This makes it difficult to fully and accurately understand network coverage in every area of ​​the building. For example, in hotels, manual testing easily covers hallways and elevators, but guest rooms are difficult to access due to their closed doors. Guest rooms are precisely where users frequently use the internet, so poor signal inside can directly impact guest satisfaction. However, due to the limited testing area, these issues cannot be identified or addressed in advance. Summary of the Invention

[0006] The purpose of the present invention is to provide a 5G indoor wireless coverage problem positioning analysis method and system based on user relationship data to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for locating and analyzing 5G indoor wireless coverage problems based on user relationship data, comprising the following steps:

[0008] Identify basic resident users within a building, establish a stable resident wireless signal feature model based on user wireless measurement report data, associate MDT and OTT precise location information, and combine with high-precision electronic maps to complete building attribution;

[0009] Establish a user relationship network, screen the call interaction signaling records accumulated over a certain period of time for basic resident users, output the first-level relationship user groups, and perform multi-layer calculations in a loop;

[0010] Calculate the spatial model of user resident signal propagation and model related signal fluctuations based on user wireless measurement report data;

[0011] Conduct user relationship training control, matching signal propagation spatial features starting from the third-level relationship output user, and terminate the training and merge the results when the matching ratio falls below the threshold;

[0012] Identify co-located users and perform feature matching calculations between source users and related training users;

[0013] Identify indoor coverage issues in buildings, establish a model library of users resident in the same building and at the same location, retain relevant data of resident users, and analyze and locate them from the aspects of overall measurement statistics, time sequence arrangement, identification of fast signal attenuation, and resident network changes and disconnection.

[0014] Preferably, in the identification of basic resident users in a building, when establishing a resident stable wireless signal feature model, clustering decisions are made on the main service cell and neighboring cell signal models of the source user's stable duration and high frequency over a long period of time.

[0015] Preferably, when establishing the user relationship network, when screening the user call interaction signaling call records, users with extremely short call interaction duration and insufficient frequency are filtered out.

[0016] Preferably, in the calculation of the user's resident signal propagation space model, a "dynamic and static" dual model is established for the user's resident signal and the round-trip signal fluctuations before entering the resident stable signal state and after leaving the resident area, wherein the "static" model is based on long-term signal model clustering decisions, and the "dynamic" model loads the user's wireless measurement report data in time sequence and matches the "static" model identification fingerprint to determine the relevant time points and network signaling change feature records.

[0017] Preferably, in locating indoor coverage problems in a building, when calculating that the overall received signal strength of the building is weak and the coverage is low, the steady-state wireless measurement report results of resident users in the building are summarized.

[0018] A system for locating and analyzing 5G indoor wireless coverage issues based on user relationship data, comprising:

[0019] Basic Resident User Identification Module: This module is used to establish a stable resident wireless signal characteristic model based on user wireless measurement report data, associate MDT and OTT precise location information with high-precision electronic maps to complete building attribution and identify basic resident users within a building.

[0020] User relationship network building module: This module is used to screen the call interaction signaling records accumulated over a certain period of time by basic resident users, output the first-level relationship user groups, and perform multi-layer calculations in a loop to build a user relationship network.

[0021] Signal propagation space model calculation module: used to model relevant signal fluctuations based on user wireless measurement report data and calculate the user resident signal propagation space model;

[0022] User relationship training control module: used to match the signal propagation space features starting from the third-level relationship output user, terminate the training and merge the results when the matching ratio falls below the threshold;

[0023] The module for determining users with co-location relationships and locating coverage issues is used to perform feature matching calculations on source users and related training users to determine users with co-location relationships. It also establishes a model library of users with co-location relationships in the same building, retains relevant data on resident users, and analyzes and locates indoor coverage issues in buildings from the perspectives of overall measurement statistics, time series arrangement to identify fast signal attenuation, and changes in the resident network causing disconnection.

[0024] Preferably, when establishing a resident stable wireless signal feature model, the basic resident user identification module is specifically used to make clustering decisions on the main service cell and neighboring cell signal models with stable duration and high frequency of source users over a long period of time, so as to obtain an accurate resident stable wireless signal feature model, and then complete the identification of basic resident users in the building.

[0025] Preferably, when screening user call interaction signaling records, the user relationship network establishment module is specifically used to filter out users with extremely short call interaction duration and insufficient frequency, so as to ensure that the screened call interaction signaling records can accurately reflect the effective relationship between users, thereby establishing an accurate user relationship network.

[0026] Preferably, when calculating the user's resident signal propagation space model, the signal propagation space model calculation module is specifically used to establish a "dynamic and static" dual model for the user's resident signal and the round-trip signal fluctuations before entering the resident stable signal state and after leaving the resident area; wherein, the "static" model is based on long-term signal model clustering decisions, and the "dynamic" model loads user wireless measurement report data in time sequence and matches the "static" model identification fingerprint to determine the relevant time points and network signaling change feature records, so as to calculate the user's resident signal propagation space model more comprehensively and accurately.

[0027] Preferably, when calculating the weak overall received signal strength and low coverage rate of a building, the module for determining users at the same location and locating coverage problems is specifically used to summarize the steady-state wireless measurement report results of resident users in the building, and to combine relevant data in the model library of resident users at the same location in the same building to conduct in-depth analysis from aspects such as overall measurement statistics, time sequence arrangement to identify fast signal attenuation, resident network changes and disconnection, so as to accurately locate the indoor coverage problem of the building.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention proposes a method and system for locating and analyzing 5G indoor wireless coverage issues based on user relationship data. By analyzing users' periodic life trajectories and commuting patterns, it achieves intelligent retention and analysis of indoor network signaling and measurement data. This intelligent data processing method can comprehensively and accurately locate various indoor coverage issues, including weak signal areas, rapid signal attenuation, and areas where users frequently disconnect from the network, without the need for on-site testing. This technology not only significantly improves the efficiency of network optimization, but also, with its non-invasive nature, minimizes interference with users' normal use, providing strong support for the high-quality development of indoor networks in the 5G era. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0031] In order to clearly and completely describe the objectives and technical solutions of the present invention and make the advantages more clearly understood, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, not all of them, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] For example 1, please refer to Figure 1 The present invention provides a technical solution: a method for locating and analyzing 5G indoor wireless coverage problems based on user relationship data, comprising the following steps:

[0033] 1. Basic resident user identification in the building

[0034] Based on user wireless measurement report data, a stable resident wireless signal characteristic model is constructed. This is then associated with precise location information such as MDT and OTT, and combined with high-precision electronic maps to determine building ownership. This step provides foundational data for subsequent analysis, accurately locating the building where the resident user resides and facilitating targeted analysis of their network status.

[0035] Method for identifying resident users in buildings

[0036] Step 1: Arrange the timing of user-level sampling points according to MmeGroupId, Cellid, MmeUeS1apId and MmeCode;

[0037] Step 2: Divide the whole day into intervals and mark the sampling points by time period

[0038] 2.1 Working hours: 7:00 am to 10:00 pm

[0039] 2.2 pm to 6 am is the rest period

[0040] Step 3: Identify the primary serving cell based on the MR sampling point data;

[0041] Step 4: If the primary service cell is a macro cell, determine whether there is an indoor cell in the adjacent cell.

[0042] 4.1: If there is an indoor cell in the neighboring area and RSRP>-90dBm, it is determined that the indoor area occupies a macro cell, and the sampling point is an indoor point;

[0043] 4.2: Based on the results of 4.1, calculate the time interval between two adjacent indoor sampling points. If the interval is within 3 minutes, the sampling point between the two indoor sampling points is considered an indoor sampling point.

[0044] Step 5: The main service cell is the macro cell. There are no indoor cells in the main service cell and its neighboring cells for 5 consecutive minutes.

[0045] 5.1: Sampling time is during the rest period

[0046] The primary service cell is a macro cell. Within 5 minutes, the primary service cell remains unchanged and the RSRP is within the 3dB range for more than 80% of the sampling points. The sampling points within this 5-minute period are indoor sampling points.

[0047] The primary serving cell is a macro cell. Within 5 minutes, the user's primary serving cell changes, but the number of changed cells is less than or equal to 3, and the RSRP fluctuation range of each primary serving cell is within 6dB, accounting for more than 80%. The sampling point within this 5-minute period is the indoor sampling point;

[0048] 5.2: Sampling time is during working hours

[0049] The primary service cell is a macro cell. Within 5 minutes, the primary service cell remains unchanged and the RSRP is within the 3dB range for more than 80% of the sampling points. The sampling points within this 5-minute period are marked as indoor attribute sampling points.

[0050] The primary serving cell is a macro cell. Within 5 minutes, the user's primary serving cell changes, but the number of changed cells is less than or equal to 3, and the RSRP fluctuation range of each primary serving cell is within 6dB, accounting for more than 80%. The sampling point within this 5-minute period is marked as a room-based sampling point.

[0051] 5.3: For the indoor attribute sampling points obtained in Section 5.2, calculate the distance between the sampling point and the primary serving cell based on the MR.LteScTadv information in the MR. Based on the minimum and maximum distances between the primary serving cell and the building outline, exclude some outdoor sampling points, and the remaining ones are indoor sampling points;

[0052] 2. Establish user relationship network

[0053] 1. First-tier user group screening: Filter the call interaction signaling records accumulated over a certain period of time for basic resident users in the building, filter out users with extremely short call interaction durations and insufficient call frequency, set a call interaction duration threshold of X seconds and the number of call interactions N, and output the first-tier user group.

[0054] 2. Multi-layer user relationship network construction: Using the first-layer target user as the source user, a multi-layer user relationship network is repeatedly calculated. By building a relationship network, co-located user groups are discovered, expanding the scope of user identification in indoor scenarios and providing more comprehensive data for analyzing indoor network coverage issues.

[0055] 3. User relationship network construction

[0056] Based on the user's IMS domain voice service call records, the call record data of identified building-based resident users is filtered out to create and output the first-level call relationship user group. The first-level relationship user group is used as the source user to calculate the second-level relationship user group, and the user relationship network is established in a multi-layered manner.

[0057] 4. Match user signal characteristics to establish a building resident user database

[0058] 1. Establishment of a "static" resident signal model: Clustering decisions are made based on the long-term stable duration and high-frequency signal models of the primary serving cell and neighboring cells, and multiple resident steady-state signal model results are output.

[0059] 2. Build a "dynamic" resident signal model for traffic to and from the resident area: Load user wireless measurement report data in chronological order, sort call records by service timestamp, match the fingerprint of the "static" resident signal model, determine the time the user entered the resident area, and search forward for network signaling change characteristics within three minutes, including serving cell changes and the maximum difference in RSRP fluctuations in the serving cell. Users that match the "dynamic and static" signal model are identified as co-located users of the source user based on signal propagation space model matching.

[0060] 5. Calculation of user resident signal propagation space model

[0061] Based on user wireless measurement report data, we analyze the fluctuations in the user's resident signal and the round-trip signal before and after entering and leaving the resident area to build a signal propagation space model. This model can more accurately reflect the user's signal changes in different states, helping to identify potential network coverage issues.

[0062] 6. User relationship training control

[0063] Starting with the third-level relationship output user results, each level of relationship users is matched against the basic input users based on the spatial characteristics of normal signal propagation in their work or residence locations, and the matching ratio is calculated. When the matching ratio falls below a certain threshold, the training loop terminates and the results for all relationship user groups are merged. This step ensures the effectiveness of relationship network training, avoids ineffective calculations, and improves analysis efficiency.

[0064] 7. Identification of users who reside at the same address

[0065] Based on the relationship training results for basic users, the source user and the relationship training users are matched with the spatial characteristics of the resident signal propagation to determine the co-located users. This step further clarifies the relationship between users in the same location and provides more accurate data support for precise analysis of indoor network coverage issues.

[0066] 8. Locating indoor coverage issues in buildings

[0067] Establish a model library of users who reside in the same building and at the same address, retain the wireless measurement reports and signaling call records of all permanent users in the building before and after entering the permanent cell, analyze them from the aspects of overall measurement statistics, time sequence arrangement to identify fast signal attenuation, resident network changes and disconnection, etc., and comprehensively locate the indoor coverage problems of the building.

[0068] In the second embodiment, based on the first embodiment, a system for locating and analyzing 5G indoor wireless coverage problems based on user relationship data is proposed, including:

[0069] Basic resident user identification module: used to establish a resident stable wireless signal characteristic model based on the user's wireless measurement report data, associate the MDT and OTT precise location information and combine it with a high-precision electronic map to complete building attribution, so as to identify the basic resident users in the building; when establishing the resident stable wireless signal characteristic model, the basic resident user identification module is specifically used to cluster the main service cell and neighboring cell signal models with long-term stable duration and high frequency of the source user, so as to obtain an accurate resident stable wireless signal characteristic model, and then complete the identification of basic resident users in the building.

[0070] User relationship network establishment module: used to screen the call interaction signaling bills accumulated by basic resident users over a certain period of time, output the first-level relationship user group and loop through multiple layers of calculation to establish a user relationship network; when screening user call interaction signaling bills, the user relationship network establishment module is specifically used to filter out users with extremely short call interaction duration and insufficient call frequency, to ensure that the screened call interaction signaling bills can accurately reflect the effective relationship between users, thereby establishing an accurate user relationship network.

[0071] Signal propagation space model calculation module: used to model relevant signal fluctuations based on user wireless measurement report data and calculate the user's resident signal propagation space model; when calculating the user's resident signal propagation space model, the signal propagation space model calculation module is specifically used to establish a "dynamic and static" dual model for the user's resident signal and the round-trip signal fluctuations before entering the resident stable signal state and after leaving the resident area; among them, the "static" model is based on long-term signal model clustering decisions, and the "dynamic" model loads user wireless measurement report data in time sequence and matches the "static" model identification fingerprint to determine the relevant time points and network signaling change feature records, so as to more comprehensively and accurately calculate the user's resident signal propagation space model.

[0072] User relationship training control module: used to match the signal propagation space features starting from the third-level relationship output user, terminate the training and merge the results when the matching ratio falls below the threshold;

[0073] The module for determining users with co-location relationships and locating coverage problems is used to perform feature matching calculations on source users and relationship training users to determine users with co-location relationships; a model library of users with co-location relationships in the same building is established to retain relevant data of resident users, and to analyze and locate indoor coverage problems in buildings from the aspects of overall measurement statistics, time series arrangement to identify fast signal attenuation, and resident network changes and disconnections; when calculating the overall weak received signal strength and low coverage rate of the building, the module for determining users with co-location relationships and locating coverage problems is specifically used to use the summary of steady-state wireless measurement report results of resident users in the building as a basis, combined with relevant data in the model library of users with co-location relationships in the same building, to conduct in-depth analysis from the aspects of overall measurement statistics, time series arrangement to identify fast signal attenuation, and resident network changes and disconnections, so as to accurately locate indoor coverage problems in the building.

[0074] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for locating and analyzing 5G indoor wireless coverage issues based on user relationship data, characterized by: The following steps are involved: Identify basic resident users within a building, establish a stable resident wireless signal feature model based on user wireless measurement report data, associate MDT and OTT precise location information, and combine with high-precision electronic maps to complete building attribution; Establish a user relationship network, screen the call interaction signaling records accumulated over a certain period of time for basic resident users, output the first-level relationship user groups, and perform multi-layer calculations in a loop; Calculate the spatial model of user resident signal propagation and model related signal fluctuations based on user wireless measurement report data; Conduct user relationship training control, matching signal propagation spatial features starting from the third-level relationship output user, and terminate the training and merge the results when the matching ratio falls below the threshold; Identify co-located users and perform feature matching calculations between source users and related training users; Identify indoor coverage issues in buildings, establish a model library of users resident in the same building and at the same location, retain relevant data of resident users, and analyze and locate them from the aspects of overall measurement statistics, time sequence arrangement, identification of fast signal attenuation, and resident network changes and disconnection.

2. The method for locating and analyzing 5G indoor wireless coverage problems based on user relationship data according to claim 1, characterized in that: In the identification of basic resident users in buildings, when establishing a resident stable wireless signal feature model, clustering decisions are made based on the main service cell and neighboring cell signal models with long-term stable duration and high frequency of the source user.

3. The method for locating and analyzing 5G indoor wireless coverage problems based on user relationship data according to claim 2, characterized in that: When establishing a user relationship network, when screening user call interaction signaling records, filter out users with extremely short call interaction duration and insufficient frequency.

4. The method for locating and analyzing 5G indoor wireless coverage problems based on user relationship data according to claim 3, characterized in that: In the calculation of the user's resident signal propagation spatial model, a "dynamic and static" dual model is established for the user's resident signal and the round-trip signal fluctuations before entering the resident stable signal state and after leaving the resident area. The "static" model is based on long-term signal model clustering decisions, and the "dynamic" model loads user wireless measurement report data in time series and matches the "static" model's identification fingerprint to determine the relevant time points and network signaling change feature records.

5. The method for locating and analyzing 5G indoor wireless coverage problems based on user relationship data according to claim 4, characterized in that: When locating indoor coverage issues in a building, when calculating the overall building received signal strength is weak and coverage is low, the steady-state wireless measurement report results of resident users in the building are summarized.

6. A system for the 5G indoor wireless coverage problem location analysis method based on user relationship data according to claim 5, characterized in that: include: Basic Resident User Identification Module: This module is used to establish a stable resident wireless signal characteristic model based on user wireless measurement report data, associate MDT and OTT precise location information with high-precision electronic maps to complete building attribution and identify basic resident users within a building. User relationship network building module: This module is used to screen the call interaction signaling records accumulated over a certain period of time by basic resident users, output the first-level relationship user groups, and perform multi-layer calculations in a loop to build a user relationship network. Signal propagation space model calculation module: used to model relevant signal fluctuations based on user wireless measurement report data and calculate the user resident signal propagation space model; User relationship training control module: used to match the signal propagation space features starting from the third-level relationship output user, terminate the training and merge the results when the matching ratio falls below the threshold; The module for determining users with co-location relationships and locating coverage issues is used to perform feature matching calculations on source users and related training users to determine users with co-location relationships. It also establishes a model library of users with co-location relationships in the same building, retains relevant data on resident users, and analyzes and locates indoor coverage issues in buildings from the perspectives of overall measurement statistics, time series arrangement to identify fast signal attenuation, and changes in the resident network causing disconnection.

7. A system according to claim 6, characterized in that: When establishing a resident stable wireless signal feature model, the basic resident user identification module is specifically used to cluster the signal models of the main service cell and neighboring cells based on the stable duration and high frequency of the source user over a long period of time, so as to obtain an accurate resident stable wireless signal feature model, and thus complete the identification of basic resident users in the building.

8. A system according to claim 7, characterized in that: When screening user call interaction signaling records, the user relationship network establishment module is specifically used to filter out users with extremely short call interaction duration and insufficient frequency, so as to ensure that the screened call interaction signaling records can accurately reflect the effective relationship between users, thereby establishing an accurate user relationship network.

9. A system according to claim 8, characterized in that: When calculating the user's resident signal propagation spatial model, the signal propagation spatial model calculation module is specifically used to establish a "dynamic and static" dual model for the user's resident signal and the round-trip signal fluctuations before entering the resident stable signal state and after leaving the resident area. The "static" model is based on long-term signal model clustering decisions, while the "dynamic" model loads user wireless measurement report data in a time series and matches the "static" model's fingerprint to determine the relevant time points and network signaling change feature records, thereby more comprehensively and accurately calculating the user's resident signal propagation spatial model.

10. A system according to claim 9, characterized in that: When calculating the weak overall received signal strength and low coverage rate of a building, the module for determining co-located users and locating coverage problems is specifically used to summarize the steady-state wireless measurement report results of resident users in the building. Combined with relevant data in the model library of resident co-located users in the same building, it conducts in-depth analysis from the aspects of overall measurement statistics, time sequence arrangement to identify fast signal attenuation, resident network changes and disconnection, etc., to accurately locate the building's indoor coverage problems.

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