Building abnormal event identification method based on signaling data

Abstract

The invention discloses a building abnormal event identification method based on signaling data, and relates to the technical field of data processing, and the method comprises the steps: determining a network abnormal event and a target moment when the network abnormal event occurs based on the signaling data of a target user for any target user in a plurality of users; determining a motion state and a user position of the target user at the target moment, and determining a resident building where the user position is located under the condition that the motion state is a resident state and the user position is a preset resident position corresponding to the target user; and constructing an abnormal event list of the resident building corresponding to each network abnormal event based on the network abnormal event of each user. According to the invention, the troubleshooting efficiency of the building coverage problem of the network signal can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method for identifying abnormal building events based on signaling data. Background Art

[0002] With the acceleration of urbanization and the continuous development of building construction, the number of buildings continues to grow. However, in terms of network signal coverage, the increase in the number of buildings may cause users to encounter poor network signals in some buildings, which not only affects the user experience, but may also cause user complaints or number portability.

[0003] At present, the investigation and discovery of building coverage problems of network signals mainly rely on regular inspections by relevant technical personnel. However, this traditional manual regular inspection method requires relevant technical personnel to arrive at each building for on-site inspection, which has the problems of high labor costs and low detection efficiency. Moreover, if this manual regular inspection method fails to discover building coverage problems in a timely manner, it may cause user complaints and lead to user loss.

[0004] Therefore, how to improve the efficiency of troubleshooting network signal building coverage problems is a problem that urgently needs to be solved. Summary of the invention

[0005] The main purpose of this application is to provide a method for identifying building abnormal events based on signaling data, aiming to improve the efficiency of troubleshooting building coverage problems of network signals.

[0006] To achieve the above objectives, the present application provides a method for identifying abnormal building events based on signaling data, and the method for identifying abnormal building events based on signaling data includes: For any target user among the multiple users, determining a network abnormal event and a target time when the network abnormal event occurs based on signaling data of the target user; Determine the motion state and user location of the target user at the target time, and if the motion state is a resident state and the user location is a preset resident location corresponding to the target user, determine the resident building where the user location is located; Based on the network abnormal events of each of the users, an abnormal event list of the resident buildings corresponding to each of the network abnormal events is constructed.

[0007] In one embodiment, before the step of determining the network abnormal event and the target time when the network abnormal event occurs based on the signaling data of the target user, the method further includes: Monitoring signaling data of the target user; The state sequence of the target user is updated based on the signaling data, wherein the state sequence records the location of the target user at each historical time point.

[0008] In one embodiment, the step of determining the motion state and user location of the target user at the target time includes: determining a user position of the target user at the target time based on the state sequence; The motion state of the target user at the target time is determined based on the target time, the user position and the state sequence.

[0009] In one embodiment, the step of determining the motion state of the target user at the target time based on the target time, the user position and the state sequence includes: Determine a duration period during which the target user is located at the user position based on the state sequence, and determine the duration of the duration period, wherein the end time of the duration period is the target time; The motion state of the target user at the target moment is determined based on the duration.

[0010] In one embodiment, the resident state includes a long-term resident state and a temporary resident state, and the step of determining the motion state of the target user at the target time based on the duration includes: When the duration is greater than or equal to a first preset duration, determining that the target user's motion state at the target time is a long-term resident state; When the duration is greater than or equal to the second preset duration and less than the first preset duration, it is determined that the motion state of the target user at the target moment is a temporary stay state, wherein the first preset duration is greater than the second preset duration.

[0011] In one embodiment, the motion state further includes an unknown state, and the step of determining the motion state of the target user at the target time based on the duration includes: When the duration is less than the second preset duration, it is determined that the motion state of the target user at the target moment is an unknown state, and the process returns to the step of monitoring the signaling data of the target user.

[0012] In one embodiment, the step of constructing an abnormal event list of a resident building corresponding to each network abnormal event based on each of the users' respective network abnormal events includes: For any target resident building among the resident buildings corresponding to each of the network abnormal events, determine a target abnormal event occurring in the target resident building among the network abnormal events of each of the users; An abnormal event list of the target resident building is constructed based on the target abnormal event and preset identification information of the target resident building.

[0013] In addition, to achieve the above-mentioned purpose, the present application also provides a device for identifying abnormal building events based on signaling data, and the device for identifying abnormal building events based on signaling data includes: A determination module, configured to determine, for any target user among a plurality of users, a network abnormality event and a target time at which the network abnormality event occurs based on signaling data of the target user; A judgment module, used to determine the motion state and user location of the target user at the target time, and when the motion state is a resident state and the user location is a preset resident location corresponding to the target user, determine the resident building where the user location is located; The statistical module is used to construct an abnormal event list of the resident buildings corresponding to each of the network abnormal events based on the network abnormal events of each of the users.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, which is a computer-readable storage medium, and the computer-readable storage medium stores a program for implementing a building abnormal event identification method based on signaling data. The program for implementing a building abnormal event identification method based on signaling data is executed by a processor to implement the steps of the building abnormal event identification method based on signaling data as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for identifying abnormal building events based on signaling data.

[0016] The present application provides a method for identifying abnormal building events based on signaling data. The present application determines, for any target user among multiple users, a network abnormal event and a target time at which the network abnormal event occurs based on the signaling data of the target user, and then determines the motion state and user location of the target user at the target time, and when the motion state is a resident state and the user location is a preset permanent location corresponding to the target user, determines the permanent building where the user location is located, and finally, based on the network abnormal events of each user, constructs an abnormal event list of the permanent building corresponding to each user.

[0017] In summary, compared to the traditional method that requires manual visits to buildings on a regular basis to troubleshoot building coverage problems, the present application determines, for each user, based on the user's signaling data, the network abnormality events that occur when the user uses the network signal, and determines the permanent building where the user resides when the network abnormality event occurs, wherein the permanent building is located at the preset permanent location corresponding to the target user. In this way, each user's respective network abnormality events and the permanent building where the user is located when each network abnormality event occurs are obtained, thereby constructing an abnormal event list for the permanent building based on the network abnormality events that occur in each permanent building. In other words, there is no need for manual visits to the site to troubleshoot building coverage problems, thereby improving the efficiency of troubleshooting building coverage problems of network signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 This is a flow chart of a first embodiment of a method for identifying abnormal building events based on signaling data of the present application; Figure 2 A schematic diagram of a building abnormal event identification process involved in an embodiment of a building abnormal event identification method based on signaling data of the present application; Figure 3 A schematic diagram of a user interface involved in an embodiment of a method for identifying abnormal building events based on signaling data of the present application; Figure 4 A schematic diagram of a state sequence involved in an embodiment of a method for identifying abnormal building events based on signaling data of the present application; Figure 5 A schematic diagram of motion state transition involved in an embodiment of a method for identifying abnormal building events based on signaling data of the present application; Figure 6 Schematic diagram of the equipment structure of the hardware operating environment involved in the method for identifying abnormal building events based on signaling data in an embodiment of the present application.

[0021] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0023] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0024] The main solution of the present application is: for any target user among multiple users, determine the network abnormal event and the target time of the occurrence of the network abnormal event based on the signaling data of the target user; determine the motion state and user location of the target user at the target time, and when the motion state is a resident state and the user location is the preset permanent location corresponding to the target user, determine the permanent building where the user location is located; based on the network abnormal events of each of the users, construct an abnormal event list of the permanent buildings corresponding to each of the network abnormal events.

[0025] At present, the investigation and discovery of building coverage problems of network signals mainly rely on regular inspections by relevant technical personnel. However, this traditional manual regular inspection method requires relevant technical personnel to arrive at each building for on-site inspection, which has the problems of high labor costs and low detection efficiency. Moreover, if this manual regular inspection method fails to discover building coverage problems in a timely manner, it may cause user complaints and lead to user loss.

[0026] Compared with the traditional method that requires manual visits to buildings on a regular basis to troubleshoot building coverage problems, the present application determines, for each user, based on the user's signaling data, network abnormal events that occur when the user uses the network signal, and determines the permanent building where the user resides when the network abnormal event occurs, wherein the permanent building is located at the preset permanent location corresponding to the target user. In this way, each user's respective network abnormal event and the permanent building where the user is located when each network abnormal event occurs are obtained, thereby constructing an abnormal event list for each permanent building based on the network abnormal events that occur in the permanent building. In other words, there is no need for manual visits to the site to troubleshoot building coverage problems, thereby improving the efficiency of troubleshooting building coverage problems of network signals.

[0027] It should be noted that the execution subject of the method in each embodiment of the signaling data-based building abnormal event identification method of the present application can be a building abnormal event identification system, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a signaling data-based building abnormal event identification device that can achieve the above functions, etc., and this embodiment does not specifically limit this. The following takes the building abnormal event identification system as an example of the execution subject to illustrate this embodiment and the following embodiments.

[0028] Based on this, the present application proposes a first embodiment of a building abnormal event identification method based on signaling data, please refer to Figure 1 The method for identifying abnormal building events based on signaling data includes steps S10 to S30: Step S10, for any target user among the multiple users, determining a network abnormal event and a target time when the network abnormal event occurs based on the signaling data of the target user; It should be noted that the multiple users mentioned above refer to users of a certain network operator. Any one of the multiple users is referred to as a target user for distinction. The signaling data of the target user includes XDR (Extended Data Representation) data. The types of network abnormal events include but are not limited to the uplink quality of quality calls, network delays, network packet drops, network throttling, data disorder, etc.

[0029] For any target user among the multiple users, a network abnormality event and a time when the network abnormality event occurs (hereinafter referred to as target time for distinction) are determined based on the signaling data of the target user.

[0030] Step S20, determining the motion state and user location of the target user at the target time, and determining the permanent building where the user location is located when the motion state is a resident state and the user location is a preset permanent location corresponding to the target user; It should be noted that the movement state of the target user includes the stationary state, the road state and the unknown state. The permanent area of ​​each user is predetermined, that is, the preset permanent location corresponding to the target user, wherein the preset permanent location includes but is not limited to the user's workplace and residence.

[0031] Determine the location of the target user's motion state at the target time (hereinafter referred to as the user location for distinction), and when the motion state is a resident state and the user location is a preset resident location corresponding to the target user, determine the resident building where the user location is located.

[0032] Exemplarily, after determining the motion state and user location of the target user when the network abnormal event occurs based on the analysis of the abnormal event data in the signaling data, if the motion state is a resident state, then determine whether the user location is the permanent location of the target user; if the user location is the user's permanent location, then calculate the permanent building where the user location is located based on the building layer information of the user's permanent location (including the building layer outline and external expansion range information) to obtain the building information where the user was located when the current network abnormal event occurred.

[0033] Step S30: Based on the network abnormal events of each of the users, an abnormal event list of the resident buildings corresponding to each of the network abnormal events is constructed.

[0034] For each of the multiple users, it is determined whether the user's movement state and user location meet the conditions when the network abnormal event of the user occurs, that is, whether the movement state is a resident state and whether the user location is a permanent location. When the conditions are met, the permanent building where the user is located when the network abnormal event occurs is determined. In this way, it is determined that the network abnormal events that occur when each user uses the network, and the permanent building corresponding to the network abnormal event when the user's movement state and user location meet the conditions when the network abnormal event occurs, based on the network abnormal events of each user, an abnormal event list of the permanent building corresponding to each network abnormal event is constructed.

[0035] For example, Figure 2 The figure shows a schematic diagram of the building abnormal event identification process. First, the user's network abnormal event is determined based on the user's signaling data, and the user's real-time motion state is identified; then the user's motion state when the network abnormal event occurs is determined. Specifically, according to the time of occurrence of the network abnormal event and the user's motion state at that time, the user's motion state when the 4G WeChat quality is poor, the 5G WeChat quality is poor, or the call quality is poor abnormal event is analyzed; then the user's user position when the network abnormal event occurs is determined, and whether the network abnormal event occurs in the user's permanent location is analyzed. Specifically, by analyzing the time when the network abnormal event occurs, the wireless cell and specific location where the user is located when the network abnormal event occurs, it is determined whether the abnormal event occurs in the user's regular residential area or its surrounding area; when the motion state is the resident state and the user's location is the user's permanent location, the permanent building where the user is located when the network abnormal event occurs is determined; finally, all network abnormal events that occur in the permanent building are counted. In this way, the embodiment of the present application can more effectively discover and solve building coverage problems in advance through active detection of network abnormal events, reduce user dissatisfaction, and improve user satisfaction and service quality.

[0036] Among them, the above-mentioned step of analyzing abnormal events at the user's permanent location can specifically include: by matching the time when the user's abnormal event occurs with the time period of the user's permanent location, analyzing whether the abnormal event occurs in the user's permanent location; analyzing the residence time of the wireless cell where the user's abnormal event occurs and the user's permanent location, and determining whether these abnormal events occur in the wireless cell occupied by the user for a long time. If the abnormal event does not occur in the wireless cell where the user resides, further analyze whether the abnormal cell has a switching (cut-in and cut-out) relationship with the user's permanent cell. Finally, output a list containing the matching situation of the user's abnormal event cell and the permanent cell; when the user's abnormal event occurs in a non-resident cell and its cut-in and cut-out cell set, by calculating the distance between the abnormal event wireless cell and the user's permanent location, evaluate whether these abnormal events actually occur in the user's regular residential area. In this way, by executing the above three steps, the detailed information of the user's abnormal event (abnormal type, occurrence time, etc.), whether the abnormal event occurs in the user's permanent location, and the user's permanent location and other information are summarized and output.

[0037] In this embodiment, the step S30 may include: Step S301, for any target resident building among the resident buildings corresponding to each of the network abnormal events, determining a target abnormal event occurring in the target resident building among the network abnormal events of each of the users; Step S302: constructing an abnormal event list of the target resident building based on the target abnormal event and preset identification information of the target resident building.

[0038] Determine any resident building among the resident buildings corresponding to each network abnormal event (hereinafter referred to as the target resident building for distinction), determine the abnormal event occurring in the target resident building among each user's respective network abnormal event (hereinafter referred to as the target abnormal event for distinction), and then construct an abnormal event list of the target resident building based on the preset identification information of the target abnormal event and the target resident building, so as to obtain the network abnormal events occurring in each building.

[0039] For example, after determining the network abnormal events corresponding to each user and the resident buildings corresponding to each network abnormal event, a statistical analysis of network abnormal events is performed on the buildings within the time range specified by the user, and a building list containing the number of abnormal events and the number of abnormal users is generated. Thus, when the user selects a specific building, the system will display a detailed list of abnormal events for the building, mark the geographical location of the building on the GIS (Geographic Information System) map, and provide the function of viewing the distribution of abnormal events in the surrounding buildings of the building. Specifically, Figure 3As shown is a user interface schematic diagram, the system displays information of each building in the building display area of ​​the user interface, such as Building 001, Building 002 and Building 003; when the user selects a specific building in the user interface, the system displays information of each network abnormal event occurring in the specific building in the building selection area of ​​the user interface, and displays map data of the area where the corresponding building is located in the map display area of ​​the user interface, so as to facilitate user viewing and improve the management of building coverage issues.

[0040] The embodiment of the present application determines the network abnormal events that occur when the user uses the network signal based on the user's signaling data for each user, and determines the permanent building where the user resides when the network abnormal event occurs, wherein the permanent building is located at the preset permanent location corresponding to the target user, so that each user's respective network abnormal event and the permanent building where the user is located when each network abnormal event occurs are obtained, thereby constructing an abnormal event list of the permanent building based on the network abnormal events that occur in each permanent building, that is, there is no need to manually go to the site to troubleshoot building coverage problems, which improves the troubleshooting efficiency of building coverage problems of network signals. In addition, the present application is also simpler in terms of business development and maintenance management, and has comprehensive advantages.

[0041] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated later. On this basis, before the step of "determining the network abnormal event and the target time when the network abnormal event occurs based on the signaling data of the target user" in step S10, the building abnormal event identification method based on signaling data also includes: Step A10, monitoring the signaling data of the target user; Step A20: updating the state sequence of the target user based on the signaling data, wherein the state sequence records the location of the target user at each historical time point.

[0042] The signaling data of the target user is monitored in real time, and the state sequence of the target user is updated based on the signaling data, wherein the state sequence records the location of the target user at each historical time point.

[0043] For example, Figure 4The figure shows a schematic diagram of a state sequence, which includes a determined sequence and an undetermined sequence. The determined sequence includes three records, namely A, B, and A. Each record includes the time when the user first appears in the cell and the time when the user last appears. Based on the determined sequence, the user's motion state in the time period corresponding to the sequence can be seen; the undetermined sequence includes two parts, one is the determined part and the other is the undetermined part. The determined part includes three records, namely C, D, and E, and the undetermined part includes two records, namely F and G. It should be noted that A, B, C, D, E, F, and G are cell identifiers. Among them, UncertainIndex records the subscript of the cell, that is, F.

[0044] And, if Figure 5 The figure shows a schematic diagram of motion state transfer, in which the user's initial state is set to unknown. The user's motion state includes unknown state, temporary residence state (corresponding to a temporary station in the state sequence), long-term residence state (corresponding to a long-term station in the state sequence) and road movement state (corresponding to the road in the state sequence), and the initial state can be updated from the unknown state to each state, and conversion can be performed between each state. After setting the user's initial state to unknown, UncertainIndex=0, then sort the user's xdr data by time, traverse the user positions corresponding to each xdr data in turn, and update the cell zipper table based on the user's xdr data, that is, detect whether the identifier of the cell where the user is currently located is the same as the identifier of the last cell in the cell zipper table. If they are the same, the current cell identifier is not added to the cell zipper table. If they are not the same, the current cell identifier is added to the end of the cell zipper table. In other words, the cell zipper table records the identifiers of each cell arranged in chronological order, that is, the identifiers of the cells that the user passes through in turn; determine whether the time when the user arrives at the current point (that is, the start time of the current point) differs from the end time of the last record in the cell zipper table by more than 90 minutes. If the time difference exceeds 90 minutes, force the undetermined sequence to be converted into a determined sequence. If the time difference does not exceed 90 minutes, determine the motion state of the current point.

[0045] In this embodiment, the step S20 may include: Step S201, determining the user position of the target user at the target time based on the state sequence; Step S202: determining the motion state of the target user at the target time based on the target time, the user position and the state sequence.

[0046] The user position corresponding to the target moment is determined based on the state sequence, that is, the user position of the target user at the target moment, and then the motion state of the target user at the target moment is determined based on the target moment, the user position and the state sequence.

[0047] In this embodiment, the step S202 may include: Step B10, determining a duration period during which the target user is located at the user position based on the state sequence, and determining the duration of the duration period, wherein the end time of the duration period is the target time; Step B20: determining the motion state of the target user at the target moment based on the duration.

[0048] Based on the state sequence, the continuous event segment of the target user at the current user location is determined, and the time length of the continuous event segment (hereinafter referred to as the continuous duration for distinction) is determined. It is worth emphasizing that the end moment of the continuous duration segment is the target moment when the current network abnormal event occurs, and then the movement state of the target user at the target moment is determined based on the continuous duration.

[0049] In this embodiment, the resident state includes a long-term resident state and a temporary resident state, and the step B20 may include: Step B201, when the duration is greater than or equal to a first preset duration, determining that the motion state of the target user at the target time is a long-term resident state; Step B202, when the duration is greater than or equal to a second preset duration and less than the first preset duration, determine that the motion state of the target user at the target moment is a temporary stay state, wherein the first preset duration is greater than the second preset duration.

[0050] It should be noted that the resident state includes a long-term resident state and a temporary resident state. The embodiment of the present application does not limit the specific durations of the first preset duration and the second preset duration, but limits the first preset duration to be greater than the second preset duration, corresponding to the long-term resident state and the temporary resident state, respectively. In this embodiment, the first preset duration is set to 30 minutes, and the second preset duration is set to 10 minutes.

[0051] Determine the relationship between the duration of the target user at the current user location and the first preset duration and the second preset duration, respectively; if the duration is greater than or equal to the first preset duration, determine that the target user's motion state at the target time is a long-term residence state; if the duration is greater than or equal to the second preset duration and less than the first preset duration, determine that the target user's motion state at the target time is a temporary residence state.

[0052] In this embodiment, the motion state also includes an unknown state, and the step B20 may include: Step B203, when the duration is less than the second preset duration, determining that the motion state of the target user at the target moment is an unknown state, and returning to execute the step of monitoring the signaling data of the target user.

[0053] It should be noted that the motion state also includes an unknown state, which means that the current XDR data is small and the user's motion state has not been identified. In addition, the motion state also includes a road movement state, which means that the user is moving on a road far away from the building.

[0054] When the duration is less than the second preset duration, it is determined that the motion state of the target user at the target time is an unknown state, and the process returns to the step of monitoring the signaling data of the target user.

[0055] In a feasible implementation, when the sequence is not determined to be an unknown state, there are several state update situations, namely: unknown state transfers to a temporary station or a station, unknown state transfers to a moving state, and unknown state continues. Among them, the situation where the unknown state transfers to a temporary station or a station is specifically: starting from UncertainIndex, the cell zipper table is traversed in sequence. If the eci (E-UTRAN Cell Identity) of the current point is the same as the eci of the traversed index cell, the difference between the end time of the current point and the start time of the traversed cell is calculated. If the time difference exceeds 10 minutes, the state of the current point is updated to a temporary station, and the user's movement state at the current point is determined to be a temporary resident state. If the time difference exceeds 30 minutes, the state of the current point is updated to a station, and the user's movement state at the current point is determined to be a long-term resident state. And update the end time of the sequence to the end time of the current point; the specific situation of the unknown state transitioning to the motion state is: starting from UncertainIndex, traverse the cell zipper table in sequence. If the eci of the current point and the eci of the traversed index cell are not the same, then calculate the distance and time difference between the cell to which the current point belongs and the last cell in the cell zipper table, where the time difference between the two cells refers to the time difference between the end time of the current point and the end time of the last cell in the cell zipper table. When the distance exceeds 2000 meters, the time difference exceeds 10 minutes, and the number of different cells between UncertainIndex and the traversed index cell exceeds 5, then the unknown state is transferred to the road state, and the user's motion state at the current point is determined to be a road moving state. UncertainIndex takes the subscript of the last cell in the cell zipper table; and the specific situation of the unknown state continuation is: in the unknown state, and the above two situations are not met, then determine that the unknown state continues, that is, the user's current motion state cannot be determined, UncertainIndex is not updated, and the end time of the sequence is changed to the end time of the last cell in the zipper table.

[0056] In another feasible implementation, when it is not determined whether the sequence is a temporary station or a long-term station, there are several status update situations, namely: station continuation, temporary station continuation or conversion to a station, [temporary] station switching to another [temporary] station and [temporary] station switching. Among them, the situation of station continuation is specifically: if the eci of the current point is the same as the eci of the station, then the end time of the station sequence is updated to the end time of the current point. UncertainIndex takes the subscript of the last cell in the cell zipper list; the situation of temporary station continuation or conversion to a station is specifically: if the state of the undetermined sequence is a temporary station state, and the eci of the current point is the same as the eci of the station, then the end time of the station sequence is updated to the end time of the current point. UncertainIndex takes the subscript of the last cell in the cell zipper list. Determine whether the end time of the current point and the start time of the temporary station sequence are greater than 30 minutes. If so, update the sequence state to a station, and determine that the user's current motion state is a long-term station state; the specific situation of switching from a [temporary] station to another [temporary] station is as follows: start from UncertainIndex and traverse the cell zipper table in sequence. If the eci of the current point is the same as the eci of the traversed index cell, and the difference between the end time of the current point and the start time of the Index cell is more than 10 minutes, then generate a determined [temporary] station sequence from the range of [0, UncertainIndex) of the cell zipper table, and the previous of the sequence points to the reference of the previous sequence, and next points to the reference of the next sequence. [UnceratianIndex, index) generates a determined road sequence, and the previous of the sequence points to the reference of the previous sequence, and next points to the reference of the next sequence. [index, current) generates an undetermined [temporary] stationary state sequence, and copies the cell zipper table from index to current to the cell zipper table in the new undetermined state sequence, and the sequence's previous points to the reference of the previous sequence. Update the start and end times of each sequence. UncertainIndex takes the subscript of the last cell in the cell zipper table; the specific situation of [temporary] stationary point turning into road is: the distance between the eci of the current point and the eci of the [temporary] stationary point exceeds 2000 meters, and the end time of the current point and the end time of the cell in the cell zipper table with the subscript UncertainIndex – 1 exceed 10 minutes, and the number of different cells between UncertainIndex and the traversed current point cell exceeds 5, then it is a road state.Then, starting from uncertainIndex, traverse the cell zipper table in sequence, calculate the distance between the cell at the current point and the traversed index cell. If the distance exceeds 2000 meters, [0, index) is a determined [temporary] station sequence, and [index, current) is an undetermined road sequence. If there is no distance exceeding 2000 meters, then [0, UncertainIndex) is a determined [temporary] station sequence, and [UncetainIndex, current) is an undetermined road sequence. The previous of the determined sequence points to the reference of the previous sequence, and next points to the reference of the next sequence; the previous of the undetermined sequence points to the reference of the previous sequence. Update the start and end time of each sequence. UncertainIndex takes the subscript of the last cell in the cell zipper table. Copy the cell zipper table in the road section to the undetermined road sequence.

[0057] In another feasible implementation, when the undetermined sequence is in a road state, there are several state update situations, namely: road to unknown (road lost) and road to [temporary] station. Among them, the situation of road to unknown (road lost) is specifically: the start time of the current point and the end time of the last record in the cell zipper table differ by more than 60 minutes, forcing the undetermined sequence to be transformed into a determined road sequence. The current point becomes an undetermined unknown state sequence, uncertainIndex is updated to 0, and the current point is added to the undetermined cell zipper table. The situation of road to [temporary] station is specifically: starting from UncertainIndex, traverse the cell zipper table in sequence. If the eci of the current point is the same as the eci of the traversed index cell, and the time difference exceeds 10 minutes, then a determined road sequence is generated from the range of [0, Index) of the cell zipper table, and the previous of the sequence points to the reference of the previous sequence, and next points to the reference of the next sequence. [index, current) generates an undetermined [temporary] stationary point sequence, and copies the cell zipper list of [index, current) to the new undetermined sequence, and UncertainIndex takes the subscript of the last cell in the cell zipper list.

[0058] In this way, the embodiment of the present application analyzes the user's signaling sequence data by utilizing big data technology to obtain an analysis model for determining the user's movement state when a network abnormal event occurs, and obtains an analysis model for the user to determine the wireless cells occupied by the network abnormal event that occurs when the user is in the resident state and the switch-in and switch-out relationship, thereby determining the user's movement state and user location when the network abnormal event occurs, so as to establish a correlation between the network abnormal event and the user's permanent area, and then count the number of abnormal events in the permanent buildings, and construct a list of abnormal events for each permanent building, thereby realizing active and continuous monitoring of abnormal events in the building. Compared with the traditional method of relying on user complaints or regular inspections, it has the advantages of short cycle, low resource consumption and high efficiency.

[0059] The present application provides a building abnormal event identification device based on signaling data, and the building abnormal event identification device based on signaling data includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the building abnormal event identification method based on signaling data in the above-mentioned embodiment one.

[0060] Reference below Figure 6 , which shows a schematic diagram of the structure of a signaling data-based building abnormal event identification device suitable for implementing the embodiment of the present application. The signaling data-based building abnormal event identification device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The signaling data-based building abnormal event identification device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0061] like Figure 6As shown, the building abnormal event identification device based on signaling data may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the building abnormal event identification device based on signaling data are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the building abnormal event identification device based on signaling data to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a building abnormal event identification device based on signaling data with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0062] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0063] The signaling data-based building abnormal event identification device provided by the present application adopts the signaling data-based building abnormal event identification method in the above-mentioned embodiment, which can solve the technical problem of how to improve the efficiency of troubleshooting the building coverage problem of the network signal. Compared with the prior art, the beneficial effects of the signaling data-based building abnormal event identification device provided by the present application are the same as the beneficial effects of the signaling data-based building abnormal event identification method provided in the above-mentioned embodiment, and the other technical features of the signaling data-based building abnormal event identification device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0064] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0065] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0066] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the signaling data-based building abnormal event identification method in the above-mentioned embodiment.

[0067] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0068] The above-mentioned computer-readable storage medium may be included in the building abnormal event identification device based on signaling data; or it may exist independently without being assembled into the building abnormal event identification device based on signaling data.

[0069] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the building abnormal event identification device based on signaling data, the building abnormal event identification device based on signaling data: for any target user among multiple users, determine the network abnormal event and the target time of the occurrence of the network abnormal event based on the signaling data of the target user; determine the movement state and user position of the target user at the target time, and when the movement state is a resident state and the user position is the preset permanent position corresponding to the target user, determine the permanent building where the user position is located; based on the network abnormal events of each of the users, construct an abnormal event list of the permanent buildings corresponding to each of the network abnormal events.

[0070] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0071] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0072] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0073] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned building abnormal event identification method based on signaling data, and can solve the technical problem of how to improve the efficiency of troubleshooting network signal building coverage problems. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the building abnormal event identification method based on signaling data provided in the above-mentioned embodiment, and will not be repeated here.

[0074] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for identifying abnormal building events based on signaling data.

[0075] The computer program product provided by the present application can improve the efficiency of troubleshooting network signal building coverage problems. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as the beneficial effects of the building abnormal event identification method based on signaling data provided by the above embodiment, which will not be repeated here.

[0076] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A method for identifying abnormal building events based on signaling data, characterized in that: The method for identifying abnormal building events based on signaling data includes: For any target user among the multiple users, determining a network abnormal event and a target time when the network abnormal event occurs based on signaling data of the target user; Determine the motion state and user location of the target user at the target time, and if the motion state is a resident state and the user location is a preset resident location corresponding to the target user, determine the resident building where the user location is located; Based on the network abnormal events of each of the users, an abnormal event list of the resident buildings corresponding to each of the network abnormal events is constructed.

2. The method according to claim 1, characterized in that Before the step of determining a network abnormality event and a target time at which the network abnormality event occurs based on the signaling data of the target user, the method further includes: Monitoring signaling data of the target user; The state sequence of the target user is updated based on the signaling data, wherein the state sequence records the location of the target user at each historical time point.

3. The method according to claim 2, characterized in that The step of determining the motion state and user position of the target user at the target time comprises: determining a user position of the target user at the target time based on the state sequence; The motion state of the target user at the target time is determined based on the target time, the user position and the state sequence.

4. The method according to claim 3, characterized in that The step of determining the motion state of the target user at the target time based on the target time, the user position and the state sequence comprises: Determine a duration period during which the target user is located at the user position based on the state sequence, and determine the duration of the duration period, wherein the end time of the duration period is the target time; The motion state of the target user at the target moment is determined based on the duration.

5. The method according to claim 4, characterized in that The resident state includes a long-term resident state and a temporary resident state. The step of determining the motion state of the target user at the target time based on the duration includes: When the duration is greater than or equal to a first preset duration, determining that the target user's motion state at the target time is a long-term resident state; When the duration is greater than or equal to a second preset duration and less than the first preset duration, it is determined that the motion state of the target user at the target moment is a temporary stay state, wherein the first preset duration is greater than the second preset duration.

6. The method according to claim 4, characterized in that The motion state also includes an unknown state, and the step of determining the motion state of the target user at the target time based on the duration includes: When the duration is less than the second preset duration, it is determined that the motion state of the target user at the target moment is an unknown state, and the process returns to the step of monitoring the signaling data of the target user.

7. The method according to any one of claims 1 to 6, characterized in that The step of constructing an abnormal event list of a resident building corresponding to each abnormal network event based on each abnormal network event of each user includes: For any target resident building among the resident buildings corresponding to each of the network abnormal events, determine a target abnormal event occurring in the target resident building among the network abnormal events of each of the users; An abnormal event list of the target resident building is constructed based on the target abnormal event and preset identification information of the target resident building.

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