Network coverage blind spot identification method and device and computer readable storage medium

Abstract

The embodiment of the invention provides a network coverage blind spot identification method and device, and a computer readable storage medium. The method can comprise the following steps: acquiring extended detailed record XDR data of vehicle-mounted terminals under a plurality of cells in a target area within a preset time period; according to the XDR data of the vehicle-mounted terminals under the plurality of cells in the preset time period, determining a network connection state change event of each cell in the plurality of cells in the preset time period; carrying out statistics on the network connection state change events of each cell in the preset time period; and according to the statistical information of the network connection state change event of each cell in the preset time period, screening out a target cell with an underground parking lot network coverage blind spot from the plurality of cells. Therefore, the cost of identifying the underground parking lot network coverage blind spots can be reduced, and the efficiency and accuracy of identifying the underground parking lot network coverage blind spots can be improved.

Description

Technical Field

[0001] This document relates to the field of communication technology, and in particular to a method, device and computer-readable storage medium for identifying network coverage blind spots. Background Technology

[0002] Underground parking lots serve as essential facilities for important locations such as residential areas and high-speed rail stations, resulting in high user density. Furthermore, the rapid development of connected vehicle services, such as remote control, vehicle monitoring, and in-car entertainment, places higher demands on network coverage in underground parking lots. However, network blind spots frequently exist in underground parking lots, impacting user experience.

[0003] Traditional methods for identifying network coverage blind spots rely primarily on on-site testing and manual analysis, which are costly, inefficient, and unable to comprehensively cover all underground parking lots. Therefore, there is an urgent need to propose a superior solution for identifying network coverage blind spots in underground parking lots. Summary of the Invention

[0004] This application provides a method, device, and computer-readable storage medium for identifying network coverage blind spots, in order to overcome at least one problem existing in current underground parking lot network coverage blind spot identification schemes.

[0005] To solve the above-mentioned technical problems, the embodiments of this application are implemented as follows: Firstly, a method for identifying network coverage blind spots is provided, the method comprising: Obtain extended detailed XDR data of vehicle terminals in multiple cells within the target area within a preset time period; Based on the XDR data of the vehicle-mounted terminals under the multiple cells within the preset time period, determine the network connection status change event that occurred in each of the multiple cells within the preset time period. The network connection status change events that occur in each cell within the preset time period are statistically analyzed. Based on the statistical information of network connection status change events that occurred in each cell within the preset time period, target cells with network coverage blind spots in underground parking lots are selected from the multiple cells.

[0006] Secondly, a network coverage blind spot identification device is provided, the device comprising: The first data acquisition module is used to acquire extended detailed record (XDR) data of vehicle terminals in multiple cells within the target area within a preset time period; The event determination module is used to determine, based on the XDR data of the vehicle-mounted terminal under the multiple cells within the preset time period, the network connection status change event that occurred in each of the multiple cells within the preset time period; The event statistics module is used to collect statistics on the network connection status change events that occur in each cell within the preset time period; The cell filtering module is used to filter out target cells with network coverage blind spots in underground parking lots from the multiple cells based on statistical information of network connection status change events that occur in each cell within the preset time period.

[0007] Thirdly, an electronic device is provided, comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in the first aspect.

[0008] Fourthly, a computer-readable storage medium is provided, wherein when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in the first aspect.

[0009] Fifthly, a computer program product including instructions is provided, characterized in that when a computer executes the instructions of the computer program product, the computer performs the method as described in the first aspect.

[0010] In this embodiment, extended detailed record (XDR) data of vehicle terminals under multiple cells in a target area can be obtained within a preset time period. Based on the XDR data of the vehicle terminals under the multiple cells within the preset time period, network connection status change events occurring in each of the multiple cells within the preset time period are determined. Then, based on the statistical information of the network connection status change events occurring in each cell within the preset time period, target cells with network coverage blind spots in underground parking lots are automatically selected from the multiple cells without manual intervention. Furthermore, since the network connection status change events occurring in each cell in the target area within the preset time period can be statistically determined based on the XDR data, and the network connection status change events of the vehicle terminals can accurately reflect the network coverage situation, the cost of identifying network coverage blind spots in underground parking lots can be reduced, and the efficiency and accuracy of identifying network coverage blind spots in underground parking lots can be improved. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating a network coverage blind spot identification method provided in an embodiment of this application.

[0013] Figure 2 This is a flowchart illustrating a network coverage blind spot identification method provided in an embodiment of this application.

[0014] Figure 3 This is a flowchart illustrating a network coverage blind spot identification method provided in an embodiment of this application.

[0015] Figure 4 This is a flowchart illustrating a network coverage blind spot identification method provided in an embodiment of this application.

[0016] Figure 5 This is a schematic diagram illustrating the principle of a network coverage blind spot identification method provided in an embodiment of this application.

[0017] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0018] Figure 7 This is a schematic diagram of the structure of a network coverage blind spot identification device provided in an embodiment of this application.

[0019] Figure 8 This is a schematic diagram of the structure of a network coverage blind spot identification device provided in an embodiment of this application.

[0020] Figure 9 This is a schematic diagram of the structure of a network coverage blind spot identification device provided in an embodiment of this application.

[0021] Figure 10 This is a schematic diagram of the structure of a network coverage blind spot identification device provided in an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in the embodiments of this application, the technical solutions in one or more embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the protection scope of this document.

[0023] The terms "first," "second," etc., used in this application and claims are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in this application and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0024] To address at least one problem with existing network coverage blind spot identification schemes in underground parking lots, this application proposes a network coverage blind spot identification method, system, device, and computer-readable storage medium. The method can be executed by an electronic device or software installed in an electronic device. The electronic device includes, but is not limited to, any of the following smart devices: smartphones, personal computers (PCs), laptops, tablets, e-readers, smart TVs, wearable devices, etc.

[0025] The following description, in conjunction with the accompanying drawings, illustrates a method for identifying network coverage blind spots provided in an embodiment of this application.

[0026] like Figure 1 As shown, one embodiment of this application provides a method for identifying network coverage blind spots, which may include: Step 101: Obtain extended detailed record (XDR) data of vehicle terminals in multiple cells within the target area during a preset time period.

[0027] The target area can be any area where blind spots in the underground parking network coverage need to be screened. For example, the target area can be a city or a specific administrative district of a city.

[0028] Several cells may be deployed within the target area. These cells may be all the cells within the target area or only a portion of the cells within the target area.

[0029] The vehicle-mounted terminal in a cell can be any vehicle-mounted terminal connected to that cell. In this embodiment, the vehicle-mounted terminal can also be considered a networked vehicle.

[0030] The preset time period can be a historical period closest to the current time, such as the past week, the past half month, or the past month. The preset time period can be a continuous period or include multiple discontinuous periods.

[0031] In this embodiment, the Extended Detail Record (XDR) data of the vehicle terminal within a preset time period can be XDR data of the vehicle terminal obtained based on Deep Packet Inspection (DPI) technology. It can be understood that XDR data expands upon traditional call detail records (CDRs) (such as call duration and data consumption) by incorporating more dimensions of user behavior data. Combined with DPI technology, it can generate more granular CDR records, thus enabling precise analysis of network usage scenarios.

[0032] XDR data from vehicle terminals obtained using DPI technology records comprehensive information about the network behavior of vehicle terminals, including offline events and network registration events, which can lay the foundation for the analysis of the network behavior of vehicle terminals.

[0033] In this embodiment, the vehicle-mounted terminal can be marked using the Access Point Name (APN), and XDR data of the vehicle-mounted terminal can be collected from massive core network DPI data. Subsequently, in-depth analysis of the XDR data of the vehicle-mounted terminal is performed to identify network coverage blind spots in underground parking lots.

[0034] As an example, if the network type covering a cell in the target area includes both 4G and 5G networks, then the XDR data of that cell within a preset time period may include 4G and 5G core network signaling. Specifically, the XDR data of that cell within the preset time period may include: 4G S1-MME interface signaling and 5G N1 / N2 interface signaling, mainly including signaling for registration, handover, service requests, and service release.

[0035] Specifically, for a cell within the target area, the DPI system can first identify and construct a database of vehicle-mounted terminals accessing that cell using information such as APN and host. This database can then be used to identify and filter XDR data generated by vehicle-mounted terminals within the DPI XDR call detail records of all terminals. Here, "host" refers to a field in the HTTP protocol header used to specify the target domain name (or hostname) requested by the client, SNI (Server Name Indication), and other information.

[0036] Step 102: Based on the XDR data of the vehicle-mounted terminals under the multiple cells within the preset time period, determine the network connection status change event that occurred in each of the multiple cells within the preset time period.

[0037] Step 103: Statistically analyze the network connection status change events that occur in each cell within the preset time period.

[0038] Step 104: Based on the statistical information of network connection status change events that occurred in each cell within the preset time period, select target cells with network coverage blind spots in underground parking lots from the multiple cells. In some embodiments, the network connection status change event may include, but is not limited to, at least one of abnormal network disconnection related events and network registration events of the vehicle terminal, wherein the abnormal network disconnection related events include network disconnection related events caused by deterioration of network coverage.

[0039] When a vehicle equipped with an onboard terminal enters a network coverage blind spot in a residential area, a network disconnection event occurs; when the vehicle leaves a network coverage blind spot, a network initial registration event occurs. In other words, network connection status change events occur when a vehicle equipped with an onboard terminal enters or leaves a network coverage blind spot in a residential area. Therefore, based on the massive XDR data of onboard terminals in that residential area, the network connection status change events occurring in that area within a preset time period can be statistically analyzed to identify areas where onboard terminals experience concentrated network disconnections and registrations, thereby screening for network coverage blind spots in underground parking lots within that residential area.

[0040] In some embodiments, the statistical information may include the number of occurrences. Accordingly, step 104 may include: determining the number of times the network connection state change event occurs in each cell within the preset time period; and identifying cells among the plurality of cells whose number of occurrences of the network connection state change event is greater than or equal to a first threshold as target cells with network coverage blind spots in underground parking lots. The first threshold may be set according to actual conditions; for example, the first threshold may be set to 50.

[0041] In one implementation, the network connection state change event includes abnormal network disconnection related events of the vehicle terminal, wherein the abnormal network disconnection related events may include, but are not limited to, at least one of UE context release events, timeout paging events, and session deletion events.

[0042] It is understandable that when a vehicle equipped with an in-vehicle terminal enters an underground parking lot without network coverage, a UE context release event will appear in the XDR data. After the UE context release, the core network will send a large number of paging requests to the offline in-vehicle terminal, but the execution results will time out. Finally, after the in-vehicle terminal has been offline for a period of time (e.g., about 1 hour, measured between 58 minutes and 1 hour and 12 minutes), the core network will perform operations such as session deletion. In this case, an example of XDR data for a cell obtained based on DPI technology can be shown in Table 1 below. Therefore, the existence of network coverage blind spots in a cell can be determined by user abnormal network disconnection events (network disconnection events caused by a sudden deterioration in network quality). For cells with a high number of abnormal network disconnection events, it can be inferred that the underground parking lot in that cell may have a network coverage problem.

[0043] Table 1. List of XDR data examples for each cell

[0044] In Table 1, “SIAP-Radit” refers to S1AP wireless interface timeout / failure, and EMM Cause is a specific code value in the NAS (Non-Access Stratum) protocol layer used to identify the root cause of mobility management process failure.

[0045] As an example, in 4G network XDR data, XDR data caused by abnormal network disconnection due to UE radio connection loss or radio interface procedure failure should be filtered according to the following rules: procedure_type=20, interface=5, and request_cause_specific value is 26 or 21. XDR data caused by inter-system redirection failure can be filtered according to the following rules: 4G UE context release (procedure_type=20 and interface=5), request_cause_specific value is 28, and there are no other signaling (except for paging messages procedure_type=4) within 1 minute after the UE context release.

[0046] As another example, in 5G network XDR data, XDR data causing abnormal network disconnection due to UE radio connection loss or radio interface procedure failure should be filtered according to the following rules: procedure_type=22 (identified as UE Context Release), interface=39 (identified as the XDR was generated by the N1 / N2 interface), and request_cause_specific is 21 or 24. XDR data causing abnormal network disconnection due to inter-system redirection failure can be filtered according to the following rules: 4G UE context release (procedure_type=22 and interface=39), request_cause_specific is 41, and there are no other signaling messages (except for paging messages procedure_type=4) within 1 minute after the UE context release.

[0047] In another implementation, the network connection state change event includes network registration events of the vehicle-mounted terminal. It is understood that, considering the behavior patterns of vehicle-mounted terminals, when a vehicle equipped with a vehicle-mounted terminal is driving normally during the day and passes through an area with network coverage, information about the N1 / N2 ports can be found in the XDR data. However, this information is not found in the early morning, and a large number of network registration events occur in the cell between 7:00 AM and 10:00 AM the following morning. In this case, an example of XDR data for a cell obtained based on DPI technology can be shown in Table 2 below. This indicates that vehicles returning to the underground parking lot at night experience network disconnection due to lack of network coverage, and a large number of initial network registration events are generated in the morning when the vehicle leaves the underground parking lot. For cells with a high number of initial network registration events, it can be inferred that the underground parking lot within that cell may have a network coverage problem.

[0048] Table 2. List of XDR data examples for each cell

[0049] In Table 2, ESM Cause is a code value in the NAS (Non-Access Stratum) protocol layer specifically used to identify the root cause of a session management process failure.

[0050] As an example, in the XDR data of a 4G network, network registration events that occur in clusters can be filtered according to the following rules: interface=5, and within one hour before procedure_type=1, there is no signaling except for paging messages procedure_type=4.

[0051] As another example, in the XDR data of 5G networks, network registration events that occur in clusters can be filtered according to the following rules: interface=39, and within one hour before procedure_type=1, there is no signaling except for paging messages procedure_type=4.

[0052] Figure 1 The embodiment shown proposes a network coverage blind spot identification method, which can acquire extended detailed record (XDR) data of vehicle terminals under multiple cells in a target area within a preset time period. Based on the XDR data of the vehicle terminals under the multiple cells within the preset time period, the method determines the network connection status change events that occurred in each of the multiple cells within the preset time period. Then, based on the statistical information of the network connection status change events that occurred in each cell within the preset time period, the method automatically filters out the target cells with network coverage blind spots in underground parking lots from the multiple cells. This method requires no manual intervention. Since the XDR data can statistically identify the network connection status change events that occurred in each cell in the target area within the preset time period, and the network connection status change events of the vehicle terminals can accurately reflect the network coverage situation, the method can reduce the cost of identifying network coverage blind spots in underground parking lots and improve the efficiency and accuracy of identifying network coverage blind spots in underground parking lots. In other words, it can effectively reduce the cost of discovering network coverage blind spots in underground parking lots and effectively improve the efficiency and accuracy of discovering network coverage blind spots in underground parking lots.

[0053] Optionally, such as Figure 2 As shown, prior to step 103 above, the network coverage blind spot identification method provided in this application embodiment may further include: Step 105: Obtain the measurement report (MR) data of the vehicle-mounted terminals under the multiple cells within the preset time period; Step 106: Based on the identification information of the vehicle terminal, the occurrence time information of the XDR data, and the reporting time information of the MR data, associate the XDR data and MR data of the vehicle terminal in each cell within the preset time period.

[0054] Typically, the MR data from a vehicle-mounted terminal can include measurement report reporting time, user information, cell information, measurement report type (periodic or event-based), current signal quality, current uplink interference, TA value, signal quality of the local cell, signal quality of neighboring cells, and signal quality of neighboring cells, etc.

[0055] As an example, the association between the XDR data and MR data of the vehicle terminal in each cell within the preset time period can be achieved by matching the quintuple information in the XDR data and MR data, as well as the occurrence time information of the XDR data and the reporting time information of the MR data. For example, in a 5G network, the five-tuple information may include AMF_REGION_ID (AMF region identifier), AMF_SET_ID (AMF set identifier), AMF_POINTER_ID (AMF pointer identifier), and AMF_UE_NGAP_ID (UE NGAP (unique temporary identifier) ​​assigned by the AMF), where AMF refers to the Access and Mobility Management (AMF) function of the core network. In a 4G network, the five-tuple information may include Public Land Mobile Network ID (PLMN ID), MME Group ID (MME_GROUP_ID), MME-side UE S1AP Identity (MME_UE_S1AP_ID), Temporary Mobile Subscriber ID (M_TMSI), and MME Equipment Code (MME_CODE).

[0056] Optionally, such as Figure 3 As shown, before step 103, after associating the XDR data and MR data of the vehicle-mounted terminal in each cell within the preset time period, the network coverage blind spot identification method provided in this application embodiment may further include: Step 107: Based on the XDR data and MR data of the vehicle terminal in each cell within the preset time period, exclude the network connection state change events that meet the first condition that occur in each cell within the preset time period. The MR data includes the signal quality characterization value of the vehicle terminal, and the first condition includes that the signal quality characterization value of the vehicle terminal when the network connection state change event occurs meets the expected value.

[0057] The signal quality characterization values ​​may include, but are not limited to, at least one of Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), and Signal-to-Noise Ratio (SNR). A signal quality characterization value meeting expectations typically means that the signal quality characterization value is greater than or equal to a threshold; for example, RSRP meeting expectations may mean that RSRP is greater than or equal to an RSRP threshold.

[0058] It is understood that the signal quality characterization value when the network connection status change event occurs in the vehicle terminal meets the expected value, indicating that the occurrence of the network connection status change event is not caused by a deterioration in network quality, but may be caused by other reasons, so it can be excluded (i.e. not included in the statistics in step 103). This can further increase the accuracy of subsequent screening of target cells with network coverage blind spots in underground parking lots from the multiple cells based on the statistical information of the network connection status change events that occur in each cell within the preset time period.

[0059] Optionally, such as Figure 4 As shown, after associating the XDR data and MR data of the vehicle-mounted terminal in each cell within the preset time period, the network coverage blind spot identification method provided in this application embodiment may further include: Step 108: Based on the XDR data and MR data of the vehicle-mounted terminal in the target cell within the preset time period, determine the most recent MR data of the vehicle-mounted terminal in the target cell before the network connection status change event occurred, wherein the MR data includes the coordinate information of the vehicle-mounted terminal.

[0060] Step 109: Based on the coordinate information of the vehicle terminal contained in the most recent MR data, determine the location information of the network coverage blind spot in the underground parking lot in the target cell.

[0061] The coordinate information of the vehicle terminal contained in the MR data can be the latitude and longitude information of the vehicle terminal.

[0062] Specifically, the process can begin by determining the most recent MR data point of the vehicle-mounted terminal in the target cell before the network connection status change event occurred, based on the correlation results of the XDR and MR data within the preset time period. Then, based on the coordinate information of the vehicle-mounted terminal contained in the most recent MR data point, the location information of the vehicle-mounted terminal in the target cell at the time of the network connection status change event is determined. Finally, based on the location information of the vehicle-mounted terminal in the target cell at the time of the network connection status change event, the location information of the network coverage blind spot in the underground parking lot within the target cell is determined. Typically, the location of the vehicle-mounted terminal in the target cell at the time of the network connection status change event is the boundary location of the network coverage blind spot in the underground parking lot within the target cell.

[0063] It is understandable that by associating the XDR data and MR data of the vehicle terminal with the occurrence time of the XDR data and the reporting time of the MR data, the attenuation degree of the signal quality characterization value reflecting the signal quality in the MR data can be used to help verify the coverage gaps in the underground parking lot. Furthermore, based on the latitude and longitude information of the vehicle terminal carried in the MR data, the location of each abnormal network disconnection event can be determined, thereby determining the approximate location of the underground parking lot with network coverage blind spots.

[0064] In some embodiments, after determining the location information of the network coverage blind spot in the underground parking lot of the target cell based on the coordinate information of the vehicle terminal contained in the most recent MR data, the network coverage blind spot identification method provided in this application embodiment may further include: Based on the location information of the underground parking network coverage blind spots in the target community, determine the distribution information of the underground parking network coverage blind spots in the grid of the target community; Based on the statistical information of network connection status change events that occurred in the target grid within the preset time period, and the signal quality characterization value before the network connection status change event occurred, the network coverage score of the target grid is determined. The target grid is a grid in the target cell that has network coverage blind spots in underground parking lots. The signal quality characterization value before the network connection status change event occurred is obtained from the most recent MR data before the network connection status change event occurred. Based on the network coverage score of the target grid, the target grids within the target cell are sorted and output, specifically in ascending order.

[0065] The statistical information of the network connection status change events occurring in the target grid within the preset time period may include, but is not limited to, at least one of the following: The number of network connection status change events that occur in the target grid within the preset time period, such as the number of abnormal disconnection-related events and the number of network registration events; The number of users whose network connection status change events occurred within the preset time period for the target grid.

[0066] The signal quality characterization value of the target grid before the network connection status change event occurs within the preset time period may include: the RSRP level value of the target grid before the network connection status change event occurs within the preset time period.

[0067] In this embodiment, a higher network coverage score for a grid indicates better network quality for that grid, and vice versa. It can be understood that for a target cell with network coverage blind spots, dividing it into grids, calculating the network coverage score for each grid, and outputting the scores in ascending order allows network maintenance personnel to identify grids with worse network coverage as early as possible, thereby pinpointing underground parking lots where network coverage is unsatisfactory, and prioritizing improvements to the network coverage of these underground parking lots.

[0068] In other embodiments, after determining the location information of the network coverage blind spot in the underground parking lot within the target cell based on the coordinate information of the vehicle terminal contained in the most recent MR data, the network coverage blind spot identification method provided in this application embodiment may further include: Based on the statistical information of the network connection status change events that occurred in the target cell within the preset time period, and the signal quality characterization value before the network connection status change event occurred, the network coverage score of the target cell is determined, wherein the signal quality characterization value before the network connection status change event occurred is obtained from the most recent MR data before the network connection status change event occurred. Based on the network coverage score of the target cell, the target cells within the target cell are sorted and output, specifically in ascending order.

[0069] The statistical information on network connection status change events occurring in the target cell within the preset time period may include, but is not limited to, at least one of the following: The number of network connection status change events that occur in the target cell within the preset time period, such as the number of abnormal network disconnection related events and the number of network registration events; The number of users in the target cell whose network connection status change events occurred within the preset time period.

[0070] The signal quality characterization value of the target cell before the network connection state change event occurs within the preset time period may include: the RSRP level value of the target cell before the network connection state change event occurs within the preset time period.

[0071] Similarly, in this embodiment, a higher network coverage score for a cell indicates better network quality, and vice versa. It is understood that calculating the network coverage score of a target cell with network coverage blind spots and outputting it in ascending order allows network maintenance personnel to identify target cells with worse network coverage as early as possible, thereby pinpointing underground parking lots within that cell where network coverage does not meet requirements, and thus prioritizing improvements to the network coverage of these underground parking lots.

[0072] like Figure 5 As shown in the embodiments of this application, a network coverage blind spot identification method can use 4G / 5G XDR data as the data source, or use 4G / 5G XDR data and MR data as the data source, to achieve active identification of network coverage blind spots in underground parking lots at least three levels: 1) Cell-level: Based on network connection status change events, identify cell-level areas with network coverage blind spots in underground parking lots.

[0073] 2) Grid level: Based on network connection status change events, combined with MR data to mine network coverage blind spots in underground parking lots, the grids of cells with network coverage blind spots are identified.

[0074] 3) Community level / grid level / physical location point level: Provide network coverage scores for blind spots in underground parking lots and output them in order.

[0075] This provides strong data support for optimizing network deployment.

[0076] Optionally, the network coverage blind spot identification method provided in this application embodiment may further include: Obtain base station deployment information within the target area; Based on the base station deployment information, the type of network coverage blind spot in the underground parking lot corresponding to the target cell is determined and output to facilitate targeted rectification.

[0077] The base station deployment information within the target area may include the type of base station deployed within the target area, such as 4G base stations or 5G base stations.

[0078] If the type of base station deployed in the target area can include 4G base stations or 5G base stations, the type of network coverage blind spot in the underground parking lot can include, but is not limited to, at least one of the following: It has 4G signal, but no 5G signal; It has 5G signal but no 4G signal. There is no network signal.

[0079] This application proposes a method for identifying network coverage blind spots, which can be used by communication network operators (hereinafter referred to as operators) to identify network coverage blind spots in underground parking lots. Through this method, operators can periodically or non-periodically identify underground parking lots without network coverage and push them to the network side for proactive optimization or base station additions, reducing the risk of user complaints regarding coverage issues in underground parking lots.

[0080] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 6 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0082] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0083] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0084] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a network coverage blind spot identification device at the logical level. The processor executes the program stored in memory and specifically performs the following operations: Obtain extended detailed XDR data of vehicle terminals in multiple cells within the target area within a preset time period; Based on the XDR data of the vehicle-mounted terminals under the multiple cells within the preset time period, determine the network connection status change event that occurred in each of the multiple cells within the preset time period. The network connection status change events that occur in each cell within the preset time period are statistically analyzed. Based on the statistical information of network connection status change events that occurred in each cell within the preset time period, target cells with network coverage blind spots in underground parking lots are selected from the multiple cells.

[0085] The above is as stated in this application. Figure 6The method executed by the network coverage blind spot identification device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0086] The electronic device can also perform Figures 1 to 4 The method shown in any of the attached figures is used to implement a network coverage blind spot identification device. Figure 6 The functions described in the illustrated embodiments will not be repeated here.

[0087] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0088] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by a portable electronic device including multiple target applications, enable the portable electronic device to perform... Figure 1 The method of the illustrated embodiment is specifically used for execution. Figures 1 to 4 The method shown in any of the accompanying figures.

[0089] This application also proposes a computer program product including instructions, wherein when a computer runs the instructions of the computer program product, the computer performs, as follows: Figures 1 to 4 The method described in any of the accompanying figures.

[0090] Figure 7 This is a schematic diagram of the structure of a network coverage blind spot identification device 700 according to an embodiment of this application. Please refer to... Figure 7 In one software implementation, the first network coverage blind spot identification device 700 may include: a first data acquisition module 701, an event determination module 702, an event statistics module 703, and a cell screening module 704.

[0091] The first data acquisition module 701 is used to acquire extended detailed record (XDR) data of vehicle terminals in multiple cells within the target area within a preset time period.

[0092] The target area can be any area where blind spots in the underground parking network coverage need to be screened. For example, the target area can be a city or a specific administrative district of a city.

[0093] Several cells may be deployed within the target area. These cells may be all the cells within the target area or only a portion of the cells within the target area.

[0094] The vehicle-mounted terminal in a cell can be any vehicle-mounted terminal connected to that cell. In this embodiment, the vehicle-mounted terminal can also be considered a networked vehicle.

[0095] The preset time period can be a historical period closest to the current time, such as the past week, the past half month, or the past month. The preset time period can be a continuous period or include multiple discontinuous periods.

[0096] In this embodiment, the Extended Detail Record (XDR) data of the vehicle terminal within a preset time period can be XDR data of the vehicle terminal obtained based on Deep Packet Inspection (DPI) technology. It can be understood that XDR data expands upon traditional call detail records (CDRs) (such as call duration and data consumption) by incorporating more dimensions of user behavior data. Combined with DPI technology, it can generate more granular CDR records, thus enabling precise analysis of network usage scenarios.

[0097] XDR data from vehicle terminals obtained using DPI technology records comprehensive information about the network behavior of vehicle terminals, including offline events and network registration events, which can lay the foundation for the analysis of the network behavior of vehicle terminals.

[0098] In this embodiment, the vehicle-mounted terminal can be marked using the Access Point Name (APN), and XDR data of the vehicle-mounted terminal can be collected from massive core network DPI data. Subsequently, in-depth analysis of the XDR data of the vehicle-mounted terminal is performed to identify network coverage blind spots in underground parking lots.

[0099] The event determination module 702 is used to determine, based on the XDR data of the vehicle-mounted terminal under the multiple cells within the preset time period, the network connection status change event that occurred in each of the multiple cells within the preset time period.

[0100] The event statistics module 703 is used to collect statistics on the network connection status change events that occur in each cell within the preset time period.

[0101] The cell filtering module 704 is used to filter out target cells with network coverage blind spots in underground parking lots from the multiple cells based on statistical information of network connection status change events that occur in each cell within the preset time period.

[0102] In some embodiments, the network connection status change event may include, but is not limited to, at least one of abnormal network disconnection related events and network registration events of the vehicle terminal, wherein the abnormal network disconnection related events include network disconnection related events caused by deterioration of network coverage.

[0103] When a vehicle equipped with an onboard terminal enters a network coverage blind spot in a residential area, a network disconnection event occurs; when the vehicle leaves a network coverage blind spot, a network initial registration event occurs. In other words, network connection status change events occur when a vehicle equipped with an onboard terminal enters or leaves a network coverage blind spot in a residential area. Therefore, based on the massive XDR data of onboard terminals in that residential area, the network connection status change events occurring in that area within a preset time period can be statistically analyzed to identify areas where onboard terminals experience concentrated network disconnections and registrations, thereby screening for network coverage blind spots in underground parking lots within that residential area.

[0104] In some embodiments, the statistical information may include the number of occurrences. Accordingly, the cell screening module 704 may be used to: determine the number of times the network connection status change event occurs in each cell within the preset time period; and identify cells among the plurality of cells whose number of occurrences of the network connection status change event is greater than or equal to a first threshold as target cells with network coverage blind spots in underground parking lots.

[0105] In one implementation, the network connection state change event includes abnormal network disconnection related events of the vehicle terminal, wherein the abnormal network disconnection related events may include, but are not limited to, at least one of UE context release events, timeout paging events, and session deletion events.

[0106] It is understandable that when a vehicle equipped with an in-vehicle terminal enters an underground parking lot without network coverage, a UE context release event will appear in the XDR data. After the UE context release, the core network will send a large number of paging requests to the offline in-vehicle terminal, but the execution results will time out. Finally, after the in-vehicle terminal has been offline for a period of time (e.g., about 1 hour, measured between 58 minutes and 1 hour and 12 minutes), the core network will perform operations such as session deletion. In this case, an example of XDR data for a cell obtained based on DPI technology can be shown in Table 1 above. Therefore, the existence of network coverage blind spots in a cell can be determined by user abnormal network disconnection events (network disconnection events caused by a sudden deterioration in network quality). For cells with a high number of abnormal network disconnection events, it can be inferred that the underground parking lot in that cell may have a network coverage problem.

[0107] In another implementation, the network connection state change event includes network registration events of the vehicle-mounted terminal. It is understood that, considering the behavior patterns of vehicle-mounted terminals, when a vehicle equipped with a vehicle-mounted terminal is driving normally during the day and passes through an area with network coverage, information about the N1 / N2 ports can be found in the XDR data. However, this information is not found in the early morning, and a large number of network registration events occur in the cell between 7:00 AM and 10:00 AM the following morning. An example of XDR data for a cell obtained based on DPI technology in this case is shown in Table 2 above. This indicates that vehicles returning to the underground parking lot at night experience network disconnection due to lack of network coverage, and a large number of initial network registration events are generated in the morning when the vehicle leaves the underground parking lot. For cells with a high number of initial network registration events, it can be inferred that the underground parking lot within that cell may have a network coverage problem.

[0108] Figure 7 The network coverage blind spot identification device 700 provided in the illustrated embodiment can also perform... Figure 1 The method, and implementation Figure 1 The embodiments shown in this application have the same functions and achieve the same technical effects, and will not be described in detail here.

[0109] Optionally, such as Figure 8 As shown in the embodiment of this application, a network coverage blind spot identification device 700 may further include: a second data acquisition module 705 and a data association module 706.

[0110] The second data acquisition module 705 is used to acquire measurement report (MR) data of the vehicle-mounted terminals under the multiple cells within the preset time period; The data association module 706 is used to associate the XDR data and MR data of the vehicle terminal in each cell within the preset time period based on the identification information of the vehicle terminal, the occurrence time information of the XDR data and the reporting time information of the MR data.

[0111] Typically, the MR data from a vehicle-mounted terminal can include measurement report reporting time, user information, cell information, measurement report type (periodic or event-based), current signal quality, current uplink interference, TA value, signal quality of the local cell, signal quality of neighboring cells, and signal quality of neighboring cells, etc.

[0112] As an example, the association between the XDR data and MR data of the vehicle terminal in each cell within the preset time period can be achieved by matching the quintuple information in the XDR data and MR data, as well as the occurrence time information of the XDR data and the reporting time information of the MR data.

[0113] Figure 8 The network coverage blind spot identification device 700 provided in the illustrated embodiment can also perform... Figure 2 The method, and implementation Figure 2 The embodiments shown in this application have the same functions and achieve the same technical effects, and will not be described in detail here.

[0114] Optionally, such as Figure 9 As shown, after associating the XDR data and MR data of the vehicle-mounted terminal in each cell within the preset time period, the network coverage blind spot identification device 700 provided in this application embodiment may further include: a data exclusion module 707, used to exclude the network connection state change event that occurs in each cell within the preset time period and meets a first condition based on the XDR data and MR data of the vehicle-mounted terminal in each cell within the preset time period, wherein the MR data includes the signal quality characterization value of the vehicle-mounted terminal, and the first condition includes that the signal quality characterization value of the vehicle-mounted terminal when the network connection state change event occurs meets the expected value.

[0115] The signal quality characterization values ​​may include, but are not limited to, at least one of Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), and Signal-to-Noise Ratio (SNR). A signal quality characterization value meeting expectations typically means that the signal quality characterization value is greater than or equal to a threshold; for example, RSRP meeting expectations may mean that RSRP is greater than or equal to an RSRP threshold.

[0116] It is understood that the signal quality characterization value when the network connection status change event occurs in the vehicle terminal meets the expected value, indicating that the occurrence of the network connection status change event is not caused by a deterioration in network quality, but may be caused by other reasons, so it can be excluded (i.e. not included in the statistics in step 103). This can further increase the accuracy of subsequent screening of target cells with network coverage blind spots in underground parking lots from the multiple cells based on the statistical information of the network connection status change events that occur in each cell within the preset time period.

[0117] Figure 9 The network coverage blind spot identification device 700 provided in the illustrated embodiment can also perform... Figure 3 The method, and implementation Figure 3 The embodiments shown in this application have the same functions and achieve the same technical effects, and will not be described in detail here.

[0118] Optionally, such as Figure 10 As shown, after associating the XDR data and MR data of the vehicle-mounted terminal in each cell within the preset time period, the network coverage blind spot identification device 700 provided in this application embodiment may further include: The data determination module 708 is used to determine the most recent MR data of the vehicle terminal in the target cell before the network connection status change event occurred, based on the XDR data and MR data of the vehicle terminal in the target cell within the preset time period, wherein the MR data includes the coordinate information of the vehicle terminal.

[0119] The location determination module 709 is used to determine the location information of the underground parking lot network coverage blind spot in the target cell based on the coordinate information of the vehicle terminal contained in the most recent MR data.

[0120] The coordinate information of the vehicle terminal contained in the MR data can be the latitude and longitude information of the vehicle terminal.

[0121] Specifically, the process can begin by determining the most recent MR data point of the vehicle-mounted terminal in the target cell before the network connection status change event occurred, based on the correlation results of the XDR and MR data within the preset time period. Then, based on the coordinate information of the vehicle-mounted terminal contained in the most recent MR data point, the location information of the vehicle-mounted terminal in the target cell at the time of the network connection status change event is determined. Finally, based on the location information of the vehicle-mounted terminal in the target cell at the time of the network connection status change event, the location information of the network coverage blind spot in the underground parking lot within the target cell is determined. Typically, the location of the vehicle-mounted terminal in the target cell at the time of the network connection status change event is the boundary location of the network coverage blind spot in the underground parking lot within the target cell.

[0122] In some embodiments, after determining the location information of the network coverage blind spot in the underground parking lot of the target cell based on the coordinate information of the vehicle terminal contained in the most recent MR data, the network coverage blind spot identification method provided in this application embodiment may further include: Based on the location information of the underground parking network coverage blind spots in the target community, determine the distribution information of the underground parking network coverage blind spots in the grid of the target community; Based on the statistical information of network connection status change events that occurred in the target grid within the preset time period, and the signal quality characterization value before the network connection status change event occurred, the network coverage score of the target grid is determined. The target grid is a grid in the target cell that has network coverage blind spots in underground parking lots. The signal quality characterization value before the network connection status change event occurred is obtained from the most recent MR data before the network connection status change event occurred. Based on the network coverage score of the target grid, the target grids within the target cell are sorted and output, specifically in ascending order.

[0123] The statistical information of the network connection status change events occurring in the target grid within the preset time period may include, but is not limited to, at least one of the following: The number of network connection status change events that occur in the target grid within the preset time period, such as the number of abnormal disconnection-related events and the number of network registration events; The number of users whose network connection status change events occurred within the preset time period for the target grid.

[0124] The signal quality characterization value of the target grid before the network connection status change event occurs within the preset time period may include: the RSRP level value of the target grid before the network connection status change event occurs within the preset time period.

[0125] In this embodiment, a higher network coverage score for a grid indicates better network quality for that grid, and vice versa. It can be understood that for a target cell with network coverage blind spots, dividing it into grids, calculating the network coverage score for each grid, and outputting the scores in ascending order allows network maintenance personnel to identify grids with worse network coverage as early as possible, thereby pinpointing underground parking lots where network coverage is unsatisfactory, and prioritizing improvements to the network coverage of these underground parking lots.

[0126] In other embodiments, after determining the location information of the network coverage blind spot in the underground parking lot within the target cell based on the coordinate information of the vehicle terminal contained in the most recent MR data, the network coverage blind spot identification method provided in this application embodiment may further include: Based on the statistical information of the network connection status change events that occurred in the target cell within the preset time period, and the signal quality characterization value before the network connection status change event occurred, the network coverage score of the target cell is determined, wherein the signal quality characterization value before the network connection status change event occurred is obtained from the most recent MR data before the network connection status change event occurred. Based on the network coverage score of the target cell, the target cells within the target cell are sorted and output, specifically in ascending order.

[0127] The statistical information on network connection status change events occurring in the target cell within the preset time period may include, but is not limited to, at least one of the following: The number of network connection status change events that occur in the target cell within the preset time period, such as the number of abnormal network disconnection related events and the number of network registration events; The number of users in the target cell whose network connection status change events occurred within the preset time period.

[0128] The signal quality characterization value of the target cell before the network connection state change event occurs within the preset time period may include: the RSRP level value of the target cell before the network connection state change event occurs within the preset time period.

[0129] Similarly, in this embodiment, a higher network coverage score for a cell indicates better network quality, and vice versa. It is understood that calculating the network coverage score of a target cell with network coverage blind spots and outputting it in ascending order allows network maintenance personnel to identify target cells with worse network coverage as early as possible, thereby pinpointing underground parking lots within that cell where network coverage does not meet requirements, and thus prioritizing improvements to the network coverage of these underground parking lots.

[0130] Figure 10 The network coverage blind spot identification device 700 provided in the illustrated embodiment can also perform... Figure 4 The method, and implementation Figure 4 The embodiments shown in this application have the same functions and achieve the same technical effects, and will not be described in detail here.

[0131] Optionally, the network coverage blind spot identification device 700 provided in this application embodiment may further include: The information acquisition module is used to acquire base station deployment information within the target area; The type determination module is used to determine and output the type of network coverage blind spot in the underground parking lot corresponding to the target cell based on the base station deployment information, so as to facilitate targeted rectification.

[0132] The base station deployment information within the target area may include the type of base station deployed within the target area, such as 4G base stations or 5G base stations.

[0133] If the type of base station deployed in the target area can include 4G base stations or 5G base stations, the type of network coverage blind spot in the underground parking lot can include, but is not limited to, at least one of the following: It has 4G signal, but no 5G signal; It has 5G signal but no 4G signal. There is no network signal.

[0134] This application provides a network coverage blind spot identification device 700, which can be used by communication network operators (hereinafter referred to as operators) to identify network coverage blind spots in underground parking lots. Through this method, operators can periodically or non-periodically identify underground parking lots without network coverage and push them to the network side for proactive optimization or base station additions, reducing the risk of user complaints regarding coverage issues in underground parking lots.

[0135] In summary, the above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0136] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0137] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0138] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0139] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

Claims

1. A method for identifying network coverage blind spots, characterized in that, The method includes: Obtain extended detailed XDR data of vehicle terminals in multiple cells within the target area within a preset time period; Based on the XDR data of the vehicle-mounted terminals under the multiple cells within the preset time period, determine the network connection status change event that occurred in each of the multiple cells within the preset time period. The network connection status change events that occur in each cell within the preset time period are statistically analyzed. Based on the statistical information of network connection status change events that occurred in each cell within the preset time period, target cells with network coverage blind spots in underground parking lots are selected from the multiple cells.

2. The method according to claim 1, characterized in that, The statistical information includes the number of occurrences. Specifically, the step of filtering target cells with network coverage blind spots in underground parking lots from the plurality of cells based on the statistical information of network connection status change events occurring in each cell within the preset time period includes: Determine the number of times the network connection status change event occurs in each cell within the preset time period; Cells among the multiple cells whose network connection status change events occur more than or equal to a first threshold are identified as target cells with network coverage blind spots in underground parking lots.

3. The method according to claim 1, characterized in that, Before statistically analyzing the network connection state change events occurring in each cell within the preset time period, the method further includes: Based on the XDR data and MR data of the vehicle terminal in each cell within the preset time period, network connection status change events that meet the first condition that occur in each cell within the preset time period are excluded. The MR data includes the signal quality characterization value of the vehicle terminal, and the first condition includes that the signal quality characterization value of the vehicle terminal when the network connection status change event occurs meets the expected value.

4. The method according to claim 1, characterized in that, The method further includes: Based on the XDR data and MR data of the vehicle-mounted terminal in the target cell within the preset time period, determine the most recent MR data of the vehicle-mounted terminal in the target cell before the network connection status change event occurs, wherein the MR data includes the coordinate information of the vehicle-mounted terminal; Based on the coordinate information of the vehicle terminal contained in the most recent MR data, the location information of the network coverage blind spot in the underground parking lot in the target cell is determined.

5. The method according to claim 4, characterized in that, The method further includes: Based on the location information of the underground parking network coverage blind spots in the target community, determine the distribution information of the underground parking network coverage blind spots in the grid of the target community; Based on the statistical information of network connection status change events that occurred in the target grid within the preset time period, and the signal quality characterization value before the network connection status change event occurred, the network coverage score of the target grid is determined. The target grid is a grid in the target cell that has network coverage blind spots in underground parking lots. The signal quality characterization value before the network connection status change event occurred is obtained from the most recent MR data before the network connection status change event occurred. Based on the network coverage score of the target grid, the target grids within the target cell are sorted and output.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Obtain base station deployment information within the target area; Based on the base station deployment information, determine and output the type of network coverage blind spot in the underground parking lot corresponding to the target cell.

7. The method according to claim 6, characterized in that, The types of blind spots in the underground parking network coverage include at least one of the following: It has 4G signal, but no 5G signal; It has 5G signal but no 4G signal. There is no network signal.

8. The method according to any one of claims 1-5 and 7, characterized in that, The network connection status change event includes at least one of the following: abnormal network disconnection related events of the vehicle terminal and network registration events, wherein the abnormal network disconnection related events include network disconnection related events caused by deterioration of network coverage.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1 to 8.

11. A computer program product comprising instructions, characterized in that, When the computer executes the instructions of the computer program product, the computer performs the method as described in any one of claims 1 to 8.

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