Underground parking lot weak coverage area scene identification method and device based on vehicle-mounted terminal, and medium

Abstract

The invention discloses an underground parking lot weak coverage area scene identification method and device based on a vehicle-mounted terminal and a medium. The method comprises the following steps: firstly, acquiring MR data of a vehicle-mounted terminal; mapping MR data of the vehicle-mounted terminal to the corresponding longitude and latitude grids according to the longitude and latitude coordinates of the vehicle-mounted terminal; then, according to MR data of the vehicle-mounted terminal, weak coverage longitude and latitude grids are identified; wherein the weak coverage latitude and longitude grid is a latitude and longitude grid corresponding to data with signal quality lower than a preset threshold value in MR data; and finally, the identified weak coverage longitude and latitude grids are matched with the parking scene to determine the weak coverage area of the underground parking lot, so that the scene identification of the weak coverage area of the underground parking lot based on the vehicle-mounted terminal is completed. According to the method, the signal coverage condition of the underground parking lot can be comprehensively, accurately and intelligently identified, and the underground parking lot with poor signal quality is effectively screened, so that the network service is optimized.

Description

Technical Field

[0001] The present invention belongs to the field of network communication technology, and in particular relates to a method, device and medium for scene recognition in a weak coverage area of ​​an underground parking lot based on a vehicle-mounted terminal. Background Art

[0002] With the rapid development of cities, the problem of weak signal coverage in underground parking lots is becoming more and more common. In the prior art, identifying weak coverage areas in underground parking lots mainly relies on on-site network testing.

[0003] Although this method is effective, it also has certain limitations. First, when testing on-site, testers may miss certain locations, resulting in some weak coverage areas not being discovered. In addition, on-site testing is not only time-consuming, but also adds additional testing fees and labor costs. Therefore, it is necessary to seek a more efficient solution to more comprehensively and accurately identify the signal coverage of underground parking lots, thereby optimizing network services. Summary of the invention

[0004] The technical problem to be solved by the present invention is to address the above-mentioned deficiencies in the prior art and to propose a scene recognition method, device and medium for weak coverage areas of underground parking lots based on a vehicle-mounted terminal. The method does not require on-site network testing in each parking lot, and can comprehensively and accurately intelligently identify the signal coverage of underground parking lots, effectively screen underground parking lots with poor signal quality, and thus optimize network services.

[0005] In a first aspect, the present invention provides a method for scene recognition of a weakly covered area of ​​an underground parking lot based on a vehicle-mounted terminal, the method comprising the following steps:

[0006] Obtain vehicle terminal MR data;

[0007] According to the latitude and longitude coordinates of the vehicle terminal, the vehicle terminal MR data is mapped to the corresponding latitude and longitude grid;

[0008] According to the vehicle terminal MR data, the weakly covered latitude and longitude grids are identified;

[0009] Wherein, the weakly covered longitude and latitude grid is the longitude and latitude grid corresponding to the data whose signal quality in the MR data is lower than a preset threshold;

[0010] The identified weak coverage latitude and longitude grids are matched with the ground parking scene to determine the weak coverage area of ​​the underground parking lot, thereby completing the scene recognition of the weak coverage area of ​​the underground parking lot based on the vehicle-mounted terminal.

[0011] Furthermore, the acquisition of vehicle-mounted terminal MR data specifically includes the following steps:

[0012] Step A1: Acquire MR data; and, acquire a vehicle terminal library;

[0013] Step A2: Match the MR data with the device information in the vehicle terminal library through IMEI-TAC to obtain the matched MR data and the matched device information;

[0014] Step A3: Filtering target MR data from the matched MR data through a set time window;

[0015] Step A4: splicing the matched device information, the target MR data, and the UE information to obtain the vehicle-mounted terminal MR data.

[0016] Furthermore, before the step A1, the method further includes a step A0;

[0017] Step A0: Build a vehicle terminal library;

[0018] The step A0 specifically includes the following steps:

[0019] Step A01: Determine whether the MR data can match the initial vehicle terminal library:

[0020] If the MR data can match the initial vehicle terminal library, go to step A03; if the MR data cannot match the initial vehicle terminal library, go to step A02;

[0021] Step A02: Generate second IMEI-TAC information;

[0022] Step A03: Matching the initial vehicle terminal library and the MR data through the first IMEI-TAC to form a first target vehicle terminal library; and matching the initial vehicle terminal library and the MR data through the second IMEI-TAC to form a second target vehicle terminal library;

[0023] The first IMEI-TAC is a unique identifier for identifying the first target device, which is a mobile device stored in the vehicle terminal library when the MR data can match the initial vehicle terminal library; the second IMEI-TAC information is inferred based on the parking time distribution and the number of samples when the second target device is used; the second target device is a mobile device actually used by the vehicle terminal when the MR data cannot match the initial vehicle terminal library;

[0024] Step A04: Merge the first target vehicle-mounted terminal library and the second target vehicle-mounted terminal library to obtain a vehicle-mounted terminal library.

[0025] Further, the signal quality in the MR data is lower than a preset threshold, specifically: the RSRP value in the MR data is lower than a preset threshold;

[0026] The RSRP value in the MR data is lower than a preset threshold, and the identification steps are as follows:

[0027] defining an RSRP preset threshold; and collecting MR data including RSRP values;

[0028] Traverse each MR record and check whether its RSRP value is lower than the RSRP preset threshold:

[0029] If the RSRP value in a certain MR record is lower than the RSRP preset threshold, it is identified that the RSRP value in the MR data is lower than the preset threshold.

[0030] Furthermore, matching the identified weak coverage latitude and longitude grid with the underground parking scene to determine the weak coverage area of ​​the underground parking lot specifically includes the following steps:

[0031] Get the AOI of the underground parking lot;

[0032] Wherein, the underground parking lot is obtained from the scene-based ground parking garage; the scene-based ground parking garage is formed according to the ground parking POI data of the network platform and matched with the building;

[0033] Determine whether the weakly covered latitude and longitude grid is located within the AOI of a certain underground parking lot:

[0034] If the weak coverage longitude and latitude grid is located within the AOI of a certain underground parking lot, the underground parking lot is matched as a weak coverage area scene.

[0035] In a second aspect, the present invention provides a scene recognition device for a weakly covered area of ​​an underground parking lot based on a vehicle-mounted terminal, the device comprising:

[0036] An acquisition unit, used for acquiring MR data of the vehicle-mounted terminal;

[0037] A mapping unit, connected to the acquisition unit, for mapping the vehicle terminal MR data to a corresponding longitude and latitude grid according to the longitude and latitude coordinates of the vehicle terminal;

[0038] An identification unit, connected to the mapping unit, for identifying weakly covered longitude and latitude grids according to the vehicle-mounted terminal MR data;

[0039] Wherein, the weakly covered longitude and latitude grid is the longitude and latitude grid corresponding to the data whose signal quality in the MR data is lower than a preset threshold;

[0040] The matching unit is connected to the identification unit and is used to match the identified weak coverage latitude and longitude grid with the underground parking scene to determine the weak coverage area of ​​the underground parking lot, thereby completing the scene identification of the weak coverage area of ​​the underground parking lot based on the vehicle-mounted terminal.

[0041] Furthermore, the acquisition unit includes:

[0042] A first acquisition module is used to acquire MR data; and, is used to acquire a vehicle terminal library;

[0043] a first matching module, connected to the first acquisition module, for matching the MR data with the device information in the vehicle terminal library through IMEI-TAC to obtain matched MR data and matched device information;

[0044] a screening module, connected to the first matching module, and configured to screen out target MR data from the matched MR data through a set time window;

[0045] The splicing module is connected to the first matching module and the screening module respectively, and is used to splice the matched device information, the target MR data, and the UE information to obtain the vehicle-mounted terminal MR data.

[0046] Furthermore, the acquisition unit further includes a construction module, which is connected to the first acquisition module and is used to construct a vehicle terminal library;

[0047] The building blocks include:

[0048] A determination submodule is used to determine whether the MR data can match the initial vehicle terminal library;

[0049] A first processing submodule, connected to the determination submodule, for matching the initial vehicle terminal library and the MR data through a first IMEI-TAC to form a first target vehicle terminal library when the determination submodule determines that the MR data can match the initial vehicle terminal library;

[0050] The first IMEI-TAC is a unique identifier for identifying the first target device, and the first target device is a mobile device stored in the vehicle terminal library when the MR data can match the initial vehicle terminal library;

[0051] A generating submodule, connected to the determining submodule, for generating second IMEI-TAC information when the determining submodule determines that the MR data cannot match the initial vehicle terminal library;

[0052] A second processing submodule, connected to the generating submodule, is used to match the initial vehicle terminal library and the MR data through a second IMEI-TAC to form a second target vehicle terminal library;

[0053] The second IMEI-TAC information is inferred based on the parking time distribution and the number of samples when the second target device is used; the second target device is the mobile device actually used by the vehicle terminal when the MR data cannot match the initial vehicle terminal library;

[0054] The merging submodule is connected to the first processing submodule and the second processing submodule respectively, and is used to merge the first target vehicle terminal library and the second target vehicle terminal library to obtain a vehicle terminal library.

[0055] Furthermore, the matching unit includes:

[0056] The second acquisition module is used to obtain the AOI of the underground parking lot;

[0057] Wherein, the underground parking lot is obtained from the scene-based ground parking garage; the scene-based ground parking garage is formed according to the ground parking POI data of the network platform and matched with the building;

[0058] A determination module, connected to the second acquisition module, for determining whether the weakly covered longitude and latitude grid is located within the AOI of a certain underground parking lot;

[0059] The second matching module is connected to the determination module, and is used to determine that the underground parking lot is a weak coverage area scene when the determination module determines that the weak coverage longitude and latitude grid is located within the AOI of a certain underground parking lot.

[0060] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the underground parking lot weak coverage area scene recognition method based on a vehicle-mounted terminal as described in the first aspect.

[0061] The present invention does not require on-site network testing in every parking lot, and can comprehensively and accurately intelligently identify the signal coverage of underground parking lots, effectively screen underground parking lots with poor signal quality, and thus optimize network services. The specific effective effects are as follows:

[0062] 1. Accurate positioning: The present invention obtains the precise position through the vehicle-mounted terminal, making the recognition process more accurate and improving the reliability of scene recognition.

[0063] 2. Real-time monitoring: The present invention collects MR data in real time through the vehicle-mounted terminal, promptly identifies weak coverage areas, ensures dynamic updating of the situation in the parking lot, and enhances the responsiveness of the system.

[0064] 3. Automatic identification: The present invention automatically identifies the latitude and longitude grids with signal quality below a preset threshold as weak coverage areas, reducing manual intervention and improving work efficiency.

[0065] 4. Efficient matching: The present invention can quickly determine the weak coverage area of ​​the underground parking lot by matching the weak coverage area with the ground parking scene, thereby optimizing the parking management and navigation system.

[0066] 5. Strong adaptability: The present invention can adapt to the complexity of different underground parking environments, thus enhancing the universality of the system.

[0067] 6. Data-driven decision making: The present invention utilizes the collected MR data to support in-depth analysis and decision making, promote intelligent management of underground parking lots, and improve operational efficiency.

[0068] 7. Resource optimization configuration: By identifying weak coverage areas, the present invention can optimize base station layout or improve signal coverage in a targeted manner, thereby improving the efficiency of infrastructure utilization.

[0069] 8. Improve user experience: By identifying and optimizing weak coverage areas, the present invention will significantly improve the user's navigation and positioning experience in underground parking lots, thereby increasing user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is an overall framework diagram of scene recognition of weak coverage areas in underground parking lots based on vehicle-mounted terminals in an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram of scene recognition of a weak coverage area in an underground parking lot based on a vehicle-mounted terminal in an embodiment of the present invention;

[0072] Figure 3 Schematic diagram of fast decay region assessment in an embodiment of the present invention;

[0073] Figure 4 Schematic diagram of a scene recognition device for a weakly covered area of ​​an underground parking lot based on a vehicle-mounted terminal in an embodiment of the present invention;

[0074] Figure 5 is a MR data path diagram in an embodiment of the present invention;

[0075] Figure 6 This is a flow chart of scene recognition of weak coverage areas in underground parking lots based on a vehicle-mounted terminal in an embodiment of the present invention;

[0076] Among them, the figure numerals are: 10, acquisition unit, 20, mapping unit, 30, identification unit, 40, matching unit. DETAILED DESCRIPTION

[0077] In order to enable those skilled in the art to better understand the technical solution of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0078] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0079] It can be understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other.

[0080] It can be understood that, for the convenience of description, the drawings of the present invention only show the parts related to the present invention, while the parts irrelevant to the present invention are not shown in the drawings.

[0081] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0082] It can be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0083] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based system that implements the specified functions, or may be implemented by a combination of hardware and computer instructions.

[0084] It can be understood that the units and modules involved in the embodiments of the present invention can be implemented by software or hardware. For example, the units and modules can be located in a processor.

[0085] Embodiment 1:

[0086] like Figure 1 and Figure 2 As shown, this embodiment provides a scene recognition method for a weak coverage area of ​​an underground parking lot based on a vehicle-mounted terminal, and the method includes the following steps:

[0087] Step S1: Acquire vehicle-mounted terminal MR data.

[0088] In the context of vehicle terminals, MR data usually refers to information related to driving status, environmental perception, vehicle dynamics, etc. MR can be expressed as "measurement records" or "driving records". These data help analyze and optimize vehicle performance, user experience, and safety. Specifically, MR data of vehicle terminals may include:

[0089] 1. Vehicle dynamic data

[0090] Speed: Real-time and average speed data.

[0091] Acceleration: The acceleration and deceleration data of the vehicle.

[0092] Directions: Direction and turn information from the vehicle's navigation system.

[0093] 2. Environmental perception data

[0094] Sensor data: Environmental data from lidar, cameras, ultrasonic sensors, etc.

[0095] Obstacle recognition: recognition information of surrounding vehicles, pedestrians, and obstacles.

[0096] 3. Driving status data

[0097] Driving mode: such as economic driving mode, sports mode, etc.

[0098] Driving habits: data records of driving behaviors such as sudden acceleration and sudden braking.

[0099] 4. Vehicle health data

[0100] Fault diagnosis: the health status of various components such as the engine, brake system, and tires.

[0101] Maintenance records: such as maintenance records of oil changes, battery checks, etc.

[0102] 5. Communication data

[0103] V2X communication (Vehicle-to-Everything): Communication data between other vehicles or infrastructure.

[0104] Data upload: Data records uploaded to the cloud or service platform.

[0105] 6. Geolocation Information

[0106] GPS data: longitude and latitude information of the vehicle’s location.

[0107] Driving path: information on historical driving trajectory.

[0108] The step of obtaining the vehicle-mounted terminal MR data specifically includes the following steps:

[0109] Step A1: Acquire MR data; and, acquire a vehicle terminal library;

[0110] Step A2: Match the MR data with the device information in the vehicle terminal library through IMEI-TAC to obtain the matched MR data and the matched device information;

[0111] Step A3: Filtering target MR data from the matched MR data through a set time window;

[0112] Step A4: splicing the matched device information, the target MR data, and the UE information to obtain the vehicle-mounted terminal MR data.

[0113] The device information in the vehicle terminal library usually refers to various types of devices and their parameters related to the vehicle terminal, including but not limited to the following aspects:

[0114] (1) Device model: The specific model and brand information of the vehicle-mounted terminal.

[0115] (2) Hardware specifications: including technical parameters such as processor, memory, storage space, and display type.

[0116] (3) Communication protocol: supported communication methods, such as 4G / 5G, Wi-Fi, Bluetooth, etc.

[0117] (4) Functional modules: such as GPS positioning module, in-vehicle entertainment system, emergency service function, etc.

[0118] (5) Software version: operating system and application software version information of the vehicle terminal.

[0119] (6) Compatibility: Compatibility with other devices or systems to ensure seamless connection.

[0120] (7) Usage status: the working status of the equipment (such as normal operation, failure, etc.).

[0121] (8) Installation location: The specific installation location of the device in the vehicle.

[0122] UE (User Equipment) information generally refers to various data and parameters related to the user terminal, including but not limited to the following aspects:

[0123] (1) Device model: The specific brand and model of the terminal.

[0124] (2) IMEI number: International Mobile Equipment Identity, used to uniquely identify a device.

[0125] (3) SIM card information: including ICCID (Integrated Circuit Card Identification Code) and operator information.

[0126] (4) Communication status: current network connection status, such as connected, signal strength, network type (4G / 5G, etc.).

[0127] (5) Software version: Version information of the device operating system and related applications.

[0128] (6) Device capabilities: supported data transmission rates and protocols (e.g., VoLTE, VoWiFi, etc.).

[0129] (7) Geographic location: real-time geographic location obtained through GPS or base station positioning.

[0130] (8) User configuration: includes user personalized settings, such as language, network preferences, etc.

[0131] (9) Usage status: such as battery level, whether the device is charging, network activity, etc.

[0132] By setting the time window, the target MR data that meets the conditions can be screened out from the matching MR data (i.e., the relevant MR data within a specific time period can be efficiently obtained). The specific steps are as follows:

[0133] (1) Define the time window: determine the start time and end time to form a valid time range.

[0134] (2) Filtering conditions: Filter the matching MR data to ensure that only data within the set time window is retained.

[0135] (3) Extract target data: Extract specific target MR data from the screened data.

[0136] IMEI (International Mobile Equipment Identity) is a unique 15-digit number that uniquely identifies mobile devices around the world. Every mobile phone and mobile device has a unique IMEI number when it leaves the factory.

[0137] IMEI has 15 digits in total, including the following:

[0138] TAC (Type Allocation Code): The first 6 digits indicate the type and manufacturer of the device; FAC (Final Assembly Code) (optional): The middle 2 digits indicate the final assembly location; SNR (Serial Number): The last 6 digits indicate the serial number of the device; Check Digit: The last digit is a number used for verification.

[0139] TAC (Type Allocation Code) is the first 6 characters of IMEI, which identifies the manufacturer and model of the device. It is used to classify devices for easy management and identification. TAC is managed by the International Telecommunication Union (ITU) and the Global System for Mobile Communications Association (GSMA). Manufacturers must apply for and register the corresponding TAC when launching new devices. TAC is used in many aspects such as device management, network security, device identification and tracking. The first 6 digits of each IMEI number are TAC. TAC provides information about the device type and manufacturer, while the complete IMEI provides unique identification capabilities for the device.

[0140] Through TAC, you can determine the basic characteristics of a device, such as brand and model.

[0141] Specific as Figure 5 As shown in the figure, when the vehicle is moving, the vehicle terminal also moves with it, and the collected MR data of the same frequency will change with the movement of the vehicle. As the vehicle drives in the parking lot, the vehicle terminal continuously updates the data, thereby reflecting the signal coverage of different locations. By tracking and monitoring the above dynamic changes, the signal quality of the underground parking lot can be monitored in real time, and more accurate scene recognition and management can be performed.

[0142] As a specific implementation, before step A1, the method further includes step A0;

[0143] Step A0: Build a vehicle terminal library;

[0144] The step A0 specifically includes the following steps:

[0145] Step A01: Determine whether the MR data can match the initial vehicle terminal library:

[0146] If the MR data can match the initial vehicle terminal library, go to step A03; if the MR data cannot match the initial vehicle terminal library, go to step A02;

[0147] Step A02: Generate second IMEI-TAC information;

[0148] Step A03: Matching the initial vehicle terminal library and the MR data through the first IMEI-TAC to form a first target vehicle terminal library; and matching the initial vehicle terminal library and the MR data through the second IMEI-TAC to form a second target vehicle terminal library;

[0149] The first IMEI-TAC is a unique identifier for identifying the first target device, which is a mobile device stored in the vehicle terminal library when the MR data can match the initial vehicle terminal library; the second IMEI-TAC information is inferred based on the parking time distribution and the number of samples when the second target device is used; the second target device is a mobile device actually used by the vehicle terminal when the MR data cannot match the initial vehicle terminal library;

[0150] Step A04: Merge the first target vehicle-mounted terminal library and the second target vehicle-mounted terminal library to obtain a vehicle-mounted terminal library.

[0151] As a specific implementation manner, the signal quality in the MR data is lower than a preset threshold, specifically: the RSRP value in the MR data is lower than a preset threshold;

[0152] The RSRP value in the MR data is lower than a preset threshold, and the identification steps are as follows:

[0153] defining an RSRP preset threshold; and collecting MR data including RSRP values;

[0154] Traverse each MR record and check whether its RSRP value is lower than the RSRP preset threshold:

[0155] If the RSRP value in a certain MR record is lower than the RSRP preset threshold, it is identified that the RSRP value in the MR data is lower than the preset threshold.

[0156] Step S2: Map the vehicle terminal MR data to the corresponding longitude and latitude grid according to the longitude and latitude coordinates of the vehicle terminal.

[0157] Step S3: Identify weakly covered longitude and latitude grids according to the vehicle-mounted terminal MR data; the weakly covered longitude and latitude grids are longitude and latitude grids corresponding to the portion of the MR data whose signal quality is lower than a preset threshold.

[0158] Step S4: Match the identified weak coverage latitude and longitude grids with the underground parking scene to determine the weak coverage area of ​​the underground parking lot, thereby completing the scene recognition of the weak coverage area of ​​the underground parking lot based on the vehicle-mounted terminal.

[0159] As a specific implementation, matching the identified weak coverage latitude and longitude grid with the underground parking scene to determine the weak coverage area of ​​the underground parking lot specifically includes the following steps:

[0160] Get the AOI of the underground parking lot;

[0161] Wherein, the underground parking lot is obtained from the scene-based ground parking garage; the scene-based ground parking garage is formed according to the ground parking POI data of the network platform and matched with the building;

[0162] Determine whether the weakly covered latitude and longitude grid is located within the AOI of a certain underground parking lot:

[0163] If the weak coverage longitude and latitude grid is located within the AOI of a certain underground parking lot, the underground parking lot is determined to be a weak coverage area scene.

[0164] AOI (Area of ​​Interest): refers to the location or area of ​​interest to the user or system in a specific area. In an underground parking lot, AOI can be a specific parking space, entrance and exit, security monitoring area, etc. In an underground parking lot, AOI is used for navigation guidance to help drivers quickly find their vehicles or available parking spaces.

[0165] In underground parking lot management, POI (Point of Interest) data is usually used to identify and manage important locations and facilities in the parking lot. This data can be used to improve the utilization rate of the parking lot, optimize the user experience, and enhance safety.

[0166] like Figure 3 As shown in the figure, the identification of underground signal fast attenuation problem is based on the location of user receiving level changes after splicing user MR data and call record information with mobility characteristics. In order to accurately identify the problem, it is necessary to identify and analyze the three dimensions of user-related scenarios, level changes and user mobility, so as to output the weak coverage underground parking lot area.

[0167] Identification of fast fading level changes: According to the principle of wireless propagation, the wireless communication channel is a time-varying channel. When the radio signal passes through the channel, it will suffer from fading from different paths. Fast fading is the phenomenon of instantaneous rapid fluctuations and rapid changes in the received signal field strength value. Fast fading is caused by the superposition of multipath propagation signals caused by various terrains, objects, and moving bodies at the receiving point. Since the phases, frequencies, and amplitudes of the received multipath signals are different, the amplitude of the superimposed signal fluctuates violently. When the mobile station is running at high speed, the carrier frequency range of the received wireless signal changes continuously over time, which can also cause dramatic changes in the amplitude of the superimposed signal. In other words, multipath effect and Doppler effect can cause fast fading. Through testing and verification, it was found that when fast fading occurs, the amplitude of the terminal receiving level change is usually greater than -10dbm. The user's RSRP level has the following three main change characteristics during fast fading:

[0168] 1) Dropped connection after fast fading: This means that after being dropped, the user reselects a new cell to join the network and stay there. This scenario usually occurs when entering indoors from outdoors, but there is no indoor coverage, resulting in no signal and fast fading. Figure 3 Condition 1 shown.

[0169] 2) Fast fading lasts for a period of time and then recovers: refers to a sudden signal drop that lasts for more than 5 seconds and then the signal gradually recovers. This scenario usually occurs when entering indoors from outdoors, when there is indoor coverage but the coverage is unbalanced or the switching zone startup settings are unreasonable, and there are no strong signal service cells around. After fast fading, the surrounding signal detection and switching measurement continue for a long time, and then the strongest cell is selected for occupation and recovery. Figure 3 Condition 2 shown.

[0170] 3) Instantaneous recovery after fast fading: The signal recovers quickly within 5 seconds after a sudden signal drop. This scenario usually occurs in places such as entering an elevator. When the elevator door closes, the wireless signal is instantly blocked, causing the fast fading signal to drop and then switch to the elevator main service cell for rapid recovery. Figure 3 Condition 3 shown.

[0171] MR is an event in which a user periodically reports the occupancy of a wireless environment signal. When a user has a service, based on the anonymized UEIP of the 4G network or the TMSI of the 5G network, the MRs of the same user over a long period of time are spliced ​​together to form a long call bill. High-speed moving users are identified based on road user identification technology (based on identification of dimensions such as moving speed and environmental changes). Long call bill splicing can determine whether fast fading occurs through changes in cells and RSRP in MR data. At the same time, by identifying high-speed moving users, it can be distinguished whether the user is moving in a vehicle or on foot, which is a way to determine fast fading on the ground.

[0172] like Figure 6 As shown, the implementation process of this embodiment includes the following steps:

[0173] Step 1: Terminal modeling:

[0174] ① Obtain IMEI-TAC information from MR data, match it with MR data, and mark the coverage information of the vehicle terminal;

[0175] ②Self-learning of vehicle information of unknown IMEI-TAC:

[0176] For MR data that cannot match the vehicle terminal library, filter the night time point data (night is the parking period) and match the ground parking scene AOI. Analyze the parking time and the number of samples with unknown IMEI-TAC to generate a self-learning vehicle terminal library;

[0177] ③ Integrate the vehicle terminals with known IMEI-TAC matching and the self-learning vehicle terminals to form a complete vehicle terminal library.

[0178] Step 2: Scene modeling:

[0179] Obtain POI data of parking lots from Huaban / Tencent and other platforms, and match them with buildings to form a scene-based parking lot library.

[0180] Step 3: Coverage evaluation of vehicle terminals

[0181] ① Based on the vehicle terminal library, splice MR data through UE information + time dimension;

[0182] ② The processed vehicle MR data is matched with the ground parking AOI border;

[0183] ③ For the matching ground parking scenario, coverage evaluation is performed based on the RSRP of MR.

[0184] Application Verification:

[0185] By using ground station identification and fast fading identification algorithms, the problem of the inability to accurately screen poor-quality ground stations when evaluating cell and grid-level coverage in traditional networks has been solved. Network blind ground station areas have been identified in a certain urban area.

[0186] This embodiment does not require on-site network testing in each parking lot, and can comprehensively and accurately intelligently identify the signal coverage of underground parking lots, effectively screen underground parking lots with poor signal quality, and thus optimize network services.

[0187] Embodiment 2:

[0188] like Figure 4 As shown, this embodiment provides a scene recognition device for a weakly covered area of ​​an underground parking lot based on a vehicle-mounted terminal, and the device includes:

[0189] An acquisition unit 10 is used to acquire MR data of the vehicle-mounted terminal;

[0190] The mapping unit 20 is connected to the acquisition unit 10 and is used to map the vehicle terminal MR data to the corresponding longitude and latitude grid according to the longitude and latitude coordinates of the vehicle terminal;

[0191] The identification unit 30 is connected to the mapping unit 20 and is used to identify the weakly covered longitude and latitude grid according to the vehicle terminal MR data;

[0192] Wherein, the weakly covered longitude and latitude grid is the longitude and latitude grid corresponding to the data whose signal quality in the MR data is lower than a preset threshold;

[0193] The matching unit 40 is connected to the identification unit 30 and is used to match the identified weak coverage latitude and longitude grid with the underground parking scene to determine the weak coverage area of ​​the underground parking lot, thereby completing the scene identification of the weak coverage area of ​​the underground parking lot based on the vehicle-mounted terminal.

[0194] As a specific implementation, the acquisition unit 10 includes:

[0195] A first acquisition module is used to acquire MR data; and, is used to acquire a vehicle terminal library;

[0196] a first matching module, connected to the first acquisition module, for matching the MR data with the device information in the vehicle terminal library through IMEI-TAC to obtain matched MR data and matched device information;

[0197] a screening module, connected to the first matching module, and configured to screen out target MR data from the matched MR data through a set time window;

[0198] The splicing module is connected to the first matching module and the screening module respectively, and is used to splice the matched device information, the target MR data, and the UE information to obtain the vehicle-mounted terminal MR data.

[0199] As a specific implementation, the acquisition unit 10 further includes a construction module, which is connected to the first acquisition module and is used to construct a vehicle terminal library;

[0200] The building blocks include:

[0201] A determination submodule is used to determine whether the MR data can match the initial vehicle terminal library;

[0202] A first processing submodule, connected to the determination submodule, for matching the initial vehicle terminal library and the MR data through a first IMEI-TAC to form a first target vehicle terminal library when the determination submodule determines that the MR data can match the initial vehicle terminal library;

[0203] The first IMEI-TAC is a unique identifier for identifying the first target device, and the first target device is a mobile device stored in the vehicle terminal library when the MR data can match the initial vehicle terminal library;

[0204] A generating submodule, connected to the determining submodule, for generating second IMEI-TAC information when the determining submodule determines that the MR data cannot match the initial vehicle terminal library;

[0205] A second processing submodule, connected to the generating submodule, is used to match the initial vehicle terminal library and the MR data through a second IMEI-TAC to form a second target vehicle terminal library;

[0206] The second IMEI-TAC information is inferred based on the parking time distribution and the number of samples when the second target device is used; the second target device is the mobile device actually used by the vehicle terminal when the MR data cannot match the initial vehicle terminal library;

[0207] The merging submodule is connected to the first processing submodule and the second processing submodule respectively, and is used to merge the first target vehicle terminal library and the second target vehicle terminal library to obtain a vehicle terminal library.

[0208] As a specific implementation, the matching unit 40 includes:

[0209] The second acquisition module is used to obtain the AOI of the underground parking lot;

[0210] Wherein, the underground parking lot is obtained from the scene-based ground parking garage; the scene-based ground parking garage is formed according to the ground parking POI data of the network platform and matched with the building;

[0211] A determination module, connected to the second acquisition module, for determining whether the weakly covered longitude and latitude grid is located within the AOI of a certain underground parking lot;

[0212] The second matching module is connected to the determination module, and is used to match the underground parking lot as a weak coverage area scene when the determination module determines that the weak coverage longitude and latitude grid is located within the AOI of a certain underground parking lot.

[0213] Embodiment 3:

[0214] This embodiment provides a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for scene recognition of weak coverage areas in underground parking lots based on a vehicle-mounted terminal described in Embodiment 1 is implemented.

[0215] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A scene recognition method for weak coverage areas of underground parking lots based on a vehicle-mounted terminal, characterized in that: The method comprises the following steps: Obtain vehicle terminal MR data; According to the latitude and longitude coordinates of the vehicle terminal, the vehicle terminal MR data is mapped to the corresponding latitude and longitude grid; According to the vehicle terminal MR data, the weakly covered latitude and longitude grids are identified; Wherein, the weakly covered longitude and latitude grid is the longitude and latitude grid corresponding to the data whose signal quality in the MR data is lower than a preset threshold; The identified weak coverage latitude and longitude grids are matched with the ground parking scene to determine the weak coverage area of ​​the underground parking lot, thereby completing the scene recognition of the weak coverage area of ​​the underground parking lot based on the vehicle-mounted terminal.

2. The scene recognition method for weak coverage area of ​​underground parking lot based on vehicle-mounted terminal according to claim 1 is characterized in that: The step of obtaining the vehicle-mounted terminal MR data specifically includes the following steps: Step A1: Acquire MR data; and, acquire a vehicle terminal library; Step A2: Match the MR data with the device information in the vehicle terminal library through IMEI-TAC to obtain the matched MR data and the matched device information; Step A3: Filtering target MR data from the matched MR data through a set time window; Step A4: splicing the matched device information, the target MR data, and the UE information to obtain the vehicle-mounted terminal MR data.

3. The scene recognition method for weak coverage area of ​​underground parking lot based on vehicle-mounted terminal according to claim 2 is characterized in that: Before step A1, the method further includes step A0; Step A0: Build a vehicle terminal library; The step A0 specifically includes the following steps: Step A01: Determine whether the MR data can match the initial vehicle terminal library: If the MR data can match the initial vehicle terminal library, go to step A03; if the MR data cannot match the initial vehicle terminal library, go to step A02; Step A02: Generate the second IMEI-TAC information, and then jump to step A03; The second IMEI-TAC information is inferred based on the parking time distribution and the number of samples when the second target device is used; the second target device is the mobile device actually used by the vehicle terminal when the MR data cannot match the initial vehicle terminal library; Step A03: Matching the initial vehicle terminal library and the MR data through the first IMEI-TAC to form a first target vehicle terminal library; and matching the initial vehicle terminal library and the MR data through the second IMEI-TAC to form a second target vehicle terminal library; The first IMEI-TAC is a unique identifier for identifying the first target device, and the first target device is a mobile device stored in the vehicle terminal library when the MR data can match the initial vehicle terminal library; Step A04: Merge the first target vehicle-mounted terminal library and the second target vehicle-mounted terminal library to obtain a vehicle-mounted terminal library.

4. The method for scene recognition of weak coverage area of ​​underground parking lot based on vehicle-mounted terminal according to claim 1 is characterized in that: The signal quality in the MR data is lower than a preset threshold, specifically: the RSRP value in the MR data is lower than a preset threshold; The RSRP value in the MR data is lower than a preset threshold, and the identification steps are as follows: defining an RSRP preset threshold; and collecting MR data including RSRP values; Traverse each MR record and check whether its RSRP value is lower than the RSRP preset threshold: If the RSRP value in a certain MR record is lower than the RSRP preset threshold, it is identified that the RSRP value in the MR data is lower than the preset threshold.

5. The method for scene recognition of weak coverage area in underground parking lot based on vehicle-mounted terminal according to any one of claims 1 to 4, characterized in that: The step of matching the identified weak coverage latitude and longitude grid with the underground parking scene to determine the weak coverage area of ​​the underground parking lot specifically includes the following steps: Get the AOI of the underground parking lot; Wherein, the underground parking lot is obtained from the scene-based ground parking garage; the scene-based ground parking garage is formed according to the ground parking POI data of the network platform and matched with the building; Determine whether the weakly covered latitude and longitude grid is located within the AOI of a certain underground parking lot: If the weak coverage longitude and latitude grid is located within the AOI of a certain underground parking lot, the underground parking lot is determined to be a weak coverage area scene.

6. A scene recognition device for weak coverage areas of underground parking lots based on a vehicle-mounted terminal, characterized in that: The device comprises: An acquisition unit, used for acquiring MR data of the vehicle-mounted terminal; A mapping unit, connected to the acquisition unit, for mapping the vehicle terminal MR data to a corresponding longitude and latitude grid according to the longitude and latitude coordinates of the vehicle terminal; An identification unit, connected to the mapping unit, for identifying weakly covered longitude and latitude grids according to the vehicle-mounted terminal MR data; Wherein, the weakly covered longitude and latitude grid is the longitude and latitude grid corresponding to the data whose signal quality in the MR data is lower than a preset threshold; The matching unit is connected to the identification unit and is used to match the identified weak coverage latitude and longitude grid with the underground parking scene to determine the weak coverage area of ​​the underground parking lot, thereby completing the scene identification of the weak coverage area of ​​the underground parking lot based on the vehicle-mounted terminal.

7. The underground parking lot weak coverage area scene recognition device based on the vehicle-mounted terminal according to claim 6 is characterized in that: The acquisition unit comprises: A first acquisition module is used to acquire MR data; and, is used to acquire a vehicle terminal library; a first matching module, connected to the first acquisition module, for matching the MR data with the device information in the vehicle terminal library through IMEI-TAC to obtain matched MR data and matched device information; a screening module, connected to the first matching module, and configured to screen out target MR data from the matched MR data through a set time window; The splicing module is connected to the first matching module and the screening module respectively, and is used to splice the matched device information, the target MR data, and the UE information to obtain the vehicle-mounted terminal MR data.

8. The underground parking lot weak coverage area scene recognition device based on the vehicle-mounted terminal according to claim 7 is characterized in that: The acquisition unit further includes a construction module, which is connected to the first acquisition module and is used to construct a vehicle terminal library; The building blocks include: A determination submodule is used to determine whether the MR data can match the initial vehicle terminal library; A first processing submodule, connected to the determination submodule, for matching the initial vehicle terminal library and the MR data through a first IMEI-TAC to form a first target vehicle terminal library when the determination submodule determines that the MR data can match the initial vehicle terminal library; The first IMEI-TAC is a unique identifier for identifying the first target device, and the first target device is a mobile device stored in the vehicle terminal library when the MR data can match the initial vehicle terminal library; A generating submodule, connected to the determining submodule, for generating second IMEI-TAC information when the determining submodule determines that the MR data cannot match the initial vehicle terminal library; A second processing submodule, connected to the generating submodule, is used to match the initial vehicle terminal library and the MR data through a second IMEI-TAC to form a second target vehicle terminal library; The second IMEI-TAC information is inferred based on the parking time distribution and the number of samples when the second target device is used; the second target device is the mobile device actually used by the vehicle terminal when the MR data cannot match the initial vehicle terminal library; The merging submodule is connected to the first processing submodule and the second processing submodule respectively, and is used to merge the first target vehicle terminal library and the second target vehicle terminal library to obtain a vehicle terminal library.

9. The underground parking lot weak coverage area scene recognition device based on the vehicle-mounted terminal according to any one of claims 6 to 8, characterized in that: The matching unit comprises: The second acquisition module is used to obtain the AOI of the underground parking lot; Wherein, the underground parking lot is obtained from the scene-based ground parking garage; the scene-based ground parking garage is formed according to the ground parking POI data of the network platform and matched with the building; A determination module, connected to the second acquisition module, for determining whether the weakly covered longitude and latitude grid is located within the AOI of a certain underground parking lot; The second matching module is connected to the determination module, and is used to determine that the underground parking lot is a weak coverage area scene when the determination module determines that the weak coverage longitude and latitude grid is located within the AOI of a certain underground parking lot.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for scene recognition of weak coverage areas in underground parking lots based on a vehicle-mounted terminal as described in any one of claims 1 to 5 is implemented.

Images