Data processing method and device, communication equipment and storage medium

Abstract

The invention discloses a data processing method and device, communication equipment and a storage medium. The method comprises the following steps: acquiring first information; the first information can reflect wireless environment feature information corresponding to a terminal participating in a communication service in a wireless communication scene; and determining a road recognition result corresponding to the terminal according to the wireless environment feature information.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data processing method, apparatus, communication equipment and storage medium. Background Art

[0002] The wireless environment covers a variety of scenarios, including buildings, suburbs, roads, etc. There are also different strategies and methods for wireless optimization, planning and operation in different scenarios, among which road scenarios are particularly important. Mobile network planning needs to be planned for wireless coverage scenarios on different roads to provide wireless communication guarantee for devices using mobile communication services on the road.

[0003] In the prior art, external data or road testing is usually used to determine the signal coverage of different roads in order to formulate optimization strategies, but such methods are inefficient and have poor real-time performance. Summary of the invention

[0004] Embodiments of the present application provide a data processing method, device, terminal, and storage medium.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] The data processing method provided in the embodiment of the present application includes:

[0007] Acquire first information; the first information can reflect the wireless environment characteristic information corresponding to the terminal participating in the communication service in the wireless communication scenario;

[0008] A road recognition result corresponding to the terminal is determined according to the wireless environment characteristic information.

[0009] In the above solution, determining the road recognition result corresponding to the terminal according to the wireless environment characteristic information includes:

[0010] The wireless environment characteristic information is processed using a network model to obtain second information; the second information is used to indicate whether the location corresponding to the terminal is a road and / or the type of road corresponding to the location, and the road identification result includes the second information.

[0011] In the above solution, determining the road recognition result corresponding to the terminal according to the wireless environment characteristic information includes:

[0012] According to the wireless environment characteristic information, third information is obtained; the third information can reflect the index data of the position corresponding to the terminal in the wireless communication scenario, and the road recognition result includes the third information.

[0013] In the above solution, the wireless environment characteristic information includes characteristic values ​​of the wireless environment and / or characteristic values ​​of the wireless environment after generalization processing.

[0014] In the above solution, the characteristic value of the wireless environment includes at least one of the following:

[0015] Wireless measurement data corresponding to the terminal;

[0016] The measurement time corresponding to the wireless measurement data;

[0017] the location of the terminal;

[0018] The identifier of the terminal; the identifier includes a terminal identifier and / or a category identifier of the terminal, and the category identifier is used to indicate whether the terminal is moving.

[0019] In the above solution, the wireless measurement data corresponding to the terminal is at least one of the following:

[0020] Physical cell identification;

[0021] reference signal received power;

[0022] signal to interference plus noise ratio;

[0023] Physical resource block.

[0024] In the above solution, the generalized feature value includes at least one of the following:

[0025] The service duration of the terminal participating in the communication service within the first time;

[0026] The number of times the terminal switches cells within a first period of time;

[0027] The number of cells experienced by the terminal within the first time;

[0028] The average cell residence time of the terminal within the first time;

[0029] A characteristic value of a primary serving cell level change trend of the terminal within the first time period.

[0030] In the above scheme, the method further comprises:

[0031] Determine a training data set; the training data set includes wireless environment characteristic information of wireless measurement data of multiple communication services and a road mark corresponding to each wireless environment characteristic information, the road mark is used to mark whether it is a road and / or road type;

[0032] According to the training data set, the network model is trained using a deep learning algorithm.

[0033] In the above solution, the road recognition result includes the second information and the third information. After determining the road recognition result corresponding to the terminal according to the wireless environment characteristic information, the method further includes:

[0034] Relevant settings of the wireless communication scenario are performed according to the second information and the third information.

[0035] The present application also provides a data processing device, including:

[0036] An acquisition unit, configured to acquire first information; the first information can reflect wireless environment characteristic information corresponding to a terminal participating in a communication service in a wireless communication scenario;

[0037] A processing unit is used to determine a road recognition result corresponding to the terminal according to the wireless environment characteristic information.

[0038] The present application also provides a communication device, including:

[0039] A memory for storing executable instructions;

[0040] The processor is used to implement the steps in any of the data processing methods described above when executing the executable instructions stored in the memory.

[0041] An embodiment of the present application further provides a storage medium, in which computer executable instructions are stored. The computer executable instructions are configured to execute any of the data processing methods provided above.

[0042] The data processing method, device, communication equipment and storage medium provided by the present application obtain first information; the first information can reflect the wireless environment characteristic information corresponding to the terminal participating in the communication service in the wireless communication scenario; according to the wireless environment characteristic information, the road identification result corresponding to the terminal is determined. The scheme provided by the present application, when the wireless environment characteristic information corresponding to the terminal participating in the communication service in the wireless communication scenario is obtained, uses the wireless environment characteristic information to identify the road corresponding to the terminal from a wireless perspective to obtain a road identification result, and the road identification result can be used for communication optimization or planning; in this way, the present application no longer relies on external data, no longer arranges road tests independently, reduces data collection, and improves the efficiency and real-time performance of obtaining road identification results. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a data processing method provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of road signal coverage in a wireless environment provided by an embodiment of the present application;

[0045] Figure 3 A schematic diagram of wireless measurement data processing provided in an embodiment of the present application;

[0046] Figure 4 A statistical diagram of terminal wireless measurement data provided by an embodiment of the present application;

[0047] Figure 5 A schematic diagram of generalization processing of wireless environment characteristic values ​​provided in an embodiment of the present application;

[0048] Figure 6 A schematic diagram of a road identification process based on minimization of drive test data provided in an embodiment of the present application;

[0049] Figure 7 A schematic diagram of a road identification process based on drive test and measurement report data provided in an embodiment of the present application;

[0050] Figure 8 A schematic diagram of the structure of a data processing device provided in an embodiment of the present application;

[0051] Fig. 9 A schematic diagram of the hardware structure of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application are further elaborated in detail below in conjunction with the drawings and embodiments. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0053] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0054] The terms "first / second / third" involved in the present application are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0056] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0057] The present application embodiment provides a data processing method, such as Figure 1 As shown, the method includes:

[0058] Step 101: Obtain first information.

[0059] Among them, the first information can reflect the wireless environment characteristic information corresponding to the terminal participating in the communication service in the wireless communication scenario.

[0060] It can be understood that wireless communication scenarios include scenarios covered by various wireless information networks, and the wireless information networks can be fifth generation mobile communication technology (5th Generation Mobile Communication Technology, 5G) network, long term evolution (Long Term Evolution, LTE) network, IP Multimedia Subsystem (IP Multimedia Subsystem, IMS) network, Global System for Mobile Communications (Global System for Mobile Communications, GSM) Enhanced Data Rate for GSM Evolution (Enhanced Data Rate for GSMEvolution, EDGE) wireless communication network / terrestrial radio access network (GSM EDGE Radio Access Network / Universal Terrestrial Radio Access, GERAN / UTRAN, G / U) network, etc.

[0061] This application takes the road scene as an example, and the wireless environment characteristic information includes but is not limited to the characteristic values ​​converted from the indicators of the wireless environment, which may include the wireless signal field strength, cell level strength information, multipath information reported by the terminal, path loss information, etc. In 5G, the wireless environment characteristic information may also include the beam ID (BeamID) information of the synchronization broadcast block (Synchronization Signal / PBCH, SSB), the reference signal receiving power (Reference Signal Receiving Power, RSRP), the physical cell identifier (Physical Cell Identifier, PCI), the physical resource block (Physical Resource Block, PRB), etc.

[0062] refer to Figure 2As shown, in 5G networks, high frequencies are used for communication, and the direction and frequency of the beams between each end user equipment (UE) and the base station are dynamically selected based on the channel quality for communication. Here, the base station includes but is not limited to: Code Division Multiple Access (CDMA) type base stations, GSM type base stations, and LTE type base stations.

[0063] The direction of the base station's transmitting beam has different angles with different roads. The base station beams received by UEs on different roads are different, and thus the signal quality received by UEs on different roads is also different. At this time, the signal quality of the corresponding road can be confirmed based on the BeamID received by the UE.

[0064] Step 102: Determine the road recognition result corresponding to the terminal according to the wireless environment characteristic information.

[0065] When wireless environment characteristic information corresponding to a terminal participating in a communication service in a wireless communication scenario is obtained, the wireless environment characteristic information is used to identify the road corresponding to the terminal from a wireless perspective to obtain a road identification result, which can be used for communication optimization or planning.

[0066] Exemplarily, the road recognition result can reflect at least one of the following: whether it is a road, the road type corresponding to the road, and indicator data in the wireless communication scenario. Figure 2 As shown, the base station beam emission direction of cell 2 is closer to road 1, and the UE on road 1 can receive more base station beams. Therefore, through the road identification result, it can be known that the signal coverage on road 1 is better than that on road 2.

[0067] The data processing method provided by the present application obtains the first information; the first information can reflect the wireless environment characteristic information corresponding to the terminal participating in the communication service in the wireless communication scenario; according to the wireless environment characteristic information, the road identification result corresponding to the terminal is determined. In other words, when the present application obtains the wireless environment characteristic information corresponding to the terminal participating in the communication service in the wireless communication scenario, the wireless environment characteristic information is used to identify the road corresponding to the terminal from a wireless perspective to obtain the road identification result, and the road identification result can be used for communication optimization or planning; in this way, the present application no longer relies on external data and no longer arranges road tests independently, which reduces data collection and improves the efficiency and real-time performance of obtaining road identification results.

[0068] It can be understood that the corresponding road category can be determined based on the wireless environment characteristic information provided by the UE. If more UE information is collected on the road section and the UE switches to more cells, it can be understood that this road section is a high-speed section; for signal coverage of high-speed sections, for example: urban express sections with heavy traffic. Usually, high-quality signal coverage is guaranteed to meet more communication needs; if less UE information is collected on the road section and the UE switches to fewer cells, it can be understood that this road section is a low-speed section. For signal coverage of low-speed sections, for example: country roads. The traffic on this section is relatively small, and the signal coverage can be relatively reduced.

[0069] In the embodiment of the present application, the wireless environment characteristic information can be processed, the characteristic values ​​of the wireless environment on different road sections can be extracted, and the network model can be trained to identify the road using the trained network model.

[0070] The 5G wireless measurement data of a single service is used as the wireless environment characteristic information, including RSRP, PCI, cell level strength information, and measurement time; users can be marked as mobile users and stationary users according to the different level trends measured by different categories of users in the wireless environment; because stationary users stay in a cell for a long time, the radio level is relatively stable in the time dimension, and the fluctuation is not obvious. The level of high-speed mobile users will experience changes in the relative distance from the main service cell, and the level fluctuation is obvious. The wireless environment characteristic value of the level change trend can be designed according to the level trend change; the PCI of the main service cell of the stationary user is basically unchanged. Based on the change of the PCI of the main service cell of the user on the road, the wireless environment characteristic value of the number of PCIs of the user's main service cell and the wireless environment characteristic value of the residence time of a single main service cell can be designed.

[0071] In practical applications, reference Figure 3 As shown in the figure, the required wireless environment feature values ​​can be generalized into statistical features, such as the variance, standard deviation, and range of the level sequence within a period of historical time; the statistical features and the labeled user data are input into the network model, and the statistical features corresponding to different categories of users are determined through the network model, that is, the statistical features corresponding to different road types are determined, such as the statistical features of roads, non-roads, highways, and low-speed roads. During the training process, cross-validation and parameter tuning can be used to improve the performance of the network model; the trained network model is used to identify new 5G wireless data and determine the corresponding road category; at the same time, the importance ranking of wireless indicators on different roads can be determined based on the wireless environment feature values. For example, the coverage of signals on different roads can be determined based on the feature values ​​of the level change trend.

[0072] It can be seen from the above content that the embodiment of the present application obtains wireless environment characteristic information corresponding to the terminal participating in the communication service in the wireless communication scenario, determines the road identification result corresponding to the terminal according to the wireless environment characteristic information, and determines the signal environment of different roads in the wireless communication scenario at the same time. In this way, the signal coverage of different roads can be obtained in real time, and targeted road signal optimization strategies can be formulated, thereby improving the efficiency of signal coverage optimization.

[0073] In some embodiments of the present application, step 102 determines the road recognition result corresponding to the terminal according to the wireless environment feature information, which can be implemented by the following steps:

[0074] The wireless environment characteristic information is processed using a network model to obtain second information; the second information is used to indicate whether the location corresponding to the terminal is a road and / or the type of road corresponding to the location, and the road recognition result includes the second information.

[0075] It is understandable that the network model can be a classifier model, such as: Support Vector Machine (SVM), Random Forest and Neural Network. The second information can include statistical features corresponding to the terminal, and the road type can include a highway and a low-speed road, which is not specifically limited in this application.

[0076] In an embodiment of the present application, wireless environment characteristic information is obtained through a terminal, and feature extraction can be performed on the wireless environment characteristic information. The statistical features of the extracted wireless environment characteristic values ​​after generalization processing are input into a classifier model, and the statistical features corresponding to the terminal are compared with the statistical feature thresholds of different roads to determine whether the location corresponding to the terminal is a road and / or the road type corresponding to the location.

[0077] In some embodiments of the present application, step 102 determines the road recognition result corresponding to the terminal according to the wireless environment feature information, and can also be implemented by the following steps:

[0078] The third information is obtained according to the wireless environment characteristic information; the third information can reflect the index data of the position corresponding to the terminal in the wireless communication scenario, and the road recognition result includes the third information.

[0079] It can be understood that the wireless environment characteristic information may also include SSB, RSRP, signal to interference plus noise ratio (SINR) and the like in 5G wireless data.

[0080] In an embodiment of the present application, the third information may include the broadcast block signal to interference plus noise ratio (SSB-SINR) and the broadcast block reference signal received power (SSB-RSRP) on the road, so as to measure the wireless indicators on the road and determine the importance ranking of the wireless indicators of the road.

[0081] In some embodiments of the present application, the wireless environment characteristic information includes characteristic values ​​of the wireless environment and / or characteristic values ​​of the wireless environment after generalization processing.

[0082] In an embodiment of the present application, after collecting 5G wireless measurement data, characteristic values ​​of the wireless environment and / or characteristic values ​​of the wireless environment after generalization processing can be obtained through processing; the 5G wireless measurement data includes but is not limited to RSRP, PCI, and cell level strength information.

[0083] In some embodiments of the present application, the characteristic value of the wireless environment includes at least one of the following:

[0084] Wireless measurement data corresponding to the terminal;

[0085] The measurement time corresponding to the wireless measurement data;

[0086] The location of the terminal;

[0087] The terminal identifier; the identifier includes the terminal identifier and / or the terminal category identifier, and the category identifier is used to indicate whether the terminal is mobile.

[0088] In the embodiment of the present application, the wireless measurement data corresponding to the terminal, the measurement time corresponding to the wireless measurement data, the location of the terminal and the identifier of the terminal can be obtained according to the data reported by the terminal to the base station.

[0089] In some embodiments of the present application, the wireless measurement data corresponding to the terminal is at least one of the following:

[0090] Physical cell identification;

[0091] reference signal received power;

[0092] signal to interference plus noise ratio;

[0093] Physical resource block.

[0094] In the present application examples, reference Figure 4 As shown, the wireless measurement data corresponding to the terminal may include a physical cell identifier, a reference signal received power, a signal to interference plus noise ratio, a physical resource block, a measurement time, and a user equipment identifier.

[0095] In some embodiments of the present application, the feature value after generalization processing includes at least one of the following:

[0096] The duration of the terminal's participation in communication services within the first time;

[0097] The number of times the terminal switches cells within the first time;

[0098] The number of cells experienced by the terminal in the first time;

[0099] Average cell dwelling time of the terminal in the first time;

[0100] The characteristic value of the primary service cell level change trend of the terminal within the first time.

[0101] It is understandable that the first time can be set according to the measurement time, and this application does not make any specific limitation on this.

[0102] In the present application examples, reference Figure 5 As shown, the characteristic value after generalization processing can be a statistical feature. Taking the cell level change trend as an example, the main service cell level sequence of the terminal within the first time can be collected and variance processing can be performed to obtain the characteristic value of the main service cell level change trend of the terminal within the first time.

[0103] In some embodiments of the present application, the method further comprises:

[0104] Determine a training data set; the training data set includes wireless environment characteristic information of wireless measurement data of multiple communication services and a road mark corresponding to each wireless environment characteristic information, where the road mark is used to mark whether it is a road and / or a road type;

[0105] Based on the training data set, the network model is trained using a deep learning algorithm.

[0106] In an embodiment of the present application, the original data of the training data set can be obtained by minimizing drive test (MDT) or associating drive test (DT) with measurement report (MR) data, and then the original data is processed to obtain the training data set.

[0107] In practical applications, the acquired raw data may include cell level strength information, multipath information, path loss information, user identification, user geographic location information, etc. Feature extraction and labeling are performed on the raw data to obtain a training data set.

[0108] In a realistic scenario, refer to Figure 6 As shown, road recognition based on MDT data can be achieved through the following steps:

[0109] Step 1: Collect MDT data, which needs to include key wireless data, such as main service level strength, multipath data and path loss data. In addition, it also needs to include longitude and latitude and user unique identification.

[0110] Step 2: Based on the user's unique identifier and time sequence, the call records of all users' single wireless visits in the cell are spliced ​​to form a record of a single user's single visit, which includes the cell sequence over time and the wireless measurement values ​​at different time points.

[0111] Step 3: Based on the single-access level call record, feature engineering is performed, including feature extraction of the wireless environment and generalized features, such as counting the number of cells experienced historically within a unit time, the variance of the level change of the main service cell within a unit time, and changing the generalized feature value to determine whether the road is a low-speed road or a high-speed road. In addition, based on the level and change amplitude information, it can be measured whether the road is a well-covered road or a road with rich multipaths.

[0112] Step 4: Build a network model based on historical data. Label the historical data to see if it is road or non-road, and add the feature values ​​from Step 3 to form a labeled training data set. Train the network model based on this data set. Once the training model meets the accuracy requirements, it can be deployed in the actual solution to complete the specific classification function.

[0113] Step 5.1: Output classification results. The newly input data at the call list level is classified into several types, such as non-road, urban and rural roads, and expressways.

[0114] Step 5.2: Combined with Step 5.1, determine the importance ranking of wireless indicators based on the eigenvalues ​​in feature engineering, make a unified summary of road levels, and determine the importance ranking of roads. Use a weighted algorithm to give the importance level of roads, such as focusing on covered roads, or focusing on switching abnormal roads, or whether it is a road with the strongest beam coverage.

[0115] Step 6: Based on the classification results and road importance, the output of the network model is provided to subsequent plans, such as key road switching optimization plans.

[0116] In a realistic scenario, refer to Figure 7 As shown, road recognition based on DT and MR data can be achieved through the following steps:

[0117] Step 1: Collect MR and historical DT data, which must include key wireless data, such as main service level strength, multipath data, and path loss data. In addition, it must also include longitude and latitude and a unique user ID.

[0118] Step 2: Since DT data is single-user data, the data splicing here uses user identification to splice DT and MR data to realize the uplink and downlink single-user wireless data call list. The road test scenario will traverse a variety of different levels of roads, such as urban main roads and densely populated roads. The data constructed by DT data can meet the diversity of road levels.

[0119] The subsequent steps may refer to the aforementioned road recognition steps based on MDT data, which will not be described in detail here.

[0120] In some embodiments of the present application, the road recognition result includes the second information and the third information. After step 102 determines the road recognition result corresponding to the terminal according to the wireless environment characteristic information, the method further includes:

[0121] According to the second information and the third information, relevant settings of the wireless communication scenario are performed.

[0122] In the embodiment of the present application, the importance ranking of the road from the wireless perspective can be given according to the second information and the third information, such as the ranking of coverage quality or the ranking of wireless multipath change degree, and instructions can be given on the wireless network coverage of the road, and relevant settings of the wireless communication scene can be performed according to the instructions, such as switching abnormal road signal coverage, adjusting the base station beam direction, etc.

[0123] In practical applications, according to the importance of roads from a wireless perspective, the methods for performing relevant settings of wireless communication scenarios may include:

[0124] 1. Enhance signal transmission strength: Enhance signal coverage by increasing the transmission power of the transmitter, and improve the coverage and signal quality of road signals.

[0125] 2. Increase the number of transmitting stations: Enhance signal coverage by increasing the number of transmitting stations. The coverage of each transmitting station can overlap with each other to improve the signal strength of the road.

[0126] 3. Optimize the installation location of the transmitter station: By optimizing the installation location of the transmitter station to improve signal coverage, the signal can be transmitted through reflection from obstructing objects such as mountains and buildings, thereby improving the quality of road signals.

[0127] 4. Improve the signal propagation environment: for example, reduce signal interference objects, such as buildings, trees, etc., and arrange more reflective objects to improve road signal quality.

[0128] 5. Improve the electromagnetic environment: Take appropriate measures to improve the electromagnetic environment, reduce electromagnetic interference, and improve the signal quality of the road.

[0129] Based on the same inventive concept as above, Figure 8A schematic diagram of the structure of a data processing device provided in an embodiment of the present invention, the data processing device 800 includes:

[0130] The acquisition unit 801 is used to acquire first information; the first information can reflect the wireless environment characteristic information corresponding to the terminal participating in the communication service in the wireless communication scenario;

[0131] The processing unit 802 is used to determine the road recognition result corresponding to the terminal according to the wireless environment characteristic information.

[0132] In some embodiments of the present application, the processing unit 802 is also used to process the wireless environment characteristic information using a network model to obtain second information; the second information is used to indicate whether the location corresponding to the terminal is a road and / or the type of road corresponding to the location, and the road recognition result includes the second information.

[0133] In some embodiments of the present application, the processing unit 802 is further used to obtain third information based on the wireless environment characteristic information; the third information can reflect the indicator data of the position corresponding to the terminal in the wireless communication scenario, and the road recognition result includes the third information.

[0134] In some embodiments of the present application, the wireless environment characteristic information includes characteristic values ​​of the wireless environment and / or characteristic values ​​of the wireless environment after generalization processing.

[0135] In some embodiments of the present application, the characteristic value of the wireless environment includes at least one of the following:

[0136] Wireless measurement data corresponding to the terminal;

[0137] The measurement time corresponding to the wireless measurement data;

[0138] The location of the terminal;

[0139] The terminal identifier; the identifier includes the terminal identifier and / or the terminal category identifier, and the category identifier is used to indicate whether the terminal is mobile.

[0140] In some embodiments of the present application, the wireless measurement data corresponding to the terminal is at least one of the following:

[0141] Physical cell identification;

[0142] reference signal received power;

[0143] signal to interference plus noise ratio;

[0144] Physical resource block.

[0145] In some embodiments of the present application, the feature value after generalization processing includes at least one of the following:

[0146] The duration of the terminal's participation in communication services within the first time;

[0147] The number of times the terminal switches cells within the first time;

[0148] The number of cells experienced by the terminal in the first time;

[0149] Average cell dwelling time of the terminal in the first time;

[0150] The characteristic value of the primary service cell level change trend of the terminal within the first time.

[0151] In some embodiments of the present application, the processing unit 802 is also used to determine a training data set; the training data set includes wireless environment characteristic information of wireless measurement data of multiple communication services and road markings corresponding to each wireless environment characteristic information, and the road markings are used to mark whether it is a road and / or road type; according to the training data set, a deep learning algorithm is used to train a network model.

[0152] In some embodiments of the present application, the processing unit 802 is further configured to perform relevant settings of the wireless communication scenario according to the second information and the third information.

[0153] Based on the foregoing embodiments, an embodiment of the present application provides a communication device, Fig. 9 Schematic diagram of the hardware structure of a communication device according to an embodiment of the present invention. The communication device 900 includes: at least one processor 901, a memory 902, and optionally, the communication device 900 may further include at least one communication interface 903. The various components in the communication device 900 are coupled together via a bus system 904. It can be understood that the bus system 904 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 904 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Fig. 9 Various buses are labeled as bus system 904.

[0154] It can be understood that the memory 902 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Sync Link Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 902 described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memories.

[0155] The memory 902 in the embodiment of the present invention is used to store various types of data to support the operation of the communication device 900. Examples of such data include: any computer program for operating on the communication device 900. The program for implementing the method of the embodiment of the present invention may be included in the memory 902.

[0156] The method disclosed in the above embodiment of the present invention can be applied to the processor 901, or implemented by the processor 901. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0157] In an exemplary embodiment, the communication device 900 can be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD), field programmable gate array (FPGA), general processor, controller, microcontroller (MCU), microprocessor, or other electronic components to execute the above method.

[0158] Based on the above embodiments, the embodiments of the present application provide a storage medium, wherein the storage medium stores computer executable instructions, and the computer executable instructions are configured to execute Figure 1 The corresponding embodiment provides a data processing method.

[0159] It should be noted that the above-mentioned computer storage medium can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM) and other memories; it can also be various communication devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0160] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0161] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0162] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0163] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0166] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire first information; the first information can reflect the wireless environment characteristic information corresponding to the terminal participating in the communication service in the wireless communication scenario; A road recognition result corresponding to the terminal is determined according to the wireless environment characteristic information.

2. The method according to claim 1, characterized in that The determining, according to the wireless environment characteristic information, a road recognition result corresponding to the terminal includes: The wireless environment characteristic information is processed using a network model to obtain second information; the second information is used to indicate whether the location corresponding to the terminal is a road and / or the type of road corresponding to the location, and the road identification result includes the second information.

3. The method according to claim 1, characterized in that The determining, according to the wireless environment characteristic information, a road recognition result corresponding to the terminal includes: According to the wireless environment characteristic information, third information is obtained; the third information can reflect the index data of the position corresponding to the terminal in the wireless communication scenario, and the road recognition result includes the third information.

4. The method according to claim 1, characterized in that The wireless environment characteristic information includes characteristic values ​​of the wireless environment and / or characteristic values ​​of the wireless environment after generalization processing.

5. The method according to claim 4, characterized in that The characteristic value of the wireless environment includes at least one of the following: Wireless measurement data corresponding to the terminal; The measurement time corresponding to the wireless measurement data; the location of the terminal; The identifier of the terminal; the identifier includes a terminal identifier and / or a category identifier of the terminal, and the category identifier is used to indicate whether the terminal is moving.

6. The method according to claim 4, characterized in that The wireless measurement data corresponding to the terminal includes at least one of the following: Physical cell identification; reference signal received power; signal to interference plus noise ratio; Physical resource block.

7. The method according to claim 4, characterized in that The generalized feature value includes at least one of the following: The service duration of the terminal participating in the communication service within the first time; The number of times the terminal switches cells within a first period of time; The number of cells experienced by the terminal within the first time; The average cell residence time of the terminal within the first time; A characteristic value of a primary serving cell level change trend of the terminal within the first time period.

8. The method according to claim 2, characterized in that: The method further comprises: Determine a training data set; the training data set includes wireless environment characteristic information of wireless measurement data of multiple communication services and a road mark corresponding to each wireless environment characteristic information, the road mark is used to mark whether it is a road and / or road type; According to the training data set, the network model is trained using a deep learning algorithm.

9. The method according to any one of claims 1 to 7, characterized in that The road recognition result includes second information and third information. After determining the road recognition result corresponding to the terminal according to the wireless environment characteristic information, the method further includes: Relevant settings of the wireless communication scenario are performed according to the second information and the third information.

10. A data processing device, characterized in that: The device comprises: An acquisition unit, configured to acquire first information; the first information can reflect wireless environment characteristic information corresponding to a terminal participating in a communication service in a wireless communication scenario; A processing unit is used to determine a road recognition result corresponding to the terminal according to the wireless environment characteristic information.

11. A communication device, characterized in that: The communication device comprises: A memory for storing executable instructions; A processor is used to implement the steps of the data processing method according to any one of claims 1 to 9 when executing the executable instructions stored in the memory.

12. A storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the data processing method provided in any one of claims 1 to 9.

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