High-speed rail 5G signal distribution circuit diagram generation method based on Bayesian estimation positioning

Abstract

The invention provides a high-speed rail 5G signal distribution circuit diagram generation method based on Bayesian estimation positioning, and the method comprises the steps: obtaining target network data, BDS positioning information and LBS ranging information, generating the positioning information of a current circuit library, and obtaining the positioning information of a historical circuit library; predicting first predicted position information at the next moment according to the current line library positioning information, and predicting second predicted position information according to the historical line library positioning information; target prediction position information at the next moment is determined through Bayesian estimation, and the actual position information is corrected into target position information; and generating a high-speed rail 5G signal distribution circuit diagram based on the target network data and the corrected target position information. According to the technical scheme provided by the embodiment of the invention, the historical experience position, the BDS positioning information and the LBS ranging information can be utilized, the next position can be predicted according to the Bayesian estimation to carry out position correction, and the high-speed rail 5G signal distribution circuit diagram can be accurately generated under the condition of reducing the hardware cost.

Description

Technical Field

[0001] This invention relates to the field of 5G signal testing technology, and in particular to a method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation localization. Background Technology

[0002] With the development of high-speed rail and the rapid advancement of 5G technology, users have increasingly higher demands for the stability of high-speed rail networks. This necessitates a network optimization solution capable of verifying the signal coverage, latency, and speed of high-speed rail lines. The high-speed rail testing and verification employs a continuous testing method along the entire tested high-speed rail line. The test begins at the originating station and continues until the train reaches its destination station.

[0003] In the current field of high-speed rail wireless network optimization, existing professional tools mostly rely on expensive inertial navigation gyroscope equipment for position positioning. Although this design meets basic optimization needs to a certain extent, the accuracy of the data still heavily depends on the signal reception of the inertial navigation equipment, and the cost is relatively high. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method for generating high-speed rail 5G signal distribution route maps based on Bayesian estimation positioning. This method can combine historical location data and utilize Bayesian estimation positioning to generate accurate high-speed rail 5G signal distribution route maps, thereby reducing hardware costs.

[0005] In a first aspect, embodiments of the present invention provide a method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation positioning, applied to a 5G wireless network testing device. The 5G wireless network testing device includes a 5G module, a location information acquisition module, and a prediction module. The method includes: The target network data is obtained through the 5G module, and the actual location information is obtained through the location information acquisition module. The target network data is collected at multiple acquisition times during the operation of the high-speed rail, and the actual location information includes BDS positioning information and LBS ranging information. Based on the first moment, the current route database positioning information is generated according to the BDS positioning information and the LBS ranging information, and the historical route database positioning information is obtained, wherein the first moment is any of the acquisition moments; Based on the prediction module, the first predicted location information at the second time is predicted according to the current route database location information, and the second predicted location information at the second time is predicted according to the historical route database location information, wherein the second time is the next collection time after the first time. Based on the first predicted location information and the second predicted location information, Bayesian estimation is performed to determine the target predicted location information at the second time moment, and the actual location information at the second time moment is corrected to the target location information based on the target predicted location information; A high-speed rail 5G signal distribution map is generated based on the target network data and the corrected target location information.

[0006] According to some embodiments of the present invention, acquiring target network data through a 5G module includes: Acquire detected terminal device data, and parse the terminal device data into device parsing data according to preset decoding configuration information; The device parsing data is converted into a target format data packet according to the preset file format type; The target format data packet is parsed and split according to the preset storage format type information to obtain the target network data.

[0007] According to some embodiments of the present invention, a 5G wireless network testing device is equipped with a BDS device, which acquires actual location information through the location information acquisition module, including: Initialize the BDS device and the 5G module; After obtaining the high-speed rail start signal, the BDS positioning information, BDS speed information and BDS time information collected by the BDS device are obtained, and the LBS ranging information, LBS speed information and LBS time information are obtained by LBS ranging through the 5G module.

[0008] According to some embodiments of the present invention, before predicting the first predicted location information at the second time based on the current route database positioning information, the method further includes: Obtain preset high-speed rail line information, wherein the high-speed rail line information includes the latitude and longitude of the high-speed rail line; The high-speed rail line information is divided into multiple line location sets based on a preset scale, wherein each line location set corresponds to a collection time.

[0009] Secondly, embodiments of the present invention provide a high-speed rail 5G signal distribution route map generation device based on Bayesian estimation localization, which is installed in a 5G wireless network testing device. The 5G wireless network testing device includes a 5G module, a location information acquisition module, and a prediction module. The high-speed rail 5G signal distribution route map generation device based on Bayesian estimation localization includes: The information acquisition module is used to acquire target network data through the 5G module and actual location information through the location information acquisition module. The target network data is acquired at multiple acquisition times during the operation of the high-speed rail, and the actual location information includes BDS positioning information and LBS ranging information. The positioning information acquisition module is used to generate current route database positioning information and acquire historical route database positioning information based on the BDS positioning information and the LBS ranging information at a first time, wherein the first time is any of the acquisition times. The location prediction module is used to predict the first predicted location information at a second time based on the current route database location information, and to predict the second predicted location information at a second time based on the historical route database location information, wherein the second time is the next acquisition time after the first time. The Bayesian estimation module is used to perform Bayesian estimation based on the first predicted location information and the second predicted location information to determine the target predicted location information at the second time, and to correct the actual location information at the second time to the target location information based on the target predicted location information. The route map generation module is used to generate a high-speed rail 5G signal distribution route map based on the target network data and the corrected target location information.

[0010] According to some embodiments of the present invention, the information acquisition module further includes: The parsing module is used to acquire the detected terminal device data and parse the terminal device data into device parsing data according to the preset decoding configuration information. The conversion module is used to convert the device-parsed data into a target format data packet according to a preset file format type; The storage module is used to parse and split the target format data packet according to the preset storage format type information to obtain the target network data.

[0011] According to some embodiments of the present invention, the 5G wireless network testing equipment is equipped with a BDS device, and the information acquisition module further includes: An initialization module is used to initialize the BDS device and the 5G module; The acquisition and startup module is used to acquire the BDS positioning information, BDS speed information and BDS time information collected by the BDS device after receiving the high-speed rail startup signal, and to obtain the LBS ranging information, LBS speed information and LBS time information through the 5G module.

[0012] According to some embodiments of the present invention, the information acquisition module further includes: The route information acquisition module is used to acquire preset high-speed rail route information, wherein the high-speed rail route information includes the latitude and longitude of the high-speed rail route; The line cutting module is used to cut the high-speed rail line information into multiple line location sets based on a preset scale, wherein each line location set corresponds to a collection time.

[0013] Thirdly, embodiments of the present invention provide a 5G wireless network testing device, including at least one control processor and a memory for communicatively connecting with the at least one control processor; the memory stores instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to execute the high-speed rail 5G signal distribution route map generation method based on Bayesian estimation positioning as described in the first aspect above.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for executing the method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation positioning as described in the first aspect above.

[0015] The method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation positioning according to an embodiment of the present invention has at least the following beneficial effects: Target network data is acquired through a 5G module; actual location information is acquired through the location information acquisition module, wherein the target network data is acquired at multiple acquisition times during high-speed rail operation; the actual location information includes BDS positioning information and LBS ranging information; based on a first time point, current route database positioning information is generated according to the BDS positioning information and the LBS ranging information, and historical route database positioning information is acquired, wherein the first time point is any one of the acquisition times; based on the prediction module, a first predicted location information for a second time point is predicted according to the current route database positioning information, and a second predicted location information for the second time point is predicted according to the historical route database positioning information, wherein the second time point is the next acquisition time point after the first time point; based on the first predicted location information and the second predicted location information, Bayesian estimation is performed to determine the target predicted location information for the second time point; based on the target predicted location information, the actual location information for the second time point is corrected to the target location information; a high-speed rail 5G signal distribution route map is generated based on the target network data and the corrected target location information. According to the technical solution of the present invention, a high-speed rail 5G signal distribution map can be accurately generated by utilizing historical experience location, BDS positioning information and LBS ranging information, dividing the high-speed rail line into multiple segments for information collection, and predicting the next location based on Bayesian estimation for location correction, thereby reducing hardware costs. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation localization, provided in one embodiment of the present invention; Figure 2 This is a structural diagram of a high-speed rail 5G signal distribution route map generation device based on Bayesian estimation localization provided in another embodiment of the present invention; Figure 3 This is a structural diagram of a 5G wireless network testing device provided in another embodiment of the present invention; Detailed Implementation Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0017] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0018] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0019] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0020] This invention provides a method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation positioning, comprising: acquiring target network data through a 5G module; acquiring actual location information through a location information acquisition module, wherein the target network data is acquired at multiple acquisition times during high-speed rail operation, and the actual location information includes BDS positioning information and LBS ranging information; generating current route database positioning information based on a first time point, according to the BDS positioning information and the LBS ranging information, and acquiring historical route database positioning information, wherein the first time point is any one of the acquisition times; predicting a first predicted location information for a second time point based on the current route database positioning information, and predicting a second predicted location information for the second time point based on the historical route database positioning information, wherein the second time point is the next acquisition time point after the first time point; performing Bayesian estimation based on the first predicted location information and the second predicted location information to determine the target predicted location information for the second time point; correcting the actual location information for the second time point to the target location information based on the target predicted location information; and generating a high-speed rail 5G signal distribution route map based on the target network data and the corrected target location information. According to the technical solution of the present invention, a high-speed rail 5G signal distribution map can be accurately generated by utilizing historical experience location, BDS positioning information and LBS ranging information, dividing the high-speed rail line into multiple segments for information collection, and predicting the next location based on Bayesian estimation for location correction, thereby reducing hardware costs.

[0021] The following is a brief explanation of the terminology used in this embodiment: BDS: Beidou Navigation Satellite System; LBS: Location Based Services.

[0022] The technical solutions of the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0023] Reference Figure 1 , Figure 1 A flowchart of a method for generating a high-speed rail 5G signal distribution map based on Bayesian estimation localization is provided in this embodiment of the invention. The method includes, but is not limited to, the following steps: S10 acquires target network data through a 5G module and actual location information through a location information acquisition module. The target network data is acquired at multiple acquisition times during the operation of the high-speed train, and the actual location information includes BDS positioning information and LBS ranging information. S20, based on the first moment, generate the current route database positioning information according to the BDS positioning information and LBS ranging information, and obtain the historical route database positioning information. The first moment can be any collection moment. S30, based on the prediction module, predict the first predicted position information at the second time according to the current line database positioning information, and predict the second predicted position information at the second time according to the historical line database positioning information, wherein the second time is the next collection time after the first time; S40, Based on the first predicted position information and the second predicted position information, Bayesian estimation is performed to determine the target predicted position information at the second time, and the actual position information at the second time is corrected to the target position information based on the target predicted position information; S50 generates a high-speed rail 5G signal distribution map based on target network data and corrected target location information.

[0024] It should be noted that the technical solution of this embodiment is applied to a 5G wireless network testing device, which includes a 5G module, a location information acquisition module, and a prediction module. The 5G module is a communication module that provides 5G network signals, the location information acquisition module is used for BDS location information and LBS location information, and the prediction module has a built-in Bayesian estimation parameter model, which can perform Bayesian estimation based on the input information.

[0025] It should be noted that the network data generated by the 5G module during high-speed rail operation, such as network speed and network latency, can be selected according to actual testing requirements. This embodiment collects and strategizes the target network data generated by the 5G module for subsequent generation of a high-speed rail 5G signal distribution map.

[0026] It should be noted that this embodiment uses a location information acquisition module to collect actual location information at each acquisition time. The actual location information can be latitude and longitude. This embodiment improves positioning accuracy by using two types of positioning information: BDS positioning information and LBS ranging information. The interval between acquisition times can be set according to actual needs. For example, in a later embodiment, after the high-speed rail line is divided into multiple segments, the time of arrival at each segment is determined as the acquisition time.

[0027] It should be noted that the technical solution of this embodiment utilizes historical information for location prediction. For ease of description, this embodiment defines the current time of each prediction as the first time and the next time as the second time. This embodiment acquires the BDS positioning information and LBS ranging information of the first time from the collected information to generate the current route database positioning information. The current route database positioning information consists of the positioning information of the high-speed train at each collection time obtained for this current journey. It also acquires the historical route database positioning information, which consists of the positioning information of the high-speed train at each collection time obtained in the past along the same route. By constructing the current route database positioning information and the historical route database positioning information, this embodiment can use historical information to predict the route.

[0028] It should be noted that this embodiment can predict the position at the second moment based on the first moment, the high-speed rail operating speed and the actual position information. First, the first predicted position information is predicted based on the current line database positioning information. The first predicted position information represents the prediction of this trip. Then, the second predicted position information is predicted based on the historical line database positioning information. The second predicted position information represents the prediction based on the historical trip.

[0029] It should be noted that the formula for Bayesian estimation is: ,in, Assuming a posterior distribution, and using location information from a historical route database, we predict the probability distribution of the location based on known location data X and parameters θ. Let be the likelihood function, representing the probability of locating data X given parameters θ. Let X be the prior distribution, representing the prior probability of the location data X given the parameter θ. As evidence factors, this embodiment performs Bayesian estimation using first and second predicted location information. The first predicted location information is the aforementioned posterior distribution, and the second predicted location information is the aforementioned prior distribution, thereby calculating... The system predicts the target's position information. After acquiring the actual position information at the second moment, it corrects the actual position information to the target's position information using the predicted position information, thereby achieving accurate positioning without an inertial navigation gyroscope.

[0030] It should be noted that after performing the above steps for each collection time, the target network data and target location information corresponding to each collection time are obtained. Based on this, a high-speed rail 5G signal distribution map is constructed. The more samples there are, the higher the accuracy of the high-speed rail 5G signal distribution map data.

[0031] In another embodiment, in step S10, target network data is obtained through the 5G module, which specifically includes, but is not limited to, the following steps: S111, acquire the detected terminal device data, and parse the terminal device data into device parsing data according to the preset decoding configuration information; S112, Convert the device parsing data into a target format data packet according to the preset file format type; S113, parse and split the target format data packet according to the preset storage format type information to obtain the target network data.

[0032] It should be noted that 5G modules generate terminal device data in the communication environment, such as the network speed mentioned above. Terminal device data is usually encapsulated through relevant protocols. Therefore, in this embodiment, the terminal device data is parsed according to the preset decoding configuration information to obtain device parsing data. Then, the device parsing data is converted into target format data packets according to the preset file format type. The target format data packets are parsed and split according to the storage format type information, so that the terminal device data is converted into target network data that can be read later. Moreover, it is stored in a fixed format, which effectively improves the convenience of data reading.

[0033] In another embodiment, the 5G wireless network testing equipment is equipped with a BDS device. In step S10, the actual location information is obtained through the location information acquisition module, which specifically includes, but is not limited to, the following steps: S121, Initialize the BDS device and 5G module; S122, after obtaining the high-speed rail start signal, acquires the BDS positioning information, BDS speed information and BDS time information collected by the BDS device, and obtains LBS ranging information, LBS speed information and LBS time information through LBS ranging via the 5G module.

[0034] It should be noted that in this embodiment, the BDS device is built into the 5G wireless network testing equipment. The BDS device and the 5G module are initialized before the high-speed train starts running, so that data can be acquired in a timely manner after the high-speed train starts.

[0035] It should be noted that in this embodiment, BDS positioning information, BDS speed information, and BDS time information are collected through BDS devices, and LBS ranging information, LBS speed information, and LBS time information are obtained through LBS ranging using a 5G module. This allows for the matching of the two types of location information by time, ensuring the accuracy of subsequent calculations.

[0036] In another embodiment, before performing step S30, the following steps are included, but are not limited to: S301, Obtain preset high-speed rail line information, including the latitude and longitude of the high-speed rail line; S302, based on a preset scale, divides the high-speed rail line information into multiple line location sets, where each line location set corresponds to a collection time.

[0037] It should be noted that since high-speed rail lines are fixed, high-speed rail line information can be preset, mainly including the latitude and longitude information along the high-speed rail line.

[0038] It should be noted that in this embodiment, the high-speed rail line is divided into data scale segments, which are divided into multiple line location sets. Each line location set includes multiple latitude and longitude information. For example, the high-speed rail line can be divided into multiple line segments. All the latitude and longitude corresponding to each line segment form a line location set. Data collection is performed once at the location corresponding to this line location set to obtain the data corresponding to a collection time.

[0039] Additionally, refer to Figure 2 This invention provides a high-speed rail 5G signal distribution route map generation device 10 based on Bayesian estimation localization, which is installed in a 5G wireless network testing device. The 5G wireless network testing device includes a 5G module, a location information acquisition module, and a prediction module. The high-speed rail 5G signal distribution route map generation device 10 based on Bayesian estimation localization includes: The information acquisition module 100 is used to acquire target network data through the 5G module and actual location information through the location information acquisition module. The target network data is acquired at multiple acquisition times during the operation of the high-speed rail, and the actual location information includes BDS positioning information and LBS ranging information. The positioning information acquisition module 200 is used to generate current route database positioning information and acquire historical route database positioning information based on BDS positioning information and LBS ranging information at a first moment. The first moment can be any collection moment. The location prediction module 300 is used to predict the first predicted location information at the second time based on the current route database location information and the second predicted location information at the second time based on the historical route database location information, wherein the second time is the next acquisition time after the first time. The Bayesian estimation module 400 is used to perform Bayesian estimation based on the first predicted position information and the second predicted position information to determine the target predicted position information at the second time, and to correct the actual position information at the second time to the target position information based on the target predicted position information. The route map generation module 500 is used to generate a high-speed rail 5G signal distribution route map based on the target network data and the corrected target location information.

[0040] It should be noted that the principle of the high-speed rail 5G signal distribution route map generation device 1010 based on Bayesian estimation positioning can be referred to the description of the above method embodiment, and will not be repeated here.

[0041] The technical solution of this embodiment can utilize historical experience location, BDS positioning information and LBS ranging information to collect information by dividing the high-speed rail line into multiple segments, and make position corrections based on Bayesian estimation to predict the next position, thereby accurately generating a high-speed rail 5G signal distribution map while reducing hardware costs.

[0042] In another embodiment, the information acquisition module 100 further includes: The parsing module is used to acquire the detected terminal device data and parse the terminal device data into device parsing data according to the preset decoding configuration information. The conversion module is used to convert device-parsed data into target format data packets according to preset file format types; The storage module is used to parse and split the target format data packets according to the preset storage format type information to obtain the target network data.

[0043] It should be noted that the principle by which the information acquisition module 100 acquires target network data can be referred to the description of the above method embodiments, and will not be repeated here.

[0044] In another embodiment, the 5G wireless network testing equipment is equipped with a BDS device, and the information acquisition module 100 further includes: Initialization module, used to initialize BDS devices and 5G modules; The acquisition and startup module is used to acquire the BDS positioning information, BDS speed information, and BDS time information collected by the BDS device after receiving the high-speed rail startup signal, and obtain LBS ranging information, LBS speed information, and LBS time information through LBS ranging via the 5G module.

[0045] It should be noted that the principle of the initialization information collection module 100 can be referred to the description of the above method embodiment, and will not be repeated here.

[0046] In another embodiment, the information acquisition module 100 further includes: The route information acquisition module 200 is used to acquire preset high-speed rail route information, which includes the latitude and longitude of the high-speed rail route. The line cutting module is used to cut high-speed rail line information into multiple line location sets based on a preset scale, where each line location set corresponds to a collection time.

[0047] It should be noted that the principle for determining the acquisition time can be found in the description of the above method embodiments, and will not be repeated here.

[0048] like Figure 3 As shown, Figure 3 This is a structural diagram of a 5G wireless network testing device provided in one embodiment of the present invention. The present invention also provides a 5G wireless network testing device, comprising: The processor 301 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 302 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 302 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302 and is called and executed by the processor 301 to execute the high-speed rail 5G signal distribution route map generation method based on Bayesian estimation positioning of the embodiments of this application. Input / output interface 303 is used to implement information input and output; The communication interface 304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 305 transmits information between various components of the device (e.g., processor 301, memory 302, input / output interface 303, and communication interface 304); The processor 301, memory 302, input / output interface 303, and communication interface 304 are connected to each other within the device via bus 305.

[0049] This application embodiment also provides a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-described method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation positioning.

[0050] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0051] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0052] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation localization, characterized in that, The method is applied to a 5G wireless network testing device, which includes a 5G module, a location information acquisition module, and a prediction module. The target network data is obtained through the 5G module, and the actual location information is obtained through the location information acquisition module. The target network data is collected at multiple acquisition times during the operation of the high-speed rail, and the actual location information includes BDS positioning information and LBS ranging information. Based on the first moment, the current route database positioning information is generated according to the BDS positioning information and the LBS ranging information, and the historical route database positioning information is obtained, wherein the first moment is any of the acquisition moments; Based on the prediction module, the first predicted location information at the second time is predicted according to the current route database location information, and the second predicted location information at the second time is predicted according to the historical route database location information, wherein the second time is the next collection time after the first time. Based on the first predicted location information and the second predicted location information, Bayesian estimation is performed to determine the target predicted location information at the second time moment, and the actual location information at the second time moment is corrected to the target location information based on the target predicted location information; A high-speed rail 5G signal distribution map is generated based on the target network data and the corrected target location information.

2. The method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation localization according to claim 1, characterized in that, Data from the target network is obtained through a 5G module, including: Acquire detected terminal device data, and parse the terminal device data into device parsing data according to preset decoding configuration information; The device parsing data is converted into a target format data packet according to the preset file format type; The target format data packet is parsed and split according to the preset storage format type information to obtain the target network data.

3. The method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation localization according to claim 1, characterized in that, The 5G wireless network testing equipment is equipped with a BDS device, which acquires actual location information through the location information acquisition module, including: Initialize the BDS device and the 5G module; After obtaining the high-speed rail start signal, the BDS positioning information, BDS speed information and BDS time information collected by the BDS device are obtained, and the LBS ranging information, LBS speed information and LBS time information are obtained by LBS ranging through the 5G module.

4. The method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation localization according to claim 1, characterized in that, Before predicting the first predicted location information at the second moment based on the current route database location information, the method further includes: Obtain preset high-speed rail line information, wherein the high-speed rail line information includes the latitude and longitude of the high-speed rail line; The high-speed rail line information is divided into multiple line location sets based on a preset scale, wherein each line location set corresponds to a collection time.

5. A high-speed rail 5G signal distribution route map generation device based on Bayesian estimation localization, installed in a 5G wireless network testing device, the 5G wireless network testing device including a 5G module, a location information acquisition module, and a prediction module, the high-speed rail 5G signal distribution route map generation device based on Bayesian estimation localization includes: The information acquisition module is used to acquire target network data through the 5G module and actual location information through the location information acquisition module. The target network data is acquired at multiple acquisition times during the operation of the high-speed rail, and the actual location information includes BDS positioning information and LBS ranging information. The positioning information acquisition module is used to generate current route database positioning information and acquire historical route database positioning information based on the BDS positioning information and the LBS ranging information at a first time, wherein the first time is any of the acquisition times. The location prediction module is used to predict the first predicted location information at a second time based on the current route database location information, and to predict the second predicted location information at a second time based on the historical route database location information, wherein the second time is the next acquisition time after the first time. The Bayesian estimation module is used to perform Bayesian estimation based on the first predicted location information and the second predicted location information to determine the target predicted location information at the second time, and to correct the actual location information at the second time to the target location information based on the target predicted location information. The route map generation module is used to generate a high-speed rail 5G signal distribution route map based on the target network data and the corrected target location information.

6. The high-speed rail 5G signal distribution route map generation device based on Bayesian estimation localization according to claim 5, characterized in that, The information acquisition module also includes: The parsing module is used to acquire the detected terminal device data and parse the terminal device data into device parsing data according to the preset decoding configuration information. The conversion module is used to convert the device-parsed data into a target format data packet according to a preset file format type; The storage module is used to parse and split the target format data packet according to the preset storage format type information to obtain the target network data.

7. The high-speed rail 5G signal distribution route map generation device based on Bayesian estimation localization according to claim 5, characterized in that, The 5G wireless network testing equipment is equipped with a BDS device, and the information acquisition module also includes: An initialization module is used to initialize the BDS device and the 5G module; The acquisition and startup module is used to acquire the BDS positioning information, BDS speed information and BDS time information collected by the BDS device after receiving the high-speed rail startup signal, and to obtain the LBS ranging information, LBS speed information and LBS time information through the 5G module.

8. The high-speed rail 5G signal distribution route map generation device based on Bayesian estimation localization according to claim 5, characterized in that, The information collection module also includes: The route information acquisition module is used to acquire preset high-speed rail route information, wherein the high-speed rail route information includes the latitude and longitude of the high-speed rail route; The line cutting module is used to cut the high-speed rail line information into multiple line location sets based on a preset scale, wherein each line location set corresponds to a collection time.

9. A 5G wireless network testing device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation positioning as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to execute the method for generating a high-speed rail 5G signal distribution route map based on Bayesian estimation positioning as described in any one of claims 1 to 4.

Images