Information determination method and device, equipment, storage medium and computer program product

Abstract

The invention discloses an information determination method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: a first device obtains a plurality of motion tracks of a target object; the plurality of motion tracks are respectively sent to the first device by a plurality of network devices; determining an actual track of the target object based on the plurality of motion tracks of the target object; determining at least two cells with optimal signal coverage according to the current position of the target object in the actual track; and determining beam weight configuration information of the at least two cells based on the multiple beam parameter combinations of the at least two cells.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to an information determination method, device, equipment, storage medium and computer program product. Background Art

[0002] At present, the beam weight optimization scheme can rely on a single site to optimize the beam weight, and completely rely on the characteristics of the communication channel to confirm the position of the target object and adjust the beam. However, the accuracy of relying on a single site to optimize the beam weight is poor, and the beam coverage effect is not good when the position of the target object is identified through the communication channel and the beam weight is adjusted. Summary of the invention

[0003] In view of this, embodiments of the present application hope to provide an information determination method, apparatus, device, storage medium and computer program product.

[0004] The technical solution of the embodiment of the present application is implemented as follows:

[0005] The present application provides an information determination method, which is applied to a first device, and the method includes:

[0006] Acquire multiple motion trajectories of the target object; the multiple motion trajectories are sent to the first device by multiple network devices respectively;

[0007] Determining an actual trajectory of the target object based on a plurality of motion trajectories of the target object;

[0008] Determine at least two cells with the best signal coverage for the current position of the target object in the actual trajectory;

[0009] Based on the multiple beam parameter combinations of the at least two cells, beam weight configuration information of the at least two cells is determined.

[0010] In addition, according to at least one embodiment of the present application, each of the multiple motion trajectories includes multiple trajectory points; the trajectory points include at least one of the following information: distance, speed, and angle.

[0011] In addition, according to at least one embodiment of the present application, determining the actual trajectory of the target object based on the multiple motion trajectories of the target object includes:

[0012] Randomly selecting two motion trajectories from a plurality of motion trajectories of the target object;

[0013] Perform joint calibration based on the two motion trajectories to obtain a calibrated motion trajectory;

[0014] The calibrated motion trajectory is used as the actual trajectory of the target object.

[0015] In addition, according to at least one embodiment of the present application, performing joint calibration based on the two motion trajectories to obtain a calibrated motion trajectory includes:

[0016] For the target object at each of the multiple same positions, perform the following operations:

[0017] Determine a first position point of the target object in the two motion trajectories to obtain two first position points;

[0018] Determine first distances between the two first location points and the respective corresponding network devices to obtain two first distances, and determine a second distance between the two first location points;

[0019] determining a position of a calibration point based on the two first distances and the second distance;

[0020] Based on the positions of the calibration points, the actual trajectory of the target object is obtained.

[0021] In addition, according to at least one embodiment of the present application, the at least two cells with the best signal coverage meet the following conditions:

[0022] The angle between the antenna coverage direction of the cell and the line connecting the base points is within a first preset range;

[0023] and / or,

[0024] The distance between the location of the cell and the base point is within a second preset range;

[0025] The base point is the current position of the target object in the actual trajectory.

[0026] In addition, according to at least one embodiment of the present application, the determining the beam weight configuration information of the at least two cells based on the multiple beam parameter combinations of the at least two cells includes:

[0027] In the first iteration, N beam parameter combinations are randomly selected from the multiple beam parameter combinations of the at least two cells; through simulation, the signal coverage rate of the actual trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations is obtained, and the beam parameter combination with the largest signal coverage rate is taken as the optimal parameter combination; wherein the signal coverage rate is determined by the signal strength at multiple sampling points in the actual trajectory; N is an integer greater than or equal to 2;

[0028] In the mth iteration, taking the optimal parameter combination after the m-1th iteration as a benchmark, randomly selecting N beam parameter combinations from the multiple beam parameter combinations, obtaining the signal coverage of the actual trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations through simulation, and taking the beam parameter combination with the largest signal coverage as the optimal parameter combination; wherein the signal coverage is determined by the signal strength at multiple sampling points in the actual trajectory; m is an integer variable that increases from 2; and N is an integer greater than or equal to 2;

[0029] In response to the number of times that the optimal parameter combination after the Mth iteration remains unchanged reaches a preset threshold, the optimal parameter combination after the Mth iteration is used as the beam weight configuration information of the at least two cells; M is an integer greater than 2.

[0030] In addition, according to at least one embodiment of the present application, the method further includes:

[0031] Based on different values ​​of the beam parameters of the at least two cells, obtaining multiple beam parameter combinations of the at least two cells;

[0032] in,

[0033] The beam parameters of each of the at least two cells include at least one of the following:

[0034] Horizontal beam width;

[0035] Beam downtilt;

[0036] Beam direction angle.

[0037] In addition, according to at least one embodiment of the present application, the step of randomly selecting N beam parameter combinations from the multiple beam parameter combinations based on the optimal parameter combination after the m-1th iteration includes:

[0038] Taking the preset fluctuation range of each beam parameter in the optimal parameter combination after the (m-1)th iteration as a reference condition, N beam parameter combinations that meet the reference condition are selected from the multiple beam parameter combinations.

[0039] In addition, according to at least one embodiment of the present application, the method further includes:

[0040] The beam weight configuration information of the at least two cells is sent to network devices corresponding to the at least two cells.

[0041] In addition, according to at least one embodiment of the present application, the method further includes:

[0042] A signal test result sent by a receiving terminal; the signal test result represents a signal coverage rate of an actual track of the target object;

[0043] When the signal test result does not meet the preset condition, the actual trajectory of the target object is re-determined based on the multiple motion trajectories of the target object.

[0044] At least one embodiment of the present application provides an information determination device, including:

[0045] An acquisition module, used for acquiring multiple motion trajectories of a target object; the multiple motion trajectories are respectively sent to the first device by multiple network devices;

[0046] A first processing module, configured to determine an actual trajectory of the target object based on a plurality of motion trajectories of the target object;

[0047] The second processing module is used to determine at least two cells with the best signal coverage based on the current position of the target object in the actual trajectory; and determine the beam weight configuration information of the at least two cells based on multiple beam parameter combinations of the at least two cells.

[0048] At least one embodiment of the present application provides a first device, including a processor and a memory for storing a computer program that can be run on the processor.

[0049] Wherein, when the processor is used to run the computer program, it executes the steps of any one of the methods described above on the first device side.

[0050] At least one embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods described above on the first device side.

[0051] At least one embodiment of the present application provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, it implements any of the methods described above on the first device side.

[0052] The information determination method, apparatus, device, storage medium and computer program product provided in the embodiments of the present application include: a first device acquires multiple motion trajectories of a target object; the multiple motion trajectories are respectively sent to the first device by multiple network devices; based on the multiple motion trajectories of the target object, an actual trajectory of the target object is determined; for the current position of the target object in the actual trajectory, at least two cells with the best signal coverage are determined; based on multiple beam parameter combinations of the at least two cells, beam weight configuration information of the at least two cells is determined.

[0053] By adopting the technical solution provided in the embodiment of the present application, the position of the target object is sensed with the help of multiple network devices, and the actual trajectory of the target object is accurately determined. After selecting at least two cells with the best signal coverage according to the current position of the target object in the actual trajectory, the optimal beam weight configuration covering the at least two cells is found to achieve precise beam coverage, which helps to improve the network coverage and quality of the target object. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the implementation process of the information determination method of the embodiment of the present application;

[0055] Figure 2 It is a schematic diagram of a specific implementation process of the information determination method of the embodiment of the present application;

[0056] Figure 3 is a schematic diagram of measuring the distance, angle and speed of a target object in an embodiment of the present application;

[0057] Figure 4 is a schematic diagram of the motion trajectory of the target object in the embodiment of the present application;

[0058] Figure 5 is a schematic diagram of two motion trajectories used in the joint calibration in an embodiment of the present application;

[0059] Figure 6 is a schematic diagram of joint calibration of two motion trajectories in an embodiment of the present application;

[0060] Figure 7 is a schematic diagram of a sensing beam according to an embodiment of the present application;

[0061] Figure 8 is a schematic diagram of a communication beam according to an embodiment of the present application;

[0062] Fig. 9 is a schematic diagram of determining at least two cells with the best signal coverage in an embodiment of the present application;

[0063] Fig.10 This is a schematic diagram of determining the optimal parameter combination in the embodiment of the present application. Figure 1 ;

[0064] Fig.11 This is a schematic diagram of determining the optimal parameter combination in the embodiment of the present application. Figure 2

[0065] Fig.12 This is a schematic diagram of determining the optimal parameter combination in the embodiment of the present application. Figure 3 ;

[0066] Fig.13 It is a schematic diagram of the composition structure of the information determination device according to an embodiment of the present application;

[0067] Fig.14 It is a schematic diagram of the composition structure of the first device in an embodiment of the present application. DETAILED DESCRIPTION

[0068] Before introducing the technical solutions of the embodiments of the present application, the related technologies are first introduced.

[0069] In the related art, the existing beam weight optimization scheme has the following technical defects: First, it mainly relies on a single site to optimize the beam weight, and does not involve a scheme for collaborative optimization of beam weights by multiple sites. Compared with the scheme for collaborative optimization of beam weights by multiple sites, the accuracy of relying on a single site to optimize the beam weight is poor; Second, it completely relies on the characteristics of the communication channel to confirm the user's position and adjust the beam, and does not use the perception channel to assist in locating the object. Since the accuracy of the synaesthesia channel in perceiving objects is sub-meter level, the weight following accuracy of the traditional scheme is not as high as the accuracy of optimizing the beam weight based on the perception channel assistance in the embodiment of the present application.

[0070] Based on this, in an embodiment of the present application, a first device obtains multiple motion trajectories of a target object; the multiple motion trajectories are respectively sent to the first device by multiple network devices; based on the multiple motion trajectories of the target object, an actual trajectory of the target object is determined; for the current position of the target object in the actual trajectory, at least two cells with the best signal coverage are determined; based on multiple beam parameter combinations of the at least two cells, beam weight configuration information of the at least two cells is determined.

[0071] See also Figure 1 , Figure 1 is a schematic diagram of the implementation flow of the information determination method of the embodiment of the present application, which is applied to the first device, such as Figure 1 As shown, the method includes steps 101 to 104:

[0072] Step 101: Acquire multiple motion trajectories of a target object; the multiple motion trajectories are sent to the first device by multiple network devices respectively.

[0073] It can be understood that the first device may refer to a device having communication and perception functions, such as a synaesthesia integrated base station (abbreviated as synaesthesia base station).

[0074] It is understandable that the network device may refer to a device with a sensing function, such as a sensing base station, or a synaesthesia integrated base station (abbreviated as synaesthesia base station).

[0075] In some embodiments, each of the plurality of motion trajectories includes a plurality of trajectory points; the trajectory points include at least one of the following information: distance, speed, and angle.

[0076] The distance may represent the distance between the target object and the corresponding network device, the angle may represent the angle between the direction of the target object and the vertical plane where the corresponding network device is located, and the speed may represent the moving speed of the target object.

[0077] As an example, the motion trajectory of the target object can be identified based on the network device. Specifically, the motion trajectory of the target object can be mainly identified based on radio waves (radar waves), analyzing the direct, reflected, and scattered characteristics of the radio waves, measuring the distance, speed, and angle of the target object, and forming the motion trajectory of the target object; wherein the distance measurement is mainly calculated by the time required for the radar wave to go back and forth, the angle measurement is mainly determined by the direction of the reflected wave, and the speed measurement is calculated based on the change in the frequency of the electromagnetic wave brought by the Doppler cell.

[0078] Here, taking the network device as a telepathic base station as an example, the telepathic base station measures the distance of the target object to obtain a distance L1=C×(t1 / 2) between the target object and the telepathic base station, wherein C represents the speed of light, and t1 represents the time difference between the radar wave emitted by the telepathic base station to the target object and the echo; the telepathic base station measures the angle of the target object, specifically, the telepathic base station emits a radar wave to the target object, if the echo is the strongest, the direction of the emitted radar wave is the direction of the target object, and the angle between the direction of the target object and the vertical plane where the telepathic base station is located is an angle; the telepathic base station measures the speed of the target object, specifically, the telepathic base station determines the moving speed V=(f1×C) / (f×2) of the target object according to the difference between the received echo frequency and the emitted radar wave frequency, i.e., the Doppler frequency, wherein f1 represents the Doppler frequency, C represents the speed of light, and f represents the emitted radar wave frequency.

[0079] Step 102: Determine the actual trajectory of the target object based on the multiple motion trajectories of the target object.

[0080] In practical applications, after obtaining the motion trajectory of the target object sent by multiple network devices, the motion trajectory of the target object can be calibrated from multiple perspectives to obtain the actual trajectory of the target object.

[0081] Based on this, in some embodiments, determining the actual trajectory of the target object based on the multiple motion trajectories of the target object includes:

[0082] Randomly selecting two motion trajectories from a plurality of motion trajectories of the target object;

[0083] Perform joint calibration based on the two motion trajectories to obtain a calibrated motion trajectory;

[0084] The calibrated motion trajectory is used as the actual trajectory of the target object.

[0085] In some embodiments, the performing joint calibration based on the two motion trajectories to obtain a calibrated motion trajectory includes:

[0086] For the target object at each of the multiple same positions, perform the following operations:

[0087] Determine a first position point of the target object in the two motion trajectories to obtain two first position points;

[0088] Determine first distances between the two first location points and the respective corresponding network devices to obtain two first distances, and determine a second distance between the two first location points;

[0089] determining a position of a calibration point based on the two first distances and the second distance;

[0090] Based on the positions of the calibration points, the actual trajectory of the target object is obtained.

[0091] As an example, the multiple network devices can collect trajectory points at the same preset time interval within the same preset time period, and obtain the position coordinates of the target object in the same coordinate system at each collection moment, and form the motion trajectory of each target object through the collected multiple trajectory points. The motion trajectory includes multiple trajectory points, each trajectory point carries the collection moment, the distance corresponding to the collection moment, and the position coordinates of the target object; wherein the distance represents the distance between the target object and the corresponding network device.

[0092] Thus, after randomly selecting two motion trajectories from the multiple motion trajectories of the target object, the process of determining the position of the calibration point may include:

[0093] First, the first device may determine, according to the same acquisition moment, trajectory points of the target object at the same position in two motion trajectories, namely, the first position points, to obtain the two first position points;

[0094] Then, according to the distance corresponding to the same collection moment, the first distance between the two first position points and the respectively corresponding network devices is determined; according to the position coordinates of the target object corresponding to the same collection moment, the position coordinates of the two first position points in the same coordinate system are determined, and then according to the position coordinates of the two first position points in the same coordinate system, the second distance between the two first position points is calculated;

[0095] Finally, determine the ratio between the two first distances; based on the ratio between the two first distances and the second distance, determine the third distance between the calibration point and one of the two first position points; and determine the position of the calibration point according to the third distance and the position coordinates of the two first position points in the same coordinate system.

[0096] Specifically, considering that the closer the target object is to the network device, the higher the measurement accuracy of the network device, that is, the more accurate the first position point measured, the closer the position of the calibration point is to the first position point measured by the network device. Assume that the two first position points are represented by point A and point B, respectively, and the corresponding network devices are the first network device and the second network device, respectively, wherein the first distance between the first position point (point A) and the first network device is S1, and the first distance between the first position point (point B) and the second network device is S2, the distance between the calibration point and the first position point (point A) measured by the first network device is Si, and the distance between the calibration point and the first position point (point B) measured by the second network device is Sj, then the following formula (1) is satisfied:

[0097]

[0098] Here, formula (2) can be derived from formula (1), and formula (3) can be derived from formula (2), and the third distance between the calibration point and the first position point (point B) measured by the second network device is determined by formula (3):

[0099]

[0100] Among them, Sj represents the third distance between the calibration point and the first position point (point B) measured by the second network device, Si represents the distance between the calibration point and the first position point (point A) measured by the first network device, S represents the second distance between the two first position points, S2 represents the first distance between the first position point (point B) of the target object measured by the second network device and the second network device, and S1 represents the first distance between the first position point (point A) of the target object measured by the first network device and the first network device.

[0101] Here, after determining the third distance between the calibration point and the first position point (point B) measured by the second network device, the position of the calibration point is determined by combining the position coordinates of the two first position points (point A and point B) in the same coordinate system.

[0102] Here, a calibrated motion trajectory is formed by using calibration points determined for each target object at the same position, and the calibrated motion trajectory is used as the actual trajectory of the target object.

[0103] Step 103: Determine at least two cells with the best signal coverage for the current position of the target object in the actual track.

[0104] In some embodiments, the at least two cells with the best signal coverage meet the following conditions:

[0105] The angle between the antenna coverage direction of the cell and the line connecting the base points is within a first preset range;

[0106] and / or,

[0107] The distance between the location of the cell and the base point is within a second preset range;

[0108] The base point is the current position of the target object in the actual trajectory.

[0109] It can be understood that the antenna coverage direction of a cell can be understood as the normal direction of the antenna surface coverage of the cell.

[0110] For example, taking the current position of the target object in the actual trajectory as the base point, filter the cells whose distance between the cell position and the base point is within the second preset range. Assuming that the three cells closest to the base point satisfy the distance between the cell position and the base point within the second preset range, then these three cells are the best coverage cells.

[0111] Step 104: Determine beam weight configuration information of the at least two cells based on multiple beam parameter combinations of the at least two cells.

[0112] It can be understood that the beam parameter combination refers to a combination of a beam downtilt angle, a beam horizontal width, and a beam direction angle.

[0113] In practical applications, in order to quickly calculate the optimal combination of weights, a particle swarm algorithm can be used to quickly approximate the optimal solution and quickly calculate the required weight configuration combination.

[0114] Based on this, in some embodiments, determining the beam weight configuration information of the at least two cells based on the multiple beam parameter combinations of the at least two cells includes:

[0115] In the first iteration, N beam parameter combinations are randomly selected from the multiple beam parameter combinations of the at least two cells; through simulation, the signal coverage rate of the actual trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations is obtained, and the beam parameter combination with the largest signal coverage rate is taken as the optimal parameter combination; wherein the signal coverage rate is determined by the signal strength at multiple sampling points in the actual trajectory; N is an integer greater than or equal to 2;

[0116] In the mth iteration, taking the optimal parameter combination after the m-1th iteration as a benchmark, randomly selecting N beam parameter combinations from the multiple beam parameter combinations, obtaining the signal coverage of the actual trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations through simulation, and taking the beam parameter combination with the largest signal coverage as the optimal parameter combination; wherein the signal coverage is determined by the signal strength at multiple sampling points in the actual trajectory; m is an integer variable that increases from 2; and N is an integer greater than or equal to 2;

[0117] In response to the number of times that the optimal parameter combination after the Mth iteration remains unchanged reaches a preset threshold, the optimal parameter combination after the Mth iteration is used as the beam weight configuration information of the at least two cells; M is an integer greater than 2.

[0118] It can be understood that through simulation, the signal coverage of the actual trajectory of the target object corresponding to each beam parameter combination is obtained, and the beam parameter combination with the largest signal coverage is taken as the optimal parameter combination; the signal coverage can represent the proportion of sampling points with signal strength greater than a preset threshold.

[0119] For example, assuming that N is equal to 3, represented by combination 11, combination 12, and combination 13 respectively, for combination 11, the signal strength at the sampling point, that is, the RSRP, is greater than the preset value such as -105dbm, the proportion of sampling points is 50%, for combination 12, the signal strength at the sampling point, that is, the RSRP, is greater than the preset value such as -105dbm, the proportion of sampling points is 60%, for combination 13, the signal strength at the sampling point, that is, the RSRP, is greater than the preset value such as -105dbm, the proportion of sampling points is 80%, then combination 13 is used as the optimal beam parameter combination.

[0120] In some embodiments, the method further comprises:

[0121] Based on different values ​​of the beam parameters of the at least two cells, obtaining multiple beam parameter combinations of the at least two cells;

[0122] in,

[0123] The beam parameters of each of the at least two cells include at least one of the following:

[0124] Horizontal beam width;

[0125] Beam downtilt;

[0126] Beam direction angle.

[0127] In some embodiments, the step of randomly selecting N beam parameter combinations from the plurality of beam parameter combinations based on the optimal parameter combination after the m-1th iteration includes:

[0128] Taking the preset fluctuation range of each beam parameter in the optimal parameter combination after the (m-1)th iteration as a reference condition, N beam parameter combinations that meet the reference condition are selected from the multiple beam parameter combinations.

[0129] For example, assuming that there are three cells, represented by cell 1, cell 2 and cell 3 respectively, the optimal parameter combination of the three cells is composed of the beam downtilt angle, beam horizontal width and beam direction angle of each cell, and the preset fluctuation range of ±10% of each beam parameter in the optimal parameter combination is used as the reference condition, and N beam parameter combinations that meet the reference condition are selected from multiple beam parameter combinations.

[0130] In some embodiments, the method further comprises:

[0131] The beam weight configuration information of the at least two cells is sent to network devices corresponding to the at least two cells.

[0132] In some embodiments, the method further comprises:

[0133] A signal test result sent by a receiving terminal; the signal test result represents a signal coverage rate of an actual track of the target object;

[0134] When the signal test result does not meet the preset condition, the actual trajectory of the target object is re-determined based on the multiple motion trajectories of the target object.

[0135] Here, the signal test result failing to meet the preset condition may mean that the signal coverage of the actual track of the target object is less than a preset value.

[0136] Here, the beam weight configuration information of the at least two cells is sent to the network device corresponding to the at least two cells to take effect, the signal strength at multiple sampling points in the actual trajectory of the target object is measured through the terminal, and the proportion of sampling points with signal strength greater than a preset value is counted according to the measurement results, and the sampling point proportion is reported to the first device as the signal coverage of the actual trajectory of the target object. The first device evaluates the effect of the beam weight configuration after application. If the signal coverage is less than the preset value, the optimal beam weight configuration is re-updated.

[0137] Here, re-updating the optimal beam weight configuration may refer to randomly re-selecting two motion trajectories from the multiple motion trajectories of the target object, performing joint calibration based on the two re-selected motion trajectories to obtain the actual trajectory of the target object, and determining at least two cells with the best signal coverage for the current position of the target object in the actual trajectory; and determining the beam weight configuration information of the at least two cells based on the beam parameter combination of the at least two cells.

[0138] In the embodiment of the present application, the following advantages are possessed:

[0139] (1) A scheme for automatically adjusting the communication beam direction based on the perceived trajectory is proposed.

[0140] In order to ensure network coverage of target objects such as drones and vehicles, the position of the target object is sensed with the help of the perception capabilities of multiple network devices such as perception base stations or integrated interawareness base stations. At the same time, a multi-perspective joint calibration algorithm is used to jointly calibrate the motion trajectories sent by multiple network devices to accurately draw the actual trajectory of the target object. After selecting at least two cells with the best signal coverage according to the current position of the target object in the actual trajectory, the particle swarm algorithm is used to optimize the beam weights to find the best beam configuration covering the at least two cells, so as to achieve precise beam coverage and improve the network coverage and quality of the detected target object.

[0141] (2) Compared with the existing network beam weight optimization solution, the embodiment of the present application solves the following problems:

[0142] First, the motion trajectory of the target object is collaboratively identified through multiple network devices (which can also be described as sites). It does not rely solely on the identification results of a single site, but rather obtains the actual trajectory of the target object by jointly calibrating the motion trajectories of the target object sent by multiple network devices. This can improve the recognition accuracy of the target object's position.

[0143] Second, the high-precision object perception capability of network equipment such as perception base stations or telepathy base stations is utilized to accurately identify the position of the target object, and then the actual trajectory of the target object is obtained through joint calibration. The cell with the best signal coverage is selected according to the current position of the target object in the actual trajectory, thereby providing a more reliable cell coverage position for beam weight optimization, and being able to more accurately guide the communication channel beam coverage, thereby improving the accuracy of beam weight optimization.

[0144] Third, in order to achieve precise beam coverage, the particle swarm algorithm is innovatively applied in the beam optimization process, which improves the optimization efficiency of beam weight optimization and greatly accelerates the beam optimization process. At the same time, it can find the optimal solution and greatly improve the efficiency of beam weight optimization.

[0145] See also Figure 2 , Figure 2 is a schematic diagram of a specific implementation flow of the information determination method of the embodiment of the present application, such as Figure 2 As shown, the method includes steps 201 to 205:

[0146] Step 201: A plurality of network devices respectively identify the motion trajectory of a target object, and respectively send the identified motion trajectory of the target object to a first device.

[0147] Here, the first device may refer to a device having communication and perception functions, such as a synaesthesia integrated base station (abbreviated as synaesthesia base station).

[0148] Here, the network device may refer to a device with a sensing function, such as a sensing base station, or a synaesthesia integrated base station (abbreviated as synaesthesia base station).

[0149] Here, each of the multiple motion trajectories includes multiple trajectory points; the trajectory points include at least one of the following information: distance, speed, and angle.

[0150] The distance may represent the distance between the target object and the corresponding network device, the angle may represent the angle between the direction of the target object and the vertical plane where the corresponding network device is located, and the speed may represent the moving speed of the target object.

[0151] Here, the motion trajectory of the target object can be identified based on network equipment such as a telepathic base station. Specifically, the telepathic base station can identify the motion trajectory of the target object mainly based on radio waves (radar waves), analyze the direct, reflected, and scattered characteristics of the radio waves, measure the distance, speed, and angle of the target object, and form the motion trajectory of the target object; among which, the distance measurement is mainly calculated by the time required for the radar wave to go back and forth, the angle measurement is mainly determined by the direction of the reflected wave, and the speed measurement is calculated based on the change in the frequency of the electromagnetic wave brought by the Doppler cell.

[0152] See also Figure 3 , Figure 3 FIG. 1 is a schematic diagram of an embodiment of the present application for measuring distance, angle and speed of a target object. Figure 3As shown, taking the network device as a telepathic base station as an example, the telepathic base station measures the distance of the target object to obtain a distance L1=C×(t1 / 2) between the target object and the telepathic base station, wherein C represents the speed of light, and t1 represents the time difference between the radar wave emitted by the telepathic base station to the target object and the echo; the telepathic base station measures the angle of the target object, specifically, the telepathic base station emits a radar wave to the target object, if the echo is the strongest, the direction of the emitted radar wave is the direction of the target object, and the angle between the direction of the target object and the vertical plane where the telepathic base station is located is an angle; the telepathic base station measures the speed of the target object, specifically, the telepathic base station determines the moving speed V=(f1×C) / (f×2) of the target object according to the difference between the received echo frequency and the emitted radar wave frequency, i.e., the Doppler frequency, wherein f1 represents the Doppler frequency, C represents the speed of light, and f represents the emitted radar wave frequency.

[0153] See also Figure 4 , Figure 4 is a schematic diagram of the motion trajectory of the target object in the embodiment of the present application, such as Figure 4 As shown, through the radar waves of the synesthesia base station, the measured target object such as a drone or a car is continuously detected, the orientation of the target object is tracked, and the motion trajectory of the target object is rendered. Each trajectory point in the motion trajectory may include information based on the straight-line distance between the synesthesia base station and the target object, the angle between the vertical plane where the synesthesia base station is located and the direction of the target object, the speed of the target object, etc. According to the trajectory points collected at preset time intervals within a preset time period, the motion trajectory of the target object that is finally tracked is accurately output.

[0154] Step 202: The first device jointly calibrates the motion trajectories of the target object respectively sent by the plurality of network devices to obtain an actual trajectory of the target object.

[0155] Here, after acquiring the motion trajectory of the target object respectively sent by a plurality of network devices, the motion trajectory of the target object may be jointly calibrated from multiple perspectives to obtain the actual trajectory of the target object.

[0156] Here, in the case where the first device is a 5G-A synesthesia base station, the motion trajectory of the target object identified by multiple sites can be accurately output through multi-perspective joint calibration based on the 5G-A synesthesia base station, and the actual trajectory of the target object can be accurately output.

[0157] See also Figure 5 , Figure 5 is a schematic diagram of two motion trajectories used in the joint calibration in the embodiment of the present application, such as Figure 5As shown, two motion trajectories can be randomly selected from the motion trajectories of the target object sent by multiple network devices respectively, represented by motion trajectory 1 and motion trajectory 2, and then a joint calibration is performed based on the two motion trajectories to obtain a calibrated motion trajectory, and the calibrated motion trajectory is used as the actual trajectory of the target object.

[0158] See also Figure 6 , Figure 6 is a schematic diagram of joint calibration of two motion trajectories in an embodiment of the present application, such as Figure 6 As shown, assuming that the motion trajectory used by the first device for joint calibration is motion trajectory 1 sent by network device 1 (represented by station A) and motion trajectory 2 sent by network device 2 (represented by station B), the multi-view joint calibration algorithm may include:

[0159] First, for the target object at each of the multiple identical positions, the following operations are performed: a first position point of the target object in two motion trajectories is determined to obtain two first position points.

[0160] Specifically, the network device 1 and the network device 2 collect trajectory points at the same preset time interval within the same preset time period, and obtain the position coordinates of the target object in the same coordinate system at each collection moment, and form the motion trajectory of each target object through the collected multiple trajectory points. The motion trajectory includes multiple trajectory points, each trajectory point carries the collection moment, the distance corresponding to the collection moment, and the position coordinates of the target object; wherein the distance represents the distance between the target object and the corresponding network device.

[0161] In this way, the first device can determine the first position point (represented by point A) in motion trajectory 1 and the first position point (represented by point B) in motion trajectory 2 of the target object at the same position based on the same acquisition time.

[0162] Second, determine the first distances between the two first location points and the corresponding network devices respectively to obtain two first distances, and determine the second distance between the two first location points.

[0163] Specifically, the network device 1 and the network device 2 collect trajectory points at the same preset time interval within the same preset time period, and obtain the position coordinates of the target object in the same coordinate system at each collection moment, and form the motion trajectory of each target object through the collected multiple trajectory points. The motion trajectory includes multiple trajectory points, each trajectory point carries the collection moment, the distance corresponding to the collection moment, and the position coordinates of the target object; wherein the distance represents the distance between the target object and the corresponding network device.

[0164] In this way, the first device can determine the first position point (represented by point A) in motion trajectory 1 and the first position point (represented by point B) in motion trajectory 2 of the target object at the same position according to the same acquisition time, and determine the first distance (represented by S1) from the first position point (point A) of the target object measured by network device 1 (station A) to station A, and the first distance (represented by S2) from the first position point (point B) of the target object measured by network device 2 (station B) to station B according to the distance corresponding to the same acquisition time. Then, according to the position coordinates of the target object corresponding to the same acquisition time, the position coordinates corresponding to the first position point (point A) and the position coordinates corresponding to the first position point (point B) in the same coordinate system are determined, and then according to the position coordinates of the two first position points in the same three-dimensional coordinate system, the second distance between the two first position points is determined.

[0165] Third, based on the two first distances and the second distance, the position of the calibration point is determined.

[0166] Here, the ratio between the two first distances is determined; based on the ratio between the two first distances and the second distance, the third distance between the calibration point and one of the two first position points is determined; according to the third distance and the position coordinates of the two first position points in the same coordinate system, the position of the calibration point is determined.

[0167] Specifically, considering that the closer the target object is to the corresponding network device such as the synesthesia base station, the higher the measurement accuracy of the perception base station, the closer the position of the determined calibration point is to the perception base station. Assuming that the two first position points are represented by point A and point B respectively, and the corresponding network devices are network device 1 (station A) and network device 2 (station B), the first distance from the first position point (point A) of the target object measured by network device 1 (station A) to network device 1 (station A) is S1, and the first distance from the first position point (point B) of the target object measured by network device 2 (station B) to network device 2 (station B) is S2, the distance from the calibration point (represented by point C) to the first position point (point A) measured by network device 1 (station A) is Si, and the third distance from the calibration point (point C) to the first position point (point B) measured by network device 2 (station B) is Sj, then the following formula (1) is satisfied:

[0168]

[0169] Here, formula (2) can be derived from formula (1), and formula (3) can be derived from formula (2), and the third distance between the calibration point (point C) and the first position point (point B) measured by network device 2 (station B) is determined by formula (3):

[0170]

[0171] Among them, Sj represents the third distance between the calibration point (point C) and the first position point (point B) measured by network device 2 (station B), Si represents the distance between the calibration point (point C) and the first position point (point A) measured by the first network device 1 (station A), S represents the second distance between the two first position points, S2 represents the first distance between the first position point (point B) of the target object measured by network device 2 (station B) and network device 2 (station B), and S1 represents the first distance between the first position point (point A) of the target object measured by network device 1 (station A) and network device 1 (station A).

[0172] Here, after determining the third distance between the calibration point (represented by point C) and the first position point (point B) measured by network device 2 (station B), the position coordinates of the two first position points (point A and point B) in the same coordinate system are combined to determine the position of the calibration point (represented by point C).

[0173] Fourth, a calibrated motion trajectory is formed by using the calibration points determined for each target object at the same position, and the calibrated motion trajectory is used as the actual trajectory of the target object.

[0174] Step 203: The first device determines at least two cells with the best signal coverage according to the current position of the target object in the actual track.

[0175] Here, in the current scenario, the perception beam of the telesensing base station is mainly used for full coverage of the airspace below 600m, while the communication beam mainly covers the ground scene. A small number of beams can cover the low altitude, and multiple narrow beam patrol scanning is used to achieve regional coverage. Due to the difference between the perception beam and the communication beam, the coverage area is inconsistent. In the area covered by the perception beam, the communication beam may have a coverage blind spot.

[0176] See also Figure 7 , Figure 7 Schematic diagram of the sensing beam in the embodiment of the present application. Figure 7 As shown, the motion trajectory of the target object can be identified through the perception beam. The perception beam used by the synaesthesia base station can be configured as a large-angle, large-lobe beam, aiming to cover the detection area more comprehensively and ensure full coverage of the detection area below 600m in the low altitude.

[0177] See also Figure 8 , Figure 8 Schematic diagram of the communication beam of the embodiment of the present application, such as Figure 8As shown, the cell can be covered by the communication beam. The maximum number of communication broadcast beams below 6GHz (also called SuB 6G) is 8. The number of beams can be different due to the differences in frequency bands and frame structures. The current network communication base stations are mainly 4.9G, currently using a 2.5ms dual-cycle frame structure, and the maximum number of patrol beams is 7.

[0178] In the embodiment of the present application, target objects such as drones, vehicles, and ships are mainly sensed through sensing beams to identify the motion trajectory of the target object; a joint calibration is performed based on the motion trajectory of the target object sent by multiple sensing base stations to obtain the actual trajectory of the target object, and after determining at least two cells with the best signal coverage, in order to perform real-time communication beam following and ensure the network coverage quality of the cell, it is necessary to generate a beam adjustment plan in real time, and high requirements are placed on the timeliness of beam optimization. The particle swarm algorithm is mainly used to reduce the number of weight optimization iterations and accelerate the optimization process.

[0179] Here, in order to find the best coverage cell, at least two cells with the best signal coverage may be determined according to the current position of the target object in the actual trajectory.

[0180] Here, at least two cells with the best signal coverage meet the following conditions:

[0181] The angle between the antenna coverage direction of the cell and the line connecting the base points is within a first preset range;

[0182] and / or,

[0183] The distance between the location of the cell and the base point is within a second preset range;

[0184] The base point is the current position of the target object in the actual trajectory.

[0185] It can be understood that the antenna coverage direction of a cell can be understood as the normal direction of the antenna surface coverage of the cell.

[0186] See also Fig. 9 , Fig. 9 is a schematic diagram of determining at least two cells with the best signal coverage in an embodiment of the present application, such as Fig. 9 As shown, the best coverage cell screening principle can be: taking the current position of the target object in the actual trajectory as the base point, retaining cells whose angle between the normal direction of the cell's antenna surface coverage and the base point connection direction is within a first preset range such as ±45°; and / or screening cells whose distance between the cell location and the base point is within a second preset range. For example, if the three cells closest to the base point meet the requirement that the distance between the cell location and the base point is within the second preset range, then these three cells are the best coverage cells.

[0187] Step 204: The first device determines beam weight configuration information of the at least two cells based on multiple beam parameter combinations of the at least two cells.

[0188] Here, taking the screening of three cells with the best coverage as an example, based on the multiple beam parameter combinations of these three cells, the best beam parameter combination of these three cells is found to ensure the best coverage of the object's motion path.

[0189] Table 1 is a schematic diagram of a beam parameter combination of three cells. As shown in Table 1, the three cells are represented by cell 1, cell 2 and cell 3 respectively. The beam parameter combination of each of the three cells refers to the combination of beam downtilt angle, beam horizontal width and beam direction angle.

[0190] Table 1

[0191] Community Horizontal beam width Beam downtilt Beam direction angle Community 1 10° 6° 80° Community 2 15° 4° 130° Community 3 10° 8° 290°

[0192] Here, based on different values ​​of the beam parameters of the at least two cells, a plurality of beam parameter combinations of the at least two cells may be obtained.

[0193] Here, beam optimization can be implemented based on the particle swarm algorithm. Assume that a total of 3 cells with the best coverage are selected. At the same time, each cell has 3 fields: horizontal width, beam downtilt angle, and beam direction angle, totaling 9 fields. Each field has multiple configurable values. There are many overall weight combinations. In order to quickly calculate the optimal weight combination, the particle swarm algorithm is used to quickly approximate the optimal solution and quickly calculate the required weight configuration combination. The specific algorithm process may include:

[0194] In the first iteration, N beam parameter combinations are randomly selected from all configurable weight combinations, i.e., multiple beam parameter combinations of three cells (the value of N can be set as needed). Through weight simulation, the signal coverage of the actual motion trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations can be calculated, and the beam parameter combination with the largest signal coverage is taken as the optimal parameter combination. The signal coverage is determined by the signal strength at multiple sampling points in the actual trajectory.

[0195] See also Fig.10 , Fig.10 is a schematic diagram of determining the optimal parameter combination in the embodiment of the present application, such as Fig.10As shown, assuming that three best coverage cells are screened out, in the first iteration, N beam parameter combinations are randomly selected from the multiple beam parameter combinations of these three cells, and are represented by combination 1 to combination N respectively. For each combination, the proportion of sampling points whose signal strength at the sampling point, i.e., RSRP, is greater than a preset value such as -105dbm is counted. If for combination 13, the proportion of sampling points whose signal strength at the sampling point, i.e., RSRP, is greater than a preset value such as -105dbm is the largest, then combination 13 is used as the optimal beam parameter combination to find the optimal solution.

[0196] In the mth iteration, taking the optimal parameter combination after the m-1th iteration as a benchmark, randomly select N beam parameter combinations from the multiple beam parameter combinations, and obtain the signal coverage of the actual trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations through simulation, and take the beam parameter combination with the largest signal coverage as the optimal parameter combination; wherein the signal coverage is determined by the signal strength at multiple sampling points in the actual trajectory; m is an integer variable increasing from 2; and N is an integer greater than or equal to 2.

[0197] Here, the optimal parameter combination after the m-1th iteration is used as a benchmark, and N beam parameter combinations are randomly selected from the multiple beam parameter combinations, including: taking the preset fluctuation range of each beam parameter in the optimal parameter combination after the m-1th iteration as a benchmark condition, and selecting N beam parameter combinations that meet the benchmark condition from the multiple beam parameter combinations.

[0198] See also Fig.11 , Fig.11 is a schematic diagram of determining the optimal parameter combination in the embodiment of the present application, such as Fig.11 As shown, assuming that three best coverage cells are screened out, in the mth iteration, taking the optimal solution of the previous round such as combination 13 as a benchmark, and taking the preset fluctuation range of ±10% of each beam parameter of the 9 key parameters involved in combination 13 as a benchmark condition, N beam parameter combinations that meet the benchmark conditions are selected from a variety of beam parameter combinations as the optimal solution calculation combination for this round, and for the selected N beam parameter combinations, weighted simulation is used again to calculate the signal coverage of the actual trajectory of the target object corresponding to each beam parameter combination to find the optimal solution such as combination 24.

[0199] In response to the number of times that the optimal parameter combination after the Mth iteration remains unchanged reaches a preset threshold, the optimal parameter combination after the Mth iteration is used as the beam weight configuration information of the at least two cells; M is an integer greater than 2.

[0200] See also Fig.12 , Fig.12 is a schematic diagram of determining the optimal parameter combination in the embodiment of the present application, such as Fig.12As shown, assuming that three cells with the best coverage are screened out, after multiple repeated iterations, the number of times the optimal parameter combination such as combination 20 remains unchanged after the Mth iteration reaches a preset threshold such as 5, that is, the optimal solution calculated until the last 5 times does not change, then this result such as combination 20 is used as the global optimal solution, that is, combination 20 is used as the beam weight configuration information of the three cells.

[0201] Step 205: The first device sends the beam weight configuration information of the at least two cells to the network devices corresponding to the at least two cells.

[0202] Here, after the first device sends the beam weight configuration information of the at least two cells to the network device corresponding to the at least two cells, it can also receive the signal test result sent by the terminal; the signal test result represents the signal coverage of the actual trajectory of the target object; when the signal test result does not meet the preset conditions, the actual trajectory of the target object is re-determined based on the multiple motion trajectories of the target object.

[0203] That is, the calculated global optimal solution is sent in real time to the network devices corresponding to the at least two cells, such as base stations, to take effect, and the signal strength at multiple sampling points in the actual trajectory of the target object is measured through the terminal. The proportion of sampling points with signal strength greater than a preset value is counted according to the measurement results, and the sampling point proportion is reported to the first device as the signal coverage of the actual trajectory of the target object. The first device evaluates the effect of the beam weight configuration after application. If the signal coverage is less than the preset value, the optimal beam weight configuration is updated again.

[0204] Here, re-updating the optimal beam weight configuration may refer to randomly re-selecting two motion trajectories from the multiple motion trajectories of the target object, performing joint calibration based on the two re-selected motion trajectories to obtain the actual trajectory of the target object, and determining at least two cells with the best signal coverage for the current position of the target object in the actual trajectory; and determining the beam weight configuration information of the at least two cells based on the beam parameter combination of the at least two cells.

[0205] In this example, the following advantages are achieved:

[0206] (1) In order to ensure network coverage of target objects such as drones and vehicles, the sensing capabilities of multiple network devices such as telepathic base stations are used to accurately sense the positions of target objects such as drones and vehicles, and the actual trajectory of the target objects is obtained by jointly calibrating the motion trajectories of the target objects sent by multiple network devices. Compared with the method of relying on a single site to sense the position of an object through a communication channel in the related art, the method of multiple network devices cooperating to sense the position of an object through a sensing channel in the embodiment of the present application is more accurate, can more accurately guide the communication channel beam coverage, can efficiently improve the network coverage and network quality of the target objects such as drones, ships, and vehicles being detected in the existing network, and improve the actual network experience of users.

[0207] Here, the traditional beam weight optimization is to identify the position through the time advance (TA) and arrival angle (AOA) of the communication channel and adjust the beam coverage. Since the accuracy of the time advance (TA) reported by the terminal is 39 meters, the recognition accuracy is poor and the beam coordination effect is not good. In the embodiment of the present application, the position of the object is sensed by means of the sensing channel, the error is at the sub-meter level, and the particle swarm algorithm is innovatively used to achieve rapid beam optimization, which can adjust the beam in real time and accurately to achieve better coverage and tracking capabilities.

[0208] (2) It can automatically output and implement the service channel beam adaptation solution based on synaesthesia around the clock without human intervention.

[0209] (3) It can effectively identify the trajectories of drones, ships, vehicles and other objects, while ensuring the quality of their networks and improving the brand competitiveness of operators. In addition, efficient beam following has broad industry application prospects and subsequent monetization capabilities.

[0210] In order to implement the information determination method of the embodiment of the present application, the embodiment of the present application also provides an information determination device, which is arranged in the first device. Fig.13 Schematic diagram of the structure of the information determination device according to the embodiment of the present application. Fig.13 As shown, the device comprises:

[0211] An acquisition module 131 is used to acquire multiple motion trajectories of a target object; the multiple motion trajectories are sent to the first device by multiple network devices respectively;

[0212] A first processing module 132, configured to determine an actual trajectory of the target object based on the multiple motion trajectories of the target object;

[0213] The second processing module 133 is used to determine at least two cells with the best signal coverage according to the current position of the target object in the actual trajectory; and determine the beam weight configuration information of the at least two cells based on multiple beam parameter combinations of the at least two cells.

[0214] In some embodiments, each of the plurality of motion trajectories includes a plurality of trajectory points; the trajectory points include at least one of the following information: distance, speed, and angle.

[0215] In some embodiments, the first processing module 132 is further configured to:

[0216] Randomly selecting two motion trajectories from a plurality of motion trajectories of the target object;

[0217] Perform joint calibration based on the two motion trajectories to obtain a calibrated motion trajectory;

[0218] The calibrated motion trajectory is used as the actual trajectory of the target object.

[0219] In some embodiments, the first processing module 132 is further configured to:

[0220] For the target object at each of the multiple same positions, perform the following operations:

[0221] Determine a first position point of the target object in the two motion trajectories to obtain two first position points;

[0222] Determine first distances between the two first location points and the respective corresponding network devices to obtain two first distances, and determine a second distance between the two first location points;

[0223] determining a position of a calibration point based on the two first distances and the second distance;

[0224] Based on the positions of the calibration points, the actual trajectory of the target object is obtained.

[0225] In some embodiments, the at least two cells with the best signal coverage meet the following conditions:

[0226] The angle between the antenna coverage direction of the cell and the line connecting the base points is within a first preset range;

[0227] and / or,

[0228] The distance between the location of the cell and the base point is within a second preset range;

[0229] The base point is the current position of the target object in the actual trajectory.

[0230] In some embodiments, the second base processing module 133 is further used for:

[0231] In the first iteration, N beam parameter combinations are randomly selected from the multiple beam parameter combinations of the at least two cells; through simulation, the signal coverage rate of the actual trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations is obtained, and the beam parameter combination with the largest signal coverage rate is taken as the optimal parameter combination; wherein the signal coverage rate is determined by the signal strength at multiple sampling points in the actual trajectory; N is an integer greater than or equal to 2;

[0232] In the mth iteration, taking the optimal parameter combination after the m-1th iteration as a benchmark, randomly selecting N beam parameter combinations from the multiple beam parameter combinations, obtaining the signal coverage of the actual trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations through simulation, and taking the beam parameter combination with the largest signal coverage as the optimal parameter combination; wherein the signal coverage is determined by the signal strength at multiple sampling points in the actual trajectory; m is an integer variable that increases from 2; and N is an integer greater than or equal to 2;

[0233] In response to the number of times that the optimal parameter combination after the Mth iteration remains unchanged reaches a preset threshold, the optimal parameter combination after the Mth iteration is used as the beam weight configuration information of the at least two cells; M is an integer greater than 2.

[0234] In some embodiments, the second processing module 133 is also used to

[0235] Based on different values ​​of the beam parameters of the at least two cells, obtaining multiple beam parameter combinations of the at least two cells;

[0236] in,

[0237] The beam parameters of each of the at least two cells include at least one of the following:

[0238] Horizontal beam width;

[0239] Beam downtilt;

[0240] Beam direction angle.

[0241] In some embodiments, the second processing module 133 is further used to:

[0242] Taking the preset fluctuation range of each beam parameter in the optimal parameter combination after the (m-1)th iteration as a reference condition, N beam parameter combinations that meet the reference condition are selected from the multiple beam parameter combinations.

[0243] In some embodiments, the apparatus further comprises:

[0244] A sending module is used to send the beam weight configuration information of the at least two cells to the network devices corresponding to the at least two cells.

[0245] In some embodiments, the acquisition module 131 is further used to receive a signal test result sent by a terminal; the signal test result represents the signal coverage rate of the actual track of the target object;

[0246] Correspondingly, the second processing module 132 is further configured to re-determine the actual trajectory of the target object based on the multiple motion trajectories of the target object when the signal test result does not meet a preset condition.

[0247] In actual application, the acquisition module 131 can be implemented by a communication interface in the information determination device; the first processing module 132 and the second processing module 133 can be implemented by a processor in the information determination device.

[0248] It should be noted that: the information determination device provided in the above embodiment only uses the division of the above program modules as an example when performing information determination. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the information determination device provided in the above embodiment and the information determination method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0249] The present application also provides a first device, such as Fig.14 As shown, including:

[0250] Communication interface 141, capable of exchanging information with other devices;

[0251] The processor 142 is connected to the communication interface 141 and is used to execute the method provided by one or more technical solutions of the first device side when running a computer program. The computer program is stored in the memory 143.

[0252] It should be noted that the specific processing process of the processor 142 and the communication interface 141 is detailed in the method embodiment and will not be repeated here.

[0253] Of course, in actual application, the various components in the first device 140 are coupled together through the bus system 144. It can be understood that the bus system 144 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 144 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Fig.14 Various buses are labeled as bus system 144 .

[0254] The memory 143 in the embodiment of the present application is used to store various types of data to support the operation of the first device 140. Examples of such data include: any computer program used to operate on the first device 140.

[0255] The method disclosed in the above embodiment of the present application can be applied to the processor 142, or implemented by the processor 142. The processor 142 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 the hardware in the processor 142 or an instruction in the form of software. The above-mentioned processor 142 may be a general-purpose processor, a digital data processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 142 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A 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 application, 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 the memory 143, and the processor 142 reads the information in the memory 143 and completes the steps of the above method in combination with its hardware.

[0256] In an exemplary embodiment, the first device 140 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 aforementioned method.

[0257] It can be understood that the memory (memory 143) of the embodiment of the present application 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 disc, 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), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0258] In an exemplary embodiment, the embodiment of the present invention further provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, for example, a memory storing a computer program, and the computer program can be executed by the processor 142 of the first device 140 to complete the steps of the first device side method. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface storage, optical disk, or CD-ROM.

[0259] Exemplarily, an embodiment of the present application further provides a computer program product, including a computer program, which can be executed by the processor 142 of the first device 140 to complete the steps of any of the aforementioned methods.

[0260] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0261] In addition, the technical solutions described in the embodiments of the present invention can be arbitrarily combined without conflict.

[0262] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A method for determining information, characterized in that: Applied to a first device, the method includes: Acquire multiple motion trajectories of the target object; the multiple motion trajectories are sent to the first device by multiple network devices respectively; Determining an actual trajectory of the target object based on a plurality of motion trajectories of the target object; Determine at least two cells with the best signal coverage for the current position of the target object in the actual trajectory; Based on the multiple beam parameter combinations of the at least two cells, beam weight configuration information of the at least two cells is determined.

2. The method according to claim 1, characterized in that Each of the multiple motion trajectories includes multiple trajectory points; the trajectory points include at least one of the following information: distance, speed, and angle.

3. The method according to claim 1, characterized in that The determining the actual trajectory of the target object based on the multiple motion trajectories of the target object comprises: Randomly selecting two motion trajectories from a plurality of motion trajectories of the target object; Perform joint calibration based on the two motion trajectories to obtain a calibrated motion trajectory; The calibrated motion trajectory is used as the actual trajectory of the target object.

4. The method according to claim 3, characterized in that The performing joint calibration based on the two motion trajectories to obtain a calibrated motion trajectory includes: For the target object at each of the multiple same positions, perform the following operations: Determine a first position point of the target object in the two motion trajectories to obtain two first position points; Determine first distances between the two first location points and the respective corresponding network devices to obtain two first distances, and determine a second distance between the two first location points; determining a position of a calibration point based on the two first distances and the second distance; Based on the positions of the calibration points, the actual trajectory of the target object is obtained.

5. The method according to claim 1, characterized in that The at least two cells with the best signal coverage meet the following conditions: The angle between the antenna coverage direction of the cell and the line connecting the base points is within a first preset range; and / or, The distance between the location of the cell and the base point is within a second preset range; The base point is the current position of the target object in the actual trajectory.

6. The method according to claim 1, characterized in that The determining, based on the multiple beam parameter combinations of the at least two cells, beam weight configuration information of the at least two cells includes: In the first iteration, N beam parameter combinations are randomly selected from the multiple beam parameter combinations of the at least two cells; through simulation, the signal coverage rate of the actual trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations is obtained, and the beam parameter combination with the largest signal coverage rate is taken as the optimal parameter combination; wherein the signal coverage rate is determined by the signal strength at multiple sampling points in the actual trajectory; N is an integer greater than or equal to 2; In the mth iteration, taking the optimal parameter combination after the m-1th iteration as a benchmark, randomly selecting N beam parameter combinations from the multiple beam parameter combinations, obtaining the signal coverage of the actual trajectory of the target object corresponding to each beam parameter combination in the N beam parameter combinations through simulation, and taking the beam parameter combination with the largest signal coverage as the optimal parameter combination; wherein the signal coverage is determined by the signal strength at multiple sampling points in the actual trajectory; m is an integer variable that increases from 2; and N is an integer greater than or equal to 2; In response to the number of times that the optimal parameter combination after the Mth iteration remains unchanged reaches a preset threshold, the optimal parameter combination after the Mth iteration is used as the beam weight configuration information of the at least two cells; M is an integer greater than 2.

7. The method according to claim 6, characterized in that The method further comprises: Based on different values ​​of the beam parameters of the at least two cells, obtaining multiple beam parameter combinations of the at least two cells; in, The beam parameters of each of the at least two cells include at least one of the following: Horizontal beam width; Beam downtilt; Beam direction angle.

8. The method according to claim 6, characterized in that The step of randomly selecting N beam parameter combinations from the plurality of beam parameter combinations based on the optimal parameter combination after the m-1th iteration includes: Taking the preset fluctuation range of each beam parameter in the optimal parameter combination after the (m-1)th iteration as a reference condition, N beam parameter combinations that meet the reference condition are selected from the multiple beam parameter combinations.

9. The method according to claim 1, characterized in that: The method further comprises: The beam weight configuration information of the at least two cells is sent to network devices corresponding to the at least two cells.

10. The method according to claim 9, characterized in that The method further comprises: A signal test result sent by a receiving terminal; the signal test result represents a signal coverage rate of an actual track of the target object; When the signal test result does not meet the preset condition, the actual trajectory of the target object is re-determined based on the multiple motion trajectories of the target object.

11. An information determination device, characterized in that: include: An acquisition module, used for acquiring multiple motion trajectories of a target object; The multiple motion trajectories are sent to the first device respectively by multiple network devices; A first processing module, configured to determine an actual trajectory of the target object based on a plurality of motion trajectories of the target object; The second processing module is used to determine at least two cells with the best signal coverage based on the current position of the target object in the actual trajectory; and determine the beam weight configuration information of the at least two cells based on multiple beam parameter combinations of the at least two cells.

12. A first device, characterized in that: comprising a processor and a memory for storing a computer program capable of being executed on the processor, Wherein, when the processor is used to run the computer program, it executes the steps of the method described in any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

14. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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