A trajectory determination, localization method, communication node and storage medium

Abstract

The application discloses a trajectory determination method, a site selection method, a communication node and a storage medium. The trajectory determination method obtains first position information in communication signaling, wherein the first position information comprises identification information and network positioning parameters of a second node; second position information of a first node is determined based on the identification information and the network positioning parameters; a plurality of third nodes associated with the first node are determined based on the second position information; reference signal receiving power of the third nodes is converted into distance estimation values, wherein the distance estimation values indicate distances between the first node and the third nodes; third position information of the first node is determined based on each distance estimation value; and a motion trajectory of the first node is generated based on the third position information. The trajectory determination method reduces cost and guarantees continuity and stability of motion trajectory determination.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a trajectory determination and location method, a communication node, and a storage medium. Background Technology

[0002] With the rapid development of drone technology, drones have been widely used in many fields such as logistics delivery, surveying, inspection, and aerial photography. Determining the trajectory of a drone is crucial in its application.

[0003] Currently, determining the flight path of a drone relies on installing high-precision positioning equipment or a dedicated data acquisition module on the drone to obtain flight path data. This not only increases the hardware load and manufacturing cost of the drone, but also requires additional investment in equipment maintenance costs, and may result in data loss or distortion due to equipment failure or signal transmission problems. Summary of the Invention

[0004] This invention provides a trajectory determination and location method, a communication node, and a storage medium to reduce costs and ensure the continuity and stability of motion trajectory determination.

[0005] According to one aspect of the present invention, a trajectory determination method is provided, applied to a first node, the method comprising:

[0006] Obtain first location information from communication signaling, the first location information including the identification information of the second node and network positioning parameters;

[0007] Based on the identification information and the network positioning parameters, the second location information of the first node is determined;

[0008] Based on the second location information, a plurality of third nodes associated with the first node are identified;

[0009] The reference signal received power of the third node is converted into a distance estimate, which indicates the distance between the first node and the third node;

[0010] Based on the distance estimates, the third location information of the first node is determined;

[0011] The motion trajectory of the first node is generated based on the third location information.

[0012] According to another aspect of the present invention, a location selection method is provided, the method comprising:

[0013] Obtain the motion trajectory of the first node, and associate the motion trajectory with the first location information in the communication signaling;

[0014] Based on the motion trajectory, hotspot areas in the motion area of ​​the first node are determined;

[0015] Based on the hotspot areas, a site selection model is solved to determine the location of the charging equipment.

[0016] According to another aspect of the present invention, a communication node is provided, the communication node comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method described in any embodiment of the present invention.

[0021] The technical solution of this invention determines preliminary second location information for a first node based on first location information in communication signaling, and then determines associated third nodes for the first node in conjunction with the second location information to assist in the first node's positioning. After determining the third node, distance conversion is performed based on the reference signal received power of the third node to obtain a distance estimate. The distance estimates between multiple third nodes and the first node determine the third location information of the first node, achieving precise positioning of the first node. After determining the third location information, the motion trajectory of the first node is generated, thus determining the motion trajectory of the first node. This solves the problems of increased costs caused by simulating external devices on the first node, as well as poor data continuity and stability. Positioning of the first node is achieved through communication signaling, which not only saves the purchase and maintenance costs of additional equipment but also leverages a mature communication network to ensure the continuity and stability of data acquisition, significantly reducing the overall cost of data acquisition.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a trajectory determination method provided in an embodiment of the present invention;

[0025] Figure 2 This is a flowchart illustrating a location selection method provided in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of the structure of a trajectory determination device provided in an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of a location selection method provided in an embodiment of the present invention;

[0028] Figure 5 This is a schematic diagram of the structure of a communication node provided in an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] In one embodiment, Figure 1This is a flowchart illustrating a trajectory determination method provided by an embodiment of the present invention. This embodiment is applicable to determining the motion trajectory of a first node. The method can be executed by a trajectory determination device, which can be implemented in hardware and / or software and can be configured within the first node. The first node can be a low-altitude operating node, such as a drone. Figure 1 As shown, the method includes:

[0032] S110. Obtain the first location information in the communication signaling, wherein the first location information includes the identification information of the second node and network positioning parameters.

[0033] Communication signaling can be obtained through the interface of a communication network. It can be obtained from a communication node or a management node. A communication node can be a base station. A management node can be a node that implements association, such as a network element. In this invention, the communication signaling is obtained from a mature communication network, which offers better stability compared to existing communication links with external devices, ensuring the continuity and stability of data acquisition.

[0034] The first location information can be location-related information. It can be considered as information contained in the communication signaling that can roughly locate the first node. The second node can be a node connected to the first node, such as a base station connected to the first node. The communication signaling can originate from the second node or from other nodes. The identification information of the second node can uniquely identify the second node. By determining the second node connected to the first node, coarse location of the first node can be achieved.

[0035] Network positioning parameters can be parameters used in a communication network to identify, measure, or calculate the location of a first node. Network positioning parameters include, but are not limited to, one or more of the following: Tracking Area Identity (TAI), Location Area Code (LAC), and Enhanced Cell ID (ECID).

[0036] S120. Based on the identification information and the network positioning parameters, determine the second location information of the first node.

[0037] The second location information can be the location information initially determined for the first node based on the first location information. In this operation, the base station database (including base station latitude, longitude, altitude, and azimuth) is queried based on the identification information of the second node, and the initial location of the UAV is obtained through network positioning parameters.

[0038] S130. Based on the second location information, determine a plurality of third nodes associated with the first node.

[0039] The third node can be determined based on its distance from the first node. For example, the third node could be the three communication nodes closest to the first node, such as a base station. The third node may or may not include the second node.

[0040] In this embodiment, after determining the second location information of the first node, a third node associated with the first node is selected based on the second location information and distance. There can be multiple third nodes, such as three.

[0041] S140. Convert the reference signal received power of the third node into a distance estimate, the distance estimate indicating the distance between the first node and the third node.

[0042] In this embodiment, the distance estimate can be the distance between the first node and the third node estimated based on the reference signal received power. Each third node corresponds to a distance estimate. In this embodiment, the conversion from reference signal received power to distance estimate can be performed using a model. This model can take into account the influence of power and / or the influence of the path loss exponent.

[0043] S150. Based on the distance estimates, determine the third location information of the first node.

[0044] The third location information can be the final location information determined by the first node. The third location information can be three-dimensional geographic coordinates, such as latitude, longitude, and altitude.

[0045] After determining the distance estimate for each third node, this embodiment can determine the third location information based on all distance estimates. For example, the third location information can be determined using triangulation.

[0046] S160. Generate the motion trajectory of the first node based on the third location information.

[0047] After determining the third position information of the first node, the third position information of the first node at different times is sorted using time series analysis, and a sequence is constructed according to the timestamps to obtain the motion trajectory of the first node. This motion trajectory can be the flight trajectory of the first node.

[0048] The trajectory determination method provided in this invention determines preliminary second location information for a first node based on first location information in communication signaling. Then, it combines this second location information to determine associated third nodes for auxiliary positioning of the first node. After determining the third nodes, distance conversion is performed based on the reference signal received power of the third nodes to obtain distance estimates. The distance estimates between multiple third nodes and the first node determine the third location information of the first node, achieving precise positioning. After determining the third location information, the motion trajectory of the first node is generated, thus determining the motion trajectory of the first node. This method solves the problems of increased costs associated with using external devices on the first node, as well as poor data continuity and stability. By achieving positioning of the first node through communication signaling, it not only eliminates the need for purchasing and maintaining additional equipment but also leverages a mature communication network to ensure the continuity and stability of data acquisition, significantly reducing the overall cost of data acquisition.

[0049] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0050] In one embodiment, converting the reference signal received power of the third node into a distance estimate includes:

[0051] The reference signal received power of the third node is converted into a distance estimate using a model. This model includes a model describing the relationship between transmission distance and path loss. The model also describes the relationship between the first path loss at the first node and the following objective term:

[0052] The path loss basic term includes a second path loss at a reference distance, and the path loss basic term also includes the logarithm of the ratio of the distance estimate to the reference distance.

[0053] The first path loss is associated with the reference signal received power of the third node.

[0054] The model can describe the linear relationship between path loss and the logarithm of transmission distance. For example, it can describe the attenuation of received signal power with transmission distance using a logarithmic function. The received reference signal power can be the received power of the reference signal transmitted from the third node, measured by the first node. The distance estimate can be an estimate of the distance between the third node and the first node. Path loss can represent the energy attenuation of the signal during transmission from the transmitting point to the receiving point.

[0055] The model can be a mathematical model describing the relationship between the first path loss and the target term. The first path loss can be the path loss from the third node to the first node. The first path loss can be calculated based on the received power of the reference signal, such as the difference between the transmitted power of the third node and the received power of the reference signal. During the calculation, the antenna gain of the third node and / or the antenna gain of the first node can also be considered. The target term is a term in the model that is related to the first path loss.

[0056] The fundamental path loss term can be a core part of the model, describing the power transmission loss as it decreases with distance. The second path loss can be the path loss from the third node to the reference distance. The reference distance can be a known distance, such as the near-field distance to the third node. The second path loss is a known quantity and can be determined based on the free-space path loss model. The logarithm of the ratio of the distance estimate to the reference distance describes the relationship between path loss and the logarithm of distance.

[0057] In one embodiment, the target item further includes one or more of the following:

[0058] Correction terms, which are used to correct the influence of the frequency of the reference signal received power fluctuation of the third node;

[0059] A penalty function is used to penalize the path loss exponent for deviations from a set value.

[0060] To address the overfitting problem, this embodiment can introduce a correction term and a penalty function.

[0061] The correction term can correct for the impact of frequency fluctuations in the received power of the reference signal. The correction term can be the logarithm of the frequency of the received power fluctuations in the reference signal. A coefficient is also added to the logarithm to penalize overfitting.

[0062] The penalty function penalizes deviations of the path loss exponent from a set value. The set value can be determined by the environment of the first node. The penalty function is the square of the difference between the path loss exponent and the set value. A penalty parameter can be multiplied by the square to control the severity of the penalty.

[0063] In one embodiment, the correction term is associated with the logarithm of the frequency, and the first path loss is linearly related to the logarithm of the frequency;

[0064] The penalty function is associated with the deviation between the path loss index and a set value.

[0065] The correction term can be the logarithm of the frequency multiplied by a coefficient. The correction term can be added to the basic path loss term to describe its relationship with the first path loss. The first path loss has a linear relationship with the logarithm of the frequency. The first path loss can be the dependent variable of the model. The objective term is the independent variable of the model.

[0066] The penalty function can be the square of the deviation between the path loss exponent and the set value, or it can be the square multiplied by the penalty coefficient. The deviation can be determined by the difference between the first path loss and the set value.

[0067] In one embodiment, the number of third nodes is three, and determining the third location information of the first node based on each distance estimate includes:

[0068] Based on the aforementioned distance estimates, the fourth location information is determined using triangulation.

[0069] Obtain the position measurement value of the first node transmitted by the satellite;

[0070] Acquire the motion data determined by the inertial measurement unit of the first node;

[0071] The fourth location information is obtained by fusing the location measurement value and the motion data.

[0072] In this embodiment, the accuracy of the first node's positioning result can be improved by fusing data from multiple sources. For example, the location determined based on the third node is called the fourth location information. That is, there are three third nodes, and three distance estimates are determined accordingly. The fourth location information of the first node is then determined using triangulation. This fourth location information can be based on the third nodes, such as the location information determined after positioning by a base station.

[0073] In this embodiment, the results of satellite and inertial measurement unit positioning are additionally incorporated to achieve multi-source positioning fusion.

[0074] Position measurements can be based on information about the location of the first node determined by satellite. The first node can receive its position measurements from the satellite.

[0075] Motion data can be data related to the motion of the first node acquired by the inertial measurement unit. This can include one or more of the following: triaxial acceleration and angular velocity.

[0076] In this embodiment, after acquiring the fourth position information, position measurement value, and motion data, the three are fused to determine the final third position information for the first node. A filter can be used for fusion. The filter can directly fuse the three data points or retain one dimension for verification; this is not limited here.

[0077] In one embodiment, fusing the fourth location information, the location measurement value, and the motion data to obtain the third location information includes:

[0078] The fourth position information, the position measurement value, and the motion data are converted to the local coordinate system to obtain the converted fourth position information, the converted position measurement value, and the converted motion data.

[0079] The converted fourth position information, the converted position measurement value, and the converted motion data are time-aligned to obtain the aligned fourth position information, the aligned position measurement value, and the aligned motion data.

[0080] The aligned fourth position information, the aligned position measurement value, and the aligned motion data are respectively encapsulated into corresponding structured data;

[0081] The structured data are fused together to obtain the third location information.

[0082] In this embodiment, when performing multi-source fusion, the data from the three sources can first be transformed to the same coordinate system. In this embodiment, the transformation is performed to the local coordinate system of the first node, such as the coordinate system used for local navigation.

[0083] After coordinate system transformation, timestamp alignment can be performed so that data from different sources have corresponding values ​​at the same timestamp, facilitating fusion based on timestamps.

[0084] In this embodiment, the method of time alignment is not limited; interpolation can be performed so that the data from the three sources have corresponding values ​​at multiple identical times.

[0085] After time alignment, the data from the three sources are encapsulated into structured data. For example, they are encapsulated into standard structured data packets containing timestamps, coordinate system identifiers, 3D position, velocity, attitude, and data quality factors (such as signal-to-noise ratio and covariance).

[0086] After obtaining the corresponding structured data from each source, all the structured data are fused to obtain the third location information, such as by using Kalman filtering to determine the third location information.

[0087] In one embodiment, the step of fusing the structured data to obtain third location information includes:

[0088] Determine the corresponding fusion mode based on the environment of the first node;

[0089] The structured data are fused according to the fusion mode described above.

[0090] When fusing structured data, a suitable fusion mode can be selected based on the environment in which the first node is located. The fusion mode can be a fusion strategy for multi-source data associated with the environment. Different fusion modes employ different multi-source data fusion strategies. In this embodiment, the fusion mode corresponding to the environment in which the first node is located can be determined, and then the multi-source structured data can be fused according to that fusion mode.

[0091] In one example, the environment could be an unobstructed environment, such as an open environment, a partially obstructed environment, such as an urban canyon, or a fully obstructed environment.

[0092] The fusion mode can be dominated by satellite-acquired data, with the third node and satellite tightly coupled, or dominated by the third node and inertial measurement unit, etc.

[0093] In one embodiment of the trajectory determination method, the method further includes:

[0094] During the fusion process, verification is performed using the second location information.

[0095] During data fusion using filters, the fusion result can be verified based on the second location information to determine its accuracy. No limitations are placed on the verification method here.

[0096] The trajectory determination method described below is an example of the present invention, which realizes the parsing of UAV position information and the improvement of position accuracy based on communication signaling. It addresses the problem of positioning accuracy and reliability of UAVs in complex environments. In areas without differential coverage, it is necessary to overcome the limitations of insufficient single-point positioning accuracy of BeiDou. A multi-source fusion positioning system capable of cross-validation, automatic diagnosis, and anomaly elimination is constructed to ensure that UAVs can obtain stable, reliable, and high-precision position services in all scenarios.

[0097] The trajectory determination method is described below as an example:

[0098] 1. A method for obtaining UAV flight trajectories based on communication signaling data

[0099] S100. Signaling Data (e.g., communication signaling) Acquisition: Location-related information and / or wireless measurement reports from the connected UAV are collected via standard open interfaces of the communication network for location calculation in the next stage. Wireless measurement reports can be reports from base stations or other UAVs or ground-based equipment, including Location Area Code (LAC), Tracking Area Identity (TAI), Enhanced Cell Identification (ECID), cell identifier, base station identifier, signal strength, and / or timestamp. Location-related information can be obtained at the communication node or the management node. The primary location information includes location-related information and / or wireless measurement reports.

[0100] S200, Location Information Parsing: A base station location database is established using base station planning data. Base station location change information is dynamically acquired through the management interface. Combined with the base station identifier (i.e., the identifier information of the first node) in the location update signaling from step S100 (i.e., communication signaling sent periodically or when switching location areas) and LAC / TAI information, area verification is performed to initially narrow down the positioning range to a specific location area / tracking area. The following further locates the drone's position (fine-grained position) by parsing the drone's location data (coarse-grained position).

[0101] S200 specifically includes S201 and S202:

[0102] S201. Base station positioning data parsing: Query the base station database (including base station latitude, longitude, altitude, and azimuth) based on the serving base station identifier, and obtain the initial position of the UAV, i.e., the second position information, through ECID / LAC / TAI (i.e., network positioning parameters).

[0103] S202. Calculate the UAV's position, i.e., the third location information, based on the multi-base station triangulation algorithm, specifically including the following steps:

[0104] Step 1: Signal Measurement and Distance Conversion

[0105] Use one of the following models to convert the Reference Signal Received Power (RSRP) value into a distance estimate:

[0106] ;

[0107] ;

[0108] ;

[0109] in, In distance The path loss (dB) at that point, i.e., the first path loss, can be determined by the received signal strength. To be at the reference distance The known path loss at a distance of 1 meter (e.g., the second path loss) is related to the frequency of the reference signal received power fluctuation. The path loss index is approximately 2 in free space and 3-4 in urban environments, depending on the scene in which the drone is located. This is the penalty coefficient, which controls the intensity of the penalty; it can be an empirical value. is a coefficient. This is the basic term for path loss.

[0110] Based on the above model, a random variable for shadow fading margin can be added. (dB), a random variable that follows a log-normal distribution, is used to simulate random signal fluctuations caused by obstacles such as hills or large buildings.

[0111] To improve the accuracy of distance calculation, this invention solves the overfitting problem by introducing a penalty function.

[0112] During fitting, if the data is noisy, add a check to the loss function. Deviation from typical values ​​(e.g.) The penalty, i.e., the penalty function. Because during fitting, the data is usually If the data is noisy, Typically less than 2, the system adds a penalty of 3, which can effectively suppress data overfitting, where the parameter... Control the penalty intensity. Use squared loss for points with small errors to adjust for underfitting in the system.

[0113] This invention introduces a higher-order term (i.e., correction term) to correct frequency The impact, and added to the loss function. By applying the coefficients of higher-order functions This can penalize overfitting in the system. It ensures that the system, under the influence of higher-order functions, neither affects the normal fit of the formula nor causes overfitting.

[0114] Finally, the distance data from RSRP to the specific base station is calculated using the following formula:

[0115] ;

[0116] ;

[0117] ;

[0118] .

[0119] S300 Position Accuracy Improvement: To improve the positioning accuracy of UAVs, this invention integrates PPP-RTK, 5G base station triangulation, and inertial measurement unit (IMU) data, and provides UAVs with continuous, stable, and high-precision positioning capabilities in various complex environments through a three-way data cross-validation strategy.

[0120] Step 1: Standardize the management of location data.

[0121] Multi-source data are synchronously acquired and preprocessed, converting the three types of measurement data into a unified standard format and adding aligned timestamps for subsequent cross-validation.

[0122] The system receives raw data streams from PPP-RTK (high-precision satellite observations and state-domain corrections), 5G base stations (raw signal strength, time difference of arrival, and other measurement information), and IMU (three-axis acceleration and angular velocity), and converts them all to a unified local navigation coordinate system. The local navigation coordinate system can be a local coordinate system with the UAV itself or a specific point as its origin, such as a Cartesian coordinate system. PPP-RTK data undergoes spatial reference transformation and map projection transformation to be converted to the local navigation coordinate system. Spatial reference transformation converts spherical latitude, longitude, and altitude coordinates to three-dimensional Cartesian coordinates, which can be coordinates in a Cartesian coordinate system with the Earth's center of mass as its origin and rotating with the Earth. Map projection transformation uses a projection algorithm to perform coordinate transformation. 5G base station data undergoes plane transformation / elevation fitting. IMU data undergoes attitude rotation transformation to be converted to the local navigation coordinate system. This yields coordinate, velocity, and attitude data under a unified reference.

[0123] The following interpolation formula is used to time-align the three non-strictly synchronized data streams within the same processing cycle, generating data packets with a common timestamp:

[0124] ;

[0125] in, and These are two known locations at different times. It is a point in time between the two points mentioned above.

[0126] For time points The corresponding physical quantity value, such as position. For time points The corresponding physical quantity value. For time points The corresponding physical quantity value.

[0127] The possible values ​​are as follows:

[0128] ms, ;

[0129] ms, ;

[0130] When merging timestamps Given two known data points and ( When the x-coordinate is between x and z, timestamp interpolation is performed. This allows for timestamp interpolation of the x-coordinate information; the same applies to the y and z-coordinates. The following description uses the x-coordinate as an example:

[0131] .

[0132] After interpolation, the data is encapsulated into a standard structured data packet containing timestamps, coordinate system IDs, 3D position, velocity, attitude, and data quality factors (such as signal-to-noise ratio and covariance). Finally, based on Kalman filtering, the multi-source information is optimally estimated, thus outputting a fusion result with higher accuracy than any single data source, i.e., third-party position information.

[0133] Step 2: Predictive maintenance of data.

[0134] 1) Set dynamic threshold: deviation between PPP-RTK and 5G positioning results (i.e., location measurement values ​​and fourth location information) The first set value, such as 5 meters, or the deviation between the IMU-estimated position (e.g., motion data) and the fused result (i.e., third-party position information). The second set value, such as 10 meters, triggers an abnormality flag.

[0135] 2) Anomaly detection mechanism: If the deviation between PPP-RTK and 5G positioning results exceeds the limit, check whether the base station signal is affected by multipath; if the IMU deviation is too large, detect whether the IMU is impacted or temperature drifted; when a data source is continuously abnormal, reduce its fusion weight or switch to backup mode.

[0136] Step 3: Adaptive positioning mode switching: Dynamically select the optimal positioning strategy based on the environment, that is, determine the corresponding fusion mode based on the environment in which the first node is located.

[0137] Open environment: preferred The dominant mode integrates 5G observations as verification, that is, verification is performed through the second location information.

[0138] City Canyon: Now Open Tightly coupled mode, using 5G base stations to compensate for satellite obstruction.

[0139] Fully occluded scene: Switch to The dominant mode is calibrated with visual odometry as an auxiliary method.

[0140] S400 Data Verification and Standardization: For the information fields such as the identifier, time, location coordinates (such as third location information), and signal strength of the connected drone in the above steps, duplicate data, erroneous data, and incomplete data are removed through data integrity verification and completion, outlier identification and filtering, so as to provide standardized information for subsequent trajectory generation.

[0141] S500 trajectory generation and integration: The time series analysis method is used to sort the parsed position points and construct a spatiotemporal coordinate sequence according to millisecond-level timestamps to obtain the motion trajectory.

[0142] Currently, most drones have short battery life, which necessitates the proper placement of charging equipment within their operating area to ensure continuous and stable operation.

[0143] Communication signaling, as a crucial carrier of control information in communication networks, contains a wealth of information about the interactions between user equipment (including the communication modules associated with drones) and the network. Currently, there is no solution for accurately and efficiently selecting drone charging point locations by fully utilizing communication signaling data. While collecting communication signaling is relatively convenient, requiring no additional complex equipment on the drone, and can reflect key information such as the drone's position and direction of movement in real time, how to utilize communication signaling for drone charging point location selection is a pressing technical problem that needs to be solved.

[0144] The site selection method provided by this invention is based on communication signaling to select charging points for drones. It automatically adjusts core parameters (such as the number of candidate points in the candidate point set, minimum remaining battery power, and other model parameters) according to different flight hotspot area types, and dynamically optimizes the search path based on the degree of compliance with business rules. Ultimately, it ensures that the generated site selection scheme not only achieves mathematical optimality but also accurately adapts to complex and ever-changing actual business scenarios.

[0145] In one embodiment, Figure 2 This is a flowchart illustrating a location selection method provided in an embodiment of the present invention. This embodiment is applicable to the location selection of charging devices. The method can be executed by a location selection device, which can be implemented in hardware and / or software and can be configured in a communication node. The communication node can be a node that performs location selection for the charging devices of the first node. For details not covered in this embodiment, please refer to the above embodiments; further elaboration is not required here.

[0146] like Figure 2 As shown, the method includes:

[0147] S210. Obtain the motion trajectory of the first node, wherein the motion trajectory is associated with the first location information in the communication signaling.

[0148] The trajectory can be the flight path of the first node, which can be determined based on the communication signaling received by the first node. The communication signaling includes first location information, which includes the identification information and network positioning parameters of the second node.

[0149] This embodiment does not limit the method of determining the motion trajectory; the method provided in this embodiment can be used for determination.

[0150] S220. Based on the motion trajectory, determine the hotspot area in the motion area of ​​the first node.

[0151] Hotspot areas can be regions where the first node is frequently active, such as geographical areas where the activity of the first node significantly exceeds a set level. Hotspot areas can be determined based on one or more of the first node's frequency of occurrence and dwell time within the area. For example, a hotspot area can be a geographical area where both the first node's frequency of occurrence and dwell time meet the requirements.

[0152] The movement region can be the area where the first node moves. In this embodiment, the movement region can be divided into multiple regions. Then, based on the movement trajectory, the hotspot regions of the first node in the movement region are determined. For example, the region where the first node frequently moves in the movement region can be determined based on the movement trajectory, and the duration of the first node's stay in the region can also be combined to determine the hotspot regions.

[0153] S230. Based on the hotspot area, solve the location model to determine the location of the charging equipment.

[0154] The charging device can be a device for charging the first node. In this embodiment, the number of first nodes can be at least one. The number of charging devices can be at least one.

[0155] The location selection model can be a mathematical model for site selection. The model can use the resource consumption of constructing charging equipment and the energy consumption of the first node flying to the charging equipment as the objective function, combined with constraints, to select the location of the charging equipment from hotspot areas. This embodiment can solve the location selection model so that the selected location of the charging equipment satisfies the minimization objective function and the constraints. When selecting the location of the charging equipment, all first nodes and all charging equipment can be considered holistically, ultimately achieving the selection of the charging equipment.

[0156] Constraints can describe the constraints on the location of charging equipment. The parameters included in the constraints are not limited, as long as they can enable the location of charging equipment in hotspot areas.

[0157] In one embodiment, the constraints of the location model are associated with one or more of the following:

[0158] The service radius of the charging equipment;

[0159] The distance between the first node and the charging device;

[0160] The charging power of the charging device;

[0161] The charging power requirement of the first node;

[0162] The lower limit of the remaining power of the first node;

[0163] The first node moves to the remaining power of the charging device.

[0164] The service radius can be the maximum distance that the charging equipment can cover. The charging equipment can supply power to the first node within the service radius. The charging power of the charging equipment can be the power that the charging equipment uses to charge the first node. The charging power requirement can be the charging power required by the first node.

[0165] The lower limit of the remaining power can be considered as the minimum remaining power of the first node. It can be the minimum remaining power required for the first node to fly to the charging device, or the minimum remaining power after flying to the charging device. Correspondingly, the remaining power of the first node when it moves to the charging device can be the actual remaining power after the first node moves to the charging device, or the remaining power required for the first node to fly to the charging device.

[0166] The above-mentioned elements included in the constraints can be combined arbitrarily to establish constraints for selecting charging equipment.

[0167] In one embodiment, the constraints include one or more of the following:

[0168] The distance between the first node and the charging device is less than or equal to the service radius;

[0169] The charging power is greater than or equal to the sum of the charging power requirements of all the first nodes;

[0170] The remaining power of the first node when it moves to the charging device is greater than or equal to the lower limit value.

[0171] The location can be the location of the charging equipment. In this embodiment, the charging equipment can be located within or around a hotspot area.

[0172] A distance less than the service radius ensures that the first node can fly to the charging device. The distance from any first node to any charging device must be less than the service radius of that charging device.

[0173] The charging power of the charging equipment must be greater than or equal to the sum of the charging power requirements of all first nodes to ensure that the charging equipment can charge all first nodes.

[0174] The remaining battery power must be greater than or equal to the lower limit to ensure that the first node has sufficient remaining battery power after flying to the charging device, or to ensure that the first node has enough battery power to fly to the charging device. For example, if any first node flies to a charging device, its remaining battery power must be greater than or equal to the lower limit.

[0175] In one embodiment, the objective function of the location model is associated with the resource consumption information for constructing the charging equipment and the resource consumption of the energy consumed by the first node moving to the charging equipment.

[0176] Resource consumption information can describe the amount of resources consumed in constructing charging equipment. Energy consumption resource consumption can refer to the amount of resources consumed by energy consumption.

[0177] The site selection method provided in this invention determines the hotspot area of ​​the first node based on its motion trajectory, and then solves the site selection model based on the hotspot area to select a location for the charging equipment of the first node from the hotspot area. This improves the accuracy of site selection, and the site location determined based on the motion trajectory is more in line with the actual flight requirements of the first node.

[0178] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0179] In one embodiment, the movement area includes multiple geographic grids, and determining the hotspot area in the movement area of ​​the first node based on the movement trajectory includes:

[0180] Based on the movement trajectory, the frequency of occurrence and dwell time of the first node in each of the geographic grids are determined;

[0181] Based on the frequency of occurrence and the dwell time, hotspot areas are selected from each of the geographic grids.

[0182] In this embodiment, the movement area can be divided into multiple geographic grids. For example, the movement area can be split according to a set size to obtain multiple geographic grids.

[0183] After dividing the data into multiple geographic grids, the frequency of occurrence and dwell time of the first node within each grid can be statistically analyzed independently. The frequency of occurrence refers to how often the first node appears within the grid. The dwell time refers to the duration the first node spends within the grid. This time can be a time interval or the duration of the dwell time, also known as the duration.

[0184] This embodiment can determine the frequency of the first node's appearance within a geographic grid based on its motion trajectory. After the motion trajectory indicates that the first node has moved into the geographic grid, signaling (such as signaling sent or received by the first node) is counted. The count is incremented by 1 each time the first node flies into the geographic grid. By counting the signaling within the geographic grid, the frequency of the UAV's appearance within the geographic grid can be determined.

[0185] This embodiment can determine the dwell time of the first node within a geographic grid by using its movement trajectory. For example, if the movement trajectory determines that the first node has moved to a geographic grid, the duration of the drone's stay from moving to to leaving the geographic grid can be determined by combining the maximum and minimum timestamps of the signals received or sent within that geographic grid.

[0186] In one example, this embodiment can determine the frequency of occurrence and / or dwell time based on the motion trajectory and the signaling sent and / or received by the first node in different geographic grids.

[0187] In one example, this embodiment can determine the total number of signaling messages from different drones flying through a geographic grid at different times within a statistical period, thus obtaining the frequency of occurrence. Within the statistical period, the duration of time a drone stays within a geographic grid can be counted, and the dwell time can be determined by the difference between the maximum and minimum timestamps of the signaling sent or received each time the drone stays within the geographic grid.

[0188] After determining the frequency of occurrence and dwell time of the corresponding geographic grid, it is determined whether the two meet the corresponding conditions. If at least one condition is met, the geographic grid can be identified as a hotspot area.

[0189] In one embodiment, selecting hotspot areas from each of the geographic grids based on the frequency of occurrence and the dwell time includes:

[0190] For each geographic grid, determine whether the occurrence frequency and dwell time of the geographic grid meet the corresponding threshold conditions. If so, the geographic grid is identified as a hotspot area.

[0191] The threshold condition for frequency of occurrence can be greater than the frequency threshold. The threshold condition for dwell time can be greater than the dwell duration threshold.

[0192] For each geographic grid, if the frequency of occurrence of the grid is greater than the corresponding frequency threshold and the dwell time is greater than the dwell time threshold, then the geographic grid is identified as a hotspot area.

[0193] The following is an exemplary description of a method for selecting charging equipment locations. This method can be considered a drone charging point location selection method based on communication signaling. The specific process is as follows:

[0194] (1) Identification of flight hotspot areas:

[0195] This invention achieves accurate judgment by setting core parameters and standardized calculation logic. First, the statistical period (T) is defined, with a default analysis window of one week (7 days); the granularity (G) of the flight area (i.e., the motion area) is configured according to accuracy requirements. A geographic grid of meters, with each grid generating a unique identifier (e.g., The core evaluation indicators include frequency of occurrence (C) and length of stay (). The former refers to the total number of independent signaling records of the UAV in the target area within the statistical period, while the latter is calculated by accumulating the time difference between adjacent signaling records to determine the cumulative dwell time. A frequency threshold needs to be set during the determination process. ) and stay threshold ( The frequency can be configured to 50-200 times and 30-120 minutes depending on the regional activity level. In the specific calculation, the signaling data is first divided into grids based on the cleaned signaling data, and then... The frequency of occurrence and dwell time in each area are statistically analyzed, and areas that simultaneously meet two threshold conditions are ultimately selected as hotspot areas for flight. For example, setting... sky, rice, Second-rate, At the specified time, a certain grid area around a logistics park in a certain city was identified as a "logistics operation hotspot area" because it appeared 320 times and stayed for 215 minutes in 7 days, while a certain grid area above a residential area was not included because it did not meet the standard of 85 occurrences.

[0196] Table 1. A schematic table of core parameters of a model provided in an embodiment of the present invention.

[0197]

[0198] Referring to Table 1, a mathematical model for charging point site selection is established, taking into account factors such as flight hotspots, the relationship between UAV flight distance and power consumption, and the resource consumption of charging point construction. The objective function is to minimize the total resource consumption for charging point construction and the total energy consumption of the UAV flying to the charging point. That is, the objective function is related to the resource consumption information for constructing charging equipment and the resource consumption of the energy consumed by the first node moving to the charging equipment. Constraints are also considered, such as the service radius of each charging point, the maximum charging power of the charging equipment, and the minimum remaining power requirement of the UAV.

[0199] The objective function can be expressed as: ;

[0200] in Indicates the first Resource consumption of each charging point (i.e., charging equipment). Indicates the first The drone flew to the Energy consumption and resource consumption at each charging point For the number of charging points, This refers to the number of drones.

[0201] The constraints can be expressed as:

[0202] The distance between the first node and the charging device is less than or equal to the service radius: ;in For the first The drone and the first The distance between charging points For the first The service radius of each charging point;

[0203] The charging power is greater than or equal to the sum of the charging power requirements of all the first nodes: ;in For the first The charging power of each charging point For the first The charging power requirements of a drone.

[0204] The remaining power of the first node when it moves to the charging device is greater than or equal to the lower limit (i.e., the minimum remaining power): ;in, For the first A drone flew to the The remaining power at each charging point.

[0205] (3) Algorithm optimization:

[0206] A genetic algorithm is used to solve the above mathematical model to find the optimal charging point location scheme. In the genetic algorithm, the location coordinates of the charging point are encoded as chromosomes. Through selection, crossover, mutation and other operations, the population is continuously evolved, and finally the chromosome with the highest fitness (i.e. the smallest objective function value) is obtained. The corresponding location coordinates of the chromosome are the optimal charging point location.

[0207] "Code" the solution, using 0 / 1 strings to represent address selection: , .

[0208] example: 1 candidate point .

[0209] Create an initial batch of schemes, and randomly generate 50 of them. String (e.g.) ( ), as an initial option.

[0210] Score and select the best option:

[0211] Total resource consumption C: The sum of resource consumption for construction and resource consumption for drone energy consumption;

[0212] Violation penalty points: Half points will be deducted for violations such as exceeding 3 kilometers or insufficient power;

[0213] Scoring rules: The smaller the C value, the more compliant the behavior (e.g., the smaller the penalty for violations), and the higher the score.

[0214] Optimize repeatedly until optimal, repeating 100 times: Keep the top 15 high-scoring solutions, then add 20 ordinary solutions; select 2 good solutions to "mix and match" (e.g., ...). and Mix out ); occasionally change 1 (like Become ), to avoid rigid thinking. Finally, the highest score The string represents the optimal solution. Example: To build the first and second charging points.

[0215] This invention provides a method for obtaining UAV flight trajectories based on communication signaling: by interfacing with the network management system of a communication operator to obtain signaling data within the UAV's communication frequency band, removing specific erroneous data and converting it into structured text or database table structure, and extracting the UAV flight trajectory from the communication signaling, this method is achieved by analyzing the connection switching relationship between the UAV and different base stations, as well as time and location information.

[0216] This invention provides a method for selecting UAV charging points based on communication signaling data: It statistically analyzes the frequency and dwell time of UAVs in a specific time period and identifies hotspot areas for UAV flights based on thresholds; using the resource consumption that minimizes total construction resources and flight energy consumption as the objective function, and combining specific constraints, it constructs a mathematical model for charging point selection, and employs a genetic algorithm to solve the optimized mathematical model for charging point selection, providing support for site selection decisions.

[0217] This invention offers a more efficient and cost-effective data acquisition method. In existing technologies, some site selection methods rely on installing high-precision positioning equipment or dedicated data acquisition modules on the UAV to obtain flight path data. This not only increases the hardware load and manufacturing cost of the UAV but also requires additional equipment maintenance costs, and data loss or distortion may occur due to equipment failure or signal transmission problems. In contrast, this invention directly utilizes existing signaling data in the communication network. By interfacing with the communication operator's network management system, it can obtain key information such as the UAV's position and direction of movement without any hardware modifications to the UAV. This data acquisition method not only eliminates the purchase and maintenance costs of additional equipment but also leverages a mature communication network to ensure the continuity and stability of data acquisition, significantly reducing the overall cost of data acquisition.

[0218] This invention's site selection is more aligned with actual flight requirements. Traditional site selection methods often rely on static data such as geographic information and population density, failing to fully consider the real-time flight trajectories, distribution patterns, and dynamic changes in demand of drones. This often leads to low utilization rates of charging points after construction, an inability to accurately match the charging needs of drones, and even situations of charging resource surplus or shortage in some areas. This invention, through in-depth analysis of communication signaling data, can accurately extract drone flight trajectories, identify flight hotspots, and comprehensively understand the actual flight patterns of drones. Based on this, charging point site selection can place charging facilities in core areas with high drone activity frequency and urgent charging needs, making the site selection results more closely match the actual operating scenarios of drones and significantly improving the actual utilization rate of charging points.

[0219] This invention presents a more scientific and accurate site selection model. Existing technologies often employ site selection models that only consider single factors such as distance or coverage area, lacking a comprehensive consideration of multi-dimensional factors such as resource consumption and power consumption during construction, resulting in insufficient practicality of the site selection schemes. The mathematical model for charging point site selection constructed in this invention uses minimizing the total resource consumption for charging point construction and the total energy consumption of drones flying to the charging point as its objective function, while incorporating multiple constraints such as service radius, charging power, and remaining power requirements. Solving the model using a genetic algorithm can find the globally optimal solution under complex constraints. This multi-objective optimization modeling approach ensures both effective charging point coverage and energy consumption optimization, making the site selection scheme more scientific and feasible.

[0220] This invention combines communication signaling with drone applications. Relying on base station networks, communication signaling can capture dynamic information such as signal strength, device distribution, and movement trajectories in real time within a region. This frees drones from dependence on a single navigation system, giving them stronger environmental adaptability and mission execution capabilities. It efficiently and cost-effectively captures drone flight trajectories and hotspot areas, achieving a high degree of matching between charging point layout and actual needs. This reduces the construction of ineffective charging points.

[0221] In one embodiment, Figure 3 This is a schematic diagram of the structure of a trajectory determination device provided in an embodiment of the present invention. Figure 3 As shown, the device includes:

[0222] The acquisition module 310 is used to acquire first location information in the communication signaling, wherein the first location information includes the identification information of the second node and network positioning parameters;

[0223] The first determining module 320 is used to determine the second location information of the first node based on the identification information and the network positioning parameters;

[0224] The second determining module 330 is used to determine a plurality of third nodes associated with the first node based on the second location information;

[0225] The conversion module 340 is used to convert the reference signal received power of the third node into a distance estimate, the distance estimate indicating the distance between the first node and the third node;

[0226] The third determining module 350 is used to determine the third location information of the first node based on each of the distance estimates;

[0227] The generation module 360 ​​is used to generate the motion trajectory of the first node based on the third location information.

[0228] In one embodiment, the conversion module 340 is specifically used for:

[0229] The reference signal received power of the third node is converted into a distance estimate using a model. This model includes a model describing the relationship between transmission distance and path loss. The model also describes the relationship between the first path loss at the first node and the following objective term:

[0230] The path loss basic term includes a second path loss at a reference distance, and the path loss basic term also includes the logarithm of the ratio of the distance estimate to the reference distance.

[0231] The first path loss is associated with the reference signal received power of the third node.

[0232] In one embodiment, the target item further includes one or more of the following:

[0233] Correction terms, which are used to correct the influence of the frequency of the reference signal received power fluctuation of the third node;

[0234] A penalty function is used to penalize the path loss exponent for deviations from a set value.

[0235] In one embodiment, the correction term is associated with the logarithm of the frequency, and the first path loss is linearly related to the logarithm of the frequency; the penalty function is associated with the deviation between the path loss exponent and a set value.

[0236] In one embodiment, the number of third nodes is three, and the third determining module 350 includes:

[0237] The third determining unit is used to determine the fourth location information based on each of the distance estimates using the triangulation method;

[0238] The first acquisition unit is used to acquire the position measurement value of the first node transmitted by the satellite;

[0239] The second acquisition unit is used to acquire the motion data determined by the inertial measurement unit of the first node;

[0240] The fusion unit is used to fuse the fourth position information, the position measurement value and the motion data to obtain the third position information.

[0241] In one embodiment, the fusion unit is specifically used for:

[0242] The transformation subunit is used to transform the fourth position information, the position measurement value and the motion data to the local coordinate system to obtain the transformed fourth position information, the transformed position measurement value and the transformed motion data;

[0243] The alignment subunit is used to perform time alignment on the converted fourth position information, the converted position measurement value and the converted motion data to obtain the aligned fourth position information, the aligned position measurement value and the aligned motion data;

[0244] The encapsulation subunit is used to encapsulate the aligned fourth position information, the aligned position measurement value and the aligned motion data into corresponding structured data respectively;

[0245] The fusion subunit is used to fuse various structured data to obtain third location information.

[0246] In one embodiment, the fusion subunit is specifically used for:

[0247] Determine the corresponding fusion mode based on the environment of the first node;

[0248] The structured data are fused according to the fusion mode described above.

[0249] In one embodiment, the device further includes a verification module for:

[0250] During the fusion process, verification is performed using the second location information.

[0251] The trajectory determination device provided in the embodiments of the present invention can execute the trajectory determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0252] In one embodiment, Figure 4 This is a schematic diagram of a location selection method provided in an embodiment of the present invention. Figure 4 As shown, the device includes:

[0253] The acquisition module 410 is used to acquire the motion trajectory of the first node, the motion trajectory being associated with the first location information in the communication signaling;

[0254] The first determining module 420 is used to determine the hotspot region in the motion region of the first node based on the motion trajectory;

[0255] The second determining module 430 is used to solve the site selection model based on the hotspot area and determine the location of the charging equipment.

[0256] In one embodiment, the movement area includes multiple geographic grids, and the first determining module 420 includes:

[0257] The determining unit is used to determine the frequency of occurrence and dwell time of the first node in each of the geographic grids based on the movement trajectory;

[0258] The selection unit is used to select hotspot areas from each of the geographic grids based on the frequency of occurrence and the dwell time.

[0259] In one embodiment, the selected unit is specifically used for

[0260] For each geographic grid, determine whether the occurrence frequency and dwell time of the geographic grid meet the corresponding threshold conditions. If so, the geographic grid is identified as a hotspot area.

[0261] In one embodiment, the objective function of the location model is associated with the resource consumption information for constructing the charging equipment and the resource consumption of the energy consumed by the first node moving to the charging equipment.

[0262] In one embodiment, the constraints of the location model are associated with one or more of the following:

[0263] The service radius of the charging equipment;

[0264] The distance between the first node and the charging device;

[0265] The charging power of the charging device;

[0266] The charging power requirement of the first node;

[0267] The lower limit of the remaining power of the first node;

[0268] The first node moves to the remaining power of the charging device.

[0269] In one embodiment, the constraints include one or more of the following:

[0270] The distance between the first node and the charging device is less than or equal to the service radius;

[0271] The charging power is greater than or equal to the sum of the charging power requirements of all the first nodes;

[0272] The remaining power of the first node when it moves to the charging device is greater than or equal to the lower limit value.

[0273] The addressing device provided in the embodiments of the present invention can execute the addressing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0274] In one embodiment, Figure 5 This is a schematic diagram of the structure of a communication node provided in an embodiment of the present invention, such as... Figure 5 The diagram illustrates a schematic of a communication node 10 that can be used to implement embodiments of the present invention. The communication node 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The communication node 10 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0275] like Figure 5As shown, the communication node 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor 11, and the computer program is executed by the at least one processor 11 to enable the at least one processor 11 to perform the method provided by the present invention.

[0276] Processor 11 can perform various appropriate actions and processes based on computer programs stored in read-only memory (ROM) 12 or loaded from storage unit 18 into random access memory (RAM) 13. RAM 13 can also store various programs and data required for the operation of communication node 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. Input / output (I / O) interface 15 is also connected to bus 14.

[0277] Multiple components in communication node 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows communication node 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0278] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the methods provided in this invention.

[0279] In some embodiments, the methods provided herein may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on communication node 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the methods by any other suitable means (e.g., by means of firmware).

[0280] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard parts (ASSPs), systems-on-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0281] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0282] In the context of this invention, a computer-readable storage medium stores computer instructions that are used to cause a processor to execute and implement the method provided by this invention.

[0283] The present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the method provided according to embodiments of the present invention. A computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0284] To provide interaction with the user, the systems and techniques described herein can be implemented on communication node 10, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to communication node 10. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0285] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0286] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0287] This invention also provides a computer program product, including a computer program that, when executed by a processor, can implement the methods provided in any embodiment of this application.

[0288] In the implementation of the computer program product, computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0289] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0290] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A trajectory determination method, characterized in that, Applied to the first node, the method includes: Obtain first location information from communication signaling, the first location information including the identification information of the second node and network positioning parameters; Based on the identification information, the base station database is queried, and the second location information of the first node is determined through the network positioning parameters; Based on the second location information, a plurality of third nodes associated with the first node are identified; The reference signal received power of the third node is converted into a distance estimate, which indicates the distance between the first node and the third node; Based on the distance estimates, the third location information of the first node is determined; The motion trajectory of the first node is generated based on the third location information; Wherein, the number of third nodes is three, and the determination of the third location information of the first node based on each of the distance estimates includes: Based on the aforementioned distance estimates, the fourth location information is determined using triangulation. Obtain the position measurement value of the first node transmitted by the satellite; Acquire the motion data determined by the inertial measurement unit of the first node; The fourth location information is obtained by fusing the location measurement value and the motion data to obtain the third location information; wherein, during the fusion process, the second location information is used for verification.

2. The method according to claim 1, characterized in that, The step of converting the reference signal received power of the third node into a distance estimate includes: The reference signal received power of the third node is converted into a distance estimate using a model. This model includes a model describing the relationship between transmission distance and path loss. The model also describes the relationship between the first path loss at the first node and the following objective term: The path loss basic term includes a second path loss at a reference distance, and the path loss basic term also includes the logarithm of the ratio of the distance estimate to the reference distance. The first path loss is associated with the reference signal received power of the third node.

3. The method according to claim 2, characterized in that, The target item also includes one or more of the following: Correction terms, which are used to correct the influence of the frequency of the reference signal received power fluctuation of the third node; A penalty function is used to penalize the path loss exponent for deviations from a set value.

4. The method according to claim 3, characterized in that, The correction term is associated with the logarithm of the frequency, and the first path loss is linearly related to the logarithm of the frequency; The penalty function is associated with the deviation between the path loss index and a set value.

5. The method according to claim 4, characterized in that, The process of fusing the fourth location information, the location measurement value, and the motion data to obtain the third location information includes: The fourth position information, the position measurement value, and the motion data are converted to the local coordinate system to obtain the converted fourth position information, the converted position measurement value, and the converted motion data. The converted fourth position information, the converted position measurement value, and the converted motion data are time-aligned to obtain the aligned fourth position information, the aligned position measurement value, and the aligned motion data. The aligned fourth position information, the aligned position measurement value, and the aligned motion data are respectively encapsulated into corresponding structured data; The structured data are fused together to obtain the third location information.

6. The method according to claim 5, characterized in that, The process of fusing the structured data to obtain the third location information includes: Determine the corresponding fusion mode based on the environment of the first node; The structured data are fused according to the fusion mode described above.

7. A site selection method, characterized in that, The method includes: Obtain the motion trajectory of the first node, wherein the motion trajectory is generated using the trajectory determination method described in any one of claims 1-6; Based on the motion trajectory, hotspot areas in the motion area of ​​the first node are determined; Based on the hotspot areas, a site selection model is solved to determine the location of the charging equipment.

8. The method according to claim 7, characterized in that, The movement area includes multiple geographic grids, and determining the hotspot area in the movement area of ​​the first node based on the movement trajectory includes: Based on the movement trajectory, the frequency of occurrence and dwell time of the first node in each of the geographic grids are determined; Based on the frequency of occurrence and the dwell time, hotspot areas are selected from each of the geographic grids.

9. The method according to claim 8, characterized in that, The selection of hotspot areas from each geographic grid based on the frequency of occurrence and the dwell time includes: For each geographic grid, determine whether the occurrence frequency and dwell time of the geographic grid meet the corresponding threshold conditions. If so, the geographic grid is identified as a hotspot area.

10. The method according to claim 7, characterized in that, The objective function of the site selection model is associated with the resource consumption information for constructing the charging equipment and the resource consumption of the energy consumed by the first node moving to the charging equipment.

11. The method according to claim 7, characterized in that, The constraints of the location selection model are associated with one or more of the following: The service radius of the charging equipment; The distance between the first node and the charging device; The charging power of the charging device; The charging power requirement of the first node; The lower limit of the remaining power of the first node; The first node moves to the remaining power of the charging device.

12. The method according to claim 11, characterized in that, The constraints include one or more of the following: The distance between the first node and the charging device is less than or equal to the service radius; The charging power is greater than or equal to the sum of the charging power requirements of all the first nodes; The remaining power of the first node when it moves to the charging device is greater than or equal to the lower limit value.

13. A communication node, characterized in that, The communication node includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-12.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to perform the method of any one of claims 1-12.

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