Multi-anchor node cooperative uwb indoor positioning method, device, equipment and medium

Abstract

This application discloses a multi-anchor node collaborative UWB indoor positioning method, apparatus, device, and medium. The method introduces multiple anchor nodes to jointly receive ultra-wideband positioning pulse signals emitted by a processing card, and constructs a signal reception time difference matrix by combining information such as signal arrival timestamps, signal reception strength, and environmental perception parameters. It calculates the non-line-of-sight propagation probability between each anchor node, quantifying the signal link reliability. Furthermore, it assigns confidence weights to the time difference measurements of different anchor nodes based on signal strength and anchor node type information, forming a weight matrix. This effectively reduces interference from anchor nodes that are significantly affected by obstruction, reflection, or attenuation in the final positioning calculation, improving positioning robustness and accuracy. The introduction of multi-source sensing data, non-line-of-sight probability modeling, and a dynamic confidence weight adjustment mechanism effectively compensates for and adaptively optimizes channel uncertainties and anchor node differences in the UWB indoor positioning system, ensuring the system's high-precision positioning capability.

Description

Technical Field

[0001] This invention belongs to the field of wireless positioning technology, specifically relating to a UWB indoor positioning method, device, equipment, and medium with multi-anchor node collaboration. Background Technology

[0002] With the increasing demand for high-precision positioning in scenarios such as intelligent manufacturing, smart logistics, and asset management, indoor positioning technology based on Ultra Wide Band (UWB) has emerged. UWB technology has high temporal resolution, low power consumption, and strong anti-interference capabilities.

[0003] Traditional UWB positioning methods often employ a single anchor node or a small number of anchor nodes, relying on ranging models such as Time of Arrival (TOA), Time Difference of Arrival (TDOA), or Angle of Arrival (AOA) for target localization. The TDOA algorithm, due to its advantage of not requiring clock synchronization, is commonly used in scenarios with multiple receiving anchor nodes. The target node transmits a UWB signal, which is received by multiple anchor nodes that record the timestamps, calculate the time difference, and construct a hyperboloid positioning model to estimate the target's position.

[0004] However, in actual deployments, signal propagation is easily interfered with by non-line-of-sight (NLOS) effects such as obstruction and reflection, leading to a significant increase in positioning errors. On the other hand, trajectory analysis of positioning results is also an important function of indoor positioning systems. Currently, simple trajectory smoothing and interpolation methods are commonly used to improve trajectory continuity and readability, but these methods struggle to accurately identify changes in device motion states, such as stillness, movement, or sudden acceleration changes, in noisy measurement environments.

[0005] Therefore, how to design a UWB indoor positioning method that can balance high-precision positioning with environmental interference is a technical problem to be solved. Summary of the Invention

[0006] Therefore, it is necessary to address the problems of existing technologies by providing a UWB indoor positioning method, device, equipment, and medium that can balance high-precision positioning with environmental interference and multi-anchor node collaboration.

[0007] In a first aspect, embodiments of this application provide a UWB indoor positioning method with multi-anchor node collaboration, comprising the following steps:

[0008] The system acquires reported data from multiple anchor nodes. The reported data includes the signal arrival timestamp, signal reception strength, and environmental perception parameters of each anchor node. The reported data is recorded by the anchor node in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card. The anchor nodes include heterogeneous anchor nodes used to acquire local environmental parameters. The ultra-wideband positioning pulse signal includes the processing card identifier and transmission timestamp.

[0009] Calculate the signal reception time difference between each pair of anchor nodes based on the arrival timestamps of each signal to obtain a reception time difference matrix;

[0010] Calculate the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtain the non-line-of-sight propagation probability matrix.

[0011] The confidence weight of each anchor node in the reception time difference matrix is ​​determined based on the non-line-of-sight propagation probability matrix, the signal reception strength, and the anchor node type of each anchor node, thus obtaining the weight matrix.

[0012] The location information of the multiple anchor nodes, the weight matrix, and the receiving time difference matrix are processed based on the dynamic weighted least squares positioning algorithm to obtain the positioning information of the processing card; the positioning information includes multiple positioning values ​​of the processing card in a continuous time period.

[0013] Preferably, the step of calculating the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data to obtain the non-line-of-sight propagation probability matrix includes:

[0014] Arrival time offset detection is performed based on the signal arrival timestamp to obtain time distribution characteristics;

[0015] Based on the received signal strength, signal strength abrupt change detection is performed to obtain the strength attenuation characteristics;

[0016] Environmental occlusion detection is performed based on the environmental perception parameters to obtain environmental occlusion features;

[0017] Based on the time distribution characteristics, intensity attenuation characteristics, and environmental occlusion characteristics, the non-line-of-sight propagation probability of each anchor node is calculated to obtain the non-line-of-sight propagation probability matrix.

[0018] Preferably, the confidence weight of each anchor node is represented by the following formula:

[0019] (1);

[0020] (2);

[0021] (3);

[0022] in, For anchor nodes Confidence weights For anchor node index; The confidence level of the received signal strength is the value of the mapped signal. The variance of ranging stability is negatively correlated with the weights; Weight for anchor node type; For anchor nodes The signal reception strength; For anchor nodes Anchor node type; For anchor nodes The non-line-of-sight propagation probability; For anchor nodes Historical ranging stability variance; This represents the total number of anchor nodes; This represents the expected value of the received signal strength. and It is a constant.

[0023] Preferably, the method further includes:

[0024] Based on the multiple positioning values ​​of the processing card within a continuous time period, an initial trajectory of the processing card is constructed; the initial trajectory includes multiple positions of the processing card and the time points corresponding to the multiple positions;

[0025] Based on spatial variation patterns and velocity smoothness constraints, anomaly detection is performed on the initial trajectory to obtain trajectory anomaly types; the trajectory anomaly types include normal, abrupt, continuous offset, and occlusion.

[0026] Based on the filtering method corresponding to the trajectory anomaly type, the initial trajectory is fitted and denoised to obtain the smooth trajectory of the processing card; the filtering method includes Kalman filtering, extended Kalman filtering and particle filtering.

[0027] Preferably, the step of performing anomaly detection on the initial trajectory to obtain the trajectory anomaly type includes:

[0028] If the initial trajectory exhibits a sudden change in velocity, then the trajectory anomaly type is determined to be the sudden change.

[0029] If the fusion mean square error of the initial trajectory exceeds a preset threshold, then the trajectory anomaly type is determined to be occlusion.

[0030] The speed is expressed by the following formula:

[0031] (4);

[0032] The fusion mean square error is expressed by the following formula:

[0033] (5);

[0034] in, The processing is stopped at a certain time point. speed, The processing is stopped at a certain time point. Location, The processing is stuck at the time point Location; The processing is stopped at a certain time point. fusion mean square error For anchor nodes The relative time difference of signal reception; For anchor nodes The expected time difference for signal reception.

[0035] Preferably, the method further includes:

[0036] In response to the frequency modulation scheduling request obtained from the anchor node, the frequency modulation spectrum is queried, and the optimal frequency band and time slot are allocated to each anchor node based on the principles of low interference and low latency.

[0037] The frequency modulation spectrum was obtained through the following method:

[0038] Obtain channel monitoring metrics for each anchor node during its idle time slots; the channel monitoring metrics include received signal strength indication, interference intensity, and signal strength change rate for each frequency band;

[0039] By using a sliding time window to estimate the interference density of the channel monitoring indicators, a historical interference heatmap of the frequency band and time slot is obtained.

[0040] The task density index is obtained based on the number of ranging operations and the frequency of positioning request responses of each anchor node within a preset period.

[0041] Based on the smoothed trajectory of the processing card within a preset period, an activity density heatmap of the processing card is obtained;

[0042] The frequency modulation spectrum is obtained based on the historical interference heatmap of the frequency band-time slot, the task density index, and the activity density heatmap of the processing card.

[0043] Preferably, the method further includes:

[0044] Based on the preset processing card movement path, the initial signal arrival timestamps of multiple anchor nodes are obtained;

[0045] Calculate the mutual measurement time difference between each pair of anchor nodes based on the initial signal arrival timestamp;

[0046] Calculate the relative distance between each pair of anchor nodes based on the mutual measurement time difference to obtain a local anchor node topology diagram;

[0047] The actual position of the anchor node is calculated based on the local anchor node topology using a graph optimization algorithm, and the position information of the anchor node is corrected based on the actual position of each anchor node.

[0048] Secondly, embodiments of this application provide a UWB indoor positioning device with multi-anchor node collaboration, including:

[0049] A data acquisition module is used to acquire reported data from multiple anchor nodes. The reported data includes the signal arrival timestamp, signal reception strength, and environmental perception parameters of each anchor node. The reported data is recorded by the anchor node in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card. The anchor nodes include heterogeneous anchor nodes used to acquire local environmental parameters. The ultra-wideband positioning pulse signal includes the processing card identifier and the transmission timestamp.

[0050] The time difference calculation module is used to calculate the signal reception time difference between each anchor node based on the arrival timestamp of each signal, and to obtain the reception time difference matrix.

[0051] The non-line-of-sight propagation calculation module is used to calculate the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtain the non-line-of-sight propagation probability matrix.

[0052] The dynamic weight calculation module is used to determine the confidence weight of each anchor node in the reception time difference matrix based on the non-line-of-sight propagation probability matrix, the signal reception strength, and the anchor node type of each anchor node, and obtain the weight matrix.

[0053] The positioning module is used to process the position information of the multiple anchor nodes, the weight matrix, and the receiving time difference matrix based on the dynamic weighted least squares positioning algorithm to obtain the positioning information of the processing card; the positioning information includes multiple positioning values ​​of the processing card within a continuous time period.

[0054] Thirdly, embodiments of this application provide an electronic device, the electronic device comprising:

[0055] processor;

[0056] Memory used to store the processor's executable instructions;

[0057] The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the above-described method steps.

[0058] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for performing the above-described method steps.

[0059] Compared with existing technologies, this application has the following advantages: By introducing multiple anchor nodes to jointly receive the ultra-wideband positioning pulse signal emitted by the processing card, and combining multi-dimensional information such as signal arrival timestamp, signal reception strength, and environmental perception parameters, a more discriminative signal reception time difference matrix is ​​constructed. Simultaneously, some anchor nodes are heterogeneous anchor nodes with environmental perception capabilities, capable of sensing non-line-of-sight propagation factors such as local obstruction and channel changes, further improving adaptability to environmental disturbances. By calculating the non-line-of-sight propagation probability between each anchor node, the reliability of the signal link is quantified. Combined with signal strength and anchor node type information, differentiated confidence weights are assigned to the time difference measurements of different anchor nodes, forming a weight matrix. This matrix effectively reduces the interference of anchor nodes significantly affected by obstruction, reflection, or attenuation in the final positioning calculation, thereby improving the robustness and accuracy of the overall positioning results. Based on weighted confidence information, a dynamic weighted least squares positioning algorithm is adopted. By fusing the position information of multiple anchor nodes and the reception time difference, the participation level of each anchor node is dynamically adjusted to achieve high-precision estimation of the processing card's position. By introducing multi-source sensing data, non-line-of-sight probability modeling, and dynamic confidence weight adjustment mechanisms, effective compensation and adaptive optimization of channel uncertainty and anchor node differences in UWB indoor positioning systems are achieved. While ensuring the system's high-precision positioning capability, its practicality and deployment robustness in complex dynamic scenarios such as industry, warehousing, and healthcare are also greatly improved. Attached Figure Description

[0060] Exemplary embodiments of the present invention can be more fully understood by referring to the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain the present invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0061] Figure 1 A flowchart of a multi-anchor node collaborative UWB indoor positioning method provided for an exemplary embodiment of this application;

[0062] Figure 2 A schematic diagram of the structure of a multi-anchor node collaborative UWB indoor positioning device provided as an exemplary embodiment of this application;

[0063] Figure 3A schematic diagram of an electronic device provided in an exemplary embodiment of this application is shown;

[0064] Figure 4 This illustration shows a schematic diagram of a computer-readable medium provided in an exemplary embodiment of this application. Detailed Implementation

[0065] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0066] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0067] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0068] Reference Figure 1 This embodiment discloses a UWB indoor positioning method with multi-anchor node collaboration, including the following steps:

[0069] S101: Acquire reported data from multiple anchor nodes; the reported data includes the signal arrival timestamp, signal reception strength, and environmental perception parameters of each anchor node; the reported data is recorded by the anchor node in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card; the anchor nodes include heterogeneous anchor nodes used to acquire local environmental parameters; the ultra-wideband positioning pulse signal includes the processing card identifier and transmission timestamp;

[0070] Indicatively, the processing card can periodically or on-demand actively transmit UWB positioning pulse signals. For example, it can actively transmit an ultra-wideband positioning pulse signal at preset time intervals. The ultra-wideband positioning pulse signal is characterized by extremely short duration and extremely wide spectral bandwidth, and can include processing card identification information, precise transmission timestamps, and task priority encoding. The pulse signal is broadcast through a UWB channel to anchor nodes within multiple coverage areas and received by multiple anchor nodes deployed in various locations. The ultra-wideband positioning pulse signal transmission behavior of the processing card is controlled by TDMA (Time Division Multiple Access) scheduling to prevent channel conflicts caused by multiple processing cards transmitting signals simultaneously. Anchor nodes refer to UWB receiving devices fixed in known locations, used to receive signals transmitted by the processing card and record relevant reception information. Some anchor nodes are equipped with environmental sensing modules, such as temperature and humidity sensors, barometers, acoustic sensors, and low-cost visual sensing modules, forming heterogeneous anchor nodes. Their purpose is to acquire local environmental parameters to assist in positioning error modeling. Upon receiving a positioning pulse signal, the anchor node simultaneously records the precise timestamp of signal arrival, the received signal strength (RSS value), and local environmental sensing parameters such as temperature, humidity, and air pressure. Optionally, the reported data is synchronously uploaded to the edge server or central positioning processing unit via the local network UWB backhaul link.

[0071] S102: Calculate the signal reception time difference between each anchor node based on the arrival timestamp of each signal to obtain the reception time difference matrix;

[0072] To illustrate, when the processing card sends a pulse signal, the reception time of different anchor nodes varies slightly. By comparing each node in pairs, a reception time difference matrix can be obtained. This matrix reflects the time delay differences along the signal propagation path and is key data for achieving TDOA positioning. It can effectively avoid clock synchronization errors between the processing card and anchor nodes, thereby improving the practicality and accuracy of positioning.

[0073] S103: Calculate the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtain the non-line-of-sight propagation probability matrix;

[0074] This diagram illustrates the possibility of non-line-of-sight (NLOS) propagation in each signal link. A signal link refers to the propagation path from the processing card to the receiving anchor node. Under NLOS conditions, signals are often reflected or blocked by walls, lengthening the propagation path and introducing reception delays and misjudgments. Specifically, based on the RSS values, environmental perception parameters, and geometric relationships between anchor nodes provided in the reported data, the NLOS probability of each signal link is calculated using multi-modal feature modeling and fusion judgment model inference. This results in the construction of a NLOS probability matrix, used to measure the reliability of the data collected by each anchor node.

[0075] For example, signal reception strength and environmental perception parameters are extracted from the reported data of each anchor node, and combined with the geometric relationships of each anchor node as link features. The environmental perception parameters include the type of obstruction, temperature, humidity, and electromagnetic interference indicators collected by the heterogeneous anchor nodes. The geometric relationships refer to the relative spatial distribution of known anchor nodes, including the relative distance and azimuth between any two anchor nodes, the angle distribution between the processing card and multiple anchor nodes, and the geometrical precision attenuation factor (GDOP) calculated based on the anchor node distribution. Further, the above link features are input into a pre-trained statistical discriminant model or machine learning model to obtain the probability value of the link being in a non-line-of-sight state. This probability value is a continuous value from 0 to 1, used to characterize the possibility of the link being obstructed or experiencing multipath effects. Furthermore, the latest collected link features can be updated and retrained based on a sliding time window to adapt to environmental changes and improve the accuracy of non-line-of-sight probability estimation. Thus, the non-line-of-sight propagation probability matrix can more realistically reflect the signal propagation state in complex indoor environments, providing a basis for the subsequent calculation of the weight matrix. Statistical discrimination models or machine learning models establish a mapping relationship between features and propagation states. The training samples of the model come from labeled LOS / NLOS measurement data in typical scenarios. As the operating environment changes, the parameters can be adjusted based on the latest collected link features using a sliding window to maintain the accuracy of discrimination.

[0076] S104: Determine the confidence weight of each anchor node in the reception time difference matrix based on the non-line-of-sight propagation probability matrix, signal reception strength, and anchor node type of each anchor node, and obtain the weight matrix.

[0077] In a schematic representation, weights are assigned based on the anchor node's signal strength, whether it is a heterogeneous anchor node, its NLOS probability, and its historical ranging stability. Specifically, anchor nodes with higher signal reception strength, lower NLOS probability, and smaller historical ranging variance receive greater weights. Data from heterogeneous anchor nodes, due to their greater environmental redundancy, are given structurally corrected weights. Optionally, a weighting mechanism based on Bayesian or empirical distributions can be introduced during the weight calculation process to further adjust the influence of each anchor node in the positioning estimation.

[0078] S105: Based on the dynamic weighted least squares positioning algorithm, the position information, weight matrix and reception time difference matrix of multiple anchor nodes are processed to obtain the positioning information of the processing card; the positioning information includes multiple positioning values ​​of the processing card in a continuous time period.

[0079] Given the known three-dimensional spatial coordinates of the anchor nodes, a dynamic weighted least squares positioning algorithm is used to estimate the spatial position of the processing card based on the receiving time difference matrix and the corresponding weight matrix. The least squares algorithm constructs a set of nonlinear equations relating the unknown coordinates of the processing card to the receiving time differences of each anchor node, and solves these equations with the objective of minimizing the sum of squared residuals. Furthermore, a dynamic weighting mechanism is introduced, where each value in the weight matrix serves as an adjustment factor for each residual term, assigning different importance to the error terms. This suppresses unreliable data and enhances highly reliable data. The weights are updated in real-time with each positioning calculation, adapting to changes in data quality under different environments. While maintaining low computational overhead, this significantly improves the final positioning accuracy and environmental adaptability.

[0080] In the multi-anchor node collaborative UWB indoor positioning method disclosed in the above embodiments, multiple anchor nodes collaboratively receive the same ultra-wideband positioning pulse signal, and a time difference matrix is ​​constructed based on the reception time difference of each anchor node. Furthermore, by combining signal strength, anchor node type, and environmental perception parameters, the non-line-of-sight propagation probability and corresponding confidence weights are calculated. A weight matrix is ​​introduced into the dynamic weighted least squares algorithm for position calculation. Multi-source information fusion improves the ability to identify non-line-of-sight channels, thus weakening the impact of abnormal links on positioning results. The introduction of dynamic confidence weights enables dynamic adjustment of the contribution of multiple anchor nodes, improving the system's adaptability to complex environmental changes. The weight-assisted least squares solution model achieves higher accuracy and stronger robustness in estimating the location of the processing card, effectively resisting the impact of multipath interference, obstruction, and signal fluctuations on positioning accuracy in complex real-world environments. Fine-grained modeling and weight adjustment of the anchor node signal reception process enhances the fault tolerance capability for abnormal links, giving the positioning system higher environmental adaptability and real-time performance. By leveraging the ability of heterogeneous anchor nodes to perceive the local environment, the dimensions of channel state discrimination are further enriched, providing a reliable basis for weight calculation and algorithm optimization. This ensures high-precision positioning while reducing deployment complexity and improving system robustness.

[0081] In a preferred embodiment, the non-line-of-sight propagation probability of the signal link at each anchor node is calculated based on the reported data to obtain a non-line-of-sight propagation probability matrix, including:

[0082] S201: Detect arrival time offset based on signal arrival timestamps to obtain time distribution characteristics;

[0083] In NLOS mode, signals typically take detours, leading to a higher TDOA. The signal arrival timestamps reported by each anchor node are compared with the propagation delay under the theoretical line-of-sight path. Significant offsets or extended distributions are considered temporal characteristics of non-line-of-sight propagation and extracted as temporal distribution features. ,in, The propagation time curve under the theoretically direct path, For historical data residual models, it characterizes the degree of deviation of the expected distribution of TDOA;

[0084] S202: Detect signal strength abrupt changes based on the received signal strength to obtain the strength attenuation characteristics;

[0085] In UWB at LOS, RSS (Relative Strength Subtraction Segment) exhibits a stable attenuation with distance. By comparing the RSS with the estimated line-of-sight intensity threshold, abrupt or nonlinear attenuation behavior is identified to form the intensity attenuation characteristics of the anchor node link. If the RSS is significantly lower than the normal attenuation curve of its TDOA (Total Distance Availability) ranging value, such as being more than 10 dB below the expected value, occlusion is considered possible, and the intensity attenuation characteristics are obtained in this case. ,in, This is the expected curve for normal decay.

[0086] S203: Perform environmental occlusion detection based on environmental perception parameters to obtain environmental occlusion features;

[0087] NLOS is commonly found in environments obstructed by walls, moisture, metal, etc. Schematic: heterogeneous anchor nodes sense environmental parameters such as sudden changes in temperature and humidity, air pressure, or abnormal lighting, using these as obstruction signals to obtain environmental obstruction characteristics. ,in, and This is a normal baseline. This refers to the sudden change in temperature and humidity. This is an abnormal lighting variable.

[0088] S204: Based on the time distribution characteristics, intensity attenuation characteristics, and environmental occlusion characteristics, calculate the non-line-of-sight propagation probability of each anchor node to obtain the non-line-of-sight propagation probability matrix.

[0089] The temporal distribution characteristics, intensity attenuation characteristics, and environmental occlusion characteristics are fused together. Then, using Bayesian inference, fuzzy logic discrimination, or lightweight neural network models, the non-line-of-sight propagation probability of the signal link at each anchor node is calculated, yielding the corresponding non-line-of-sight propagation probability matrix. Illustratively, a multi-feature joint evaluation model based on a multilayer perceptron (MLP) is constructed for each anchor node. The observation results and their NLOS probabilities were calculated. This matrix will serve as the basis for subsequent positioning weight adjustments and trajectory confidence updates.

[0090] In a preferred embodiment, the confidence weight of each anchor node in the reception time difference matrix is ​​determined based on the non-line-of-sight propagation probability matrix, signal reception strength, and anchor node type, resulting in a weight matrix, including:

[0091] The confidence weight of each anchor node is represented by the following formula:

[0092] (1);

[0093] (2);

[0094] (3);

[0095] in, For anchor nodes Confidence weights For anchor node index; The confidence level of the received signal strength is the value of the mapped signal. The variance of ranging stability is negatively correlated with the weights; Weight for anchor node type; For anchor nodes The signal reception strength; For anchor nodes Anchor node type; For anchor nodes The non-line-of-sight propagation probability; For anchor nodes Historical ranging stability variance; This represents the total number of anchor nodes; This represents the expected value of the received signal strength. and It is a constant.

[0096] In a preferred embodiment, the method further includes:

[0097] S301: Based on the multiple positioning values ​​of the processing card within a continuous time period, construct the initial trajectory of the processing card; the initial trajectory includes multiple positions of the processing card and the time points corresponding to the multiple positions;

[0098] This illustration demonstrates how spatial positioning values ​​of a processing card are extracted at various time points within a continuous time period and used to construct an initial trajectory. Each trajectory data point contains a specific timestamp and three-dimensional spatial coordinates, reflecting the movement path and rhythm of the processing card within that time period. The initial trajectory of the processing card is constructed from positioning values ​​recorded by a UWB indoor positioning system, arranged in a time series. This trajectory is in the form of… ,in Indicates the first Two-dimensional positioning coordinates at each time point This indicates the corresponding timestamp.

[0099] S302: Based on spatial variation patterns and velocity smoothness constraints, anomaly detection is performed on the initial trajectory to obtain the trajectory anomaly type; the trajectory anomaly types include normal, abrupt, continuous offset, and occlusion;

[0100] Multiple statistical and pattern recognition methods were applied to the initial trajectory to detect trajectory anomalies and identify non-physical changes or unreasonable jumps in the trajectory. Spatial variation refers to the fact that the spatial position of the processing card, determined by physical constraints, should change continuously and gradually over time during actual movement, without large jumps or discontinuous backtracking. Velocity smoothness constraint means that the speed change of the processing card should be relatively smooth over a short period. If the speed difference derived from two adjacent positioning points is too large, or the instantaneous speed far exceeds the system's allowed movement limit, it can be preliminarily judged that there is a data anomaly or positioning drift. Specifically, parameters such as the rate of change of speed, path continuity, and spatial position drift amplitude are analyzed to classify trajectory anomaly types. "Normal" indicates that the trajectory basically conforms to the continuous motion law and requires no additional correction; "abrupt" indicates a significant difference in position before and after a certain point in time, inconsistent with the physical movement capability of the processing card; "continuous offset" indicates that the trajectory continuously deviates from the actual channel of the processing card, possibly due to occlusion or NLOS error; "occlusion" indicates that there are no effective positioning points or abnormal signal attenuation over multiple consecutive time periods, making it difficult to reconstruct the path. Optionally, the drift trend can be determined by the systematic accumulation of deviations between the trajectory and auxiliary inertial trajectories such as IMU (Inertial Measurement Unit), and by the angle or offset between the trajectory fitting and the IMU reference trajectory.

[0101] S303: Based on the filtering method corresponding to the trajectory anomaly type, the initial trajectory is fitted and denoised to obtain a smooth trajectory of the processing card; the filtering methods include Kalman filtering, extended Kalman filtering and particle filtering.

[0102] Indicatively, for normal and abrupt trajectory types, a Kalman filter is used to smooth high-frequency jumps through a linear state-space model. For continuous offset and occlusion types, an Extended Kalman Filter (EKF) or Particle Filter (PF) is used. By introducing nonlinear modeling and Bayesian estimation, trajectory reconstruction under multi-source constraints is achieved. The reconstructed smooth trajectory retains the true movement trend while effectively suppressing system noise and non-line-of-sight interference, providing reliable input for subsequent production behavior modeling, process status identification, and process anomaly diagnosis.

[0103] Specifically, Kalman filtering is a recursive estimation algorithm for linear systems. It achieves optimal state estimation by introducing a system state transition model and a measurement model, combining current observations with historical state predictions. In positioning trajectories, if the error follows a random Gaussian distribution and changes steadily, Kalman filtering can effectively smooth the positioning points, reducing random fluctuations and making it suitable for handling abrupt trajectory anomalies. Specifically, it predicts the current position value based on the state transition equation. Covariance Matrix Furthermore, the predicted values ​​are revised based on the current actual measurements. , , ,in, This indicates the estimated current position of the processing card. These are the observed values, and A, B, H, Q, and R are the model parameters.

[0104] Extended Kalman Filter (EKF) is a nonlinear extension of the Kalman Filter, used to handle situations where system state transitions and observation models do not satisfy linear assumptions. Due to nonlinear disturbances introduced by occlusion or reflection, trajectory changes often exhibit nonlinear patterns. In such cases, ordinary Kalman Filter cannot model the behavior, and EKF is required to achieve stronger state correction capabilities. Specifically, EKF first modulates the nonlinear state transition function... With observation function Linearization is achieved by replacing the original model with its first-order Taylor expansion Jacobian matrix, thus transforming it into a linear filtering problem, exemplified in the prediction phase. , Update phase , , ,in and These are the first-order Jacobian matrices for the state function and the observation function, respectively, which are dynamically constructed at each step.

[0105] Particle filtering is a Bayesian filter based on the Monte Carlo method, applicable to any nonlinear, non-Gaussian system. It simulates the probability distribution of system states by generating a large number of possible state samples (i.e., particles), and iteratively updates particle weights through importance sampling and resampling strategies to estimate the optimal state. When the trajectory contains severe occlusion, continuous offsets, or other high-noise non-Gaussian interference, the accuracy of Kalman filters decreases, while particle filtering can achieve robust trajectory reconstruction in complex scenarios through nonparametric modeling. Specifically, initializing the particle set involves generating… One possible state Set initial weights Predictions are made for each particle based on the system state transition model, combined with measured values. The weight of each particle is calculated using an observation model. To prevent particle degradation, the particle swarm is resampled according to weights, and the current particle states are weighted and averaged to obtain the estimated positions.

[0106] In a preferred embodiment, anomaly detection is performed on the initial trajectory to obtain trajectory anomaly types, including:

[0107] S401: If a velocity jump occurs in the initial trajectory, the trajectory anomaly type is determined to be the aforementioned sudden change;

[0108] S402: If the fusion mean square error of the initial trajectory exceeds the preset threshold, the trajectory anomaly type is determined to be occlusion.

[0109] The speed is expressed by the following formula:

[0110] (4);

[0111] The fusion mean square error is expressed by the following formula:

[0112] (5);

[0113] in, To process the time point speed, To process the time point Location, To process the time point Location; To process the time point fusion mean square error For anchor nodes The relative time difference of signal reception; For anchor nodes The expected time difference for signal reception.

[0114] Indicative, if If the speed exceeds the set speed threshold, it indicates that the positioning is unreliable and the signal may be blocked. Alternatively, if the weights in the weight matrix are concentrated in a very small number of anchor nodes or the weight matrix is ​​extremely sparse, it is determined to be channel blockage.

[0115] In a preferred embodiment, the method further includes:

[0116] S501: In response to the frequency modulation scheduling request obtained from the anchor node, query the frequency modulation map and allocate the optimal frequency band and time slot to each anchor node based on the principles of low interference and low latency.

[0117] The frequency modulation spectrum was obtained using the following method:

[0118] S5011: Obtain channel monitoring indicators for each anchor node during idle time slots; channel monitoring indicators include received signal strength indication, interference intensity, and signal strength change rate for each frequency band;

[0119] S5012: Using a sliding time window, interference density is estimated for channel monitoring indicators to obtain a frequency band-time slot historical interference heatmap.

[0120] S5013: Based on the number of ranging operations and the frequency of positioning request responses of each anchor node within a preset period, a task density index is obtained;

[0121] S5014: Obtain the activity density heat map of the processing card based on the historical smooth trajectory of the processing card within a preset period;

[0122] S5015: Based on the historical interference heatmap of the frequency band-time slot, the task density index, and the activity density heatmap of the processing card, the frequency modulation spectrum is obtained.

[0123] Indicatively, each anchor node actively conducts channel monitoring operations during its idle time slots to obtain channel monitoring indicators within the covered frequency band. Among these, the Received Signal Strength Indication (RSSI) is used to assess the presence of high-power interference sources in a given frequency band; Interference Power reflects the degree of electromagnetic interference caused by sources other than the system's own transmissions in that frequency band; and the rate of change of signal strength is used to determine the interference fluctuations and stability of a given frequency band.

[0124] Furthermore, to dynamically assess the interference situation of frequency bands and time slots over historical dimensions, a sliding time window mechanism is used to aggregate and statistically analyze monitoring indicators. Specifically, within the sliding window, the interference density estimates for each frequency band in different time slots are continuously accumulated, forming a two-dimensional interference heatmap of frequency band-time slot mapping. This heatmap can intuitively reflect the trend of interference intensity changes of a certain frequency band over a certain period of time, thus providing historical basis for interference avoidance.

[0125] Furthermore, the task density index of each anchor node within a preset period is statistically analyzed. Task density refers to the sum of the number of ranging operations undertaken by a certain anchor node and the frequency of participating in positioning requests within that period. It reflects the load of the anchor node in the network and is one of the key factors to be considered when allocating frequency band resources.

[0126] To further improve the foresight and accuracy of spectrum scheduling, we combined historical trajectory data of processing cards to analyze the activity density of processing cards. Using the trajectory sequences previously generated by the trajectory smoothing module, we can statistically analyze the distribution frequency of processing cards in different regions at different times, constructing a heatmap of processing card activity density. This heatmap can be used to predict potential peaks in positioning demand in certain areas in the near future.

[0127] Optionally, a global frequency modulation spectrum can be constructed by fusing together the interference heatmap, task density index, and processing card activity density heatmap. ,in, frequency band In time Interference density, Anchor Node In time Task density, frequency band In time The corresponding processing card heat map. This map provides each anchor node with information on when and in which frequency band communication interference is minimized and response is most timely.

[0128] When an anchor node needs to participate in a positioning task and initiates a frequency modulation scheduling request, the frequency modulation spectrum is queried, and frequency band-time slot pairs are selected based on two key scheduling principles: first, low interference priority, that is, frequency bands and time slot combinations with lower interference intensity are selected for the node; second, low latency response priority, that is, spectrum resources with lower task density and shorter response latency are selected. Based on satisfying these two priorities, the currently optimal frequency band and time slot pair is allocated to the requesting node, completing the spectrum scheduling task.

[0129] This mechanism enables data-aware dynamic spectrum management without altering the hardware structure of the anchor nodes, effectively improving the efficiency of collaborative communication between anchor nodes, avoiding redundant occupation and interference overlap of spectrum resources, thereby enhancing the stability and robustness of the entire UWB indoor positioning system.

[0130] In a preferred embodiment, the method further includes:

[0131] S601: Based on the preset processing card movement path, obtain the preset signal arrival timestamps of multiple anchor nodes;

[0132] In a schematic manner, during deployment, the positional information of some anchor nodes is typically statically calibrated. However, due to limitations such as anchor node installation errors, environmental disturbances, or the fact that some anchor nodes are mobile, their actual positions may deviate from the preset values, leading to a decrease in overall positioning accuracy. Specifically, based on a preset processing card movement path, the processing card is controlled to move within a designated area or trajectory. Since the processing card periodically broadcasts UWB signals, multiple anchor nodes distributed within the area can simultaneously receive the signal and record its arrival timestamp.

[0133] S602: Calculate the mutual measurement time difference between each pair of anchor nodes based on the signal arrival timestamp;

[0134] The arrival timestamps of signals recorded between multiple anchor nodes are compared pairwise to calculate the TDOA of any two anchor nodes for the same signal.

[0135] S603: Calculate the relative distance between each pair of anchor nodes based on the time difference of each mutual measurement, and obtain the local anchor node topology diagram:

[0136] Since the speed of light is a constant for signal propagation in space, the time difference can be multiplied by the propagation speed to convert it into a spatial distance difference. By aggregating the mutual time difference data between multiple anchor nodes, a local topology graph representing the relative geometric relationships between the anchor nodes can be constructed. In this topology graph, the edges represent the relative distances between anchor nodes, and the nodes represent the individual anchor nodes.

[0137] S604: Calculate the actual position of the anchor node based on the local anchor node topology using a graph optimization algorithm, and correct the known anchor node position information based on the actual position of each anchor node.

[0138] Specifically, nonlinear least squares optimization algorithms such as the Levenberg-Marquardt algorithm or pose graph optimization methods can be used. The relative distance constraints between anchor nodes are used as the cost function for graph edges, and the actual spatial coordinates of each anchor node are calculated through iterative optimization. The actual positions of the anchor nodes in the optimization results are compared with the preset calibration positions. For anchor nodes that deviate by more than a set threshold, their position information is corrected, thereby constructing a more accurate global coordinate map of anchor nodes. This achieves a self-correction mechanism for anchor node position information, significantly improving positioning stability and maintenance convenience without requiring external manual recalibration.

[0139] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0140] Based on the same inventive concept, this application also provides a UWB indoor positioning device for implementing the aforementioned multi-anchor node collaborative UWB indoor positioning method. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more UWB indoor positioning device embodiments provided below can be found in the limitations of the multi-anchor node collaborative UWB indoor positioning method described above, and will not be repeated here.

[0141] See Figure 2 This embodiment discloses a UWB indoor positioning device with multi-anchor node collaboration, including:

[0142] The data acquisition module 201 is used to acquire reported data from multiple anchor nodes. The reported data includes the signal arrival timestamp, signal reception strength, and environmental perception parameters of each anchor node. The reported data is recorded by the anchor node in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card. The anchor nodes include heterogeneous anchor nodes used to acquire local environmental parameters. The ultra-wideband positioning pulse signal includes the processing card identifier and the transmission timestamp.

[0143] Time difference calculation module 202 is used to calculate the signal reception time difference between each anchor node based on the arrival timestamp of each signal, and obtain the reception time difference matrix;

[0144] The non-line-of-sight propagation calculation module 203 is used to calculate the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtain the non-line-of-sight propagation probability matrix.

[0145] The dynamic weight calculation module 204 is used to determine the confidence weight of each anchor node in the reception time difference matrix based on the non-line-of-sight propagation probability matrix, the signal reception strength and the anchor node type of each anchor node, and obtain the weight matrix.

[0146] The positioning module 205 is used to process the position information, weight matrix and reception time difference matrix of multiple anchor nodes based on the dynamic weighted least squares positioning algorithm to obtain the positioning information of the processing card; the positioning information includes multiple positioning values ​​of the processing card in a continuous time period.

[0147] In a preferred embodiment, the non-line-of-sight propagation calculation module 203 is further configured to:

[0148] Arrival time offset is detected based on the signal arrival timestamp to obtain time distribution characteristics;

[0149] Signal strength abrupt changes are detected based on the received signal strength to obtain the intensity attenuation characteristics;

[0150] Environmental occlusion detection is performed based on environmental perception parameters to obtain environmental occlusion features;

[0151] Based on the time distribution characteristics, intensity attenuation characteristics, and environmental occlusion characteristics, the non-line-of-sight propagation probability of each anchor node is calculated, and the non-line-of-sight propagation probability matrix is ​​obtained.

[0152] In a preferred embodiment, a trajectory smoothing module is further included, for:

[0153] Based on multiple positioning values ​​corresponding to continuous time of the processing card, an initial trajectory of the processing card is constructed; the initial trajectory includes multiple positions of the processing card and the time points corresponding to the multiple positions.

[0154] Based on spatial variation patterns and velocity smoothness constraints, anomaly detection is performed on the initial trajectory to obtain trajectory anomaly types; trajectory anomaly types include normal, abrupt, continuous offset, and occlusion.

[0155] Based on the filtering method corresponding to the trajectory anomaly type, the initial trajectory is fitted and denoised to obtain a smooth trajectory of the processing card; the filtering methods include Kalman filtering, extended Kalman filtering and particle filtering.

[0156] In a preferred embodiment, a frequency modulation scheduling module is further included, for:

[0157] In response to the frequency modulation scheduling request obtained from the anchor node, the frequency modulation spectrum is queried, and the optimal frequency band and time slot are allocated to each anchor node based on the principles of low interference and low latency.

[0158] The frequency modulation spectrum was obtained using the following method:

[0159] Obtain channel monitoring metrics for each anchor node during idle time slots; channel monitoring metrics include received signal strength indication, interference intensity, and signal strength change rate for each frequency band;

[0160] By using a sliding time window to estimate the interference density of channel monitoring indicators, a historical interference heatmap of frequency band-time slot is obtained.

[0161] The task density index is obtained based on the number of ranging operations and the frequency of positioning request responses of each anchor node within a preset period.

[0162] Based on the historical smoothed trajectory of each processing card within a preset period, a heat map of processing card activity density is obtained;

[0163] The frequency modulation spectrum is obtained based on the historical interference heatmap of the frequency band-time slot, the task density index, and the activity density heatmap of the processing card.

[0164] In a preferred embodiment, a calibration module is further included, for:

[0165] Based on the preset processing card movement path, obtain the initial signal arrival timestamps of multiple anchor nodes;

[0166] Calculate the mutual measurement time difference between each pair of anchor nodes based on the initial signal arrival timestamp;

[0167] The relative distance between each anchor node is calculated based on the time difference of each mutual measurement, and a local anchor node topology is obtained.

[0168] The actual positions of anchor nodes are calculated based on the local anchor node topology using a graph optimization algorithm, and the position information of anchor nodes is corrected based on the actual positions of each anchor node.

[0169] This application also provides an electronic device corresponding to the method provided in the foregoing embodiments. The electronic device can be a server-side electronic device, such as a server, including independent servers and distributed server clusters, to execute the above method; the electronic device can also be a client-side electronic device, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the above method.

[0170] Please refer to Figure 3 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 3 As shown, the electronic device 30 includes: a processor 300, a memory 301, a bus 302, and a communication interface 303. The processor 300, the communication interface 303, and the memory 301 are connected via the bus 302. The memory 301 stores a computer program that can run on the processor 300. When the processor 300 runs the computer program, it executes the method described above in this application.

[0171] The memory 301 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 303 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0172] Bus 302 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs. After receiving an execution instruction, the processor 300 executes the program. The methods disclosed in any of the foregoing embodiments of this application can be applied to the processor 300, or implemented by the processor 300.

[0173] The processor 300 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 300 or by instructions in software form. The processor 300 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 301. The processor 300 reads the information in memory 301 and, in conjunction with its hardware, completes the steps of the above method.

[0174] The electronic devices and methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0175] This application also provides a computer-readable medium corresponding to the method provided in the foregoing embodiments. Please refer to... Figure 4 The computer-readable storage medium shown is an optical disc 40, on which a computer program (i.e., a program product) is stored, which, when run by a processor, performs the aforementioned method.

[0176] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0177] The computer-readable storage medium provided in the above embodiments of this application and the method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0178] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0179] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0180] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0181] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0182] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0183] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application.

Claims

1. A multi-anchor node cooperative UWB indoor positioning method, characterized in that, Includes the following steps: The system acquires reported data from multiple anchor nodes. The reported data includes the signal arrival timestamp, signal reception strength, and environmental perception parameters of each anchor node. The reported data is recorded by the anchor node in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card. The anchor nodes include heterogeneous anchor nodes used to acquire local environmental parameters. The ultra-wideband positioning pulse signal includes the processing card identifier and the transmission timestamp. Calculate the signal reception time difference between each pair of anchor nodes based on the arrival timestamps of each signal to obtain a reception time difference matrix; Calculate the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtain the non-line-of-sight propagation probability matrix. The confidence weight of each anchor node in the reception time difference matrix is ​​determined based on the non-line-of-sight propagation probability matrix, the signal reception strength, and the anchor node type of each anchor node, thus obtaining the weight matrix. The location information of the multiple anchor nodes, the weight matrix, and the receiving time difference matrix are processed based on the dynamic weighted least squares positioning algorithm to obtain the positioning information of the processing card; the positioning information includes multiple positioning values ​​of the processing card in a continuous time period; The step of calculating the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtaining the non-line-of-sight propagation probability matrix, includes: Arrival time offset detection is performed based on the signal arrival timestamp to obtain time distribution characteristics; Based on the received signal strength, signal strength abrupt change detection is performed to obtain the strength attenuation characteristics; Environmental occlusion detection is performed based on the environmental perception parameters to obtain environmental occlusion features; Based on the time distribution characteristics, intensity attenuation characteristics, and environmental occlusion characteristics, the non-line-of-sight propagation probability of each anchor node is calculated to obtain the non-line-of-sight propagation probability matrix. The confidence weight of each anchor node is represented by the following formula: （1）； （2）； （3）； in, For anchor nodes Confidence weights For anchor node index; The confidence level of the received signal strength is the value of the mapped signal. The variance of ranging stability is negatively correlated with the weights; Weight for anchor node type; For anchor nodes The signal reception strength; For anchor nodes Anchor node type; For anchor nodes The non-line-of-sight propagation probability; For anchor nodes Historical ranging stability variance; This represents the total number of anchor nodes; This represents the expected value of the received signal strength. and It is a constant.

2. The method of claim 1, wherein, The method further includes: Based on the multiple positioning values ​​of the processing card within a continuous time period, an initial trajectory of the processing card is constructed; the initial trajectory includes multiple positions of the processing card and the time points corresponding to the multiple positions; Based on spatial variation patterns and velocity smoothness constraints, anomaly detection is performed on the initial trajectory to obtain trajectory anomaly types; the trajectory anomaly types include normal, abrupt, continuous offset, and occlusion. Based on the filtering method corresponding to the trajectory anomaly type, the initial trajectory is fitted and denoised to obtain the smooth trajectory of the processing card; the filtering method includes Kalman filtering, extended Kalman filtering and particle filtering.

3. The method of claim 2, wherein, The anomaly detection of the initial trajectory to obtain trajectory anomaly types includes: If the initial trajectory exhibits a sudden change in velocity, then the trajectory anomaly type is determined to be the sudden change. If the fusion mean square error of the initial trajectory exceeds a preset threshold, then the trajectory anomaly type is determined to be occlusion. The speed is expressed by the following formula: （4）； The fusion mean square error is expressed by the following formula: （5）； in, The processing is stopped at a certain time point. speed, The processing is stopped at a certain time point. Location, The processing is stuck at the time point Location; The processing is stopped at a certain time point. fusion mean square error For anchor nodes The relative time difference of signal reception; For anchor nodes The expected time difference for signal reception.

4. The method according to claim 3, characterized in that, The method further includes: In response to the frequency modulation scheduling request obtained from the anchor node, the frequency modulation spectrum is queried, and the optimal frequency band and time slot are allocated to each anchor node based on the principles of low interference and low latency. The frequency modulation spectrum was obtained through the following method: Obtain channel monitoring metrics for each anchor node during its idle time slots; the channel monitoring metrics include received signal strength indication, interference intensity, and signal strength change rate for each frequency band; By using a sliding time window to estimate the interference density of the channel monitoring indicators, a historical interference heatmap of the frequency band and time slot is obtained. The task density index is obtained based on the number of ranging operations and the frequency of positioning request responses of each anchor node within a preset period. Based on the historical smooth trajectory of the processing card within a preset period, an activity density heatmap of the processing card is obtained; The frequency modulation spectrum is obtained based on the historical interference heatmap of the frequency band-time slot, the task density index, and the activity density heatmap of the processing card.

5. The method of claim 2, wherein, The method further includes: Based on the preset processing card movement path, the initial signal arrival timestamps of multiple anchor nodes are obtained; Calculate the mutual measurement time difference between each pair of anchor nodes based on the initial signal arrival timestamp; Calculate the relative distance between each pair of anchor nodes based on the mutual measurement time difference to obtain a local anchor node topology map; The actual position of the anchor node is calculated based on the local anchor node topology using a graph optimization algorithm, and the position information of the anchor node is corrected based on the actual position of each anchor node.

6. A multi-anchor node coordinated UWB indoor positioning apparatus, characterized in that, include: The data acquisition module is used to acquire the reported data from multiple anchor nodes; The reported data includes the signal arrival timestamp, signal reception strength, and environmental perception parameters of each anchor node; the reported data is recorded by the anchor node in response to receiving the same ultra-wideband positioning pulse signal sent by the processing card; the anchor node includes heterogeneous anchor nodes for acquiring local environmental parameters; the ultra-wideband positioning pulse signal includes the processing card identifier and the transmission timestamp; The time difference calculation module is used to calculate the signal reception time difference between each anchor node based on the arrival timestamp of each signal, and to obtain the reception time difference matrix. The non-line-of-sight propagation calculation module is used to calculate the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtain the non-line-of-sight propagation probability matrix. The dynamic weight calculation module is used to determine the confidence weight of each anchor node in the reception time difference matrix based on the non-line-of-sight propagation probability matrix, the signal reception strength, and the anchor node type of each anchor node, and obtain the weight matrix. The positioning module is used to process the position information of the multiple anchor nodes, the weight matrix, and the receiving time difference matrix based on a dynamic weighted least squares positioning algorithm to obtain the positioning information of the processing card; the positioning information includes multiple positioning values ​​of the processing card within a continuous time period; The step of calculating the non-line-of-sight propagation probability of the signal link of each anchor node based on the reported data, and obtaining the non-line-of-sight propagation probability matrix, includes: Arrival time offset detection is performed based on the signal arrival timestamp to obtain time distribution characteristics; Based on the received signal strength, signal strength abrupt change detection is performed to obtain the strength attenuation characteristics; Environmental occlusion detection is performed based on the environmental perception parameters to obtain environmental occlusion features; Based on the time distribution characteristics, intensity attenuation characteristics, and environmental occlusion characteristics, the non-line-of-sight propagation probability of each anchor node is calculated to obtain the non-line-of-sight propagation probability matrix. The confidence weight of each anchor node is represented by the following formula: （1）； （2）； （3）； in, For anchor nodes Confidence weights For anchor node index; The confidence level of the received signal strength is the value of the mapped signal. The variance of ranging stability is negatively correlated with the weights; Weight for anchor node type; For anchor nodes The signal reception strength; For anchor nodes Anchor node type; For anchor nodes The non-line-of-sight propagation probability; For anchor nodes Historical ranging stability variance; This represents the total number of anchor nodes; This represents the expected value of the received signal strength. and It is a constant.

7. An electronic device, comprising: The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program for performing the method according to any one of claims 1 to 5.

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