TOPO positioning implementation method and device based on WISUN communication

Abstract

The invention discloses a TOPO positioning implementation method and device based on WISUN communication, and the method comprises the following steps: a master module transmits positioning signals to a plurality of slave modules; a plurality of slave modules receive the positioning signals, measure the time difference of arrival of the signals, and upload the time difference of arrival data to an algorithm engine; the algorithm engine calculates initial three-dimensional coordinates of the slave module relative to the master module by using a TDOA algorithm based on the time difference of arrival data; the algorithm engine obtains the RSSI value from the slave module and preprocesses the RSSI value; and the algorithm engine performs correction through a data fusion algorithm according to the initial three-dimensional coordinates and the RSSI values to obtain final three-dimensional coordinates of the slave modules, and draws a network topology architecture diagram containing physical position information based on the final three-dimensional coordinates of all the slave modules. According to the invention, the technical problems of how to accurately obtain the accurate position information of each module and how to meet the visual management of the whole PAN network topology architecture are solved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method and apparatus for implementing TOPO positioning based on WISUN communication. Background Technology

[0002] Destination-Oriented Directed Acyclic Graph (DODAG) is a core concept in the Routing Protocol for Low-Power and Lossy Networks (RPL) for organizing network topology. It is a special type of directed acyclic graph used to guide the transmission path of data packets in the network, ensuring that data can be transmitted efficiently and reliably from the source node to the destination node. WISUN TOPO graphs (topology diagrams) are usually presented as multi-level tree-like connection structures, but nodes can dynamically adjust forwarding routes through the RPL protocol mechanism to realize a mesh network communication structure. In the WISUN network TOPO architecture diagram, the structure of DODAG can be displayed graphically, but this routing TOPO diagram can only show the connection relationship between nodes and which level they are at; more information cannot be shown.

[0003] Patent application CN202411969037.5 discloses a positioning method and system for a hybrid topology network of dual-layer optical fiber heterogeneous communication, relating to the field of communication technology. The solution involves: constructing a dual-layer optical fiber multi-node communication architecture to fuse heterogeneous networks into a dual-layer optical fiber heterogeneous communication positioning network, and optimizing network channels; constructing micro-nano devices and building a multi-signal cooperative communication positioning model based on ultra-wideband and orthogonal frequency division multiplexing (OFDM) communication technology; optimizing the network architecture of the multi-signal cooperative communication positioning model based on a star-ring topology; dynamically polling the dual-layer optical fiber heterogeneous communication positioning network based on the optimized model to obtain real-time node information; and using a combination of circular positioning and time difference of arrival (TDOA) positioning methods to estimate and locate nodes based on their real-time information, thereby obtaining and outputting a positioning report for the dual-layer optical fiber heterogeneous communication network. In the existing WISUN large-scale network communication environment, although the main module can obtain the topological connection relationship of the entire PAN network through the routing table, it can only know the logical routing paths between modules, but cannot determine the specific location information of the modules in physical space. This limits the network's fine-grained management and optimization capabilities. Even when module location information is obtained through certain methods, the accuracy of this information in existing technologies is often insufficient. For example, relying solely on Time Difference of Arrival (TDOA) for ranging is susceptible to environmental interference and signal attenuation, leading to significant location errors and failing to meet high-precision positioning requirements. Furthermore, the inability to accurately obtain the specific location information of modules prevents existing WISUN networks from accurately mapping the entire PAN topology. This not only affects network visualization and management but also hinders network structure optimization and expansion, limiting overall network performance and scalability. Therefore, there is an urgent need to propose a TOPO positioning implementation method and device based on WISUN communication to solve the technical problem of accurately obtaining the precise location information of each module and performing visualized management of the entire PAN network topology. Summary of the Invention

[0004] The main objective of this invention is to propose a TOPO positioning method and apparatus based on WISUN communication, aiming to solve the technical problem of how to accurately obtain the precise location information of each module and meet the requirements of visual management of the entire PAN network topology.

[0005] To achieve the above objectives, the present invention provides a TOPO positioning implementation method based on WISUN communication, wherein the TOPO positioning implementation method based on WISUN communication includes the following steps:

[0006] S1. The main module sends positioning signals to several slave modules;

[0007] S2. Several of the slave modules receive the positioning signal, measure the time difference of arrival of the signal, and upload the time difference of arrival data to the algorithm engine;

[0008] S3. The algorithm engine calculates the initial three-dimensional coordinates of the slave module relative to the master module based on the arrival time difference data using the TDOA algorithm;

[0009] S4. The algorithm engine obtains the RSSI value from the slave module and preprocesses the RSSI value.

[0010] S5. The algorithm engine corrects the initial three-dimensional coordinates and the RSSI value using a data fusion algorithm to obtain the final three-dimensional coordinates of the slave module, and draws a network topology diagram containing physical location information based on the final three-dimensional coordinates of all slave modules.

[0011] In one preferred embodiment, step S1 includes at least four slave modules.

[0012] In one preferred embodiment, step S3 uses the TDOA algorithm to calculate the initial three-dimensional coordinates of the slave module relative to the master module, specifically as follows:

[0013] S31. Establish a system of nonlinear equations based on the time difference of signal arrival;

[0014] S32. Linearize the nonlinear equation system using Taylor expansion;

[0015] S33. Calculate the linearized system of equations using the least squares method to obtain the initial three-dimensional coordinates of the target module relative to the main module.

[0016] One preferred embodiment is that the nonlinear equation set is:

[0017]

[0018] in, For signal propagation speed, These represent the times when the signal reaches the first slave module, the second slave module, the third slave module, and the fourth slave module, respectively. The position coordinates of the first slave module, The coordinates of the second slave module are: The position coordinates of the third module. The position coordinates of the fourth module. These represent the times when the signal reaches the first slave module, the second slave module, the third slave module, and the fourth slave module, respectively. These are the position coordinates of the signal source.

[0019] One preferred embodiment, step S32, specifically includes:

[0020] Construct initial estimated coordinates The nonlinear equations are expanded using Taylor expansion at the initial estimated coordinates to obtain the linear equations.

[0021] in, This is the time difference used for transmission between the first slave module and the second slave module. This is the time difference used for transmission between the first slave module and the third slave module. This is the time difference used for transmission between the first slave module and the fourth slave module.

[0022] In one preferred embodiment, step S4, which preprocesses the RSSI value, includes:

[0023] S41. Calculate the mean and standard deviation of RSSI values, and use a Gaussian filtering algorithm to filter the original RSSI values ​​and remove abrupt signals.

[0024] S42. Sort and evaluate the filtered RSSI values ​​to obtain a threshold, and adjust the RSSI values ​​according to the threshold and weight.

[0025] One preferred embodiment, step S41, specifically includes:

[0026]

[0027] in, The probability density function for filtering the original RSSI values. The standard deviation of the original RSSI values. The mean of the original RSSI values. These are the original RSSI values; the standard deviation of the original RSSI values ​​is:

[0028]

[0029] in, For measurement The number of times the value is, For the first Second measurement Value; the mean of the original RSSI values ​​is:

[0030]

[0031] in, This is the mean of the original RSSI values, i.e., the actual mean. This is the initial mean.

[0032] One preferred embodiment, step S42, specifically includes:

[0033] The filtered RSSI values ​​are sorted and evaluated to obtain the threshold; the threshold is:

[0034]

[0035] in, This is the threshold for adjacent RSSI, which is also the minimum RSSI value at which two modules can be connected. This is the median RSSI value after sorting and evaluating the filtered RSSI values.

[0036] The RSSI value is adjusted according to the threshold and weight; the RSSI value is:

[0037]

[0038] in, The adjusted RSSI value. For the first Second measurement The weight of the value; the weight for:

[0039]

[0040] in, For the first The communication quality threshold for each module.

[0041] In one preferred embodiment, step S5 is corrected using a data fusion algorithm to obtain the final three-dimensional coordinates of the module, specifically as follows:

[0042] S51. Convert the initial three-dimensional coordinates of the module into TDOA ranging values, and use the K-means clustering algorithm to perform cluster analysis on the TDOA ranging values ​​and RSSI values ​​to identify the clusters of the corresponding line-of-sight channels;

[0043] S52. Average the TDOA ranging values ​​within the line-of-sight channel cluster to obtain a preliminary distance estimate. When the preliminary distance estimate is less than or equal to a preset threshold, calculate the corrected distance using a weighted formula, and convert the corrected distance into three-dimensional coordinates, which are the final three-dimensional coordinates of the module. The weighted formula is:

[0044]

[0045] in, This is the corrected distance. This is a preliminary distance estimate. For weighted parameters, For RSSI reference values ​​at distances of 1m and above, The RSSI value of the current cluster center. This is the road loss coefficient.

[0046] A TOPO positioning implementation device based on WISUN communication, including the aforementioned TOPO positioning implementation method based on WISUN communication, comprises: a main module, an algorithm engine, and several slave modules; the main module is connected to the several slave modules, and the several slave modules are connected to the algorithm engine.

[0047] In the above technical solution of the present invention, the TOPO positioning implementation method based on WISUN communication includes the following steps: a master module transmits positioning signals to several slave modules; several slave modules receive the positioning signals, measure the time difference of arrival (TDOA), and upload the TDOA data to the algorithm engine; the algorithm engine calculates the initial three-dimensional coordinates of the slave modules relative to the master module using the TDOA algorithm based on the TDOA data; the algorithm engine obtains the RSSI values ​​from the slave modules and preprocesses the RSSI values; the algorithm engine corrects the initial three-dimensional coordinates and the RSSI values ​​using a data fusion algorithm to obtain the final three-dimensional coordinates of the slave modules, and draws a network topology diagram containing physical location information based on the final three-dimensional coordinates of all slave modules. The present invention solves the technical problem of how to accurately obtain the precise location information of each module to meet the requirements of visual management of the entire PAN network topology.

[0048] In this invention, the time difference of signal arrival is measured using the TDOA algorithm to calculate the precise position information of each module relative to the main module.

[0049] In this invention, the initial three-dimensional coordinates obtained by the TDOA algorithm are optimized based on the ranging correction of RSSI values ​​to reduce position errors and improve positioning accuracy.

[0050] In this invention, based on the precise location data of each module, a network topology diagram containing physical location information can be drawn. On the basis of the original routing table, the physical location information of the modules is added, so that network management is no longer limited to logical routing paths, but also includes specific locations in physical space, providing support for the visualization management, optimization and expansion of the network. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0052] Figure 1This is a schematic diagram of a TOPO positioning implementation method based on WISUN communication according to an embodiment of the present invention;

[0053] Figure 2 This is a coordinate signal intersection diagram of the four slave modules in an embodiment of the present invention;

[0054] Figure 3 This is a line graph showing the coordinates of four modules in an embodiment of the present invention;

[0055] Figure 4 This is a graph showing the relationship between TDOA ranging results and RSSI values ​​in an embodiment of the present invention.

[0056] Figure 5 This is a graph showing the relationship between the TDOA ranging results and the corrected RSSI values ​​in an embodiment of the present invention.

[0057] Figure 6 This is a schematic diagram of a TOPO positioning device based on WISUN communication according to an embodiment of the present invention;

[0058] Figure 7 This is a TOPO diagram of the WISUN FAN network location in an embodiment of the present invention;

[0059] Figure 8 This is a TOPO diagram of the WISUN FAN network hierarchy positions in an embodiment of the present invention.

[0060] The realization of the objective, functional characteristics and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0063] See Figures 1-5 According to one aspect of the present invention, the present invention provides a TOPO positioning implementation method based on WISUN communication, wherein the TOPO positioning implementation method based on WISUN communication includes the following steps:

[0064] S1. The main module sends positioning signals to several slave modules;

[0065] S2. Several of the slave modules receive the positioning signal, measure the time difference of arrival of the signal, and upload the time difference of arrival data to the algorithm engine;

[0066] S3. The algorithm engine calculates the initial three-dimensional coordinates of the slave module relative to the master module based on the arrival time difference data using the TDOA algorithm;

[0067] S4. The algorithm engine obtains the RSSI value from the slave module and preprocesses the RSSI value.

[0068] S5. The algorithm engine corrects the initial three-dimensional coordinates and the RSSI value using a data fusion algorithm to obtain the final three-dimensional coordinates of the slave module, and draws a network topology diagram containing physical location information based on the final three-dimensional coordinates of all slave modules.

[0069] Specifically, in this embodiment, TDOA positioning technology determines the location of a signal source by measuring the time difference of a signal arriving at multiple receivers. The signal propagates at the speed of light in space. Assuming the location of the signal source... When a signal is transmitted, multiple receivers receive the signal at different times. These time differences reflect the relative distance difference between the signal source and the receivers.

[0070] Specifically, in this embodiment, step S1 includes at least four slave modules; the positions of the four slave modules are as follows: , , , .

[0071] Specifically, in this embodiment, see Figures 2-3 Step S3 uses the TDOA algorithm to calculate the initial three-dimensional coordinates of the slave module relative to the master module, specifically as follows:

[0072] S31. Establish a system of nonlinear equations based on the time difference of signal arrival;

[0073] S32. Linearize the nonlinear equation system using Taylor expansion;

[0074] S33. Calculate the linearized system of equations using the least squares method to obtain the initial three-dimensional coordinates of the target module relative to the main module.

[0075] Specifically, in this embodiment, at least four slave modules are needed to determine the location of the signal source in three-dimensional space. The time difference between the signal arrival at the slave modules is represented by the nonlinear equations, which describe the distance difference relationship between the master module and the slave modules. The nonlinear equations are:

[0076]

[0077] in, For signal propagation speed, These represent the times when the signal reaches the first slave module, the second slave module, the third slave module, and the fourth slave module, respectively. The position coordinates of the first slave module, The coordinates of the second slave module are: The position coordinates of the third module. The position coordinates of the fourth module. These represent the times when the signal reaches the first slave module, the second slave module, the third slave module, and the fourth slave module, respectively. These are the position coordinates of the signal source.

[0078] Specifically, in this embodiment, step S32 is as follows:

[0079] Construct initial estimated coordinates The nonlinear equations are expanded using Taylor expansion at the initial estimated coordinates to obtain the linear equations.

[0080] in, This is the time difference used for transmission between the first slave module and the second slave module. This is the time difference used for transmission between the first slave module and the third slave module. This is the time difference used for transmission between the first slave module and the fourth slave module.

[0081] Specifically, in this embodiment, step S33 uses the least squares method to calculate the linearized system of equations to obtain the initial three-dimensional coordinates of the target module relative to the main module. Specifically, this involves calculating the linear equations using the least squares method to obtain the coordinates. Then add the initial estimated coordinates. This yields the initial three-dimensional coordinates of the target module relative to the main module.

[0082] Specifically, in this embodiment, because the signal has a certain degree of uncertainty during transmission, it is necessary to preprocess the collected RSSI values ​​to meet the requirements of RSSI ranging; the preprocessing of RSSI values ​​in step S4 includes:

[0083] S41. Calculate the mean and standard deviation of RSSI values, and use a Gaussian filtering algorithm to filter the original RSSI values, removing abrupt and non-compliant signals;

[0084] S42. Sort and evaluate the filtered RSSI values ​​to obtain a threshold, and adjust the RSSI values ​​according to the threshold and weight.

[0085] Specifically, in this embodiment, step S41 is as follows:

[0086]

[0087] in, The probability density function for filtering the original RSSI values. The standard deviation of the original RSSI values. The mean of the original RSSI values. These are the original RSSI values; the standard deviation of the original RSSI values ​​is:

[0088]

[0089] in, For measurement The number of times the value is, For the first Second measurement Value; the mean of the original RSSI values ​​is:

[0090]

[0091] in, For measurement The number of times the value is, For the first Second measurement value.

[0092] Specifically, in this embodiment, step S42 is as follows:

[0093] The filtered RSSI values ​​are sorted and evaluated to obtain the threshold; the threshold is:

[0094]

[0095] in, This is the threshold for adjacent RSSI, which is also the minimum RSSI value at which two modules can be connected. This is the median RSSI value after sorting and evaluating the filtered RSSI values.

[0096] The RSSI value is adjusted according to the threshold and weight; the RSSI value is:

[0097]

[0098] in, The adjusted RSSI value. For the first Second measurement The weight of the value; the weight for:

[0099]

[0100] in, For the first The communication quality threshold for each module.

[0101] Specifically, in this embodiment, some nonlinear results may occur in close-range measurements, leading to a decrease in distance estimation accuracy. It is necessary to combine RSSI results for correction. The data obtained from multiple measurements has poor consistency. In order to accurately estimate the straight-line distance between the positioning anchor point and the target node, the algorithm engine corrects the initial three-dimensional coordinates and RSSI values ​​through a data fusion algorithm to obtain the final three-dimensional coordinates of the slave module. Based on the final three-dimensional coordinates of all slave modules, a network topology diagram containing physical location information is drawn.

[0102] Specifically, in this embodiment, step S5 uses a data fusion algorithm to correct the data and obtain the final three-dimensional coordinates of the module, specifically:

[0103] S51. Convert the initial three-dimensional coordinates of the module into TDOA ranging values. Use the K-means clustering algorithm to perform cluster analysis on the TDOA ranging values ​​and RSSI values. By analyzing the TDOA ranging values ​​of the cluster center points, the clusters of the corresponding line-of-sight channels can be identified. The characteristic is that the TDOA ranging value of the cluster center point is the smallest among all clusters.

[0104] S52. Average the TDOA ranging values ​​within the line-of-sight channel cluster to obtain a preliminary distance estimate. When the preliminary distance estimate is less than or equal to a preset threshold, use a weighted formula to calculate the corrected distance and convert the corrected distance into three-dimensional coordinates, which are the final three-dimensional coordinates of the module.

[0105] Specifically, in this embodiment, to improve the accuracy of distance estimation, it is further corrected by incorporating RSSI values. When the initial distance estimate is... When the distance is less than or equal to 100m, the TDOA ranging value and RSSI value are fused using a weighted formula. This formula includes weighting parameters, path loss coefficient, RSSI reference values ​​for distances greater than 1m, and the RSSI value of the current cluster center. This weighted formula can adjust for nonlinear results caused by hardware clock drift, thereby improving the accuracy of distance estimation in close-range measurements and ultimately obtaining a more accurate distance estimate. The weighting formula is as follows:

[0106]

[0107] in, This is the corrected distance. This is a preliminary distance estimate. For weighted parameters, For RSSI reference values ​​at distances of 1m and above, The RSSI value of the current cluster center. This is the road loss coefficient.

[0108] Specifically, in this embodiment, the present invention provides a precise module location for the WISUN large-scale network communication environment by combining the TDOA algorithm and the RSSI value ranging correction algorithm. This method can not only determine the logical routing paths between modules, but also accurately obtain their specific location information in physical space, thereby significantly improving the network's precise management capabilities and making network optimization and resource allocation more efficient. By drawing a network topology architecture diagram based on this precise location data, network administrators can more intuitively understand the network structure, thereby enabling more effective network planning and troubleshooting. Based on the final three-dimensional coordinates of each slave module, a network topology architecture diagram containing physical location information can be drawn, thereby enhancing the network's visual management and making network expansion and maintenance more convenient. Precise location information helps to quickly identify and resolve network problems, reducing network fault response time. At the same time, this method also improves the network's scalability, providing flexible adaptability for future network growth and changes. Through these improvements, the present invention can significantly improve the overall performance and reliability of the WISUN network, bringing users a better network experience.

[0109] According to another aspect of the invention, see Figures 6-8 This invention provides a TOPO positioning implementation device based on WISUN communication. The TOPO positioning implementation device based on WISUN communication includes a main module, an algorithm engine, a database, and several slave modules. The main module is connected to several slave modules, and several slave modules are connected to the algorithm engine. The database is used to store the constructed equation system and distance estimation results, providing data support for drawing network topology architecture diagrams and for the visualization management, optimization, and expansion of the network.

[0110] To facilitate understanding of the terminology related to this invention, the following explanations are provided:

[0111] Intelligent Utility Networks (WISUN) is a collective term for a series of standard wireless communication networks based on the IEEE 802.15.4 underlying protocol.

[0112] Received Signal Strength Indication (RSSI) is a metric for measuring the strength of wireless signals and is commonly used in wireless communication systems such as Wi-Fi, Bluetooth, Zigbee, and cellular networks.

[0113] Topology (abbreviated as TOPO) refers to the physical or logical layout structure of components in a network or system.

[0114] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present invention specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A TOPO positioning implementation method based on WISUN communication, characterized in that, Includes the following steps: S1. The main module sends positioning signals to several slave modules; S2. Several of the slave modules receive the positioning signal, measure the time difference of arrival of the signal, and upload the time difference of arrival data to the algorithm engine; S3. The algorithm engine calculates the initial three-dimensional coordinates of the slave module relative to the master module based on the arrival time difference data using the TDOA algorithm; S4. The algorithm engine obtains the RSSI value from the slave module and preprocesses the RSSI value. S5. The algorithm engine corrects the initial three-dimensional coordinates and the RSSI value using a data fusion algorithm to obtain the final three-dimensional coordinates of the slave module, and draws a network topology diagram containing physical location information based on the final three-dimensional coordinates of all slave modules.

2. The TOPO positioning method based on WISUN communication according to claim 1, characterized in that, At least four slave modules are provided in step S1.

3. A TOPO positioning implementation method based on WISUN communication according to any one of claims 1-2, characterized in that, Step S3 uses the TDOA algorithm to calculate the initial three-dimensional coordinates of the slave module relative to the master module, specifically as follows: S31. Establish a system of nonlinear equations based on the time difference of signal arrival; S32. Linearize the nonlinear equation system using Taylor expansion; S33. Calculate the linearized system of equations using the least squares method to obtain the initial three-dimensional coordinates of the target module relative to the main module.

4. The TOPO positioning method based on WISUN communication according to claim 3, characterized in that, The nonlinear equation system is as follows: ； in, For signal propagation speed, These represent the times when the signal reaches the first slave module, the second slave module, the third slave module, and the fourth slave module, respectively. The coordinates of the first slave module are: The coordinates of the second slave module are: The position coordinates of the third module. The position coordinates of the fourth module are... These represent the times when the signal reaches the first slave module, the second slave module, the third slave module, and the fourth slave module, respectively. These are the position coordinates of the signal source.

5. A TOPO positioning implementation method based on WISUN communication according to claim 4, characterized in that, Step S32 specifically includes: Construct initial estimated coordinates The nonlinear equations are expanded using Taylor expansion at the initial estimated coordinates to obtain the linear equations. in, This is the time difference used for transmission between the first slave module and the second slave module. This is the time difference used for transmission between the first slave module and the third slave module. This is the time difference used for transmission between the first slave module and the fourth slave module.

6. A TOPO positioning method based on WISUN communication according to any one of claims 1-2, characterized in that, Step S4, which preprocesses the RSSI value, includes: S41. Calculate the mean and standard deviation of RSSI values, and use a Gaussian filtering algorithm to filter the original RSSI values ​​and remove abrupt signals. S42. Sort and evaluate the filtered RSSI values ​​to obtain a threshold, and adjust the RSSI values ​​according to the threshold and weight.

7. A TOPO positioning implementation method based on WISUN communication according to claim 6, characterized in that, Step S41 specifically includes: ； in, The probability density function for filtering the original RSSI values. The standard deviation of the original RSSI values. The mean of the original RSSI values. These are the original RSSI values; the standard deviation of the original RSSI values ​​is: ； in, For measurement The number of times the value is, For the first Second measurement Value; the mean of the original RSSI values ​​is: ； in, This is the mean of the original RSSI values, i.e., the actual mean. This is the initial mean.

8. A TOPO positioning method based on WISUN communication according to claim 7, characterized in that, Step S42 specifically includes: The filtered RSSI values ​​are sorted and evaluated to obtain the threshold; the threshold is: ； in, This is the threshold for adjacent RSSI, which is also the minimum RSSI value at which two modules can be connected. This is the median RSSI value after sorting and evaluating the filtered RSSI values. The RSSI value is adjusted according to the threshold and weight; the RSSI value is: ； in, The adjusted RSSI value. For the first Second measurement The weight of the value; the weight for: ； in, For the first Communication quality thresholds for each module.

9. A TOPO positioning method based on WISUN communication according to any one of claims 1-2, characterized in that, Step S5 uses a data fusion algorithm to correct the data and obtain the final three-dimensional coordinates of the module, specifically: S51. Convert the initial three-dimensional coordinates of the module into TDOA ranging values, and use the K-means clustering algorithm to perform cluster analysis on the TDOA ranging values ​​and RSSI values ​​to identify the clusters of the corresponding line-of-sight channels; S52. Average the TDOA ranging values ​​within the line-of-sight channel cluster to obtain a preliminary distance estimate. When the preliminary distance estimate is less than or equal to a preset threshold, calculate the corrected distance using a weighted formula, and convert the corrected distance into three-dimensional coordinates, which are the final three-dimensional coordinates of the module. The weighted formula is: ； in, This is the corrected distance. This is a preliminary distance estimate. For weighted parameters, For RSSI reference values ​​at distances of 1m and above, The RSSI value of the current cluster center. This is the road loss coefficient.

10. A TOPO positioning implementation device based on WiSUN communication, comprising the TOPO positioning implementation method based on WiSUN communication as described in any one of claims 1-9, characterized in that, include: The system comprises a main module, an algorithm engine, and several slave modules; the main module is connected to the several slave modules, and the several slave modules are connected to the algorithm engine.

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