A multi-mode fusion positioning method

Abstract

The present invention discloses a multi-mode fusion positioning method, which relates to the field of wireless positioning technology. In this method, multiple fixed reference points are selected from a preset area; reference wired positioning data and reference wireless positioning data corresponding to each fixed reference point are obtained; multiple deviation values ​​are obtained based on multiple reference wired positioning data and multiple reference wireless positioning data; the current measurement point is determined, and the current wireless positioning data corresponding to the current measurement point is obtained; the current wireless positioning data is corrected based on multiple deviation values, multiple reference wireless positioning data and multiple fixed reference points to obtain the current corrected positioning data; the current corrected positioning data is verified to determine whether the current corrected positioning data is verified; when the current corrected positioning data is not verified, the current corrected positioning data is corrected to obtain the current actual positioning data. The implementation of the technical solution of the present application can improve the accuracy of wireless positioning in complex environments.

Description

Technical Field

[0001] The present application relates to the field of wireless positioning technology, and in particular to a multi-mode fusion positioning method. Background Art

[0002] In modern industrial and commercial applications, positioning technology has become the core technology for realizing smart factories, intelligent logistics, personnel management and other fields. With the development of wireless sensor technology, positioning systems based on wireless signals have been widely adopted in practical applications.

[0003] In the prior art, although the traditional GPS system can provide high positioning accuracy in outdoor environments, its positioning accuracy is greatly reduced in indoor environments due to signal blocking and reflection problems. In order to solve the problem of indoor positioning accuracy, wireless positioning technologies such as Bluetooth and UWB have been widely used. The Bluetooth positioning system has been widely used in various indoor positioning applications due to its low power consumption and high positioning accuracy. However, the Bluetooth system relies on the measurement of signal strength and is greatly affected by the environment. The positioning error is usually around 3 meters, which is difficult to meet high-precision requirements. Although UWB technology can provide high positioning accuracy, its positioning data will still be interfered by environmental factors in complex environments, resulting in positioning errors.

[0004] Therefore, how to improve the accuracy of wireless positioning in complex environments has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a multi-mode fusion positioning method, system, electronic device and storage medium, which can improve the accuracy of wireless positioning in complex environments.

[0006] In a first aspect, the present application provides a multi-mode fusion positioning method, the method comprising: selecting multiple fixed reference points from a preset area; obtaining reference wired positioning data and reference wireless positioning data corresponding to each of the fixed reference points; obtaining multiple deviation values ​​based on the multiple reference wired positioning data and the multiple reference wireless positioning data; determining a current measurement point, and obtaining current wireless positioning data corresponding to the current measurement point; correcting the current wireless positioning data based on the multiple deviation values, the multiple reference wireless positioning data and the multiple fixed reference points to obtain current corrected positioning data; verifying the current corrected positioning data to determine whether the current corrected positioning data has been verified; when the current corrected positioning data has been verified, using the current corrected positioning data as the current actual positioning data; when the current corrected positioning data has not been verified, correcting the current corrected positioning data to obtain the current actual positioning data.

[0007] By adopting the above technical solution, a stable reference network is established by selecting multiple fixed reference points from a preset area, providing reliable reference points for subsequent positioning data correction, so that the system can accurately correct wireless positioning data at different locations. Next, the reference wired positioning data and reference wireless positioning data corresponding to each fixed reference point are obtained to obtain a high-precision reference position and an actual measurement position. These data are used to calculate the deviation value, which effectively quantifies the error of the wireless positioning system. Based on multiple reference wired positioning data and multiple reference wireless positioning data, multiple deviation values ​​are calculated. This step clarifies the error of the wireless positioning data relative to the actual position, provides specific error information for subsequent data correction, and improves the accuracy of the correction. Subsequently, the current measurement point is determined, and the current wireless positioning data of the measurement point is obtained, providing preliminary positioning information for the actual position at the current moment, which will be used for subsequent error correction and accuracy improvement. By correcting the current wireless positioning data based on multiple deviation values, multiple reference wireless positioning data, and multiple fixed reference points, the current corrected positioning data is obtained. This step significantly improves the positioning accuracy of the current measurement point, making the corrected positioning data closer to the actual position and reducing the positioning error. Verify the current corrected positioning data to determine whether it has passed the verification, ensuring that the corrected positioning data meets the expected accuracy requirements and improving the reliability of the system and data accuracy. When the current corrected positioning data passes the verification, it is used as the current actual positioning data, which ensures that the output positioning data has high accuracy and reliability and can be directly used for subsequent applications and decisions. If the current corrected positioning data fails the verification, it needs to be corrected. Through further correction, the accuracy of the positioning data can be further improved, thereby improving the accuracy of wireless positioning in complex environments.

[0008] Optionally, the current wireless positioning data is corrected based on the multiple deviation values, the multiple reference wireless positioning data and the multiple fixed reference points to obtain current corrected positioning data, specifically including: constructing a positioning correction formula based on the multiple reference wireless positioning data, the multiple deviation values ​​and the multiple fixed reference points; substituting the current wireless positioning data into the positioning correction formula to obtain the current corrected positioning data.

[0009] By adopting the above technical solution, a positioning correction formula is constructed based on multiple reference wireless positioning data, multiple deviation values ​​and multiple fixed reference points, so that the error information of known reference points can be effectively used to establish a mathematical model to correct the errors in the wireless positioning data. The current wireless positioning data is substituted into the positioning correction formula to obtain the current corrected positioning data. In this way, the error compensation of the wireless positioning data of the current measurement point can be accurately performed.

[0010] Optionally, constructing a positioning correction formula based on the multiple reference wireless positioning data, the multiple deviation values ​​and the multiple fixed reference points specifically includes: obtaining the Lagrangian basis function corresponding to each of the fixed reference points based on the multiple reference wireless positioning data; obtaining an interpolation function based on each of the Lagrangian basis functions and each of the deviation values; and obtaining the positioning correction formula based on the interpolation function.

[0011] By adopting the above technical solution, the Lagrangian basis functions corresponding to each fixed reference point are obtained based on multiple reference wireless positioning data, thereby constructing a mathematical basis that can reflect the positional relationship of each reference point. Next, based on each Lagrangian basis function and each deviation value, an interpolation function is obtained, thereby effectively extending the error information of the known reference point to the entire positioning area. Finally, based on the interpolation function, a positioning correction formula is obtained, thereby concretizing the error compensation into an operational mathematical expression. The positioning correction formula calculates the error correction value of the current wireless positioning data through the interpolation function, making the corrected data more accurate.

[0012] Optionally, obtaining the Lagrangian basis function corresponding to each of the fixed reference points based on the plurality of reference wireless positioning data specifically includes:

[0013] Each of the Lagrangian basis functions is obtained by the following formula:

[0014]

[0015] The interpolation function is obtained based on each of the Lagrangian basis functions and each of the deviation values, specifically comprising:

[0016] The interpolation function is obtained by the following formula:

[0017]

[0018] The obtaining of the positioning correction formula based on the interpolation function specifically includes:

[0019] The positioning correction formula is obtained by the following formula:

[0020] P c =(P x +f x (P x ),P y +f y (P y ),P z +f z (P z ));

[0021] Where n is the number of fixed reference points, li (x) is the Lagrangian basis function in the x dimension corresponding to the i-th fixed reference point, l i (y) is the Lagrangian basis function of the y dimension corresponding to the i-th fixed reference point, l i (z) is the Lagrangian basis function of the z dimension corresponding to the i-th fixed reference point, (x j ,y j ,z j ) is the reference wireless positioning data corresponding to the jth fixed reference point; x (x) is the interpolation function in the x dimension, f y (y) is the interpolation function in the y dimension, f z (z) is the interpolation function in the z dimension, Δ ix is the deviation value of the x dimension, Δ iy is the deviation value in the y dimension, Δ iz is the deviation value of the z dimension, P c is the current correction positioning data, P x is the x-coordinate of the current wireless positioning data, P y is the y coordinate of the current wireless positioning data, P z is the z coordinate of the current wireless positioning data.

[0022] Optionally, the verification of the current corrected positioning data to determine whether the current corrected positioning data has been verified specifically includes: determining a plurality of adjacent reference points adjacent to the current measurement point from a plurality of fixed reference points; determining adjacent wireless positioning data corresponding to each of the adjacent reference points; calculating the Euclidean distance between the current corrected positioning data and each of the adjacent wireless positioning data to obtain a plurality of error values; calculating an average error value of the plurality of error values; when the average error value is not greater than a preset error threshold, determining that the current corrected positioning data has been verified; when the average error value is greater than a preset error threshold, determining that the current corrected positioning data has not been verified.

[0023] By adopting the above technical solution, by determining multiple adjacent reference points adjacent to the current measurement point from multiple fixed reference points, the reference point that is most representative of the current measurement point position can be effectively selected, providing a reliable benchmark for subsequent data verification. Next, by determining the adjacent wireless positioning data corresponding to each adjacent reference point, the actual wireless positioning data of these reference points is obtained. Then, by calculating the Euclidean distance between the current corrected positioning data and each adjacent wireless positioning data, multiple error values ​​are obtained to quantify the deviation between the current corrected positioning data and the actual reference point data. Then, by calculating the average error value of multiple error values, the overall deviation of the current corrected positioning data is obtained. When the average error value is not greater than the preset error threshold, it is determined that the current corrected positioning data has been verified, thereby confirming that the corrected data has met the expected accuracy requirements and ensuring the reliability and accuracy of the positioning data. When the average error value is greater than the preset error threshold, it is determined that the current corrected positioning data has not been verified, thereby prompting that the correction data needs to be further corrected to ensure that the final output positioning data meets the accuracy requirements.

[0024] Optionally, the current corrected positioning data is corrected to obtain current actual positioning data, specifically including: acquiring real-time environmental data within a preset area; preprocessing the real-time environmental data to obtain preprocessed real-time environmental data; inputting the preprocessed real-time environmental data into a preset environmental impact correction model to obtain a correction factor; and correcting the current corrected positioning data based on the correction factor to obtain the current actual positioning data.

[0025] Optionally, before inputting the preprocessed real-time environmental data into a preset environmental impact correction model to obtain a correction factor, the method also includes: acquiring historical environmental data within a preset area; preprocessing the historical environmental data to obtain preprocessed historical environmental data; extracting multiple initial environmental features from the preprocessed historical environmental data, and screening out relevant environmental features that affect positioning accuracy from the multiple initial environmental features; determining an initial model; using the relevant environmental features as input features and the deviation value as a target variable, performing model training on the initial model to obtain the environmental impact correction model.

[0026] By adopting the above technical solution, by obtaining the historical environmental data in the preset area, a rich data foundation is provided for building the environmental impact correction model. Next, by preprocessing the historical environmental data, the preprocessed historical environmental data is obtained to ensure the quality and consistency of the data. Then, by extracting multiple initial environmental features from the preprocessed historical environmental data, and screening out the relevant environmental features that affect the positioning accuracy from the multiple initial environmental features, the feature set is optimized to ensure that the features used in model training have high relevance and effectiveness. Then, by determining the initial model, a basic environmental impact correction framework is established. Finally, by taking the relevant environmental features as input features and the deviation value as the target variable, the initial model is trained to obtain the environmental impact correction model, thereby optimizing the model parameters so that it can accurately predict the impact of environmental features on positioning errors.

[0027] In a second aspect of the present application, a multi-mode fusion positioning system is provided, the system comprising: a processing module and an acquisition module; the processing module is used to select multiple fixed reference points from a preset area; the acquisition module is used to acquire reference wired positioning data and reference wireless positioning data corresponding to each of the fixed reference points; the processing module is also used to obtain multiple deviation values ​​based on the multiple reference wired positioning data and the multiple reference wireless positioning data; the processing module is also used to determine a current measurement point and acquire current wireless positioning data corresponding to the current measurement point; the processing module is also used to correct the current wireless positioning data based on the multiple deviation values, the multiple reference wireless positioning data and the multiple fixed reference points to obtain current corrected positioning data; the processing module is also used to verify the current corrected positioning data to determine whether the current corrected positioning data has been verified; the processing module is also used to use the current corrected positioning data as the current actual positioning data when the current corrected positioning data has been verified; the processing module is also used to correct the current corrected positioning data to obtain the current actual positioning data when the current corrected positioning data has not been verified.

[0028] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any method as described in the first aspect of the present application.

[0029] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program that can be loaded by a processor and execute the method as described in any one of the first aspects of the present application.

[0030] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0031] By selecting multiple fixed reference points from the preset area, a stable reference network is established, which provides reliable reference points for subsequent positioning data correction, so that the system can accurately correct wireless positioning data at different locations. Next, the reference wired positioning data and reference wireless positioning data corresponding to each fixed reference point are obtained to obtain high-precision reference positions and actual measurement positions. These data are used to calculate deviation values, which effectively quantify the error of the wireless positioning system. Based on multiple reference wired positioning data and multiple reference wireless positioning data, multiple deviation values ​​are calculated. This step clarifies the error of the wireless positioning data relative to the actual position, provides specific error information for subsequent data correction, and improves the accuracy of the correction. Subsequently, the current measurement point is determined, and the current wireless positioning data of the measurement point is obtained, which provides preliminary positioning information for the actual position at the current moment. This information will be used for subsequent error correction and accuracy improvement. By correcting the current wireless positioning data based on multiple deviation values, multiple reference wireless positioning data, and multiple fixed reference points, the current corrected positioning data is obtained. This step significantly improves the positioning accuracy of the current measurement point, making the corrected positioning data closer to the actual position and reducing the positioning error. Verify the current corrected positioning data to determine whether it has passed the verification, ensuring that the corrected positioning data meets the expected accuracy requirements and improving the reliability of the system and data accuracy. When the current corrected positioning data passes the verification, it is used as the current actual positioning data, which ensures that the output positioning data has high accuracy and reliability and can be directly used for subsequent applications and decisions. If the current corrected positioning data fails the verification, it needs to be corrected. Through further correction, the accuracy of the positioning data can be further improved, thereby improving the accuracy of wireless positioning in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is one of the flow charts of a multi-mode fusion positioning method provided in an embodiment of the present application;

[0033] Figure 2 This is the second flow chart of a multi-mode fusion positioning method provided in an embodiment of the present application;

[0034] Figure 3 This is a third flow chart of a multi-mode fusion positioning method provided in an embodiment of the present application;

[0035] Figure 4 It is a structural diagram of a multi-mode fusion positioning system provided in an embodiment of the present application;

[0036] Figure 5 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0037] Explanation of the reference numerals: 1. Acquisition module; 2. Processing module; 500. Electronic device; 501. Processor; 502. Communication bus; 503. User interface; 504. Network interface; 505. Memory. DETAILED DESCRIPTION

[0038] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0039] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0040] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0041] This application provides a multi-mode fusion positioning method, referring to Figure 1 , which shows one of the flow diagrams of a multi-mode fusion positioning method provided in an embodiment of the present application. The method includes steps S1-S8, which are as follows:

[0042] Step S1: Select multiple fixed reference points from a preset area.

[0043] When implementing step S1, multiple fixed reference points are selected from the preset area in order to establish a high-precision reference positioning network. Specifically, these fixed reference points will serve as reference points of known positions for subsequent calibration and verification of positioning data.

[0044] First, the selection of the preset area should take into account the coverage requirements of the entire target area. The preset area can be a factory workshop, warehouse, or office building where high-precision positioning is required. In the preset area, multiple fixed reference points are reasonably distributed according to the complexity of the environment and the positioning requirements. The distribution of these reference points should ensure that a sufficient number of reference point signals can be received at any location to perform effective positioning data correction.

[0045] During the specific operation, high-precision measurement tools (such as total stations, laser rangefinders, etc.) can be used to determine the precise position of each fixed reference point. These position data are the reference wired positioning data. At the same time, wireless positioning devices (such as UWB tags or Bluetooth tags) are installed at each fixed reference point, and the wireless positioning data of these points are obtained through the wireless positioning system.

[0046] Through such arrangement and measurement, reference wired positioning data and reference wireless positioning data of each fixed reference point can be obtained, which will be used to calculate multiple deviation values ​​in the subsequent step S3.

[0047] The effect of selecting multiple fixed reference points is that a stable and accurate reference positioning network is established, which can provide necessary reference information for the wireless positioning system. In this way, in the actual positioning process, even if the environment is complex and the signal is interfered, the wireless positioning data of the current measurement point can still be corrected through the data of these fixed reference points, significantly improving the positioning accuracy.

[0048] In summary, by selecting multiple fixed reference points in a preset area, a high-precision benchmark positioning network can be effectively established, providing a reliable basis for the correction and verification of subsequent positioning data.

[0049] Step S2: Obtain reference wired positioning data and reference wireless positioning data corresponding to each fixed reference point.

[0050] When implementing step S2, the reference wired positioning data and reference wireless positioning data corresponding to each fixed reference point are obtained in order to perform positioning data correction and deviation value calculation in subsequent steps, thereby improving the accuracy and reliability of the positioning system.

[0051] First, after multiple fixed reference points have been selected in the preset area, the precise location information of these reference points needs to be obtained. This precise location information, i.e., reference wired positioning data, is obtained through high-precision measurement tools such as total stations or laser rangefinders. The position (x, y, z coordinates) of each fixed reference point is accurately measured and recorded.

[0052] Next, a wireless positioning device (such as a UWB tag or a Bluetooth tag) is installed at each fixed reference point, and the wireless positioning data of these reference points is obtained through the wireless positioning system. The wireless positioning data is obtained by measuring the propagation characteristics of wireless signals in space, such as signal strength or arrival time difference. Similarly, the (x, y, z) coordinates of the wireless positioning data of each reference point are recorded.

[0053] By acquiring these data, two sets of data for each fixed reference point can be obtained: one set is the precise reference wired positioning data, and the other set is the corresponding reference wireless positioning data. The acquisition of these data is the basis for subsequent steps, especially in step S3, calculating multiple deviation values.

[0054] The effect of obtaining these two sets of data is that the reference wired positioning data provides a high-precision reference position, while the reference wireless positioning data reflects the measurement results of the actual wireless positioning system. By comparing these two sets of data, errors in the wireless positioning system can be identified and quantified, providing a basis for subsequent positioning data correction.

[0055] Step S3: Based on a plurality of reference wired positioning data and a plurality of reference wireless positioning data, a plurality of deviation values ​​are obtained.

[0056] When implementing step S3, multiple deviation values ​​are obtained based on multiple reference wired positioning data and multiple reference wireless positioning data in order to identify and quantify errors in the wireless positioning system and provide a basis for subsequent positioning data correction, thereby improving the accuracy and reliability of the positioning system.

[0057] First, the reference wired positioning data and reference wireless positioning data of multiple fixed reference points have been obtained through the previous step S2. The reference wired positioning data is obtained through high-precision measurement tools and represents the real position of each fixed reference point. The reference wireless positioning data is obtained through a wireless positioning system and may have certain errors due to environmental factors.

[0058] In order to quantify these errors, it is necessary to calculate the deviation value of each fixed reference point. The deviation value is defined as the difference between the reference wired positioning data and the reference wireless positioning data. Specifically, for the i-th fixed reference point, the deviation value Δi is calculated as follows: in, is the reference wired positioning data of the i-th fixed reference point, is the reference wireless positioning data of the i-th fixed reference point. Since the positioning data includes three-dimensional coordinates (x, y, z), the deviation value also includes the corresponding three dimensions: Δ i =(Δ ix ,Δ iy ,Δ iz ).

[0059] Step S4: determine the current measurement point, and obtain the current wireless positioning data corresponding to the current measurement point.

[0060] When implementing step S4, the current measurement point is determined and the current wireless positioning data corresponding to the current measurement point is obtained in order to obtain the initial positioning information of the target position in real time, thereby providing data support for subsequent correction and accuracy improvement, and ensuring that the system maintains high-precision positioning capabilities in a dynamic environment.

[0061] When a target needs to be located, the system will start the wireless positioning device in the preset area and obtain the preliminary location data of the current measurement point (i.e. the current wireless positioning data) by capturing the wireless signal of the current measurement point. The wireless positioning device calculates the three-dimensional coordinates (x, y, z) of the current measurement point by measuring the time difference of arrival (TDOA) or signal strength (RSSI) of the signal. Although these wireless positioning data may be affected by environmental factors and have errors, they are the basis for subsequent correction steps.

[0062] Specifically, when the system starts the wireless positioning devices, these devices will begin to capture the signals sent by the target object or person. After receiving the signals, the wireless positioning system processes these signals through algorithms to obtain preliminary location data of the current measurement point. These data include (x, y, z) coordinates in three-dimensional space, reflecting the location information of the target object or person at the current moment.

[0063] The process of obtaining the current wireless positioning data is not only to provide the target location data in real time, but more importantly to provide the necessary input for the subsequent positioning data correction. Since wireless signals are interfered and affected by various factors in the actual environment, there may be large errors when directly using these data for positioning. Therefore, by obtaining preliminary wireless positioning data, the system can correct these data in combination with the deviation value of the reference point in the subsequent steps, thereby significantly improving the positioning accuracy.

[0064] Step S5: Correct the current wireless positioning data based on multiple deviation values, multiple reference wireless positioning data and multiple fixed reference points to obtain current corrected positioning data.

[0065] When implementing step S5, the current wireless positioning data is corrected based on multiple deviation values, multiple reference wireless positioning data and multiple fixed reference points to obtain current corrected positioning data. This is to correct errors in the wireless positioning data, thereby improving positioning accuracy and ensuring that the system can provide high-precision positioning services in various environments.

[0066] In one possible implementation, refer to Figure 2, which shows the second flow diagram of a multi-mode fusion positioning method provided in an embodiment of the present application. Step S5 specifically includes steps S51-S52:

[0067] Step S51: Construct a positioning correction formula based on multiple reference wireless positioning data, multiple deviation values ​​and multiple fixed reference points.

[0068] In one possible implementation, refer to Figure 3 , which shows the third flow chart of a multi-mode fusion positioning method provided in an embodiment of the present application. Step S51 specifically includes steps S511-S513:

[0069] Step S511: Based on a plurality of reference wireless positioning data, obtain the Lagrangian basis function corresponding to each fixed reference point.

[0070] Specifically, each Lagrangian basis function is obtained by the following formula:

[0071] Step S512: Based on each Lagrangian basis function and each deviation value, an interpolation function is obtained.

[0072] Specifically, the interpolation function is obtained by the following formula:

[0073] Step S513: Based on the interpolation function, a positioning correction formula is obtained.

[0074] Specifically, the positioning correction formula is obtained by the following formula: P c =(P x +f x (P x ),P y +f y (P y ),P z +f z (P z )).

[0075] In the above formula, n is the number of fixed reference points, l i (x) is the Lagrangian basis function of the x dimension corresponding to the i-th fixed reference point, l i (y) is the Lagrangian basis function of the y dimension corresponding to the i-th fixed reference point, l i (z) is the Lagrangian basis function of the z dimension corresponding to the i-th fixed reference point, (x j ,y j ,z j ) is the reference wireless positioning data corresponding to the jth fixed reference point; f x(x) is the interpolation function in the x dimension, f y (y) is the interpolation function in the y dimension, f z (z) is the interpolation function in the z dimension, Δ ix is the deviation value of the x dimension, Δ iy is the deviation value in the y dimension, Δ iz is the deviation value of the z dimension, P c is the current correction positioning data, P x is the x-coordinate of the current wireless positioning data, P y is the y coordinate of the current wireless positioning data, P z It is the z coordinate of the current wireless positioning data.

[0076] When implementing step S511, the Lagrangian basis functions corresponding to each fixed reference point are obtained based on multiple reference wireless positioning data in order to construct an interpolation function using the data of known reference points, so that the error of any point in the entire preset area can be estimated and corrected, thereby improving the accuracy of the positioning system.

[0077] First, through the previous steps, reference wireless positioning data of multiple fixed reference points have been obtained. Reference wireless positioning data refers to the position information of each reference point measured by the wireless positioning system. Although this information has certain errors, its distribution in space is known. In order to effectively correct the errors in the current wireless positioning data, it is necessary to construct an interpolation function through these known data.

[0078] In the specific operation process, the Lagrangian basis functions corresponding to each fixed reference point are first calculated based on multiple reference wireless positioning data. The Lagrangian basis function is a polynomial interpolation method that can construct a continuous interpolation polynomial through known discrete data points. These basis functions will be used for subsequent interpolation calculations to construct interpolation functions. The interpolation function can be weighted summed by these basis functions and deviation values ​​to obtain the error correction value of the current measurement point.

[0079] When implementing step S512, an interpolation function is obtained based on each Lagrangian basis function and each deviation value in order to construct a mathematical model that can reflect the error distribution, so as to correct the current wireless positioning data in practical applications and improve the positioning accuracy and reliability.

[0080] In the previous step, by calculating the Lagrangian basis functions of each fixed reference point, we obtained a function that reflects the spatial position relationship of each reference point. Next, we need to construct interpolation functions based on these Lagrangian basis functions and known deviation values. These interpolation functions can use the known reference point data to infer the error value of any point.

[0081] In the specific operation process, each deviation value is first multiplied by the corresponding Lagrangian basis function to obtain the error distribution of each reference point in space. Then, these results are weighted and summed to form the final interpolation function. These interpolation functions correspond to error correction in the three dimensions of x, y and z respectively. By constructing these interpolation functions, the deviation value of the known reference point can be extended to the entire measurement space, thereby estimating the error value at any position. The effect of this step is that the system can use these interpolation functions to accurately correct the current wireless positioning data and significantly reduce the positioning error.

[0082] When implementing step S513, the positioning correction formula is obtained based on the interpolation function in order to construct a mathematical model for correcting the current wireless positioning data, thereby improving the overall accuracy and reliability of the positioning system. By constructing the positioning correction formula, error compensation can be applied to the actual positioning process, making the positioning data more accurate.

[0083] In the previous steps, interpolation functions have been obtained based on various Lagrangian basis functions and deviation values. These interpolation functions correspond to the error correction values ​​of the three dimensions of x, y and z respectively. Next, it is necessary to apply these interpolation functions to the current wireless positioning data and construct a positioning correction formula so that the corrected positioning data is closer to the actual position. Through the above positioning correction formula, the error in the current wireless positioning data can be compensated to obtain the corrected positioning data Pc.

[0084] Step S52: Substitute the current wireless positioning data into the positioning correction formula to obtain the current corrected positioning data.

[0085] When implementing step S52, the current wireless positioning data is substituted into the positioning correction formula to obtain the current corrected positioning data. This is to accurately correct the current wireless positioning data through the mathematical model constructed previously, thereby improving the positioning accuracy and ensuring the reliability and accuracy of the system in complex environments.

[0086] In the previous step, a positioning correction formula has been constructed based on the interpolation function. This formula can use the known error information of the reference point to compensate and correct the current wireless positioning data. Therefore, when implementing step S52, it is necessary to substitute the wireless positioning data of the current measurement point into the positioning correction formula to obtain the corrected positioning data.

[0087] Step S6: verify the current correction positioning data to determine whether the current correction positioning data has passed the verification.

[0088] In a possible implementation, step S6 specifically includes the following steps:

[0089] A plurality of adjacent reference points adjacent to the current measurement point are determined from the plurality of fixed reference points.

[0090] Determine the neighboring wireless positioning data corresponding to each neighboring reference point.

[0091] The Euclidean distance between the current corrected positioning data and each adjacent wireless positioning data is calculated to obtain multiple error values.

[0092] Calculate the average error value of multiple error values.

[0093] When the average error value is not greater than the preset error threshold, it is determined that the current corrected positioning data has passed the verification.

[0094] When the average error value is greater than a preset error threshold, it is determined that the current corrected positioning data verification fails.

[0095] When implementing step S6, the current corrected positioning data is verified to determine whether the current corrected positioning data has been verified, in order to ensure that the corrected positioning data meets the expected accuracy requirements, thereby improving the reliability and stability of the system. Through the verification step, possible errors can be effectively identified and corrected to ensure that the final output positioning data is accurate and reliable.

[0096] In the previous steps, the current corrected positioning data has been obtained by correcting the current wireless positioning data. However, in order to ensure the accuracy of the corrected data, it is necessary to verify it. In the specific operation process, the error values ​​are calculated by comparing the current corrected positioning data with the data of the adjacent fixed reference points, and it is determined whether these error values ​​are within an acceptable range.

[0097] First, multiple adjacent reference points adjacent to the current measurement point are selected from multiple fixed reference points. The adjacent reference points are selected for comparison and verification using their known high-precision data. After the adjacent reference points are determined, the adjacent wireless positioning data of these reference points are obtained.

[0098] Next, the Euclidean distance between the current corrected positioning data and the neighboring wireless positioning data of each neighboring reference point is calculated. The Euclidean distance is a commonly used distance measurement method that can quantify the straight-line distance between two points, thereby reflecting the error between the current corrected positioning data and the neighboring reference points.

[0099] The specific calculation steps are as follows:

[0100] Determine the coordinates of the adjacent reference point (x i ,y i ,z i ) and nearby wireless positioning data.

[0101] Calculate the current correction positioning data (P cx ,P cy ,Pcz ) to each neighboring reference point: Repeat the above calculation to obtain multiple error values. Then, calculate the average error value of these error values. The average error value reflects the overall accuracy of the current correction positioning data. Calculate the average error value: Where m is the number of neighboring reference points.

[0102] Finally, the average error value is compared with the preset error threshold. If the average error value is not greater than the preset error threshold, the current corrected positioning data is determined to have passed the verification, indicating that the accuracy of the corrected positioning data meets the requirements; if the average error value is greater than the preset error threshold, the current corrected positioning data is determined to have failed the verification, indicating that further correction is required.

[0103] Through the verification step, the system can ensure that the corrected positioning data meets the expected accuracy requirements. In the specific implementation process, the verification program can be written to realize automatic verification and judgment of the correction data. The effect of the verification process is to identify and correct possible errors to ensure that the positioning data output by the system is highly accurate and reliable.

[0104] Step S7: When the current corrected positioning data passes the verification, the current corrected positioning data is used as the current actual positioning data.

[0105] When implementing step S7, when the current corrected positioning data passes the verification, the current corrected positioning data is used as the current actual positioning data. This is to ensure that the positioning data output by the system is accurate and reliable after the verification data meets the accuracy requirements, and is directly used for subsequent applications and decisions. This step confirms the validity of the correction data, thereby providing high-precision positioning information for practical applications.

[0106] In the previous steps, by correcting and verifying the current wireless positioning data, the current corrected positioning data has been obtained, and the verification is passed, which means that the error of the corrected positioning data is within an acceptable range and meets the preset accuracy requirements. Therefore, these verified corrected data can be used as the current actual positioning data and directly used for the actual positioning output of the system.

[0107] Step S8: When the current corrected positioning data fails the verification, the current corrected positioning data is corrected to obtain the current actual positioning data.

[0108] When the current corrected positioning data fails to be verified during the implementation of step S8, the current corrected positioning data is corrected to obtain the current actual positioning data. This is to ensure that even if the preliminary corrected data fails to meet the accuracy requirements, high-precision actual positioning data can still be output through further correction. By introducing the environmental impact correction model, the interference of the environment on the wireless signal can be effectively compensated, and the positioning accuracy and system reliability can be improved.

[0109] In a possible implementation, step S8 specifically includes the following steps:

[0110] Get real-time environmental data within a preset area.

[0111] Specifically, in actual application scenarios, the propagation of wireless signals is easily affected by environmental factors such as temperature, humidity, obstacles, electromagnetic interference, etc. Therefore, obtaining real-time environmental data in the preset area can provide key reference information for subsequent positioning data correction. The purpose of this step is to accurately reflect the impact of the current environment on wireless signals through real-time environmental data, laying the foundation for further error compensation.

[0112] During the specific operation, various environmental sensors need to be arranged in the preset area. These sensors can include temperature sensors, humidity sensors, infrared sensors, acoustic sensors, and electromagnetic interference detectors, etc., which are used to monitor environmental parameters in real time. Through these sensors, changes in the environment can be fully captured to ensure the comprehensiveness and accuracy of the data.

[0113] After the sensors are deployed, the system will periodically or continuously collect real-time environmental data. The collected data includes temperature, humidity, obstacle distribution, electromagnetic interference intensity, etc. in the current area. In order to ensure the timeliness and accuracy of the data, the sensors should have high precision and fast response capabilities. The collected data will be transmitted to the central processing system through the data acquisition terminal for further processing and analysis.

[0114] For example, in a large warehouse, real-time environmental data can be collected by placing temperature and humidity sensors in various areas to monitor temperature and humidity changes in different areas; at the same time, infrared and acoustic sensors can be placed to detect the movement of people and objects; in addition, electromagnetic interference detectors can be used to monitor the interference of wireless signals. After all these data are aggregated, they can fully reflect the impact of the current environment on wireless signals.

[0115] The real-time environmental data is preprocessed to obtain preprocessed real-time environmental data.

[0116] Specifically, the acquired real-time environmental data may contain noise, outliers or missing values, which will affect the accuracy of the modified model. Therefore, preprocessing the real-time environmental data can improve the data quality and make the model calculation more reliable and accurate.

[0117] In the specific operation process, the collected environmental data is first subjected to noise filtering. Noise filtering is to remove the interference signals received by the environmental sensor during the collection process. Common signal processing methods such as moving average method and median filtering method can be used to smooth the data and reduce the impact of random noise. For example, for the data of the temperature sensor, the moving average method can be used to average the temperature value at each time point with the temperature values ​​at several time points before and after it, so as to obtain the smoothed temperature data.

[0118] Next, detect and process outliers in the data. Outliers are values ​​that deviate significantly from the normal range, which may be caused by sensor failure or sudden environmental changes. Statistical methods can be used to detect outliers, such as setting a reasonable threshold range or using box plots to identify outliers. Once an outlier is detected, interpolation or replacement of data at previous and subsequent time points can be used to ensure the continuity and rationality of the data. For example, if the humidity value at a certain moment deviates significantly from the normal range, the outlier can be replaced by linear interpolation of humidity values ​​at several moments before and after.

[0119] Then, deal with the missing value problem. Missing values ​​are data gaps caused by sensor failure or data transmission interruption. There are many ways to deal with missing values, and you can choose the appropriate method to fill them according to the specific situation. For example, you can use the mean filling method to fill the missing values ​​with the average value of the variable over a period of time; you can also use the nearest neighbor interpolation method to interpolate and fill the missing values ​​with the data of the adjacent time points before and after.

[0120] After noise filtering, outlier detection and missing value processing, the pre-processed real-time environmental data is of higher quality and can more accurately reflect the current environmental conditions. These high-quality data will be provided as input to the environmental impact correction model to ensure that the correction factors calculated by the model are more accurate, thereby effectively correcting the positioning data.

[0121] The preprocessed real-time environmental data is input into a preset environmental impact correction model to obtain a correction factor.

[0122] Specifically, the preprocessed real-time environmental data contains various parameters of the current environment, such as temperature, humidity, obstacle distribution, and electromagnetic interference, which will affect the propagation of wireless signals and thus affect the positioning accuracy. In order to compensate for these environmental impacts, these real-time environmental data need to be input into the environmental impact correction model to calculate the correction factor used to correct the positioning data.

[0123] During the specific operation, first ensure that the preset environmental impact correction model has been fully trained with historical environmental data. The model can predict the impact of the current environment on the positioning error based on the input environmental data. The model can use a variety of machine learning algorithms, such as linear regression, decision trees, neural networks, etc., to adapt to different environmental characteristics and positioning requirements.

[0124] Then, the preprocessed real-time environmental data is used as input and imported into the environmental impact correction model. The model will calculate the corresponding correction factor based on this data. The correction factor reflects the degree of influence of the environment on the current positioning data and is an important parameter for compensating and correcting the positioning data. For example, if the current environmental data indicates that the temperature is high, the humidity is low, and there is strong electromagnetic interference, the model may calculate a large correction factor to significantly adjust the positioning data to compensate for the errors caused by these environmental factors.

[0125] The current corrected positioning data is corrected based on the correction factor to obtain the current actual positioning data.

[0126] Specifically, in the previous step, the correction factors have been calculated through the environmental impact correction model, which reflect the specific impact of the current environmental conditions on the positioning error. In order to make the corrected positioning data more accurate, these correction factors need to be applied to the current corrected positioning data to obtain the final actual positioning data.

[0127] In the specific operation process, first obtain the current correction positioning data (P cx ,P cy ,P cz ). These data are obtained through preliminary calibration in the previous steps, but they may still be affected by environmental factors and have certain errors.

[0128] Next, apply the correction factor to adjust the current corrected positioning data. The correction factor is usually calculated based on real-time environmental data and represents the compensation value of the environment for positioning errors. These correction factors are added to the current corrected positioning data to obtain more accurate actual positioning data. The specific formula is as follows: P actual =(P cx +Δx,P cy +Δy,P cz +Δz); where Δx, Δy, Δz are the values ​​of the environmental correction factor in the x, y, and z dimensions respectively.

[0129] In a possible implementation manner, before inputting the preprocessed real-time environmental data into a preset environmental impact correction model to obtain the correction factor, the method further includes the following steps:

[0130] Get historical environmental data in a preset area.

[0131] Specifically, the specific implementation method of obtaining historical environmental data in a preset area is consistent with the specific implementation method of obtaining real-time environmental data in a preset area, which has been described in detail, so it will not be repeated here.

[0132] The historical environmental data is preprocessed to obtain preprocessed historical environmental data.

[0133] Specifically, the specific implementation method of preprocessing historical environmental data to obtain preprocessed historical environmental data is consistent with the specific implementation method of preprocessing real-time environmental data to obtain preprocessed real-time environmental data in the aforementioned embodiment, which has been described in detail, so it will not be elaborated here.

[0134] A plurality of initial environmental features are extracted from the preprocessed historical environmental data, and relevant environmental features that affect the positioning accuracy are screened out from the plurality of initial environmental features.

[0135] Specifically, after obtaining the pre-processed historical environmental data, these data may contain a variety of environmental characteristics, such as temperature, humidity, light intensity, air flow speed, obstacle density, and electromagnetic interference intensity. These initial environmental characteristics are important variables for analyzing and understanding the impact of the environment on wireless signal propagation. However, not all initial environmental characteristics will have a significant impact on positioning accuracy. Therefore, it is necessary to screen out relevant environmental characteristics that have a greater impact on positioning accuracy from these initial environmental characteristics to improve the effectiveness and accuracy of the correction model.

[0136] In the specific operation process, the historical environmental data is firstly extracted to obtain multiple initial environmental features. These features may include but are not limited to: temperature, humidity, light intensity, air flow speed, obstacle density, and electromagnetic interference intensity. Each feature needs to be annotated and recorded in the historical environmental data set.

[0137] Next, feature screening is performed. The purpose of feature screening is to find features that are highly correlated with positioning errors from the initial environmental features, thereby simplifying the model and improving its prediction ability. Feature screening can be performed using a variety of methods, such as correlation analysis, principal component analysis (PCA), recursive feature elimination (RFE), and feature importance evaluation based on tree models.

[0138] Specifically, we can first perform a correlation analysis to calculate the correlation coefficient between each initial environmental feature and the positioning error. Features with high correlation coefficients indicate that they have a greater impact on the positioning error and should be retained. For example, by calculating the correlation coefficient, we may find that temperature, humidity, and electromagnetic interference intensity have a significant impact on the positioning error, while light intensity and air flow speed have a smaller impact.

[0139] Then, principal component analysis (PCA) can be used to reduce the dimensionality of the initial environmental features and extract the main components that can explain most of the data variance. These main components represent the comprehensive information of the initial environmental features, which can simplify the model and improve training efficiency.

[0140] In the process of feature screening, the recursive feature elimination (RFE) method can also be combined to optimize the feature selection results by gradually removing features with less influence. At the same time, the feature importance evaluation method based on the tree model can also be used to judge the importance of each feature and screen out features that have a significant impact on positioning accuracy.

[0141] Through feature extraction and screening, we finally get a set of relevant environmental features that have a significant impact on positioning accuracy. These features will be used to train the environmental impact correction model, so that the model can accurately predict the impact of the environment on positioning errors, thereby improving the accuracy and reliability of the positioning system.

[0142] Determine the initial model.

[0143] Specifically, in the previous step, relevant environmental features that have a significant impact on positioning accuracy have been extracted and screened from the preprocessed historical environmental data. These features provide input variables for the model, and they have a high correlation with the positioning error. Next, an initial environmental impact correction model needs to be constructed based on these features.

[0144] In the specific operation process, first select the appropriate model type. Common model types include linear regression model, decision tree model, support vector machine (SVM), neural network model, etc. When selecting the model type, you should make a comprehensive consideration based on the data characteristics and application requirements. For example, the linear regression model is suitable for processing linear relationships, while the neural network model is suitable for complex nonlinear relationships.

[0145] The relevant environmental characteristics are used as input features, the deviation values ​​are used as target variables, and the initial model is trained to obtain the environmental impact correction model.

[0146] Specifically, in the previous step, the initial model has been determined, and the relevant environmental features have been selected as input features and the positioning error as the target variable. Next, the initial model needs to be optimized through the model training process so that it can accurately predict the impact of the environment on the positioning error.

[0147] In the specific operation process, the selected relevant environmental features and the corresponding positioning error data are first paired to form a training data set. The training data set contains a large amount of historical environmental data and corresponding positioning error values, which will be used to train the initial model.

[0148] During the training process, each data sample in the training data set (i.e., a set of environmental features and corresponding positioning errors) is input into the initial model to calculate the prediction error. The model continuously adjusts the model parameters through optimization algorithms (such as gradient descent) to minimize the prediction error. Through iterative optimization, the model gradually learns the relationship between environmental features and positioning errors, which continuously improves the prediction accuracy.

[0149] During the training process, the cross-validation method can be used to evaluate the model performance and prevent overfitting. Specifically, the training data set is divided into several subsets, and training and validation are performed separately to ensure that the model performs stably on different data sets. In addition, indicators such as mean square error (MSE) can be used to measure the prediction performance of the model and select the best parameter combination.

[0150] Through such a training process, an optimized environmental impact correction model is finally obtained. This model can accurately predict the positioning error based on the input real-time environmental characteristics, so as to correct the positioning data in practical applications. For example, when the real-time environmental data is input into the model, the model can quickly calculate the correction factor under the current environmental conditions to compensate for the positioning error and ensure the accuracy of the final positioning data.

[0151] Reference Figure 4 , which shows a schematic diagram of the structure of a multi-mode fusion positioning system provided in an embodiment of the present application, the system includes: a processing module 1 and an acquisition module 2;

[0152] Processing module 1, used for selecting a plurality of fixed reference points from a preset area;

[0153] Acquisition module 2, used to acquire reference wired positioning data and reference wireless positioning data corresponding to each fixed reference point;

[0154] The processing module 1 is also used to obtain multiple deviation values ​​based on multiple reference wired positioning data and multiple reference wireless positioning data; the processing module 1 is also used to determine the current measurement point and obtain the current wireless positioning data corresponding to the current measurement point;

[0155] The processing module 1 is further used to correct the current wireless positioning data based on the multiple deviation values, the multiple reference wireless positioning data and the multiple fixed reference points to obtain the current corrected positioning data;

[0156] The processing module 1 is also used to verify the current correction positioning data and determine whether the current correction positioning data has passed the verification;

[0157] The processing module 1 is also used to use the current corrected positioning data as the current actual positioning data when the current corrected positioning data passes the verification;

[0158] The processing module 1 is also used to correct the current corrected positioning data to obtain the current actual positioning data when the current corrected positioning data fails to be verified.

[0159] In a possible implementation, the processing module 1 is further used to construct a positioning correction formula based on multiple reference wireless positioning data, multiple deviation values, and multiple fixed reference points;

[0160] The processing module 1 is also used to substitute the current wireless positioning data into the positioning correction formula to obtain the current corrected positioning data.

[0161] In a possible implementation, the processing module 1 is further used to obtain a Lagrangian basis function corresponding to each fixed reference point based on a plurality of reference wireless positioning data;

[0162] The processing module 1 is further used to obtain an interpolation function based on each Lagrangian basis function and each deviation value;

[0163] The processing module 1 is also used to obtain a positioning correction formula based on the interpolation function.

[0164] In a possible implementation, the processing module 1 is further configured to obtain each Lagrangian basis function by using the following formula:

[0165]

[0166] The interpolation function is obtained by the following formula:

[0167]

[0168] The positioning correction formula is obtained by the following formula:

[0169] P c =(P x +f x (P x ),P y +f y (P y ),P z +f z (Pz ));

[0170] Where n is the number of fixed reference points, l i (x) is the Lagrangian basis function of the x dimension corresponding to the i-th fixed reference point, l i (y) is the Lagrangian basis function of the y dimension corresponding to the i-th fixed reference point, l i (z) is the Lagrangian basis function of the z dimension corresponding to the i-th fixed reference point, (x j ,y j ,z j ) is the reference wireless positioning data corresponding to the jth fixed reference point; f x (x) is the interpolation function in the x dimension, f y (y) is the interpolation function in the y dimension, f z (z) is the interpolation function in the z dimension, Δ ix is the deviation value of the x dimension, Δ iy is the deviation value in the y dimension, Δ iz is the deviation value of the z dimension, P c is the current correction positioning data, P x is the x-coordinate of the current wireless positioning data, P y is the y coordinate of the current wireless positioning data, P z It is the z coordinate of the current wireless positioning data.

[0171] In a possible implementation, the processing module 1 is further used to determine a plurality of adjacent reference points adjacent to the current measurement point from a plurality of fixed reference points;

[0172] The processing module 1 is also used to determine the adjacent wireless positioning data corresponding to each adjacent reference point;

[0173] The processing module 1 is also used to calculate the Euclidean distance between the current corrected positioning data and each adjacent wireless positioning data to obtain multiple error values;

[0174] The processing module 1 is further used to calculate an average error value of multiple error values;

[0175] The processing module 1 is further used to determine that the current corrected positioning data has passed the verification when the average error value is not greater than a preset error threshold;

[0176] The processing module 1 is further used to determine that the current corrected positioning data verification fails when the average error value is greater than a preset error threshold.

[0177] In a possible implementation, the acquisition module 2 is further used to acquire real-time environmental data within a preset area;

[0178] The processing module 1 is also used to preprocess the real-time environmental data to obtain preprocessed real-time environmental data;

[0179] The processing module 1 is also used to input the pre-processed real-time environmental data into a preset environmental impact correction model to obtain a correction factor;

[0180] The processing module 1 is also used to correct the current corrected positioning data based on the correction factor to obtain the current actual positioning data.

[0181] In a possible implementation, the acquisition module 2 is further used to acquire historical environmental data within a preset area;

[0182] The processing module 1 is also used to preprocess the historical environmental data to obtain preprocessed historical environmental data;

[0183] The processing module 1 is further used to extract a plurality of initial environmental features from the preprocessed historical environmental data, and screen out relevant environmental features that affect the positioning accuracy from the plurality of initial environmental features;

[0184] The processing module 1 is also used to determine the initial model;

[0185] The processing module 1 is also used to use the relevant environmental features as input features and the deviation value as the target variable to perform model training on the initial model to obtain the environmental impact correction model.

[0186] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0187] The present application also discloses an electronic device. Figure 5 , Figure 5 The electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0188] The communication bus 502 is used to realize the connection and communication between these components.

[0189] The user interface 503 may include a display screen (Display) and a camera (Camera), and the optional user interface 503 may also include a standard wired interface and a wireless interface.

[0190] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0191] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Optionally, the processor 501 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 501 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 501, and it can be implemented by a single chip.

[0192] Among them, the memory 505 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 505 may optionally be at least one storage device located away from the aforementioned processor 501. Refer to Figure 5 , the memory 505 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module and an application program.

[0193] exist Figure 5 In the electronic device 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call an application stored in the memory 505, and when executed by one or more processors 501, the electronic device 500 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0194] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0196] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0197] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0198] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0199] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0200] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A multi-mode fusion positioning method, characterized in that: The method comprises: Select multiple fixed reference points from the preset area; Acquire reference wired positioning data and reference wireless positioning data corresponding to each of the fixed reference points; Based on the plurality of reference wired positioning data and the plurality of reference wireless positioning data, a plurality of deviation values ​​are obtained; Determine a current measurement point, and obtain current wireless positioning data corresponding to the current measurement point; Correcting the current wireless positioning data based on the multiple deviation values, the multiple reference wireless positioning data and the multiple fixed reference points to obtain current corrected positioning data; The correcting the current wireless positioning data based on the plurality of deviation values, the plurality of reference wireless positioning data and the plurality of fixed reference points to obtain the current corrected positioning data specifically includes: Constructing a positioning correction formula based on the plurality of reference wireless positioning data, the plurality of deviation values ​​and the plurality of fixed reference points; Substituting the current wireless positioning data into the positioning correction formula to obtain current corrected positioning data; Verifying the current corrected positioning data to determine whether the current corrected positioning data has passed the verification; The verifying of the current corrected positioning data to determine whether the current corrected positioning data has been verified specifically includes: Determine a plurality of adjacent reference points adjacent to the current measurement point from the plurality of fixed reference points; Determine the adjacent wireless positioning data corresponding to each of the adjacent reference points; Calculate the Euclidean distance between the current corrected positioning data and each of the adjacent wireless positioning data to obtain a plurality of error values; Calculate an average error value of a plurality of the error values; When the average error value is not greater than a preset error threshold, determining that the current corrected positioning data has passed verification; When the average error value is greater than a preset error threshold, determining that the current corrected positioning data verification fails; When the current corrected positioning data passes the verification, the current corrected positioning data is used as the current actual positioning data; When the current corrected positioning data fails verification, the current corrected positioning data is corrected to obtain current actual positioning data; The current corrected positioning data is corrected to obtain the current actual positioning data, specifically including: Obtain real-time environmental data within a preset area; Preprocessing the real-time environmental data to obtain preprocessed real-time environmental data; Inputting the preprocessed real-time environmental data into a preset environmental impact correction model to obtain a correction factor; The construction of the environmental impact correction model includes: extracting and screening relevant environmental features that affect positioning accuracy from pre-processed historical environmental data; using the relevant environmental features as input features and positioning errors as target variables to form a training data set; selecting a suitable model type based on the training data set, the model type including at least one of a linear regression model, a decision tree model, a support vector machine, and a neural network model; through the model training process, using an optimization algorithm to continuously adjust model parameters to minimize prediction errors and obtain an initial environmental impact correction model; using a cross-validation method to evaluate the performance of the initial environmental impact correction model, optimizing model parameters based on a mean square error index, and obtaining an optimized environmental impact correction model; The current corrected positioning data is corrected based on the correction factor to obtain the current actual positioning data.

2. The method according to claim 1, characterized in that The constructing of a positioning correction formula based on the plurality of reference wireless positioning data, the plurality of deviation values ​​and the plurality of fixed reference points specifically includes: Based on the plurality of reference wireless positioning data, obtaining the Lagrangian basis function corresponding to each of the fixed reference points; Based on each of the Lagrangian basis functions and each of the deviation values, an interpolation function is obtained; Based on the interpolation function, the positioning correction formula is obtained.

3. The method according to claim 2, characterized in that The obtaining, based on the plurality of reference wireless positioning data, a Lagrangian basis function corresponding to each of the fixed reference points specifically includes: Each of the Lagrangian basis functions is obtained by the following formula: ； ； ； The interpolation function is obtained based on each of the Lagrangian basis functions and each of the deviation values, specifically comprising: The interpolation function is obtained by the following formula: ； ； ； The obtaining of the positioning correction formula based on the interpolation function specifically includes: The positioning correction formula is obtained by the following formula: ； Wherein, n is the number of the fixed reference points, is the Lagrangian basis function of the x dimension corresponding to the i-th fixed reference point, is the Lagrangian basis function of the y dimension corresponding to the i-th fixed reference point, is the Lagrangian basis function of the z dimension corresponding to the i-th fixed reference point, is the reference wireless positioning data corresponding to the j-th fixed reference point; is the interpolation function in the x dimension, is the interpolation function in the y dimension, is the interpolation function in the z dimension, is the deviation value of the x dimension, is the deviation value in the y dimension, is the deviation value in the z dimension, where for the i-th fixed reference point, the deviation value Δi is calculated as follows: ;in, is the reference wired positioning data of the i-th fixed reference point, is the reference wireless positioning data of the i-th fixed reference point. The positioning data includes three-dimensional coordinates (x, y, z), and the deviation value includes the corresponding three dimensions: , is the current correction positioning data, is the x-coordinate of the current wireless positioning data, is the y coordinate of the current wireless positioning data, is the z coordinate of the current wireless positioning data.

4. The method according to claim 1, characterized in that: Before inputting the pre-processed real-time environmental data into a preset environmental impact correction model to obtain a correction factor, the method further includes: Obtain historical environmental data in a preset area; Preprocessing the historical environmental data to obtain preprocessed historical environmental data; Extracting a plurality of initial environmental features from the preprocessed historical environmental data, and screening out relevant environmental features that affect positioning accuracy from the plurality of initial environmental features; Determine the initial model; The relevant environmental features are used as input features, the deviation values ​​are used as target variables, model training is performed on the initial model, and the environmental impact correction model is obtained.

5. A multi-mode fusion positioning system, characterized in that: The system comprises: a processing module and an acquisition module; The processing module is used to select a plurality of fixed reference points from a preset area; The acquisition module is used to acquire the reference wired positioning data and the reference wireless positioning data corresponding to each of the fixed reference points; The processing module is further used to obtain a plurality of deviation values ​​based on the plurality of reference wired positioning data and the plurality of reference wireless positioning data; The processing module is further used to determine a current measurement point and obtain current wireless positioning data corresponding to the current measurement point; The processing module is further configured to correct the current wireless positioning data based on the plurality of deviation values, the plurality of reference wireless positioning data, and the plurality of fixed reference points to obtain current corrected positioning data; the correcting the current wireless positioning data based on the plurality of deviation values, the plurality of reference wireless positioning data, and the plurality of fixed reference points to obtain current corrected positioning data specifically comprises: constructing a positioning correction formula based on the plurality of reference wireless positioning data, the plurality of deviation values, and the plurality of fixed reference points; substituting the current wireless positioning data into the positioning correction formula to obtain current corrected positioning data; The processing module is further used to verify the current corrected positioning data and determine whether the current corrected positioning data has been verified; the verification of the current corrected positioning data and determining whether the current corrected positioning data has been verified specifically includes: determining a plurality of adjacent reference points adjacent to the current measurement point from a plurality of the fixed reference points; determining adjacent wireless positioning data corresponding to each of the adjacent reference points; calculating the Euclidean distance between the current corrected positioning data and each of the adjacent wireless positioning data to obtain a plurality of error values; calculating an average error value of the plurality of error values; when the average error value is not greater than a preset error threshold, determining that the current corrected positioning data has been verified; when the average error value is greater than a preset error threshold, determining that the current corrected positioning data has not been verified; The processing module is further configured to use the current corrected positioning data as the current actual positioning data when the current corrected positioning data passes verification; The processing module is also used to correct the current corrected positioning data to obtain the current actual positioning data when the current corrected positioning data verification fails; the current corrected positioning data is corrected to obtain the current actual positioning data, specifically including: obtaining real-time environmental data in a preset area; preprocessing the real-time environmental data to obtain preprocessed real-time environmental data; inputting the preprocessed real-time environmental data into a preset environmental impact correction model to obtain a correction factor; the construction of the environmental impact correction model includes: extracting and screening relevant environmental features that affect the positioning accuracy from the preprocessed historical environmental data; inputting the relevant environmental features into a preset environmental impact correction model to obtain a correction factor. The method comprises the following steps: taking the positioning error as the target variable and taking the positioning error as the input feature to form a training data set; selecting a suitable model type based on the training data set, and the model type includes at least one of a linear regression model, a decision tree model, a support vector machine, and a neural network model; adjusting the model parameters through the model training process by using an optimization algorithm to minimize the prediction error and obtain an initial environmental impact correction model; performing a performance evaluation on the initial environmental impact correction model by using a cross-validation method, optimizing the model parameters based on a mean square error index, and obtaining an optimized environmental impact correction model; and correcting the current corrected positioning data based on the correction factor to obtain the current actual positioning data.

6. An electronic device, characterized in that: The electronic device (500) comprises a processor (501), a memory (505), a user interface (503) and a network interface (504), wherein the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device (500) executes the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 4 is performed.

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