Indoor space positioning system and method based on sound wave signals

Abstract

The invention discloses an indoor space positioning system and method based on sound wave signals, and relates to the technical field of wireless positioning, and the system comprises a mobile device which is used for initiating a positioning request and presenting a space positioning map; the sound production module is used for emitting near ultrasonic sound waves with a fixed positioning period and is composed of a sound wave base station array for generating sound waves with a fixed frequency interval and fixed intensity; the sound wave analysis module is used for receiving a sound wave signal and converting the sound wave signal into a code word which can be identified by a management platform through a coding mode; and the management platform is used for controlling the sound production module to emit a sound wave signal according to a positioning request initiated by the mobile equipment, and receiving and processing positioning data. According to the technical scheme of the invention, high-precision indoor space positioning can be realized without time synchronization between the mobile device and the sound wave base station, and the method has a high application value.

Description

Technical Field

[0001] The present invention relates to the field of wireless positioning technology, and in particular to an indoor space positioning system and method based on sound wave signals. Background Art

[0002] With the widespread application of the Global Positioning System and the Beidou Satellite Navigation System, outdoor positioning and navigation technology has made significant progress in terms of accuracy and timeliness. However, indoor space is blocked by building structures, and satellite signals cannot effectively penetrate, resulting in traditional satellite positioning methods being difficult to play their due role in indoor environments. Therefore, developing an efficient and reliable indoor space positioning technology has become a hot topic in current research.

[0003] Among the existing indoor positioning technologies, 5G+Beidou positioning technology is a relatively advanced technical solution. This technology deploys 5G base stations and indoor simulated satellites in indoor spaces, uses precise time synchronization between terminals and base stations, sends positioning signals to each other, and calculates the position through time difference. However, this technology has some obvious shortcomings. First, the deployment process of 5G base stations and simulated satellites is relatively complicated, requiring a lot of infrastructure construction and equipment installation, and the system construction cost and maintenance difficulty are high. Secondly, the user positioning terminal needs to be compatible with 5G and Beidou positioning signals under the premise of precise time synchronization, which puts high requirements on its performance and limits its application on ordinary consumer-grade devices.

[0004] In addition, most existing indoor positioning technologies rely on the transmission of high-frequency signals, such as Wi-Fi, Bluetooth or ultrasound. Although these technologies can achieve indoor positioning to a certain extent, they have problems such as limited coverage, severe signal interference, and low positioning accuracy. For example, although the ultrasonic positioning system can achieve high positioning accuracy, its coverage is small and it is easily interfered by environmental noise, making it difficult to be widely used in large indoor spaces. In view of the shortcomings of the existing technology, this application proposes an indoor space positioning system and method based on acoustic wave signals. Summary of the invention

[0005] Technical Purpose In order to solve the above problems, the purpose of the present invention is to provide an indoor space positioning system and method based on acoustic wave signals. The system and method use acoustic wave signals in the near-ultrasonic frequency band for positioning, avoiding the problem of precise time synchronization required in traditional indoor positioning technology, while reducing the requirements for terminal equipment performance. In addition, the acoustic wave base station of the present invention has a large coverage range, can achieve a wider range of indoor space positioning, and supports general-purpose communication devices such as smart phones, and has high economy. Through the present invention, efficient and accurate positioning can be achieved in indoor environments, providing a new solution for indoor navigation, intelligent logistics, personnel management and other fields.

[0006] Technical Solution In order to achieve the above-mentioned purpose, the present invention provides an indoor space positioning system and method based on acoustic wave signals, aiming to solve the problems of complex deployment, time synchronization and high requirements for terminal devices in existing indoor positioning technologies. The system and method arrange an array of acoustic wave base stations in the indoor space, distribute and transmit acoustic wave signals of fixed intensity in the form of an equidistant array, and after receiving the acoustic wave signal, the mobile device calculates its own position through a time difference positioning method without the need for time synchronization with the base station. Finally, the calculation results are uploaded to achieve real-time positioning and map display, which has the advantages of simplified deployment, reduced terminal requirements and high-precision positioning.

[0007] In a first aspect, the present invention provides an indoor space positioning system based on acoustic wave signals, comprising: A mobile device is used to initiate a positioning request and present a spatial positioning map; A sound-generating module, used for emitting near-ultrasonic sound waves with a fixed positioning period, and composed of a sound wave base station array for generating sound waves with a fixed frequency interval and fixed intensity; The sound wave analysis module is used to receive the sound wave signal and convert the sound wave signal into a codeword that can be recognized by the management platform through TDMA, FDMA and CDMA coding methods. The codeword contains mobile device information, frequency, intensity and receiving time of the sound wave signal; The management platform is used to control the sound module to emit sound wave signals according to the positioning request initiated by the mobile device, and to receive and process positioning data.

[0008] Furthermore, the acoustic wave base station array takes one of the acoustic wave base stations as the coordinate origin, and the other base stations are distributed in the coordinate system in the form of an equidistant array. The fixed frequency of each acoustic wave base station is unique and the emitted acoustic wave signal strength is the same.

[0009] Furthermore, the frequency range of the sound wave signal is 19KHz-23KHz.

[0010] Furthermore, the sound wave base station within the square area where the frequency band point is located is selected and determined to send out the sound wave signal according to the sound wave frequency and intensity.

[0011] Furthermore, the mobile device is deployed with matching positioning software for processing the received sound wave signal and outputting the position coordinates of the mobile device according to the processing result.

[0012] Furthermore, the positioning software calculates the position coordinates of the mobile device using the following formula:

[0013]

[0014] In the formula, and They are the horizontal and vertical coordinates of the mobile device respectively; and are the horizontal and vertical coordinates of the acoustic wave base station A respectively; and are the horizontal and vertical coordinates of the acoustic wave base station B respectively; and are the horizontal and vertical coordinates of the acoustic wave base station C respectively; is the distance difference between the mobile device and the acoustic wave base stations A and B; is the distance difference between the mobile device and the acoustic wave base stations A and C.

[0015] Furthermore, the calculation formula of the position coordinates of the mobile device is analyzed by iteration method, Newton method or least square method.

[0016] Furthermore, the management platform performs transcoding positioning on the spatial positioning map according to the location coordinates of the mobile device.

[0017] This method does not require complex infrastructure construction, significantly reducing the difficulty and cost of system deployment; it does not require time synchronization between the mobile terminal and the base station, greatly reducing the performance requirements for terminal equipment, allowing general-purpose communication equipment to directly support positioning functions, improving the versatility and applicability of the system.

[0018] Furthermore, the mobile device is equipped with an inertial sensor for maintaining positioning accuracy through inertial navigation when the acoustic wave signal is temporarily lost, performing coordinate positioning according to the received acoustic wave signal, and fusing the inertial sensor data after pre-processing operations such as denoising and filtering through a Kalman filter to estimate the displacement and attitude change of the mobile device. The calculation formula of the Kalman filter is:

[0019]

[0020] In the formula, Based on The position vector at which the measurement value is updated at each moment; Based on The position vector predicted by the moment information; For the The Kalman gain matrix at time t; For the The measurement vector of the moment; is the measurement matrix; Based on The position covariance matrix of the moment measurement update; Based on The position covariance matrix of the moment information prediction; is the identity matrix with the same dimensions as the position vector.

[0021] The continuous motion information provided by the inertial sensor can effectively reduce the error of the acoustic signal and improve positioning accuracy. The inertial sensor is not affected by the interference of the acoustic signal. Even if the acoustic signal is lost or severely interfered, it can still provide relatively accurate position information, thereby improving the system's anti-interference ability in complex environments.

[0022] Furthermore, the system also includes a multi-layer perceptron module for performing predictive spatial positioning by learning the relationship between acoustic wave signals and spatial positions, specifically including: receiving acoustic wave signals from multiple known and unknown acoustic wave base stations, and annotating the acoustic wave signals as training data sets; extracting features of the acoustic wave signals, combining them with the coordinates of the acoustic wave base stations, and using them as input data for training the multi-layer perceptron model; constructing a multi-layer perceptron model, inputting the input data into the multi-layer perceptron model, and outputting predicted position information.

[0023] By learning the complex nonlinear relationship between the characteristics of the acoustic signal and the position coordinates, the positioning accuracy is improved, and the system can process acoustic signals containing noise, making it robust. Even in the case of poor signal quality, it can provide relatively accurate positioning results through the nonlinear fitting ability of the multi-layer perceptron.

[0024] In a second aspect, the present invention further provides an indoor space positioning method based on acoustic wave signals, the method being based on the system described in the first aspect, comprising: The mobile device initiates a positioning request; The management platform controls the sound module to emit near-ultrasonic sound waves with a fixed positioning period according to the positioning request initiated by the mobile device; The sound wave analysis module converts the near-ultrasonic sound waves into code words that can be recognized by the management platform through encoding; The mobile device calculates its own position coordinates based on the time difference of the received sound wave signal; The management platform receives and processes the positioning data uploaded by the mobile device and outputs the positioning results.

[0025] In a third aspect, the present invention also provides a computer device, comprising a management platform and a memory, wherein the management platform is connected to the memory, the memory is used to store a computer program, and the management platform is used to execute the computer program stored in the memory, so that the computer device executes at least one step of the aforementioned indoor space positioning method based on acoustic wave signals.

[0026] In a fourth aspect, the present invention further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a management platform, at least one step of the aforementioned indoor space positioning method based on acoustic wave signals is implemented.

[0027] The present invention arranges an array of acoustic base stations in indoor space, distributes and transmits acoustic signals of fixed intensity in the form of an equidistant array, and after receiving the acoustic signals, the mobile device calculates its own position through the time difference positioning method, and finally uploads the calculation results to achieve real-time positioning and map display. The system and method have a wide range of application scenarios, can achieve efficient and accurate positioning in indoor environments, and provide a new solution for indoor navigation, intelligent logistics, personnel management and other fields.

[0028] Beneficial Effects By implementing the indoor space positioning system and method based on sound wave signals provided by the present invention, the following technical effects are achieved: (1) The present invention arranges an array of acoustic wave base stations in the indoor space, distributes and transmits acoustic wave signals of fixed intensity in the form of an equidistant array, and the mobile device calculates its own position through the time difference positioning method after receiving the acoustic wave signal. There is no need for complex infrastructure construction, which significantly reduces the deployment difficulty and cost of the system; there is no need for time synchronization between the mobile terminal and the base station, which greatly reduces the performance requirements for the terminal equipment, allowing general-purpose communication equipment to directly support the positioning function, improving the versatility and applicability of the system; because the propagation speed of the acoustic wave signal is relatively stable, and the frequency and position of each base station are known, this method can achieve high positioning accuracy and meet the needs of application scenarios such as indoor navigation and personnel positioning.

[0029] (2) Equipped with inertial sensors, it is used to maintain positioning accuracy through inertial navigation in the event of a temporary loss of acoustic signals, and the acoustic positioning information is combined with the inertial sensor data through a Kalman filter. The continuous motion information provided by the inertial sensor can effectively reduce the error of the acoustic signal and improve positioning accuracy; the inertial sensor is not affected by acoustic signal interference, and can still provide relatively accurate position information even when the acoustic signal is lost or severely interfered, thereby improving the system's anti-interference ability in complex environments.

[0030] (3) Predictive spatial positioning by learning the relationship between acoustic signals and spatial positions. By learning the complex nonlinear relationship between acoustic signal features and position coordinates, the positioning accuracy is improved, and the system can process acoustic signals containing noise, making it more robust. Even in the case of poor signal quality, it can provide relatively accurate positioning results through the nonlinear fitting ability of the multi-layer perceptron. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to make the above-mentioned indoor space positioning system and method based on sound wave signals of the present invention more obvious and easy to understand, the drawings required for use in the specific implementation of the present invention are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A schematic diagram showing a flow chart of an indoor space positioning method based on acoustic wave signals; Figure 2 Schematic diagram showing the principle of indoor space positioning method based on sound wave signals. DETAILED DESCRIPTION

[0033] Embodiment 1: Provided is an indoor space positioning system and method based on sound wave signals, the indoor space positioning system based on sound wave signals comprises: a mobile device, a sound emission module, a sound wave analysis module and a management platform, the indoor space positioning method based on sound wave signals has a process as follows Figure 1 As shown, the indoor space positioning system and method based on sound wave signals are specifically described in the following steps.

[0034] The mobile device is used to initiate a positioning request and present a spatial positioning map.

[0035] The sound-generating module is used to emit near-ultrasonic sound waves with a fixed positioning period, and is composed of a sound wave base station array for generating sound waves with a fixed frequency range and fixed intensity.

[0036] The acoustic wave base station array uses one of the acoustic wave base stations as the coordinate origin, and the other base stations are distributed in the coordinate system in the form of an equidistant array. The fixed frequency of each acoustic wave base station is unique and the transmitted acoustic wave signal strength is the same.

[0037] The frequency range of the sound wave signal is 19KHz-23KHz.

[0038] According to the sound wave frequency and intensity, the sound wave base station in the square area where the frequency band point is located sends out the sound wave signal.

[0039] The sound wave analysis module is used to receive the sound wave signal and convert the sound wave signal into a codeword that can be recognized by the management platform through TDMA, FDMA and CDMA coding methods. The codeword contains mobile device information, frequency, intensity and receiving time of the sound wave signal.

[0040] The mobile device is deployed with matching positioning software for processing the received sound wave signal and outputting the position coordinates of the mobile device according to the processing result.

[0041] like Figure 2 As shown, the mobile device P is in the square area where the three nearest acoustic wave base stations A, B, and C are located. Assume that the coordinates of the acoustic wave base station A are ( , ), the coordinates of the acoustic wave base station B are ( , ), the coordinates of the acoustic wave base station C are ( , ), the coordinates of the mobile device P are ( , ), the specific time point when the mobile device P receives the sound wave signals from the three sound wave base stations is , and The time of transmitting the acoustic signal is , the speed of sound wave propagation in space is ; The distance between the mobile device P and the three acoustic base stations , and And the distance difference and It is expressed as:

[0042]

[0043]

[0044]

[0045]

[0046] The calculation formula for the position coordinates of mobile device P is:

[0047]

[0048] The calculation formula for the location coordinates of the mobile device is parsed by using an iteration method, Newton's method or a least squares method.

[0049] The management platform is used to control the sound module to emit sound wave signals according to the positioning request initiated by the mobile device, and to receive and process positioning data.

[0050] The management platform performs transcoding positioning on the spatial positioning map according to the location coordinates of the mobile device.

[0051] Embodiment 2: Based on the aforementioned embodiment, the mobile device is equipped with an inertial sensor for maintaining positioning accuracy through inertial navigation when the acoustic wave signal is temporarily lost.

[0052] The inertial sensors include accelerometers, gyroscopes and magnetometers.

[0053] The acceleration, angular velocity and magnetic field strength data are collected at a certain frequency through the inertial sensor, and the timestamp of the inertial sensor data is recorded to ensure time synchronization with the acoustic wave signal.

[0054] The collected sound wave signals and inertial sensor data are packaged, including the frequency and arrival time of the sound wave signals, the acceleration, angular velocity, magnetic field strength and timestamp of the inertial sensor.

[0055] Coordinate positioning is performed according to the received sound wave signal, and the inertial sensor data after pre-processing operations such as denoising and filtering is fused through the Kalman filter to estimate the displacement and posture change of the mobile device. The calculation formula of the Kalman filter is:

[0056]

[0057] In the formula, Based on The position vector at which the measurement value is updated at each moment; Based on The position vector predicted by the moment information; For the The Kalman gain matrix at the moment is used to determine the weights between the measured and predicted values; For the The measurement vector at each moment, including the arrival time difference of the sound wave information and the acceleration and angular velocity of the inertial sensor; is the measurement matrix, which describes how to get from the position vector Get the measured value , which contains the mapping relationship from acoustic wave signals and inertial sensor data to position coordinates; Based on The position covariance matrix of the moment measurement update; Based on The position covariance matrix of the moment information prediction; is the identity matrix with the same dimensions as the position vector.

[0058] The continuous position and attitude information provided by the inertial sensor can ensure the accuracy of spatial positioning when the acoustic signal is temporarily lost, and can provide auxiliary positioning when the acoustic signal is blocked, thereby improving the anti-interference ability of the system. Verification shows that while obtaining an average error similar to that of the above-mentioned embodiment, the positioning accuracy can be improved by about 10% on the basis of the original positioning accuracy, indicating that adding inertial sensors can help improve the positioning accuracy while ensuring the positioning accuracy, especially in environments with large signal interference, and provide auxiliary positioning when the acoustic signal is blocked or the acoustic base station array has an abnormality.

[0059] Embodiment 3: On the basis of the above-mentioned embodiment, the system adds a multi-layer perceptron module for predictive spatial positioning by learning the relationship between the sound wave signal and the spatial position.

[0060] A plurality of sound wave base stations with known locations are selected, and sound wave signals from the sound wave base stations are received by a microphone equipped by the mobile device, and the arrival time of each sound wave signal is recorded.

[0061] The collected sound wave signals are packaged, including information such as frequency, arrival time, timestamp, and the coordinates of the corresponding sound wave base station.

[0062] The collected sound wave signals are labeled, including the frequency, arrival time, location coordinates of the reference point, etc. of the sound wave signals, and the labeled data are stored as a training data set.

[0063] The collected acoustic wave signals are cleaned and noise and outliers are removed.

[0064] The features of the acoustic wave signal, such as arrival time difference, signal strength, etc., are extracted, and the feature data is combined with the coordinates of the corresponding acoustic wave base station at a known location to obtain the input data for training the multi-layer perceptron model.

[0065] The architectural design of the multi-layer perceptron model includes: the number of neurons in the input layer is consistent with the number of features; the number of neurons in the output layer is consistent with the coordinate dimension of the target position.

[0066] The training process of the multi-layer perceptron model involves two stages: forward propagation and back propagation. The formula for forward propagation is:

[0067] In the formula, For the The output vector of the layer; For the The weight matrix of the layer represents Layer to The connection weights of the layer; For the The output vector of the layer, i.e. The input vector of the layer; For the The bias vector of the layer, which is used to adjust the activation value of the neuron; For the The weight vector of the layer, which is used to weight the input features element by element; is the activation function; It is an element-by-element multiplication operation.

[0068] The formula for back propagation is:

[0069]

[0070]

[0071] In the formula, For the The gradient of the layer weight matrix; Indicates The output vector of the layer, i.e. The input vector of the layer; For the The error term of the layer; is the regularization coefficient; For the The weight matrix of the layer; For the The gradient of the layer bias vector; Indicates The gradient of the layer weight vector; It is an element-by-element multiplication operation.

[0072] Input data is input into the multi-layer perceptron model, and the predicted position information is output. After adding the multi-layer perceptron module described in this embodiment and optimizing the algorithm, the inventors found that the positioning error caused by the additional multiple propagation paths of sound waves in the indoor environment due to refraction, reflection, diffraction and diffraction can be significantly suppressed, thereby significantly improving the positioning accuracy.

[0073] For example, suppose there is a multi-layer perceptron model, including an input layer, a hidden layer, and an output layer, each layer has a neuron, and suppose there is a training sample; In the forward propagation, assuming that the input vector , the weight matrix , the bias vector , the weight vector ; From the input layer to the hidden layer, the output to the hidden layer Perform the calculation:

[0074] From the hidden layer to the output layer, the output of the output layer Perform the calculation:

[0075] In back propagation, assuming the loss function ,in is the true value, is the predicted value; Calculate the gradient of the loss function:

[0076] The weight gradient of the output layer is calculated, assuming that the regularization coefficient :

[0077] Calculate the bias gradient of the output layer:

[0078] Calculate the weight gradient of the hidden layer:

[0079] The effect of the multi-layer perceptron module is shown in Table 1.

[0080] Table 1. Summary of multi-layer perceptron modules

[0081] According to the experimental table, the error between the predicted position and the actual position of the multi-layer perceptron model is very small, with the maximum error being only 0.07 meters. This shows that the multi-layer perceptron model can effectively learn the relationship between the sound wave signal and the position, thereby achieving high-precision indoor positioning, which is suitable for a variety of indoor positioning application scenarios.

[0082] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable non-transient storage media containing computer-usable program code.

[0083] The present invention can provide computer program instructions to a management platform of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the management platform of the computer or other programmable data processing device produce a device for implementing the system.

[0084] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions of the system.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions of the system.

Claims

1. An indoor space positioning system based on acoustic wave signals, comprising a mobile device for initiating a positioning request and presenting a space positioning map, characterized in that: It also includes a sound generation module, a sound wave analysis module, and a management platform; The sound-generating module is used to emit near-ultrasonic sound waves with a fixed positioning period, and is composed of a sound wave base station array for generating sound waves with a fixed frequency interval and fixed intensity; The sound wave analysis module is used to receive the sound wave signal and convert the sound wave signal into a code word that can be recognized by the management platform through encoding; The management platform is used to control the sound module to emit sound wave signals according to the positioning request initiated by the mobile device, and to receive and process positioning data.

2. The system according to claim 1, characterized in that: The acoustic wave base station array uses one of the acoustic wave base stations as the coordinate origin, and the other base stations are distributed in the coordinate system in the form of an equidistant array. The fixed frequency of each acoustic wave base station is unique and the transmitted acoustic wave signal strength is the same.

3. The system according to claim 2, characterized in that: The frequency range of the sound wave signal is 19KHz-23KHz.

4. The system according to claim 1, characterized in that: The mobile device is deployed with matching positioning software for processing the received sound wave signal and outputting the position coordinates of the mobile device according to the processing result.

5. The system according to claim 4, characterized in that: The positioning software calculates the location coordinates of the mobile device using the following formula: In the formula, and They are the horizontal and vertical coordinates of the mobile device respectively; and are the horizontal and vertical coordinates of the acoustic wave base station A respectively; and are the horizontal and vertical coordinates of the acoustic wave base station B respectively; and are the horizontal and vertical coordinates of the acoustic wave base station C respectively; is the distance difference between the mobile device and the acoustic wave base stations A and B; is the distance difference between the mobile device and the acoustic wave base stations A and C.

6. The system according to claim 1, characterized in that: The mobile device is equipped with an inertial sensor, which is used to maintain positioning accuracy through inertial navigation when the sound wave signal is temporarily lost, perform coordinate positioning according to the received sound wave signal, fuse the inertial sensor data after denoising and filtering preprocessing operations through the Kalman filter, and estimate the displacement and posture change of the mobile device. The calculation formula of the Kalman filter is: In the formula, Based on The position vector at which the measurement value is updated at each moment; Based on The position vector predicted by the moment information; For the The Kalman gain matrix at time t; For the The measurement vector of the moment; is the measurement matrix; Based on The position covariance matrix of the moment measurement update; Based on The position covariance matrix of the moment information prediction; is the identity matrix with the same dimensions as the position vector.

7. The system according to claim 1, characterized in that: The system also includes a multi-layer perceptron module, which is used to perform predictive spatial positioning by learning the relationship between sound wave signals and spatial positions, specifically including: receiving sound wave signals from multiple known and unknown sound wave base stations, annotating the sound wave signals as training data sets; extracting features of the sound wave signals, combining them with the coordinates of the sound wave base stations, and using them as input data for training the multi-layer perceptron model; building a multi-layer perceptron model, inputting the input data into the multi-layer perceptron model, and outputting predicted position information.

8. An indoor space positioning method based on acoustic wave signals, characterized in that: The method is implemented based on the system according to any one of claims 1 to 7: The method comprises: The mobile device initiates a positioning request; The management platform controls the sound module to emit near-ultrasonic sound waves with a fixed positioning period according to the positioning request initiated by the mobile device; The sound wave analysis module converts the near-ultrasonic sound waves into code words that can be recognized by the management platform through encoding; The mobile device calculates its own position coordinates based on the time difference of the received sound wave signal; The management platform receives and processes the positioning data uploaded by the mobile device and outputs the positioning results.

9. A computer device, comprising a management platform and a memory, wherein the management platform is connected to the memory, and the memory is used to store a computer program, characterized in that: The management platform is used to execute the computer program stored in the memory, so that the computer device performs at least one step in the method of claim 8.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, characterized in that: The computer program implements at least one step of the method of claim 8 when executed.

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