Outdoor equipment positioning method, electronic equipment and computer storage medium

Abstract

The invention discloses an outdoor equipment positioning method. The method comprises the steps that first positioning data based on a GPS, second positioning data based on WIFI and third positioning data based on a base station are acquired; performing signal state analysis on the first positioning data and the second positioning data, and allocating weight coefficients corresponding to the first positioning data, the second positioning data and the third positioning data according to a signal state analysis result; and obtaining the correction state of the outdoor equipment at the current moment, and obtaining the current position of the outdoor equipment according to the correction state and the weight coefficients corresponding to the first positioning data, the second positioning data and the third positioning data. According to the invention, by combining multiple positioning technologies, the weight coefficient of each positioning technology can be dynamically adjusted according to different environments, respective advantages can be exerted, and mutual defects can be made up, so that under the synergistic effect of multiple positioning modes, accurate positioning of the outdoor equipment is realized, effective supervision of the outdoor equipment is ensured, and the positioning efficiency of the outdoor equipment is improved. And the working efficiency of the outdoor equipment and the use experience of a user are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of outdoor equipment positioning, and in particular to an outdoor equipment positioning method, electronic equipment and computer storage medium. Background Art

[0002] GPS, or the Global Positioning System, is undoubtedly the most widely used outdoor positioning technology. It accurately calculates a device's geographic location by receiving signals transmitted by multiple satellites. However, GPS accuracy is often affected by various factors, such as satellite signal strength, weather conditions, and terrain obstruction. In complex environments such as urban canyons, tunnels, and forests, GPS signals are easily blocked or reflected, resulting in significant increases in positioning errors.

[0003] In addition to GPS, Wi-Fi positioning and base station positioning are also common positioning methods. Wi-Fi positioning calculates the device's approximate location by detecting the signal strength of Wi-Fi hotspots around the device and combining it with a database of known Wi-Fi hotspot locations. Wi-Fi positioning is generally suitable for indoor use and has short communication ranges. Base station positioning, on the other hand, estimates the device's location based on the signal strength or time difference between the device and multiple base stations. It is suitable for outdoor use and has relatively stable communication. Both technologies are widely used indoors and in urban environments, but their accuracy is generally inferior to GPS in open areas outdoors.

[0004] Outdoor equipment requires real-time positioning during operation to improve operational accuracy and efficiency, adapt to changing terrain, and facilitate user management and monitoring. However, relying solely on any of the aforementioned positioning technologies often struggles to achieve accurate positioning in complex and changing real-world environments, hindering the supervision of outdoor equipment, affecting operational efficiency, and reducing the user experience. Summary of the Invention

[0005] The purpose of this application is to provide an outdoor equipment positioning method, electronic device and computer storage medium, which significantly improves the positioning accuracy of outdoor equipment in complex and changeable real environments by integrating GPS, Wi-Fi and base station positioning technologies, ensures effective supervision of outdoor equipment, and improves the working efficiency of outdoor equipment and the user experience.

[0006] To achieve the above objectives:

[0007] In a first aspect, an embodiment of the present application provides a method for positioning an outdoor device, the method comprising:

[0008] obtaining two of the first positioning data, the second positioning data, and the third positioning data based on different references;

[0009] performing a signal status analysis on the first positioning data and the second positioning data, and assigning corresponding weight coefficients to at least two of the first positioning data, the second positioning data, and the third positioning data according to a signal status analysis result;

[0010] Obtaining a current correction state of the outdoor device, and obtaining a current location of the outdoor device based on the correction state, a corresponding weight coefficient of at least two of the first positioning data, the second positioning data, and the third positioning data. In one embodiment, before performing signal state analysis on the first positioning data and the second positioning data, the method further includes:

[0011] The first positioning data, the second positioning data, and the third positioning data are converted into metric values in a unified coordinate system and noise filtering is performed.

[0012] In one embodiment, the performing signal status analysis on the first positioning data and the second positioning data includes:

[0013] Obtaining a first initial weight coefficient of the first positioning data and a second initial weight coefficient of the second positioning data;

[0014] determining signal stability of the first positioning data and the second positioning data;

[0015] When the signal stability of the first positioning data is less than a preset first stability threshold, reducing the first initial weight coefficient to obtain a first revised weight coefficient;

[0016] When the signal stability of the second positioning data is greater than a preset second stability threshold, the second initial weight coefficient is increased to obtain a second revised weight coefficient, wherein the second stability threshold is greater than the first stability threshold.

[0017] In one embodiment, allocating weight coefficients corresponding to the first positioning data, the second positioning data, and the third positioning data according to the signal state analysis result includes:

[0018] Determine the first modified weight coefficient and the second modified weight coefficient as weight coefficients of the first positioning data and the second positioning data respectively;

[0019] The first revised weight coefficient, the second revised weight coefficient, and the preset third initial weight coefficient of the third positioning data are normalized to obtain weight coefficients corresponding to the first positioning data, the second positioning data, and the third positioning data, respectively.

[0020] In one embodiment, obtaining the current corrected state of the outdoor device, and obtaining the current position of the outdoor device according to corresponding weight coefficients of at least two of the corrected state, the first positioning data, the second positioning data, and the third positioning data, includes:

[0021] Initializing state parameters of the outdoor equipment;

[0022] Obtaining a predicted state of the outdoor device at a current moment according to a preset motion model;

[0023] Obtaining a corrected state of the outdoor device at a current moment according to the predicted state, at least one of the first positioning data, the second positioning data, and the third positioning data;

[0024] The weight coefficients corresponding to the correction state, the first positioning data, the second positioning data, and the third positioning data are input into a preset positioning model to obtain the current position of the outdoor device.

[0025] In one embodiment, obtaining the predicted state of the outdoor device at the current moment according to a preset motion model includes:

[0026] Obtaining a control input for changing the state of the outdoor device at a current moment and a historical state of the outdoor device at a previous moment;

[0027] The control input and the historical state are input into the motion model to obtain the predicted state of the outdoor equipment at the current moment.

[0028] In one embodiment, obtaining the corrected state of the outdoor device at a current moment based on the predicted state, the first positioning data, the second positioning data, and the third positioning data includes:

[0029] Obtaining a Kalman gain of the outdoor device at a current moment according to the predicted state, the first positioning data, the second positioning data, and the third positioning data;

[0030] Obtaining a priori estimated state of the outdoor device at the current moment given a previous moment and an observed value of the outdoor device at the current moment;

[0031] The Kalman gain, the prior estimated state, and the observed value are input into a preset state update equation to obtain the corrected state of the outdoor device at the current moment.

[0032] In one embodiment, after inputting the weight coefficients corresponding to the correction state, the first positioning data, the second positioning data, and the third positioning data into a preset positioning model to obtain the current position of the outdoor device, the method further includes:

[0033] Determining whether the current location is valid;

[0034] If so, in response to receiving a communication signal sent by the control terminal, establishing a communication connection with the control terminal, and / or, when the communication connection with the control terminal has been established, maintaining the communication connection with the control terminal;

[0035] If not, the outdoor device is locked.

[0036] In a second aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the outdoor device positioning method described above when executing the computer program.

[0037] In a third aspect, an embodiment of the present application provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the outdoor equipment positioning method described above are implemented.

[0038] By combining multiple positioning technologies, this application can dynamically adjust the weight coefficient of each positioning technology according to different environments, so that each can play its own advantages and make up for each other's shortcomings. In this way, with the synergistic effect of multiple positioning methods, precise positioning of outdoor equipment is achieved, ensuring effective supervision of outdoor equipment, and improving the working efficiency of outdoor equipment and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flowchart of a method for locating outdoor equipment provided by an embodiment of the present invention.

[0040] Figure 2 A schematic diagram of a vehicle locking method according to an embodiment of the present invention.

[0041] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0042] Description of reference numerals:

[0043] 10. Client; 11. Central control module; 12. Storage module; 13. Protocol module; 14. First cache module;

[0044] 20. Server; 21. Protocol verification module; 22. Functional module; 23. Second cache module. DETAILED DESCRIPTION

[0045] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0046] It should be noted that, in this document, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0047] It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the term "if" as used herein may be interpreted as "at the time of," "when," or "in response to a determination." Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms "comprising" and "including" indicate the presence of the described features, steps, operations, elements, components, items, types, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, types, and / or groups. The terms "or" and "and / or" as used herein are to be interpreted as inclusive, meaning any one or any combination. Thus, “A, B, or C” or “A, B, and / or C” means “any of: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition occurs only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0048] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and they can be performed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0049] It should be noted that in this article, step codes such as S1 and S2 are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial restriction on the order. When implementing the step, those skilled in the art may execute S2 first and then S1, etc., but these should all be within the scope of protection of this application.

[0050] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0051] In the subsequent description, the use of suffixes such as "module", "component" or "unit" to represent elements is only for the purpose of facilitating the description of the present application and has no specific meaning. Therefore, "module", "component" or "unit" can be used interchangeably.

[0052] The following is an explanation of the terms that may be involved in this application, as follows:

[0053] Common positioning benchmarks include GPS, Wi-Fi, and base stations.

[0054] GPS, or the Global Positioning System, accurately calculates the geographic location of a device by receiving signals transmitted by multiple satellites. Its basic principle is to determine the location of the device by measuring the distance between the device and multiple satellites using triangulation. The accuracy of GPS positioning is usually affected by the following factors: satellite signal strength (the stronger the signal strength, the higher the positioning accuracy), the number of satellites (the more satellite signals received, the higher the positioning accuracy), atmospheric conditions (the refraction effect of the ionosphere and troposphere will affect signal propagation and cause errors), and multipath effects (the signal will cause measurement errors after being reflected by surfaces such as buildings and terrain). Under ideal circumstances, the positioning accuracy of GPS can reach the meter level or even the sub-meter level. However, in complex environments such as urban canyons, tunnels, and forests, GPS signals are easily blocked or reflected, resulting in increased positioning errors or even inability to locate.

[0055] Wi-Fi positioning technology captures the signal strength of Wi-Fi hotspots around a device and compares it to a database of known Wi-Fi hotspot locations, performing complex calculations to approximate the device's location. Its core principle is to exploit the variation in signal strength with distance and use triangulation to accurately determine the device's location. The accuracy of Wi-Fi positioning is often affected by the following key factors: Wi-Fi hotspot density (the greater the number of hotspots, the higher the positioning accuracy), signal strength measurement error (environmental factors such as obstacles and walls can interfere with signal strength, leading to measurement errors), and the accuracy of the Wi-Fi hotspot location database (the accuracy of the database directly affects the final positioning accuracy). In indoor and urban environments, due to the widespread distribution of Wi-Fi hotspots, Wi-Fi positioning can typically achieve meter-level accuracy. However, in open outdoor areas, where Wi-Fi hotspots are more sparse, positioning accuracy often falls short of GPS systems.

[0056] Base station positioning technology relies on the signal strength or time difference between a device and multiple base stations to estimate the device's geographic location. Its core principle is to use the correlation between signal strength and distance to accurately determine the device's location through triangulation. The accuracy of base station positioning is generally affected by the following key factors: base station density (in areas with high base station density, positioning accuracy will be significantly improved because more base stations participate in the calculation, which can reduce errors), signal strength measurement errors (environmental factors such as obstacles and terrain changes will affect the measurement of signal strength, which may cause positioning errors), and the accuracy of base station location data (the accuracy of the base station's own location data will directly affect the accuracy of the positioning results). In urban environments, due to the dense distribution of base stations, base station positioning can generally provide positioning accuracy of tens to hundreds of meters. In remote areas, due to the smaller number of base stations, positioning accuracy may be reduced.

[0057] The Kalman filter is a recursive filter that effectively processes noisy measurement data and provides more accurate state estimates by fusing data from multiple sensors. The Extended Kalman Filter (EKF) is an extension of the Kalman filter and is suitable for nonlinear systems. In positioning systems, the Kalman filter can be used to fuse data from different positioning technologies, improving positioning accuracy through continuous state estimation and updates.

[0058] Please refer to Figure 1 , Figure 1 A flow chart of a method for locating outdoor equipment is shown, the method comprising:

[0059] S1. Acquire first positioning data based on GPS, second positioning data based on WIFI, and third positioning data based on a base station.

[0060] S2. Performing a signal state analysis on the first positioning data and the second positioning data, and assigning weight coefficients for the first positioning data, the second positioning data, and the third positioning data based on the signal state analysis results. Specifically, in this embodiment, it is necessary to perform a signal state analysis on the preprocessed first positioning data and the second positioning data, and to evaluate the sensor data quality (e.g., signal strength and noise level) in real time in order to dynamically adjust the weight coefficients of the three types of positioning data.

[0061] S3. Obtain the current correction state of the outdoor device, and obtain the current position of the outdoor device based on the correction state, the first positioning data, the second positioning data, and the weight coefficient corresponding to the third positioning data. Specifically, in this embodiment, the current position of the outdoor device can be obtained through the following steps:

[0062] S31. Weighting of homogeneous data based on variance component estimation based on statistical characteristics: For sensors of the same type (such as multi-base station TOA measurement), an improved variance component estimation method is used. The variance of each data source is calculated by maximum likelihood estimation or least squares method, and the weights are inversely proportionally assigned. The following formula is used:

[0063]

[0064] Among them, w i represents the weight of the i-th data point, is the variance of the i-th data point, and the denominator is the sum of the inverses of the variances of all data points. This weight reflects the relative accuracy of each data point, that is, the smaller the variance (the more accurate the data point), the greater its weight. i It also represents the weight of the i-th data point, which takes into account the variance of all data points and the calculated weight w j , which first calculates an intermediate weight w j , and then adjust this intermediate weight based on the variance of each data point. For different sensor types (such as distance and angle measurement), heterogeneous data fusion requires combining model structure analysis and variance analysis. For example, a multi-structure nonlinear regression model can be established, structural characteristic factors can be introduced through parameter sensitivity analysis, and the blending weights can be calculated after correcting the variance components.

[0065] S32, Dynamic Adaptive Weighted Kalman Filtering and Prediction Covariance: Within a recursive filtering framework, weights are dynamically adjusted based on the predicted state covariance matrix and the measurement noise covariance. For example, when a sensor's short-term noise increases, its weight is automatically reduced. Robust Weighting Function: In complex environments, the Huber function and Tukey bi-weighted function are used instead of least squares to suppress the influence of outliers on weights.

[0066] S33. Data-driven intelligent weighted machine learning models: Utilize deep networks (such as LSTM) to learn the spatiotemporal correlations between multi-source data. For example, input is a historical error sequence for each sensor, and output is a real-time weight distribution. Reinforcement learning optimization: Using algorithms such as Q-learning, weighting strategies are trained in a simulation environment to minimize the long-term reward of positioning error.

[0067] S34. Fusion algorithm implementation: One is centralized fusion, which inputs weighted multi-source observations into a unified model (such as the extended Kalman filter). This is suitable for scenarios with sufficient computing resources. For example, tightly coupled positioning can be achieved by fusing UWB distance, IMU angular velocity, and visual SLAM pose. The other is distributed fusion, which performs weighted fusion after local estimation of each sensor to reduce communication overhead. For example, convex combination fusion is performed on local KF results, with weights determined based on the accuracy and reliability of the local estimates.

[0068] Thus, the aforementioned steps S1 to S3 (S31 to S34) effectively improve the accuracy and robustness of the positioning system by dynamically adjusting sensor weights. In practical applications, different implementation steps and methods can be selected based on specific environments and needs. The core is to dynamically adjust weights based on the reliability of the data source, measurement accuracy, and environmental adaptability. Different positioning methods each play their own advantages and compensate for each other's shortcomings, achieving precise positioning of outdoor equipment, ensuring effective supervision of outdoor equipment, and improving the working efficiency of outdoor equipment and the user experience.

[0069] Optionally, before performing signal state analysis on the first positioning data and the second positioning data, the method further includes: converting the first positioning data, the second positioning data, and the third positioning data into measurement values under a unified coordinate system, and performing noise filtering. Specifically, in this embodiment, the three positioning data obtained cannot be used directly, and each positioning data needs to be preprocessed separately. First, the asynchronous data must be time-aligned using a unified time scale such as an interpolation method or a sliding window method. Next, the time-synchronized data needs to be converted from different sensor coordinate systems (local coordinate system, geographic coordinate system) to a unified geocentric coordinate system reference through a rotation matrix or a quaternion method. Finally, a preliminary filtering operation must be performed on the noise, and outlier detection based on a statistical model is used to eliminate outliers and smooth the noise through low-pass filtering.

[0070] Optionally, performing a signal status analysis on the first positioning data and the second positioning data includes: obtaining a first initial weight coefficient for the first positioning data and a second initial weight coefficient for the second positioning data; determining the signal stability of the first positioning data and the second positioning data; when the signal stability of the first positioning data is less than a preset first stability threshold, reducing the first initial weight coefficient to obtain a first revised weight coefficient; and when the signal stability of the second positioning data is greater than a preset second stability threshold, increasing the second initial weight coefficient to obtain a second revised weight coefficient, wherein the second stability threshold is greater than the first stability threshold. Specifically, in this embodiment, an initial weight coefficient is preset for each of the first positioning data and the second positioning data. Since GPS positioning accuracy and coverage area are significantly greater than Wi-Fi positioning, the first initial weight coefficient is by default greater than the second initial weight coefficient. For example, the first initial weight coefficient may be 0.4 to 0.7, and the second initial weight coefficient may be less than 0.2.

[0071] Furthermore, the first positioning data and the second positioning data after preprocessing need to be pre-analyzed for signal stability and accuracy, and the sensor data quality (such as signal strength and noise level) needs to be evaluated in real time in order to dynamically adjust the weights of the three signals. When the signal stability of the first positioning data is less than the preset first stability threshold, that is, when the first positioning data repeatedly shows a signal jump offset value that is too large or a weak signal, it indicates that the outdoor device may be in an indoor or outdoor obstructed location. It is necessary to appropriately reduce the first positioning data, that is, the weight of the GPS signal, based on the signal stability to obtain the first correction weight coefficient, thereby achieving the purpose of improving positioning accuracy.

[0072] Similarly, analyzing the Wi-Fi signal strength can reveal the distribution of Wi-Fi hotspots around the outdoor device. Initially, the weight of the second positioning data is low. When the signal stability of the second positioning data exceeds the preset second stability threshold, that is, when the Wi-Fi signal strength is high, it indicates that the outdoor device may be indoors. At this time, the weight of the second positioning data is dynamically increased to obtain a second correction weight coefficient, thereby achieving the purpose of improving positioning accuracy.

[0073] Optionally, allocating weight coefficients corresponding to the first positioning data, the second positioning data, and the third positioning data based on the signal state analysis results includes: determining the first revised weight coefficient and the second revised weight coefficient as the weight coefficients of the first positioning data and the second positioning data, respectively; and normalizing the first revised weight coefficient, the second revised weight coefficient, and the third initial weight coefficient of the preset third positioning data to obtain weight coefficients corresponding to the first positioning data, the second positioning data, and the third positioning data, respectively. Specifically, in this embodiment, the first revised weight coefficient and the second revised weight coefficient updated in the previous embodiment are respectively determined as the weight coefficients of the first positioning data and the second positioning data. The third positioning data also has an initial weight coefficient preset, namely, the third initial weight coefficient. Due to the high stability of base station positioning, the third initial weight coefficient is used as a constant value. After adjusting the weights of the first positioning data (GPS) and the second positioning data (Wi-Fi signal), the weight of the third positioning data is adjusted by renormalizing the three weights, that is, the sum of the weights of the three is now equal to one.

[0074] Optionally, the correction state of the outdoor device at the current moment is obtained, and the current position of the outdoor device is obtained according to the weight coefficient corresponding to the correction state, the first positioning data, the second positioning data and the third positioning data, including: initializing the state parameters of the outdoor device; obtaining the predicted state of the outdoor device at the current moment according to the preset motion model; obtaining the correction state of the outdoor device at the current moment according to the predicted state, the first positioning data, the second positioning data and the third positioning data; inputting the weight coefficient corresponding to the correction state, the first positioning data, the second positioning data and the third positioning data into the preset positioning model to obtain the current position of the outdoor device. Specifically, in this embodiment, Kalman filtering can be performed after the initial dynamic adjustment of the weights of the three positioning data. First, the state variables such as the position, speed, and acceleration of the outdoor device are initialized to provide an initial reference point (facilitating subsequent iterative calculations), improve prediction accuracy (reduce prediction error and optimize filtering algorithm), and enhance system stability (avoid algorithm divergence and improve system robustness). Secondly, based on the state and control quantity of the previous moment, the state of the outdoor device at the current moment can be obtained and predicted by the preset motion model, and then the predicted state is obtained. Then, the predicted state is updated based on the first, second, and third positioning data to obtain the corrected state of the outdoor device at the current moment. Finally, a weighted fusion calculation is performed based on the three weight coefficients obtained previously and the corrected state to obtain the current location of the outdoor device.

[0075] Optionally, obtaining the predicted state of the outdoor device at the current moment based on a preset motion model includes: obtaining the control input for changing the state of the outdoor device at the current moment and the historical state of the outdoor device at the previous moment; inputting the control input and the historical state into the motion model to obtain the predicted state of the outdoor device at the current moment. Specifically, in this embodiment, a Kalman filter is used to determine the current position of the outdoor device. Among them, the Kalman filter is a highly efficient recursive filter (autoregressive filter) that can estimate the state of a dynamic system from a series of incomplete and noisy measurements. It will generate an estimate of the unknown variable based on the values of each measurement at different times, considering the joint distribution at each time, and thus will be more accurate than an estimation method based on only a single measurement. The Kalman filter is based on linear algebra and hidden Markov models. Its basic dynamic system can be represented by a Markov chain, which is based on a linear operator interfered by Gaussian noise (i.e., normally distributed noise). The state of the system can be represented by a vector whose elements are real numbers. With each increment of discrete time, this linear operator acts on the current state to produce a new state, which also introduces some noise, and some control information of the system's known controller is also added. At the same time, another linear operator perturbed by noise produces the visible output of these hidden states.

[0076] The specific implementation steps should include:

[0077] (1) Initialization: Set the initial state equation and observation equation according to the actual application scenario

[0078] x k =F k x k-1 +B k u k +w k

[0079] Among them, x k is the state at time k, F k Is acting on x k-1 State transformation model (matrix or vector) at (previous moment); B k is the vector acting on the controller u k Input-control model on w k is the process noise, and it is assumed to have a mean of zero, and the covariance matrix is Q k The multivariate normal distribution of .

[0080] z k =H k x k +v k

[0081] Among them, z k is the observed value at time k, H k is the observation model, which maps the real state space into the observation space and obeys the normal distribution. k is the observation noise, which has zero mean.

[0082] (2) Initial parameter setting of the algorithm: setting the process noise covariance (Q), observation noise covariance (R), state transfer matrix (F), control matrix (B) and observation matrix (H).

[0083] (3) Based on the historical state of the outdoor equipment at the previous moment and the control input (control quantity) used to change the state of the outdoor equipment at the current moment, the current state is predicted. This predicted value is essentially an estimate because it does not yet incorporate the observation information at the current moment. The error covariance matrix of the predicted value is calculated by calculating the error covariance matrix of the previous moment and the system noise covariance matrix.

[0084] The preset motion models are as follows:

[0085]

[0086]

[0087] in, is the predicted state at the current time k (state estimate at time k), is the state estimate of the previous moment k-1, F k is the state transition matrix (describing how the state is transferred from time k-1 to time k), is the control input, B k is the control input matrix;

[0088] Furthermore, P k is the error covariance matrix at the current moment k (describing the uncertainty of state estimation), P k-1 is the error covariance matrix of the previous moment k-1, Q k is the process noise covariance matrix (describing the uncertainty in the system model).

[0089] In this way, by inputting the control input and historical state into the above motion model, the predicted state of the outdoor equipment at the current moment can be obtained.

[0090] Optionally, obtaining a corrected state of the outdoor device at the current moment based on the predicted state, the first positioning data, the second positioning data, and the third positioning data includes: obtaining the Kalman gain of the outdoor device at the current moment based on the predicted state, the first positioning data, the second positioning data, and the third positioning data; obtaining a priori estimated state of the outdoor device at the current moment given the previous moment and an observed value of the outdoor device at the current moment; and inputting the Kalman gain, the priori estimated state, and the observed value into a preset state update equation to obtain the corrected state of the outdoor device at the current moment. Specifically, in this embodiment, the predicted state is corrected in combination with the observed data, and the Kalman gain is first calculated. The Kalman gain calculation formula is as follows:

[0091]

[0092] Among them, K k is the Kalman gain at the current time k; P k∣k-1 It is the prediction error covariance matrix of the current moment k given the k-1 information of the previous moment, that is, the prior error covariance; H k is the observation matrix at time k, which maps the state space to the observation space; R k is the observation noise covariance matrix at time k; is the transpose of the observation matrix. The Kalman gain is calculated to balance the weights between the prior estimate and the new observations. It achieves the optimal estimate by minimizing the variance of the estimation error. The Kalman filter gain balances the reliability of predictions and observations. When the observation noise R is small, a larger Kalman gain increases the weight of new observations in updating the state estimate, giving greater confidence in the measured value. Conversely, a smaller Kalman gain increases the weight of the prior estimate.

[0093] Furthermore, the Kalman gain, the prior estimated state, and the observed value are input into a preset state update equation to obtain the corrected state of the outdoor device at the current moment. The state update equation is as follows:

[0094]

[0095] in, The corrected state of the outdoor device at the current time k; It is the predicted state estimate (prior estimate) at time k given the information at time k-1; K k is the Kalman gain, which is used to weigh the weight between the prior estimate and the new observation; z k is the observation value at the current time k; H k is the observation matrix at time k, which maps the state space to the observation space; is the observation prediction of the prior state estimate.

[0096] Furthermore, the error covariance update equation is as follows:

[0097] P k∣k =(IK k H k )P k∣k-1

[0098] Among them, P k∣k-1 is the prediction error covariance (prior error covariance) of the current moment k given the previous moment k-1 information; I is the unit matrix; K k H k is the product of the Kalman gain and the observation matrix, which represents the weight of the observation value when updating the state estimate.

[0099] The combination of these two update equations allows the Kalman filter to estimate the state of a dynamic system from a series of incomplete and noisy measurements in the presence of noise. By recursively applying the prediction and update steps, the Kalman filter can provide the best estimate of the system state.

[0100] Alternatively, the complete process of the Kalman filter algorithm includes two steps: prediction and update. Kalman filtering is a recursive algorithm used to estimate the state of a linear dynamic system. The following is a detailed explanation of this set of formulas:

[0101] 1. Prediction step (based on the system model and prior estimates, predict the current state and error covariance):

[0102] 1. Prediction residuals

[0103]

[0104] in, is the prediction residual, z k is the observation value at time k; H k is the observation matrix at time k; It is the predicted state estimate (prior estimate) at time k given the information at time k-1.

[0105] 2. Forecast Error Covariance

[0106]

[0107] Among them, S k is the prediction error covariance; P k∣k-1 is the prediction error covariance matrix; R k is the observation noise covariance matrix.

[0108] 3. Kalman Gain

[0109]

[0110] Among them, K k is the Kalman gain, which is used to weigh the weight between the prior estimate and the new observation; P k∣k-1 is the prediction error covariance matrix; S k is the prediction error covariance; H k is the observation matrix at time k.

[0111] 2. Update step (using new observation data to correct the predicted state estimate and error covariance to obtain a more accurate posterior estimate)

[0112] 1. Update state estimate

[0113]

[0114] in, The posterior state estimate for time k follows the information at time k; It is the predicted state estimate (prior estimate) at time k given the information at time k-1; K k is the Kalman gain; is the prediction residual.

[0115] 2. Update error covariance

[0116] P k∣k =(IK k H k )P k∣k-1

[0117] Among them, P k∣k is the posterior error covariance matrix; I is the identity matrix; H k is the observation matrix at time k; K k is the Kalman gain; P k∣k-1 is the prediction error covariance matrix.

[0118] In this way, by recursively executing the above prediction steps and update steps, the Kalman filter can estimate the state of the dynamic system from a series of incomplete and noisy measurements in the presence of noise, and achieve real-time recursive estimation.

[0119] Optionally, inputting the weight coefficients corresponding to the correction state, the first positioning data, the second positioning data, and the third positioning data into a preset positioning model to obtain the current position of the outdoor device can also be achieved in the following manner:

[0120] The particle filter (PF), also known as the Sequential Monte Carlo method, is a state estimation technique specifically designed for nonlinear, non-Gaussian dynamic systems. This method discretizes the state space and employs a random sampling strategy to estimate the system state. Unlike the extended Kalman filter (EKF), the particle filter does not require linearization of the system model, making it particularly effective in dealing with highly nonlinear problems, demonstrating its unique advantages.

[0121] First, a set of particles is generated to represent the probability distribution of the initial state. Random sampling can be performed based on prior knowledge of the initial state.

[0122] At each time step, a prediction is made for each particle using the system’s state transition model. The particle prediction update is typically based on the randomness of the control input and the state transition model.

[0123]

[0124] in, is the possible state at time k,

[0125] It is a state transition model and a conditional probability density function, which indicates the state at a given previous moment k-1. and control input u k In the case of k The probability of this formula is: the state at time k According to the conditional probability distribution To generate, where the condition is the state of the previous moment and control input u k .

[0126] In Kalman filtering and other recursive estimation algorithms, the above formula is used to describe the state transition model—how to predict the current state based on the previous state and control inputs. This state transition model is fundamental to dynamic system modeling, helping us understand and predict system behavior.

[0127] Then, the weight of each particle is updated according to the observed data. The weight reflects the degree of match between the particle and the observed data. The weight of the i-th particle at time t is obtained :

[0128]

[0129] in, is the observation model, that is, the conditional probability, which represents the state at a given time k When zk This weight reflects the ability of each particle to explain the current observation data, that is, the degree to which each particle matches the actual observation data.

[0130] In a particle filter, each particle represents a possible realization of the system's state, and the weight reflects the credibility of that realization. Calculating weights is a key step in particle filtering, as it determines which particles should be retained or replicated and which should be discarded or reduced. By iteratively updating these weights, the particle filter can approximate the system's posterior distribution, thereby achieving an estimate of the system's state.

[0131] To avoid the "particle degeneration" problem (where most particles have weights close to zero), particles need to be resampled. The goal of resampling is to select particles with high weights from the current particles, thereby concentrating the state distribution. The state of the system is estimated based on the particle weights and states.

[0132]

[0133] in, is the estimated value at time k; N is the total number of particles; is the weight of the i-th particle at time k; is the state of the i-th particle at time k.

[0134] Particle filtering has great advantages in processing highly nonlinear and non-Gaussian dynamic system signals, but its computational complexity will also increase with the increase of the number of particles.

[0135] Optionally, after inputting the weight coefficients corresponding to the correction state, the first positioning data, the second positioning data, and the third positioning data into a preset positioning model to obtain the current position of the outdoor device, the method further includes: determining whether the current position is valid; if so, establishing a communication connection with the control terminal in response to receiving the communication signal sent by the control terminal, or, when a communication connection has been established with the control terminal, maintaining a communication connection with the control terminal; if not, locking the outdoor device. Specifically, in this embodiment, after obtaining the current position of the outdoor device, it is necessary to determine the position of the outdoor device. If it is determined that the current position is valid, there are two situations. Situation 1: The user is using the control terminal to try to establish a communication connection with the outdoor device. At this time, the outdoor device will receive the communication signal sent by the control terminal and establish a communication connection with the control terminal. Situation 2: The outdoor device and the control terminal are already in a communication connection state. At this time, the outdoor device will maintain a communication connection with the control terminal.

[0136] Furthermore, if there is a large position deviation or the position signal is lost, the current position is determined to be invalid, and the outdoor device needs to be locked directly with one button and the outdoor device position is updated again until the outdoor device and the mobile terminal (mobile phone or other smart device) can be connected via Bluetooth or the distance is within the specified range. After that, the outdoor device can be unlocked via Bluetooth or other wireless communication methods.

[0137] Among them, the method of judging whether the current location is valid may include at least one of the following: (1) judging whether any one of the first positioning data, the second positioning data and the third positioning data obtained is consistent with common sense. For example, the outdoor device is only sold domestically, and any one of the three positioning data shows that the outdoor device is currently abroad. In this case, it is considered inconsistent with common sense, that is, the current location can be judged to be invalid; (2) whether any two of the first positioning data, the second positioning data and the third positioning data are equal to zero. For example, when the first positioning data and the third positioning data are both equal to zero, it proves that the outdoor device can neither receive GPS signals nor base station signals at this time, then it is considered that the outdoor device can be taken into areas such as deserts, oceans, caves, forests, etc., that is, the current location can be judged to be invalid; (3) obtaining the current location of the outdoor device multiple times in a row, and judging whether the fluctuation of the current location between two adjacent times is obvious. If so, it proves that the data does not converge, that is, the current location can be judged to be invalid.

[0138] Optionally, the vehicle locking function can be divided into two parts: software implementation and structural implementation. The software part of the one-touch vehicle locking is achieved by forcibly shutting down the entire machine, including the cutter motor, travel motor, lights, and all vehicle switches. The motor is set by software to fully open the lower bridge MOS transistor of the drive circuit and close the upper bridge MOS transistor, putting the motor in a clamped state, making it difficult to rotate it by external force, thus reaching the software implementation of the motor locking. Secondly, the switch detection, control panel, and light display of the outdoor equipment are all in standby mode, not receiving external hardware feedback, and always remain in the off state to avoid disrupting the vehicle's locking state through physical switches. Finally, the entire vehicle function returns to its initial state, with only the positioning-related devices and wireless communication components in operation. After the entire machine is locked with one button, if the external device needs to be restarted, it must be relocated, paired again via Bluetooth, and re-authenticated with the outdoor device through a mobile phone or other terminal device before the one-touch vehicle locking state can be released and reused. Structural locking is achieved by setting a locking device on the structure. A structural lock is set at the travel motor and the cutter motor. When the software receives a one-button vehicle lock command, it runs the drive motor of the structural lock. After the motor is locked by the software, it drives the structural lock motor to lock the travel motor tire and hinder the operation of the cutter until the whole machine lock command is released.

[0139] Optionally, upon receiving a one-touch lock command from the outdoor device, the device's cloud database can send a message to a designated mobile phone via the cloud, informing the user that the device is in the one-touch lock state. If the device remains in the one-touch lock state for an extended period, a phone call or text message can be sent to the local police station to alert the user, preventing financial losses or other unforeseen circumstances. This feature can also be used during outdoor device rentals. The device is first locked with a single click at the rental location, and temporary unlocking permissions are sent to the tenant. Upon receiving the device, the user can unlock the vehicle and use and unlock the device normally for a period of time. If the device is in an area with poor positioning signal, the tenant's phone can still wirelessly communicate with the device, providing real-time positioning information through integrated positioning, preventing the device from stalling in areas with poor GPS signal. Furthermore, if the user's phone is out of range of the device and the device experiences poor signal, the one-touch lock function can be restored, providing timely feedback on the device's last known location, preventing theft or accidental loss of the vehicle during rental. When the user has finished using the device or the usage time limit has been reached, the temporary permissions of the user's mobile phone can be remotely revoked, and the outdoor device can be remotely locked with one click through the cloud and the communication base station near the outdoor device.

[0140] The outdoor equipment positioning method proposed in this application can achieve precise positioning of outdoor equipment in the practical application scenarios described below.

[0141] Understandably, GPS works well outdoors, but is easily affected by tall buildings, weather, and other factors, making it nearly unusable indoors. Wi-Fi and base station positioning work well indoors, but may not be as accurate as GPS, especially in areas with sparse base station locations. In this case, these positioning methods can be combined. Depending on the environment, GPS, Wi-Fi, and base station positioning data can be used as observations. Kalman filtering is used to fuse the data, and by assigning different weighting parameters to the corresponding positioning data, accurate positioning can be achieved in complex environments.

[0142] Case 1: In the open air or in the wild without any shelter:

[0143] The GPS system on outdoor devices receives satellite signals (time, ephemeris, etc.), measures signal propagation delay, and uses at least three satellites for two-dimensional positioning, or four satellites to correct for time errors. This allows for accurate positioning within a hundred meters in scenarios with global coverage. However, Wi-Fi positioning may generate significant deviations or even fail to locate the device due to the lack of nearby Wi-Fi hotspots in the wild. Base station positioning accuracy depends on the number of base stations available. Based on the base station ID currently connected to the device, when an outdoor device is within the range of a single base station, the base station coverage area is used as the potential location of the device, or triangulation is performed in multiple scenarios. The accuracy is somewhere in between. In remote areas with sparse base stations, errors can reach several kilometers. In these cases, GPS positioning can be prioritized, with a higher weight given to its data. When base station positioning data and GPS data are close, the two data sets can be merged, giving less weight to the Wi-Fi positioning signal, resulting in more accurate positioning data for the outdoor device. In outdoor obstruction scenarios, the GPS signal will also become weak. At this time, the location information obtained according to the weight and analysis method in the unobstructed outdoor situation may have lower credibility, and the positioning data may fail multiple times or have large position jumps. In this case, you can directly lock the outdoor equipment with one button, and use the software to energize the lower tubes of the travel motor and cutter motor drive circuit. The software level can realize the locking of the wheels and cutter of the entire vehicle to avoid the outdoor equipment from being accidentally started in a different place.

[0144] Case 2: In an unobstructed urban environment:

[0145] In this scenario, GPS signals for outdoor devices are unaffected, maintaining high positioning accuracy. In scenarios with multiple Wi-Fi hotspots or familiar locations with a pre-existing location fingerprint database, triangulation or database-based positioning can achieve positioning accuracy within 5 meters. In urban settings, base station positioning combines the user's historical movement trajectory (base station handover sequence) with a Hidden Markov Model (HMM) or Viterbi algorithm to predict the current location. In densely populated base station locations, device location is calculated using the Time Difference of Arrival (TOA) or Time Difference of Arrival (TDOA) of multiple base station signals. The distance between the device and each base station is estimated based on a signal attenuation model, and trilateration (three-point positioning) is used to determine the position. Alternatively, the outdoor device records the signal propagation time difference and calculates the distance difference based on the speed of light, forming a hyperbolic intersection positioning method with accuracy within 100 meters. This is the ideal positioning scenario, where all positioning methods have very similar accuracy, covering most outdoor device operating scenarios in urban areas. The location signals collected by all three positioning methods are then filtered through a Kalman filter to directly update the device's position in real time, without applying weights to individual signals.

[0146] Case 3: In the case of urban indoor obstruction:

[0147] In outdoor locations, GPS signals are obstructed, resulting in the lowest positioning accuracy. Base station positioning accuracy is largely unaffected, as it depends solely on base station density. In urban areas, where base stations are densely populated, accuracy can reach within 100 meters. In indoor locations with numerous Wi-Fi hotspots, Wi-Fi positioning accuracy can approach or even exceed base station positioning. In locations with fewer Wi-Fi hotspots, positioning accuracy approaches that of GPS. In these situations, location information should be based on the value obtained from base station positioning, giving it a higher priority. Wi-Fi positioning takes second place, with GPS performing the worst.

[0148] Based on the same inventive concept as the above embodiments, an embodiment of the present invention provides an electronic device, such as Figure 3 As shown, the device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 3 The processor 310 shown in the figure is not used to indicate that the number of processors 310 is one, but is only used to indicate the positional relationship of the processor 310 relative to other devices. In actual applications, the number of processors 310 may be one or more; similarly, Figure 3 The memory 311 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 311 relative to other devices. In actual application, the number of memories 311 can be one or more. When the processor 310 runs the computer program, the method applied to the above device is implemented.

[0149] The device may also include: at least one network interface 312. The various components in the device are coupled together via a bus system 313. It is understood that the bus system 313 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 3 Various buses are labeled as bus system 313.

[0150] Memory 311 may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM); magnetic surface memory may include magnetic disk memory or magnetic tape memory. Volatile memory may include random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memory 311 described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memories.

[0151] The memory 311 in the embodiment of the present invention is used to store various types of data to support the operation of the device. Examples of such data include: any computer program used to operate on the device, such as an operating system and an application; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program can include various applications, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. Here, the program that implements the method of the embodiment of the present invention can be included in the application program.

[0152] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium. The computer-readable storage medium may be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a read-only optical disc (CD-ROM) or other memory; or it may be various devices including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer-readable storage medium is executed by the processor, the above-mentioned method is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 The description of the illustrated embodiment will not be repeated here.

[0153] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion of elements other than the listed elements and may also include additional elements not specifically listed.

[0155] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for positioning outdoor equipment, characterized in that: The method comprises: obtaining two of the first positioning data, the second positioning data, and the third positioning data based on different references; performing a signal status analysis on the first positioning data and the second positioning data, and assigning corresponding weight coefficients to at least two of the first positioning data, the second positioning data, and the third positioning data according to a signal status analysis result; A current correction state of the outdoor device is obtained, and a current position of the outdoor device is obtained according to a corresponding weight coefficient of at least two positioning data among the correction state, the first positioning data, the second positioning data, and the third positioning data.

2. The method according to claim 1, characterized in that Before performing signal status analysis on the first positioning data and the second positioning data, the method further includes: The first positioning data, the second positioning data, and the third positioning data are converted into metric values in a unified coordinate system, and noise filtering is performed.

3. The method according to claim 1, characterized in that The performing signal status analysis on the first positioning data and the second positioning data includes: Obtaining a first initial weight coefficient of the first positioning data and a second initial weight coefficient of the second positioning data; determining signal stability of the first positioning data and the second positioning data; When the signal stability of the first positioning data is less than a preset first stability threshold, reducing the first initial weight coefficient to obtain a first revised weight coefficient; When the signal stability of the second positioning data is greater than a preset second stability threshold, the second initial weight coefficient is increased to obtain a second revised weight coefficient, wherein the second stability threshold is greater than the first stability threshold.

4. The method according to claim 3, characterized in that The allocating weight coefficients corresponding to the first positioning data, the second positioning data, and the third positioning data according to the signal state analysis result includes: Determine the first modified weight coefficient and the second modified weight coefficient as weight coefficients of the first positioning data and the second positioning data respectively; The first revised weight coefficient, the second revised weight coefficient, and the preset third initial weight coefficient of the third positioning data are normalized to obtain weight coefficients corresponding to the first positioning data, the second positioning data, and the third positioning data, respectively.

5. The method according to claim 1, wherein The obtaining of a current correction state of the outdoor device, and obtaining a current position of the outdoor device according to corresponding weight coefficients of at least two of the correction state, the first positioning data, the second positioning data, and the third positioning data, includes: Initializing state parameters of the outdoor equipment; Obtaining a predicted state of the outdoor device at a current moment according to a preset motion model; Obtaining a corrected state of the outdoor device at a current moment according to the predicted state, at least one of the first positioning data, the second positioning data, and the third positioning data; The weight coefficients corresponding to the correction state, the first positioning data, the second positioning data, and the third positioning data are input into a preset positioning model to obtain the current position of the outdoor device.

6. The method according to claim 5, characterized in that The obtaining of the predicted state of the outdoor device at the current moment according to the preset motion model includes: Obtaining a control input for changing the state of the outdoor device at a current moment and a historical state of the outdoor device at a previous moment; The control input and the historical state are input into the motion model to obtain the predicted state of the outdoor equipment at the current moment.

7. The method according to claim 5, characterized in that The obtaining of a corrected state of the outdoor device at a current moment according to the predicted state, the first positioning data, the second positioning data, and the third positioning data includes: Obtaining a Kalman gain of the outdoor device at a current moment according to the predicted state, the first positioning data, the second positioning data, and the third positioning data; Obtaining a priori estimated state of the outdoor device at the current moment given a previous moment and an observed value of the outdoor device at the current moment; The Kalman gain, the prior estimated state, and the observed value are input into a preset state update equation to obtain the corrected state of the outdoor device at the current moment.

8. The method according to claim 5, characterized in that After inputting the weight coefficients corresponding to the correction state, the first positioning data, the second positioning data, and the third positioning data into a preset positioning model to obtain the current position of the outdoor device, the method further includes: Determining whether the current location is valid; If so, in response to receiving a communication signal sent by the control terminal, establishing a communication connection with the control terminal, and / or, when the communication connection with the control terminal has been established, maintaining the communication connection with the control terminal; If not, the outdoor device is locked.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the outdoor equipment positioning method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that The readable storage medium stores a computer program, and when the processor executes the computer program, the steps of the outdoor equipment positioning method according to any one of claims 1 to 8 are implemented.

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