A comprehensive positioning method and system for high-precision tilt monitoring of a power transmission tower

Abstract

The application discloses a kind of comprehensive positioning method and system for transmission tower high-precision tilt monitoring, it is related to transmission tower tilt monitoring technical field, including the fusion model of IMU and UWB is constructed, initialization system sampling rate;IMU and UWB data are fused using EKF algorithm, switch to IMU dominant mode when UWB signal quality drops, update positioning state;Real-time monitoring transmission tower tilt state, short-time motion model is constructed based on acceleration and differential information, and generates tilt monitoring report.The method disclosed in the application solves the problems of discontinuous positioning, insufficient accuracy and high algorithm complexity in traditional transmission tower tilt monitoring system by innovative IMU and UWB data fusion model, dynamic switching algorithm and lightweight real-time monitoring mechanism;The overall effect is to realize high-precision, continuous and efficient transmission tower tilt monitoring, and provide a good adaptive technical solution for high-precision positioning and state monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transmission tower tilt monitoring, in particular to a comprehensive positioning method and system for high-precision tilt monitoring of a transmission tower. BACKGROUND

[0002] Transmission towers are an important part of power transmission systems, and their stability and safety are directly related to the normal operation of the power grid and the service life of the equipment. In recent years, with the rapid development of intelligent power systems and Internet of Things technology, high-precision tilt monitoring has gradually become an important direction for transmission tower structure health monitoring. In existing technologies, inertial measurement units (IMU) are widely used in tilt monitoring due to their high sensitivity and dynamic response characteristics. At the same time, ultra-wideband (UWB) sensors have gradually become one of the key technologies for indoor and outdoor positioning due to their robustness in complex environments with centimeter-level positioning accuracy. For the monitoring needs of transmission towers, which are static targets, researchers have attempted to combine IMU and UWB technologies in order to achieve higher-precision tilt monitoring. However, how to integrate the data of the two sensors to overcome their respective technical limitations has become a key and difficult point in current research.

[0003] Although IMU and UWB sensors have wide applications in high-precision positioning, existing technologies still face many deficiencies in the context of high-precision tilt monitoring of transmission towers. The inertial navigation method based on acceleration integration of IMU will accumulate significant errors over time, leading to rapid decline in positioning accuracy during long-term monitoring. While UWB sensors can provide high-precision positioning, their positioning performance is easily disturbed by environmental factors, especially in non-line-of-sight (NLOS) conditions, where signal quality rapidly declines, making it difficult to ensure stable monitoring results. In addition, existing fusion methods mostly use simple data weighting strategies or Kalman filtering, but lack optimized design for nonlinear complex systems, making it difficult to meet the high requirements of transmission tower tilt monitoring for continuity and stability. In particular, during the monitoring process, when UWB signal quality fluctuates, existing technologies cannot achieve seamless switching between the two sensor modes, leading to monitoring interruption or false alarm problems. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the existing transmission tower monitoring method has the problems of excessive IMU data accumulation error, UWB signal interference leading to unstable monitoring, inability to achieve seamless switching between the two modes, and insufficient monitoring accuracy in nonlinear complex systems, as well as how to achieve high-precision, continuous, and robust tilt state monitoring.

[0006] To solve the above technical problems, the application provides the following technical scheme: a comprehensive positioning method for high-precision tilt monitoring of a power transmission tower, comprising constructing an IMU and UWB fusion model, initializing a system sampling rate; fusing IMU and UWB data by using an EKF algorithm, switching to an IMU dominant mode when UWB signal quality decreases, and updating a positioning state; monitoring a tilt state of the power transmission tower in real time, constructing a short-time motion model based on acceleration and differential speed information, and generating a tilt monitoring report.

[0007] As a preferred scheme of the comprehensive positioning method for high-precision tilt monitoring of a power transmission tower, the constructing of the IMU and UWB fusion model comprises that an IMU sensor provides three-axis acceleration information a b and three-axis gyroscope data, inertia-based position information and speed information are calculated, UWB sensor calculates position information and speed information based on ultra-wideband technology through distance measurement between unknown tags and different known base stations, after the IMU sensor and the UWB sensor are processed by independent positioning calculation modules respectively, data is transmitted to a fusion positioning calculation module, the fusion positioning calculation module outputs comprehensive position information and speed information through fusion of output information of the IMU sensor and the UWB sensor, and tilt monitoring of the power transmission tower is performed.

[0008] As a preferred scheme of the comprehensive positioning method for high-precision tilt monitoring of a power transmission tower, the initializing of the system sampling rate comprises that a tag is installed on the tower, X(k) is defined as a state vector at time k, and contains position x(k), speed v(k) and acceleration a(k), and is expressed as:

[0009] X(k)=[x x (k)x y (k)v x (k)v y (k)a x (k)a y (k)] T

[0010] X(k) represents a state at time k, x x (k) is a position in the x-axis direction at time k, x y (k) is a position in the y-axis direction at time k, v x (k) is a speed in the x-axis direction at time k, v y (k) is a speed in the y-axis direction at time k, a x (k) is an acceleration in the x-axis direction at time k, and a y(k) is the acceleration in the y-axis direction at time k; when the sampling period is T, Tω(k) represents the acceleration process noise, (T 2 / 2)ω(k) represents the velocity process noise, (T 3 / 6)ω(k) represents the position process noise caused by the double integral of acceleration, and the state equation is represented as:

[0011]

[0012] wherein x x (k+1) is the position in the x-axis direction at time k+1, x y (k+1) is the position in the y-axis direction at time k+1, v x (k+1) is the velocity in the x-axis direction at time k+1, v y (k+1) is the velocity in the y-axis direction at time k+1, and the state equation in the matrix form is represented as:

[0013] X(k+1) = FX(k) + GW(k)

[0014] wherein X(k+1) is the state vector at time k+1, F is the state transition matrix, G is the noise input matrix, and W(k) is the noise vector at time k; Z(k) represents the observation vector, including the true distance d i (k) at time k and the observation noise v i (k), and the observation equation is represented as:

[0015]

[0016] wherein H(k) represents the observation matrix, V(k) = [v1(k) v2(k)...v n (k)] T represents the observation noise vector with zero mean and covariance matrix at time k

[0017] As a preferred scheme of the comprehensive positioning method for high-precision tilt monitoring of a power transmission tower, the fusion of IMU and UWB data includes calculating a detailed distance, represented as:

[0018]

[0019] wherein d n (k) is the actual distance at time k, (x1, y1) is the position coordinate of base station 1, and (x2, y2) is the position coordinate of base station 2; the distance calculation is linearized, the EKF algorithm is adopted, and at each time step, the Jacobian matrix is obtained through the first-order Taylor expansion, represented as:

[0020]

[0021] wherein H'(k) is the Jacobian matrix.

[0022] As a preferred scheme of the comprehensive positioning method for high-precision tilt monitoring of a power transmission tower, the updating of the positioning state comprises analyzing an accuracy model of the power transmission tower tilt monitoring, DOP represents the geometric distribution of the tags and the BSs, the DOP includes geometric DOP, position DOP, horizontal DOP, vertical DOP and time DOP, the smaller the HDOP is, the higher the positioning accuracy is under the same distance measurement accuracy, and the horizontal DOP is expressed as:

[0023]

[0024] wherein σ d is the standard deviation of the distance measurement error, and are the variances of the x-axis and the y-axis respectively, and the derivative at the approximate position (x', y') is expressed as:

[0025]

[0026] wherein d1-d'1 is the error between the actual distance and the estimated distance, the partial derivative of the distance with respect to the y-axis direction position is expressed as:

[0027]

[0028] When n≥3, the position error covariance matrix is expressed as:

[0029] C=(B T B) -1 B T D

[0030] wherein B is the partial derivative matrix composed of the partial derivatives of the distance with respect to the position, and D is the matrix of the error between the actual distance and the estimated distance, which is expressed as:

[0031]

[0032] The covariance matrix of the distance error R is the same as the covariance matrix in the EKF algorithm, and the positioning error covariance matrix Q c caused by the distance error is expressed as:

[0033]

[0034] wherein E represents the symmetric matrix of the influence of the distance error on the positioning error, is the equivalent ranging error variance of the user, and the symmetric matrix is expressed as:

[0035]

[0036] obtained E 11 , E 12 , E 21 , E 22 is each element of the matrix E, representing the error contribution in different directions.

[0037] As a preferred scheme of the comprehensive positioning method for high-precision tilt monitoring of power transmission towers, the short-time motion model is constructed by measuring the distance, denoted as:

[0038] d = [d1d2...d n ]

[0039] The LS position is calculated, denoted as:

[0040] ε(k) = (A T A) -1 A T b

[0041] Wherein, A is the system matrix, used to describe the relationship between observation and position, b is the observation value vector, containing the observed measurement data, the differential speed v(k) is calculated, denoted as:

[0042]

[0043] Wherein, t is the time interval.

[0044] As a preferred scheme of the comprehensive positioning method for high-precision tilt monitoring of power transmission towers, the short-time motion model is constructed by analyzing the conversion acceleration coordinate system, denoted as:

[0045]

[0046] The position state is updated, denoted as:

[0047]

[0048] Wherein, ε(k+1) is the position vector at time k+1.

[0049] Another object of the present application is to provide a comprehensive positioning system for high-precision tilt monitoring of power transmission towers, which can fuse IMU and UWB data by using EKF algorithm, switch to IMU dominant mode when UWB signal quality decreases, update the positioning state, and solve the problem of low reliability in the current power transmission tower tilt monitoring technology.

[0050] As a preferred scheme of the comprehensive positioning system for high-precision tilt monitoring of a power transmission tower, the system comprises a model construction module, a state updating module and a tilt monitoring module; the model construction module is configured to construct an IMU and UWB fusion model and initialize a system sampling rate; the state updating module is configured to fuse IMU and UWB data by using an EKF algorithm, switch to an IMU dominant mode when UWB signal quality decreases, and update a positioning state; and the tilt monitoring module is configured to monitor a tilt state of the power transmission tower in real time, construct a short-time motion model based on acceleration and differential speed information, and generate a tilt monitoring report.

[0051] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement steps of the comprehensive positioning method for high-precision tilt monitoring of a power transmission tower.

[0052] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement steps of the comprehensive positioning method for high-precision tilt monitoring of a power transmission tower.

[0053] The comprehensive positioning method for high-precision tilt monitoring of a power transmission tower provided by the application solves the problems of discontinuous positioning, insufficient precision and high algorithm complexity in a traditional power transmission tower tilt monitoring system by using an innovative IMU and UWB data fusion model, a dynamic switching algorithm and a lightweight real-time monitoring mechanism, and realizes high-precision, continuous and efficient power transmission tower tilt monitoring, thereby effectively ensuring the operation safety of a power system and providing a good adaptive technical solution for high-precision positioning and state monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort.

[0055] Figure 1 A comprehensive positioning method for high-precision tilt monitoring of a power transmission tower is provided for the first embodiment of the application.

[0056] Figure 2 An IMU and UWB integrated system architecture diagram of a comprehensive positioning method for high-precision tilt monitoring of a power transmission tower is provided for the second embodiment of the application.

[0057] Figure 3A whole flow chart of a comprehensive positioning system for high-precision tilt monitoring of a power transmission tower is provided for a third embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the above objectives, features and advantages of the present application more apparent, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should belong to the protection scope of the present application.

[0059] Embodiment 1, with reference to Figure 1 For an embodiment of the present application, a comprehensive positioning method for high-precision tilt monitoring of a power transmission tower is provided, comprising:

[0060] S1: constructing an IMU and UWB fusion model, and initializing a system sampling rate.

[0061] Further, the construction of the IMU and UWB fusion model includes that the IMU sensor provides three-axis acceleration information a b and three-axis gyroscope data, calculates inertial-based position information and velocity information, and the UWB sensor calculates ultra-wideband technology-based position information and velocity information through distance measurement between unknown tags and different known base stations. After the IMU sensor and the UWB sensor are processed by independent positioning calculation modules respectively, the data is transmitted to a fusion positioning calculation module. The fusion positioning calculation module outputs comprehensive position information and velocity information by fusing the output information of the IMU sensor and the UWB sensor, and performs tilt monitoring of the power transmission tower.

[0062] It should be noted that initializing the system sampling rate includes that the tag is installed on the tower, and X(k) is defined as a state vector at time k, containing position x(k), velocity v(k) and acceleration a(k), and is expressed as:

[0063] X(k) = [x x (k)x y (k)v x (k)v y (k)a x (k)a y (k)] T

[0064] Wherein, X(k) represents the state at time k, x x (k) is the position in the x-axis direction at time k, x y (k) is the position in the y-axis direction at time k, v x (k) is the velocity in the x-axis direction at time k, vy (k) is the velocity in the y-axis direction at time k, a x (k) is the acceleration in the x-axis direction at time k, a y (k) is the acceleration in the y-axis direction at time k; when the sampling period is T, Tω(k) represents the acceleration process noise, (T 2 / 2)ω(k) represents the velocity process noise, (T 3 / 6)ω(k) represents the position process noise caused by the double integral of acceleration, and the state equation is represented as:

[0065]

[0066] wherein x x (k+1) is the position in the x-axis direction at time k+1, x y (k+1) is the position in the y-axis direction at time k+1, v x (k+1) is the velocity in the x-axis direction at time k+1, v y (k+1) is the velocity in the y-axis direction at time k+1, and the state equation in the matrix form is represented as:

[0067] X(k+1) = FX(k) + GW(k)

[0068] wherein X(k+1) is the state vector at time k+1, F is the state transition matrix, G is the noise input matrix, and W(k) is the noise vector at time k; Z(k) represents the observation vector, which contains the true distance d i (k) at time k and the observation noise v i (k), and the observation equation is represented as:

[0069]

[0070] wherein H(k) represents the observation matrix, V(k) = [v1(k) v2(k)...v n (k)] T represents the observation noise vector with zero mean and covariance matrix at time k

[0071] It should also be noted that the synchronization of the two sensors in the time and data dimensions is ensured by initializing the sampling rate (100 Hz for the IMU and 50 Hz for the UWB) and defining the state vector; the purpose is to solve the problem of large differences in the characteristics of the two sensor data sources and complex data fusion, and to ensure that the system can provide time-continuous and clear-structured input when collecting data; the final beneficial effect is that a unified sensor data expression framework is established, which enables the subsequent fusion algorithm to operate efficiently and accurately, and lays a reliable foundation for high-precision tilt monitoring.

[0072] S2: fusing IMU and UWB data by EKF algorithm, switching to IMU dominant mode when UWB signal quality is degraded, updating positioning state.

[0073] Further, fusing IMU and UWB data includes calculating detailed distance, denoted as:

[0074]

[0075] where d n (k) is actual distance at time k, (x1, y1) is position coordinate of base station 1, (x2, y2) is position coordinate of base station 2, distance calculation is linearized, EKF algorithm is adopted, at each time step, Jacobian matrix is obtained through first-order Taylor expansion, denoted as:

[0076]

[0077] where H'(k) is Jacobian matrix.

[0078] It should be noted that updating positioning state includes analyzing precision model of transmission tower tilt monitoring, DOP represents geometric distribution of tag and BSs, DOP includes geometric DOP, position DOP, horizontal DOP, vertical DOP and time DOP, under the same distance measurement precision, the smaller the HDOP, the higher the positioning precision, horizontal DOP is denoted as:

[0079]

[0080] where σ d is standard deviation of distance measurement error, and are variances of x-axis and y-axis respectively, derivative at approximate position (x', y') is denoted as:

[0081]

[0082] where d1-d'1 is error between actual distance and estimated distance, partial derivative of distance to y-axis direction position is denoted as:

[0083]

[0084] When n≥3, position error covariance matrix is denoted as:

[0085] C=(B T B) -1 B T D

[0086] where B is the partial derivative matrix composed of the partial derivatives of the distance with respect to the position, D is the matrix of the actual distance and the error of the estimated distance, and is expressed as:

[0087]

[0088] The covariance matrix of the distance error R is the same as that in the EKF algorithm, and the covariance matrix Q of the positioning error caused by the distance error is expressed as: c

[0089]

[0090] where E represents a symmetric matrix of the influence of the distance error on the positioning error, is the equivalent ranging error variance of the user, and the symmetric matrix is expressed as:

[0091]

[0092] It is obtained that E 11 , E 12 , E 21 , E 22 is each element of the matrix E, representing the error contribution in different directions.

[0093] It should also be noted that the extended Kalman filter (EKF) algorithm is used to realize dynamic data fusion between the two sensors, ensuring that stable tilt monitoring data can be provided in multiple scenarios; when the UWB signal is blocked or interfered by the environment, causing a decrease in accuracy, the system can switch to the IMU dominant mode in time to avoid interruption of the monitoring data; the effect is to solve the problem of UWB signal vulnerability and IMU long-term drift, making the advantages of the two sensors complementary; ultimately, the beneficial effect is that the system can provide continuous and stable positioning and tilt monitoring results under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, enhancing the robustness and environmental adaptability of the system.

[0094] S3: Real-time monitoring of the tilt state of the power transmission tower, constructing a short-time motion model based on acceleration and differential information, and generating a tilt monitoring report.

[0095] Further, constructing a short-time motion model includes measuring the distance, which is expressed as:

[0096] d = [d1d2...d n ]

[0097] Calculate the LS position ε(k), which is expressed as:

[0098] ε(k) = (A T A) -1 A T b ​

[0099] wherein A is a system matrix for describing the relationship between observation and position, b is an observation value vector containing measured data, and the differential velocity v(k) is calculated and expressed as:

[0100]

[0101] wherein t is a time interval.

[0102] It should be noted that constructing the short-time motion model also includes analyzing the conversion of the acceleration coordinate system, which is expressed as:

[0103]

[0104] The position state is updated, which is expressed as:

[0105]

[0106] wherein ε(k+1) is the position vector at time k+1.

[0107] It should also be noted that the system introduces an efficient algorithm for real-time calculation, combines the acceleration data of the IMU and the differential velocity information of the UWB, constructs a motion model (AUAM) in a short time, and generates a tilt monitoring report; this process effectively solves the problem of high complexity and poor real-time performance of long-time motion models, providing a lightweight and embedded device running calculation scheme; the effect is to ensure that the system can still provide fast and accurate tilt state data even in the case of limited computing resources; the final beneficial effect is to improve the real-time performance and accuracy of the system in monitoring tilt trends and generating high-precision reports, while extending the battery life of the device, which is suitable for long-term deployment of power transmission towers.

[0108] Embodiment 2, with reference to Figure 2 As an embodiment of the present application, a comprehensive positioning method for high-precision tilt monitoring of power transmission towers is provided, and in order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiments are used for scientific demonstration.

[0109] Firstly, the experiment designed four groups of experimental conditions: experimental condition 1 tests the drift error and response performance when using an inertial measurement unit (IMU) alone; experimental condition 2 tests the signal accuracy when the ultra-wideband (UWB) sensor works independently; experimental condition 3 verifies the tilt correction effect when the IMU and UWB are combined but not fused by extended Kalman filter (EKF); and experimental condition 4 tests the performance when the IMU, UWB and EKF fusion algorithm are combined; Figure 2A positioning system architecture integrating IMU (Inertial Measurement Unit) and UWB (Ultra-Wideband) sensors is demonstrated. In the figure, the IMU sensor provides three-axis acceleration information and three-axis gyroscope data for calculating inertial-based position information and velocity information. The UWB sensor calculates ultra-wideband technology-based position information and velocity information through distance measurement between an unknown tag and multiple known base stations. After being processed by independent positioning calculation modules, the data of the two sensors are transmitted to the fusion positioning calculation module, which further improves the overall positioning accuracy and system stability by fusing the output information (position and velocity) of the two sensors. Finally, the system outputs comprehensive position information and velocity information for high-precision tilt monitoring of the transmission tower. This architecture realizes the complementary and collaborative work of IMU and UWB in complex environments, improving the adaptability and reliability of the system. In the experimental preparation stage, IMU and UWB sensors are installed on the top of the transmission tower, and the synchronous transmission of sampling data is realized through the communication module. The IMU sampling frequency is set to 100 Hz, and the UWB sampling frequency is set to 50 Hz. The dynamic fusion of the data of the two sensors is realized by using the invented EKF algorithm, and the tilt angle and environmental impact factors are monitored in real time. During the experimental implementation process, an initial tilt angle of 5.0° is applied to simulate the actual scene. The corrected tilt angle, IMU drift error, UWB signal accuracy, and EKF fusion error are measured and recorded respectively. At the same time, the response time and power consumption of the system are counted to evaluate its actual performance in the embedded environment. Refer to Table 1 for recording and analyzing the experimental data.

[0110] Table 1 Experimental data recording table

[0111]

[0112]

[0113] The experimental results show that when the complete invention scheme (experimental condition 4) is adopted, the system has obvious advantages in tilt angle correction, error suppression and response performance; specifically, in terms of tilt correction capability, the IMU alone (experimental condition 1) cannot correct the initial deviation, and the corrected tilt angle is still 5.0°; the UWB alone (experimental condition 2) corrects the tilt to 3.0°, but is limited by signal accuracy; and after the IMU and UWB are combined and the EKF is used (experimental condition 4), the corrected tilt angle reaches 0.5°, which is significantly better than other conditions, fully verifying the correction accuracy of the EKF algorithm; in terms of error suppression, the drift error of the IMU is reduced from 2.5° / hour when used alone to 0.5° / hour, indicating that the EKF algorithm effectively reduces the cumulative error of inertial navigation; the UWB signal accuracy is improved from 10.0 cm to 2.0 cm, and the error is further reduced to 1.0 cm through EKF fusion; in terms of response time and power consumption, the response time of the complete scheme is 20 ms, which is at least 30% faster than other conditions, proving that the optimization algorithm can meet the real-time computing requirements in the embedded environment; at the same time, the power consumption is only 2.0 W, which is suitable for long-term deployment of power transmission towers; in summary, through the deep fusion of IMU and UWB and the introduction of the EKF algorithm, the present application greatly improves the accuracy, reliability and adaptability of the tilt monitoring of the power transmission tower, solves the problems of drift accumulation, unstable signal and high power consumption in the prior art, and fully demonstrates the innovation and practical value.

[0114] In Example 3, with reference to Figure 3 For an embodiment of the present application, a comprehensive positioning system for high-precision tilt monitoring of a power transmission tower is provided, which includes a model construction module, a state update module, and a tilt monitoring module.

[0115] The model construction module is used to construct an IMU and UWB fusion model and initialize the system sampling rate; the state update module is used to fuse IMU and UWB data using the EKF algorithm, switch to the IMU dominant mode when the UWB signal quality decreases, and update the positioning state; and the tilt monitoring module is used to monitor the tilt state of the power transmission tower in real time, construct a short-time motion model based on acceleration and differential speed information, and generate a tilt monitoring report.

[0116] If the functions are implemented in software, the functions can be stored in or implemented as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium can be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, or twisted pair, then the coaxial cable, fiber optic cable, or twisted pair are included in the definition of medium. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), and Blu-Ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0117] In other words, like a human driver of a vehicle, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on. In some embodiments, an autonomous vehicle can be programmed to follow traffic laws and to make decisions based on its environment using a machine learning algorithm. For example, an autonomous vehicle can be programmed to follow a speed limit, to stop at a stop sign, to yield to a pedestrian, to merge onto a highway, to change lanes, to park, and so on using a machine learning algorithm.

[0118] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0119] It should be understood that portions of the present application can be implemented with hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be understood that the foregoing embodiments are merely illustrative of the present application and are not to be used to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, it will be apparent to those skilled in the art that various changes and modifications can be contributed to the present application without departing from the spirit and scope of the present application, and such changes and modifications should be encompassed within the scope of the appended claims.

[0120] It should be understood that the foregoing embodiments are merely illustrative of the present application and are not to be used to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, it will be apparent to those skilled in the art that various changes and modifications can be contributed to the present application without departing from the spirit and scope of the present application, and such changes and modifications should be encompassed within the scope of the appended claims.

Claims

1. A comprehensive positioning method for high-precision tilt monitoring of transmission towers, characterized in that, include: Construct an IMU-UWB fusion model and initialize the system sampling rate; The EKF algorithm is used to fuse IMU and UWB data. When the UWB signal quality deteriorates, the system switches to IMU-dominated mode to update the positioning status. Real-time monitoring of the tilt status of transmission towers; construction of short-time motion models based on acceleration and differential speed information; and generation of tilt monitoring reports. The construction of the IMU and UWB fusion model includes triaxial acceleration information provided by the IMU sensor. b Using data from a three-axis gyroscope, inertial-based position and velocity information are calculated. The UWB sensor calculates position and velocity information based on ultra-wideband technology by measuring the distance between an unknown tag and known base stations. After the IMU sensor and UWB sensor are processed by independent positioning calculation modules, the data is transmitted to the fusion positioning calculation module. The fusion positioning calculation module outputs comprehensive position and velocity information by fusing the output information from the IMU sensor and UWB sensor to monitor the tilt of the transmission tower. The initialization system sampling rate includes tags installed on the tower. X(k) is defined as the state vector at time k, containing position x(k), velocity v(k), and acceleration a(k), expressed as: X(k)=[x x (k)x y (k)v x (k)v y (k)a x (k)a y (k)] T Where X(k) represents the state at time k, x x (k) represents the position of x-axis at time k, where x y (k) represents the position of v in the y-axis direction at time k. x (k) represents the velocity v in the x-axis direction at time k. y (k) represents the velocity in the y-axis direction at time k, a x (k) represents the acceleration in the x-axis direction at time k, a y (k) represents the acceleration in the y-axis direction at time k; When the sampling period is T, according to the uniform acceleration motion equation at time k+1, Tω(k) represents the noise of the acceleration process, (T 2 / 2)ω(k) represents the velocity process noise, (T 3 / 6)ω(k) represents the position process noise caused by the double integral of acceleration, and the state equation is expressed as: Where, x x (k+1) represents the position of time k+1 in the x-axis direction, x y (k+1) represents the position of time k+1 in the y-axis direction, v x The velocity v of time k+1 in the x-axis direction y The velocity of k+1 in the y-axis direction at time (k+1) can be expressed in matrix form as follows: X(k+1) = FX(k) + GW(k) Where X(k+1) is the state vector at time k+1, F is the state transition matrix, G is the noise input matrix, and W(k) is the noise vector at time k. Let Z(k) denote the observation vector, which contains the true distance d at time k. i (k) and observation noise v i (k), the observation equation is expressed as: Where H(k) represents the observation matrix, and V(k) = [v1(k)v2(k)...v n (k)] T This represents the observation noise vector with zero mean and covariance matrix at time k.

2. The integrated positioning method for high-precision tilt monitoring of transmission towers as described in claim 1, characterized in that: The fusion of IMU and UWB data includes calculating detailed distances, expressed as follows: Where, d n (k) represents the actual distance at time k, (x1, y1) represents the location coordinates of base station 1, and (x2, y2) represents the location coordinates of base station 2. The distance calculation is linearized using the EKF algorithm. At each time step, the Jacobian matrix is ​​obtained through a first-order Taylor expansion, expressed as: Where H′(k) is the Jacobian matrix.

3. The integrated positioning method for high-precision tilt monitoring of transmission towers as described in claim 2, characterized in that: The updated positioning status includes analyzing the accuracy model of transmission tower tilt monitoring. DOP represents the geometric distribution of tags and BSs. DOP includes geometric DOP, positional DOP, horizontal DOP, vertical DOP, and time DOP. Under the same distance measurement accuracy, the smaller the HDOP, the higher the positioning accuracy. Horizontal DOP is represented as: Where σ d This represents the standard deviation of the distance measurement error. and Let be the variances of the x-axis and y-axis, respectively. The derivative at the approximate position (x′, y′) is expressed as: Where d1-d1 is the error between the actual distance and the estimated distance, and the partial derivative of the distance with respect to the position along the y-axis is expressed as: When n≥3, the position error covariance matrix is ​​expressed as: C=(B T B) -1 B T D Where B is the partial derivative matrix, composed of the partial derivatives of distance with respect to position, and D is the matrix representing the error between the actual distance and the estimated distance, expressed as: The covariance matrix of the distance error R is the same as that in the EKF algorithm, and the covariance matrix of the positioning error caused by the distance error is Q. c Represented as: Where E represents the symmetric matrix representing the influence of distance error on positioning error. The variance of the user's equivalent ranging error is represented by a symmetric matrix as follows: get E 11 E 12 E 21 E 22 Let E be the elements of matrix E, representing the error contribution in different directions.

4. The integrated positioning method for high-precision tilt monitoring of transmission towers as described in claim 3, characterized in that: The construction of the short-time motion model includes measuring distance, expressed as: d=[d1 d2...d n ] The LS position ε(k) is calculated as follows: ε(k)=(A T A) -1 A T b Where A is the system matrix, used to describe the relationship between observation and location, b is the observation vector containing the observed measurement data, and the differential velocity v(k) is calculated as follows: Where t is the time interval.

5. The integrated positioning method for high-precision tilt monitoring of transmission towers as described in claim 4, characterized in that: The construction of the short-time motion model also includes analyzing and transforming the acceleration coordinate system, expressed as: Update the location status, represented as: Where ε(k+1) is the position vector at time k+1.

6. A system employing the integrated positioning method for high-precision tilt monitoring of transmission towers as described in any one of claims 1 to 5, characterized in that: Includes a model building module, a state update module, and a tilt monitoring module; The model building module is used to build an IMU and UWB fusion model and initialize the system sampling rate; The status update module is used to fuse IMU and UWB data using the EKF algorithm, and switch to IMU-dominated mode to update the positioning status when the UWB signal quality deteriorates. The tilt monitoring module is used to monitor the tilt status of transmission towers in real time, construct a short-time motion model based on acceleration and differential speed information, and generate a tilt monitoring report.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the integrated positioning method for high-precision tilt monitoring of transmission towers as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the integrated positioning method for high-precision tilt monitoring of transmission towers as described in any one of claims 1 to 5.

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