Comprehensive positioning method and system for high-precision inclination monitoring of transmission tower

Abstract

The invention discloses a comprehensive positioning method and system for high-precision inclination monitoring of a power transmission tower, and relates to the technical field of inclination monitoring of the power transmission tower, and the method comprises the steps: constructing an IMU and UWB fusion model, and initializing a system sampling rate; fusing IMU and UWB data by using an EKF algorithm, switching to an IMU dominant mode when the UWB signal quality is reduced, and updating a positioning state; and monitoring the inclination state of the transmission tower in real time, constructing a short-time motion model based on acceleration and differential information, and generating an inclination monitoring report. According to the method, through an innovative IMU and UWB data fusion model, a dynamic switching algorithm and a lightweight real-time monitoring mechanism, the problems of discontinuous positioning, insufficient precision and high algorithm complexity in a traditional transmission tower inclination monitoring system are solved; the overall effect is that high-precision, continuous and high-efficiency inclination monitoring of the transmission tower is realized, and a technical scheme with good adaptability is provided for high-precision positioning and state monitoring.

Description

Technical Field

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

[0002] Transmission towers are an important part of the power transmission system. Their stability and safety are directly related to the normal operation of the power grid and the service life of equipment. In recent years, with the rapid development of intelligent power systems and Internet of Things technologies, high-precision tilt monitoring has gradually become an important direction for transmission tower structural health monitoring. In existing technologies, inertial measurement units (IMUs) are widely used in the field of 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 centimeter-level positioning accuracy and robustness in complex environments. In response to the monitoring needs of static targets such as transmission towers, researchers have attempted to combine IMU and UWB technologies to achieve more accurate tilt monitoring. However, how to fuse the data of the two sensors to overcome their respective technical limitations has become the focus and difficulty of current research.

[0003] Although IMU and UWB sensors are widely used in the field of high-precision positioning, existing technologies still face many shortcomings in the scenario of high-precision tilt monitoring of transmission towers. The IMU's inertial navigation method based on acceleration integration will accumulate significant errors over time, resulting in a rapid decrease in positioning accuracy in long-term monitoring. Although UWB sensors can provide high-precision positioning, their positioning performance is extremely susceptible to interference from environmental factors, especially in non-line-of-sight (NLOS) conditions, where signal quality drops sharply, making it difficult to ensure stable monitoring results. In addition, most existing fusion methods use simple data weighting strategies or Kalman filtering, but lack optimized design for nonlinear complex systems, making it difficult to meet the high requirements for continuity and stability in transmission tower tilt monitoring. In particular, during the monitoring process, when the UWB signal quality fluctuates, existing technologies cannot achieve seamless switching between the two sensor modes, resulting in monitoring interruptions or false alarms. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing transmission tower monitoring methods have problems such as excessively large IMU data cumulative errors, UWB signals being susceptible to interference resulting in unstable monitoring, inability to achieve seamless switching between the two modes, and insufficient monitoring accuracy under nonlinear complex systems, as well as the problem of how to achieve high-precision, continuous and robust tilt state monitoring.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a comprehensive positioning method for high-precision tilt monitoring of transmission towers, including constructing an IMU and UWB fusion model and initializing the system sampling rate; using the EKF algorithm to fuse IMU and UWB data, switching to the IMU-dominant mode when the UWB signal quality degrades, and updating the positioning status; real-time monitoring of the transmission tower tilt state, constructing a short-term motion model based on acceleration and differential information, and generating a tilt monitoring report.

[0007] As a preferred solution of the comprehensive positioning method for high-precision tilt monitoring of transmission towers described in the present invention, wherein: the construction of the IMU and UWB fusion model includes the IMU sensor providing three-axis acceleration information a b The data from the IMU and UWB sensors are processed by independent positioning calculation modules, and then the data is passed to the fusion positioning calculation module. The fusion positioning calculation module outputs comprehensive position and speed information by fusing the output information of the IMU and UWB sensors to monitor the tilt of the transmission tower.

[0008] As a preferred solution of the integrated positioning method for high-precision tilt monitoring of transmission towers described in the present invention, the initialization system sampling rate includes the tag being installed on the tower, defining X(k) as the state vector at time k, including position x(k), velocity ν(k) and acceleration a(k), expressed as:

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

[0010] Among them, 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, v y (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, according to the uniform acceleration motion equation at time k+1, Tω(k) represents the acceleration process noise, (T 2 / 2)ω(k) represents the speed 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:

[0011]

[0012] Among them, x x (k+1) is the position of k+1 in the x-axis direction, x y (k+1) is the position of k+1 in the y-axis direction, v x (k+1) The velocity of k+1 in the x-axis direction at time k+1, v y The velocity of k+1 in the y-axis direction at time (k+1) is expressed in matrix form as follows:

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

[0014] Where X(k+1) is the state vector at time k+1, F is the state transfer matrix, G is the noise input matrix, and W(k) is the noise vector at time k. Let Z(k) represent 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:

[0015]

[0016] Where 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 solution of the integrated positioning method for high-precision tilt monitoring of transmission towers according to the present invention, the fusion of IMU and UWB data includes calculating a detailed distance, which is expressed as:

[0018]

[0019] Among them, d i (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 using the EKF algorithm. At each time step, the Jacobian matrix is ​​obtained through the first-order Taylor expansion, which is expressed as:

[0020]

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

[0022] As a preferred solution of the integrated positioning method for high-precision tilt monitoring of transmission towers according to the present invention, the updating of the positioning state includes analyzing the accuracy model of the transmission tower tilt monitoring. The DOP represents the geometric distribution of tags and BSs. The DOP includes geometric DOP, position DOP, horizontal DOP, vertical DOP, and time DOP. Under the same distance measurement accuracy, the smaller the HDOP, the higher the positioning accuracy. The horizontal DOP is expressed as:

[0023]

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

[0025]

[0026] Among them, d1-d′1 is the error between the actual distance and the estimated distance, and the partial derivative of the distance with respect to the y-axis 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] Among them, B is the partial derivative matrix, which is composed of the partial derivatives of distance with respect to 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. The covariance matrix of the positioning error caused by the distance error is Q c Expressed as:

[0033]

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

[0035]

[0036] get E 11 、E 12 、E 21 、E 22 are the elements of the matrix E, representing the error contributions in different directions.

[0037] As a preferred solution of the comprehensive positioning method for high-precision tilt monitoring of transmission towers according to the present invention, the construction of the short-term motion model includes measuring the distance, which is expressed as:

[0038] d=[d1 d2 ... d n ]

[0039] Calculate the LS position ε(k), expressed as:

[0040] ε(k)=(A T A) -1 A T b

[0041] Where A is the system matrix, which is used to describe the relationship between observation and position, and b is the observation value vector, which contains the observed measurement data. The differential velocity v(k) is calculated as:

[0042]

[0043] Where t is the time interval.

[0044] As a preferred solution of the comprehensive positioning method for high-precision tilt monitoring of transmission towers described in the present invention, the construction of the short-term motion model also includes analyzing and converting the acceleration coordinate system, which is expressed as:

[0045]

[0046] Update the location status, expressed as:

[0047]

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

[0049] Another object of the present invention is to provide an integrated positioning system for high-precision tilt monitoring of transmission towers. The system can fuse IMU and UWB data using the EKF algorithm, switch to IMU-dominant mode when the UWB signal quality degrades, and update the positioning status, thereby solving the problem of low reliability of current transmission tower tilt monitoring technology.

[0050] As a preferred solution of the integrated positioning system for high-precision tilt monitoring of transmission towers described in the present invention, it includes: a model construction module, a state update module, and a tilt monitoring module; the model construction module is used to build an IMU and UWB fusion model and initialize the system sampling rate; the state update module is used to use the EKF algorithm to fuse IMU and UWB data, switch to IMU-dominant mode when the UWB signal quality degrades, and update the positioning status; the tilt monitoring module is used to monitor the tilt status of the transmission tower in real time, build a short-term motion model based on acceleration and differential information, and generate a tilt monitoring report.

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

[0052] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a comprehensive positioning method for high-precision tilt monitoring of transmission towers.

[0053] Beneficial effects of the present invention: The comprehensive positioning method for high-precision tilt monitoring of transmission towers provided by the present invention solves the problems of discontinuous positioning, insufficient accuracy and high algorithm complexity in traditional transmission tower tilt monitoring systems through an innovative IMU and UWB data fusion model, a dynamic switching algorithm and a lightweight real-time monitoring mechanism; the overall effect is to achieve high-precision, continuous and high-efficiency transmission tower tilt monitoring, effectively ensure the safe operation of the power system, and provide a well-adaptable technical solution for high-precision positioning and status monitoring. The present invention achieves better results in terms of reliability, efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 This is an overall flow chart of a comprehensive positioning method for high-precision tilt monitoring of transmission towers provided by the first embodiment of the present invention.

[0056] Figure 2 A system architecture diagram of an IMU and UWB integrated positioning method for high-precision tilt monitoring of transmission towers provided in a second embodiment of the present invention.

[0057] Figure 3This is an overall flow chart of a comprehensive positioning system for high-precision tilt monitoring of transmission towers provided by the third embodiment of the present invention. DETAILED DESCRIPTION

[0058] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0059] Example 1, with reference to Figure 1 , as one embodiment of the present invention, provides a comprehensive positioning method for high-precision tilt monitoring of transmission towers, comprising:

[0060] S1: Build the IMU and UWB fusion model and initialize the system sampling rate.

[0061] Furthermore, the IMU and UWB fusion model is constructed, including the IMU sensor providing three-axis acceleration information a b The data from the IMU and UWB sensors are processed by independent positioning calculation modules, and then the data is passed to the fusion positioning calculation module. The fusion positioning calculation module outputs comprehensive position and speed information by fusing the output information of the IMU and UWB sensors to monitor the tilt of the transmission tower.

[0062] It should be noted that the initialization system sampling rate includes the installation of the tag on the tower, defining X(k) as the state vector at time k, including position x(k), velocity v(k) and acceleration a(k), 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] Among them, 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, v y (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, according to the uniform acceleration motion equation at time k+1, Tω(k) represents the acceleration process noise, (T 2 / 2)ω(k) represents the speed 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:

[0065]

[0066] Among them, x x (k+1) is the position of k+1 in the x-axis direction, x y (k+1) is the position of k+1 in the y-axis direction, v x (k+1) The velocity of k+1 in the x-axis direction at time k+1, v y The velocity of k+1 in the y-axis direction at time (k+1) is expressed in matrix form as follows:

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

[0068] Where X(k+1) is the state vector at time k+1, F is the state transfer matrix, G is the noise input matrix, and W(k) is the noise vector at time k. Let Z(k) represent 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:

[0069]

[0070] Where H(k) represents the observation matrix, V(k)=[ν1(k) ν2(t) ... ν n (k)] T represents the observation noise vector with zero mean and covariance matrix at time k

[0071] It should also be noted that by initializing the sampling rate (100 Hz for IMU and 50 Hz for UWB) and defining the state vector, the synchronization of the two sensors in time and data dimensions is ensured; 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 clearly structured input when collecting data; the ultimate beneficial effect is that a unified sensor data expression framework is established, which enables the subsequent fusion algorithm to run efficiently and accurately, laying a solid foundation for high-precision tilt monitoring.

[0072] S2: Use the EKF algorithm to fuse IMU and UWB data, switch to IMU-dominated mode when the UWB signal quality degrades, and update the positioning status.

[0073] Furthermore, fusing IMU and UWB data involves calculating detailed distances, expressed as:

[0074]

[0075] Among them, d i (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 using the EKF algorithm. At each time step, the Jacobian matrix is ​​obtained through the first-order Taylor expansion, which is expressed as:

[0076]

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

[0078] It should be noted that updating the 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, position DOP, horizontal DOP, vertical DOP, and time DOP. Under the same distance measurement accuracy, the smaller the HDOP, the higher the positioning accuracy. The horizontal DOP is expressed as:

[0079]

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

[0081]

[0082] Among them, d1-d′1 is the error between the actual distance and the estimated distance, and the partial derivative of the distance with respect to the y-axis position is expressed as:

[0083]

[0084] When n≥3, the position error covariance matrix is ​​expressed as:

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

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

[0087]

[0088] The covariance matrix of the distance error R is the same as the covariance matrix in the EKF algorithm. The covariance matrix of the positioning error caused by the distance error is Q c Expressed as:

[0089]

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

[0091]

[0092] get E 11 、E 12 、E 21 、E 22 are the elements of the matrix E, representing the error contributions in different directions.

[0093] It should also be noted that the extended Kalman filter (EKF) algorithm is used to achieve dynamic data fusion between the two sensors, ensuring that stable tilt monitoring data can be provided in a variety of scenarios; when the UWB signal is blocked or interfered with by the environment, resulting in a decrease in accuracy, the system can switch to the IMU-dominated mode in time to avoid interruption of monitoring data; its role is to solve the problems of UWB signal fragility and long-term drift of IMU, so that the advantages of the two sensors complement each other; the ultimate beneficial effect is that the system can provide continuous and stable positioning and tilt monitoring results regardless of line-of-sight (LOS) or non-line-of-sight (NLOS) conditions, thereby enhancing the system's robustness and environmental adaptability.

[0094] S3: Monitor the tilt status of transmission towers in real time, build a short-term motion model based on acceleration and differential information, and generate a tilt monitoring report.

[0095] Furthermore, constructing a short-term motion model involves measuring distance, which can be expressed as:

[0096] d=[d1 d2 ... d n ]

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

[0098] ε(k)=(A T A) -1 A T b

[0099] Where A is the system matrix, which is used to describe the relationship between observation and position, and b is the observation value vector, which contains the observed measurement data. The differential velocity v(k) is calculated as:

[0100]

[0101] Where t is the time interval.

[0102] It should be noted that constructing a short-term motion model also includes analyzing and transforming the acceleration coordinate system, which can be expressed as:

[0103]

[0104] Update the location status, 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, combining the acceleration data of the IMU and the differential information of the UWB to construct a short-term motion model (AUAM) and generate a tilt monitoring report. This process effectively solves the problems of high complexity and poor real-time performance of long-term motion models, and provides a lightweight computing solution suitable for operation on embedded devices. Its role is to ensure that the system can still provide fast and accurate tilt status data even when computing resources are limited. The ultimate beneficial effect is that it improves the real-time and accuracy of the system in monitoring tilt trends and generating high-precision reports, while extending the battery life of the equipment, making it suitable for long-term deployment on transmission towers.

[0108] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides a comprehensive positioning method for high-precision tilt monitoring of transmission towers. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0109] First, this experiment designed four sets of experimental conditions: Experimental condition 1 tests the drift error and response performance when using the 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 without extended Kalman filter (EKF) fusion; Experimental condition 4 tests the performance when the IMU and UWB are fully combined and the EKF fusion algorithm is used; Figure 2 The paper shows the positioning system architecture of IMU (inertial measurement unit) and UWB (ultra-wideband) sensor integration. In the figure, IMU sensor provides three-axis acceleration information and three-axis gyroscope data for calculating inertial-based position and velocity information. UWB sensor calculates the position and velocity information based on ultra-wideband technology by measuring the distance between unknown tags and multiple known base stations. After the two sensors are processed by independent positioning calculation modules, the data is passed 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 and velocity information for high-precision tilt monitoring of transmission towers. This architecture realizes the integration of IMU and UWB in complex The complementary and collaborative work in a complex environment improves the adaptability and reliability of the system. During the experimental preparation phase, IMU and UWB sensors were installed on the top of the transmission tower, and the sampled data were synchronously transmitted through the communication module. The IMU sampling frequency was set to 100Hz and the UWB sampling frequency was set to 50Hz. The invented EKF algorithm was used to achieve dynamic fusion of the two sensor data, while monitoring the tilt angle and environmental influencing factors in real time. During the experimental implementation, an initial tilt angle of 5.0° was applied to simulate the actual scenario. The corrected tilt angle, IMU drift error, UWB signal accuracy, and EKF fusion error were measured and recorded respectively. At the same time, the system response time and power consumption were calculated to evaluate its actual performance in an embedded environment. Refer to Table 1 for the recorded and analyzed experimental data.

[0110] Table 1 Experimental data record table

[0111]

[0112] The experimental results show that when the complete invention scheme is adopted (experimental condition 4), the system shows significant 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 tilt angle after correction is still 5.0°; the UWB alone (experimental condition 2) corrects the tilt to 3.0°, but is limited by the signal accuracy; and after the IMU is combined with the UWB and the EKF is adopted (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 is more reliable. The KF algorithm effectively reduces the cumulative error of inertial navigation; the UWB signal accuracy is improved from 10.0cm to 2.0cm, and the error is finally further reduced to 1.0cm through EKF fusion; in terms of response time and power consumption, the response time of the complete solution is 20ms, which is at least 30% faster than other conditions, proving that the optimization algorithm can meet the real-time computing needs in an embedded environment; at the same time, the power consumption is only 2.0W, which is suitable for the long-term deployment of transmission towers; in summary, the present invention greatly improves the accuracy, reliability and adaptability of transmission tower tilt monitoring through the deep fusion of IMU and UWB and the introduction of the EKF algorithm, solves the problems of drift accumulation, signal instability and excessive power consumption in the existing technology, and fully demonstrates its innovativeness and practical value.

[0113] Example 3, reference Figure 3 , as an embodiment of the present invention, provides a comprehensive positioning system for high-precision tilt monitoring of transmission towers, including a model building module, a state updating module, and a tilt monitoring module.

[0114] The model construction module is used to build an IMU and UWB fusion model and initialize the system sampling rate; the state update module is used to use the EKF algorithm to fuse IMU and UWB data, switch to the IMU-dominant mode when the UWB signal quality degrades, and update the positioning status; the tilt monitoring module is used to monitor the tilt status of the transmission tower in real time, build a short-term motion model based on acceleration and differential information, and generate a tilt monitoring report.

[0115] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0116] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0117] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0118] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc. It should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention and are not intended to be limiting. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced with equivalents without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications should be encompassed by the claims of the present invention.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A comprehensive positioning method for high-precision tilt monitoring of transmission towers, characterized in that: include: Build the IMU and UWB fusion model and initialize the system sampling rate; The EKF algorithm is used to fuse IMU and UWB data, and when the UWB signal quality decreases, the system switches to the IMU-dominated mode to update the positioning status. Monitor the tilt status of transmission towers in real time, build a short-term motion model based on acceleration and differential information, and generate a tilt monitoring report.

2. The integrated positioning method for high-precision tilt monitoring of transmission towers according to claim 1, characterized in that: The construction of the IMU and UWB fusion model includes the IMU sensor providing three-axis acceleration information a b The inertial position and velocity information is calculated based on the three-axis gyroscope data. The UWB sensor measures the distance between the unknown tag and different known base stations to calculate the position and velocity information based on ultra-wideband technology. After the IMU sensor and UWB sensor are processed by independent positioning calculation modules, the data is passed to the fusion positioning calculation module. The fusion positioning calculation module outputs comprehensive position and velocity information by fusing the output information of the IMU sensor and the UWB sensor to monitor the inclination of the transmission tower.

3. The comprehensive positioning method for high-precision tilt monitoring of transmission towers according to claim 2, characterized in that: The initialization system sampling rate includes the tag installed on the tower, defining X(k) as the state vector at time k, including 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 Among them, 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, v y (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, according to the uniform acceleration motion equation at time k+1, Tω(k) represents the acceleration process noise, (T 2 / 2)ω(k) represents the speed 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: Among them, x x (k+1) is the position of k+1 in the x-axis direction, x y (k+1) is the position of k+1 in the y-axis direction, v x (k+1) The velocity of k+1 in the x-axis direction at time k+1, v y The velocity of k+1 in the y-axis direction at time (k+1) is 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 transfer matrix, G is the noise input matrix, and W(k) is the noise vector at time k; Let Z(k) denote the observation vector, containing the true distance d at time k i (k) and the observation noise v i (k), the observation equation is expressed as: Where 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 4. The integrated positioning method for high-precision tilt monitoring of transmission towers according to claim 3, characterized in that: The fusion of IMU and UWB data includes calculating the detailed distance, expressed as: Among them, d i (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 and the EKF algorithm is used. At each time step, the Jacobian matrix is ​​obtained through the first-order Taylor expansion, which is expressed as: Where H′(k) is the Jacobian matrix.

5. The integrated positioning method for high-precision tilt monitoring of transmission towers according to claim 4, characterized in that: The updating of the positioning state includes analyzing the accuracy model of the transmission tower tilt monitoring. DOP represents the geometric distribution of tags and BSs. DOP includes geometric DOP, position DOP, horizontal DOP, vertical DOP and time DOP. Under the same distance measurement accuracy, the smaller the HDOP, the higher the positioning accuracy. The horizontal DOP is expressed as: where σ d is the standard deviation of the distance measurement error, and are the variances of the x-axis and y-axis respectively, and the derivative at the approximate position (x′, y′) is expressed as: Among them, d1-d′1 is the error between the actual distance and the estimated distance, and the partial derivative of the distance with respect to the position in the y-axis direction is expressed as: When n ≥ 3, the position error covariance matrix is ​​expressed as: C=(B T B) -1 B T D Among them, B is the partial derivative matrix, which is composed of the partial derivatives of distance to position, and D is the matrix of the error between the actual distance and the estimated distance, expressed as: The covariance matrix of the distance error R is the same as the covariance matrix in the EKF algorithm. The covariance matrix of the positioning error caused by the distance error is Q c It is expressed as: Where E represents the symmetric matrix of the influence of distance error on positioning error, is the user equivalent ranging error variance, and the symmetric matrix is ​​expressed as: get E 11 、E 12 、E 21 、E 22 are the elements of the matrix E, representing the error contributions in different directions.

6. The integrated positioning method for high-precision tilt monitoring of transmission towers according to claim 5, characterized in that: The construction of the short-term motion model includes measuring the distance, which is expressed as: d=[d1 d2 … d n ] Calculate the LS position ε(k), expressed as: ε(k)=(A T A) -1 A T b Among them, A is the system matrix, which is used to describe the relationship between observation and position, b is the observation value vector, which contains the observed measurement data, and calculates the differential speed v(k), which is expressed as: Where t is the time interval.

7. The integrated positioning method for high-precision tilt monitoring of transmission towers according to claim 6, characterized in that: The construction of the short-term motion model also includes analyzing and converting the acceleration coordinate system, which is expressed as: Update the location status, represented by: Among them, ε(k+1) is the position vector at time k+1.

8. A system using the comprehensive positioning method for high-precision tilt monitoring of transmission towers as claimed in any one of claims 1 to 7, characterized in that: Including model building module, status update module, tilt monitoring module; The model building module is used to build 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; The tilt monitoring module is used to monitor the tilt state of the transmission tower in real time, build a short-term motion model based on acceleration and differential information, and generate a tilt monitoring report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the comprehensive positioning method for high-precision tilt monitoring of transmission towers according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the comprehensive positioning method for high-precision tilt monitoring of transmission towers according to any one of claims 1 to 7 are implemented.

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