Intelligent monitoring and precise docking algorithm for submersible sewage pump

Abstract

The invention relates to an intelligent monitoring and precise docking algorithm for a submersible sewage pump. The intelligent monitoring and precise docking algorithm comprises the steps of 1-5, collecting an angular velocity signal, a linear acceleration signal, a geomagnetic field intensity signal and an environment temperature signal of a pump body; performing multi-stage filtering and data fusion on the sensor parameters obtained in the step 1 by using error state extended Kalman filtering, and outputting an attitude quaternion of the submersible sewage pump and a position vector and a speed vector under a world coordinate system; a homogeneous transformation matrix is constructed according to the attitude quaternion and the position vector solved in the second step, real-time alignment of the submersible sewage pump virtual model and real pump body motion is achieved, and an underwater coupler virtual scene is constructed; the temperature signals collected in the step 1 and the related motion parameters solved in the step 2 are analyzed, whether vibration, posture or temperature abnormity exists in the submersible sewage pump or not is judged, and if yes, early warning is triggered; and synchronizing the 3D simulation result in the step 3 and the abnormal monitoring result in the step 4 to a plurality of terminals to realize the butt joint guide and operation state visual monitoring of the submersible sewage pump.

Description

Technical Field

[0001] This invention relates to the field of underwater equipment monitoring and control technology, specifically to an intelligent monitoring and precise docking algorithm for submersible sewage pumps. Background Technology

[0002] A submersible sewage pump is a general-purpose machine specifically designed for transporting sewage, wastewater, and mine water containing solid particles, fibers, and other impurities. Its core function is to use an underwater motor to drive an impeller, generating negative pressure to efficiently extract and transport underwater sewage, industrial wastewater, or mine water to designated pipelines or treatment systems. The traditional installation process for a submersible sewage pump mainly consists of three steps: First, operators use lifting equipment (such as a crane or winch) to hoist the pump above the underwater work area. Second, relying on visual inspection combined with the lifting equipment's pulling force feedback, the approximate underwater position of the pump is determined. The hoisting speed and boom angle are manually adjusted to attempt to align the pump with the pre-set underwater coupling base. Finally, after the pump initially contacts the coupling base, repeated fine-tuning of the lifting force and direction ensures the pump and coupling base are properly engaged, completing the mechanical fixation and sealing connection.

[0003] The existing installation method has the following problems: (1) Poor installation and docking accuracy: Traditional installation mainly relies on the experience and visual inspection of operators, making it difficult to accurately control the three-dimensional attitude and position of the pump body underwater, resulting in a low success rate of docking between the pump body and the underwater coupling base. Repeated attempts are not only time-consuming and labor-intensive, but may also cause mechanical collisions and damage the equipment.

[0004] (2) Lack of status monitoring methods: When the equipment is running, it will vibrate and drift due to factors such as water flow impact, impeller imbalance and bearing wear. Traditional monitoring is usually limited to electrical parameters such as current and voltage on the shore, or requires periodic pump shutdown and hoisting inspection. It cannot accurately sense the real-time mechanical status of the pump body (such as three-dimensional vibration and abnormal swaying), and the early warning capability is insufficient. It is very easy for serious equipment damage or shutdown to occur due to the accumulation of small faults.

[0005] (3) Lack of intuitive operation guidance: The underwater environment is invisible, and the operators are like "blind operators". They cannot intuitively understand the relative position relationship between the pump body and the coupler. They rely entirely on their sense of touch and experience, which is inefficient and risky.

[0006] Therefore, there is a need to develop a technology that can realize real-time attitude perception, intelligent guidance and docking, and online status monitoring of underwater equipment. Summary of the Invention

[0007] To address the existing problems in the installation and monitoring of submersible sewage pumps, an intelligent monitoring and precise docking algorithm for submersible sewage pumps is proposed. By collecting parameters of the submersible sewage pump and using a differential state extended Kalman filter data fusion algorithm, 3D spatial simulation, and AR augmented reality technology, the algorithm simulates the state and position of the submersible sewage pump after it enters the water, thereby achieving precise docking guidance and routine monitoring of the submersible sewage pump during underwater installation.

[0008] To achieve the above objectives, this invention proposes an intelligent monitoring and precise docking algorithm for submersible sewage pumps, comprising the following steps: Step 1: Collect the pump body's angular velocity signal, linear acceleration signal, geomagnetic field intensity signal, and ambient temperature signal; Step 2: Use error state extended Kalman filter to perform multi-stage filtering and data fusion on the sensor parameters obtained in Step 1, and output the attitude quaternion of the submersible pump, the position vector and velocity vector in the world coordinate system. Step 3: Construct a homogeneous transformation matrix based on the attitude quaternion and position vector calculated in Step 2 to achieve real-time alignment between the virtual model of the submersible sewage pump and the actual pump body motion, and construct a virtual scene of the underwater coupler. Step 4: Analyze the temperature signal collected in Step 1 and the relevant motion parameters calculated in Step 2 to determine whether the submersible pump has abnormal vibration, attitude or temperature. If an abnormality is found, trigger an early warning. Step 5: Synchronize the 3D simulation results from Step 3 with the anomaly monitoring results from Step 4 to multiple terminals to achieve submersible pump docking guidance and visual monitoring of operating status.

[0009] Further, step 2 includes: Step 2.1: Define the system state vector ,in, q Let be the quaternion of the pose. p Let be the position vector of the submersible sewage pump in the world coordinate system. v Let V be the velocity vector of the submersible sewage pump in the world coordinate system. b_ω This is the zero bias vector of the gyroscope. b_a This is the zero bias vector of the accelerometer; Step 2.2: Implement using quaternion integration q The prediction, as shown in formula (1), is based on the optimal estimate at the previous moment and the current angular velocity measurement. ω_m The current state is predicted as shown in formula (2): (1); Among them, Ω( oh ) is a skew-symmetric matrix composed of angular velocity vectors; (2); in, f(...)It is the nonlinear state transition function of the system, where Δt is the time step; Prediction error covariance matrix P As shown in formula (3): (3); in, F k It is a state transition function f Jacobian matrix, Q k It is the process noise covariance matrix; Step 2.3: Calculate the optimal correction weights K k As shown in formula (4): (4); in, H k It is the observation function h(x) Jacobian matrix, R k It is the observation noise covariance matrix; Using observations Actual gravity direction and predicted observations The difference between them is used to correct the predicted state: (5); The updated error covariance matrix is: .

[0010] By clearly defining the system state vector, using precise attitude prediction and state update formulas, and combining the dynamic evolution of the error covariance matrix, the high accuracy and stability of the submersible pump's attitude, position, and velocity vector calculations are ensured, providing a reliable data foundation for subsequent 3D simulation and anomaly monitoring.

[0011] Furthermore, step 2 also includes: Anti-disturbance strategy: Incorporate short-term high-frequency disturbances from the accelerometer into the observation noise. R k Adaptive filtering is achieved by monitoring the variance of accelerometer data. When the vibration is severe and the variance is large, the accelerometer automatically adjusts the speed. R k Conversely, under stable conditions, lower R k This allows the accelerometer to fully correct for the gyroscope's drift; Relative position estimation and drift suppression: A high-pass filter is applied to the calculated velocity and translation to filter out extremely low-frequency drift signals; When the algorithm determines through variance analysis that the pump body is in a state of instantaneous relative stillness, it forcibly resets the changes in velocity and position to zero.

[0012] The anti-disturbance strategy, high-pass filtering, and zero-rate update design effectively suppress the impact of high-frequency disturbances and extremely low-frequency drift signals from the accelerometer on the measurement results, significantly improving the anti-interference capability of data fusion in complex underwater environments and ensuring the long-term accuracy of position and attitude estimation.

[0013] Further, step 3 includes: Step 3.1: Define world coordinates {W} and pump body coordinates {B}. The transformation matrix ᵂTᴮ from pump body coordinates {B} to world coordinates {W} is: ; Where ᵂTᴮ is a 3x3 rotation matrix, which is the optimal estimated quaternion output by the Kalman filter. Transformed from, ᵂRᴮ is: ; ᵂpᴮ is a 3x1 translation vector, derived from the state variables of the Kalman filter. p ; Step 3.2: In the 3D rendering engine, set an initial transformation matrix for the pump's virtual model. Before rendering each frame, set the transformation matrix of the pump model in the engine to ᵂTᴮ. The rendering engine will automatically transform each vertex v_vertex of the pump model to the world coordinate system based on this matrix. The process of v_world=ᵂTᴮ*v_vertex is performed dozens of times per second, enabling the virtual model to track and reproduce the real pump's motion in real time and accurately.

[0014] By defining a dual coordinate system and constructing a homogeneous transformation matrix, real-time and precise alignment between the virtual model and the actual pump body movement was achieved. Furthermore, the high-frequency rendering updates ensured the synchronization of the visualization, providing operators with an intuitive positional reference and significantly reducing the operational difficulty of underwater docking.

[0015] Further, step 4 includes: Vibration detection: The high-frequency acceleration signal collected is analyzed in the time and frequency domains to monitor the vibration intensity of the pump body in real time and identify the main vibration frequency. Once the safety threshold is exceeded or abnormal characteristics of the spectrum appear, the system will immediately trigger an alarm. Attitude stability monitoring: Real-time monitoring of whether the pump body tilts or sways beyond the allowable range due to water flow impact during operation.

[0016] Temperature monitoring: Monitors temperature changes in the sealed wiring chamber to help diagnose faults such as motor overheating and seal failure.

[0017] By combining vibration analysis, attitude stability monitoring, and temperature monitoring in the time and frequency domains, a comprehensive coverage of the mechanical and environmental conditions of the submersible sewage pump is achieved, enabling operational monitoring of the pump.

[0018] Furthermore, it also includes an intelligent monitoring and precise docking device for submersible sewage pumps. The intelligent monitoring and precise docking device for submersible sewage pumps includes a sensing unit, a data processing and transmission unit, and a multi-terminal interaction unit. The sensing unit is installed in the sealed wiring chamber of the submersible sewage pump and is fixedly connected to the pump body near the center. The sensing unit includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The three-axis gyroscope is used to collect the angular velocity of the pump body, the three-axis accelerometer is used to detect the linear acceleration of the pump body, and the three-axis magnetometer is used to detect the geomagnetic field strength signal. It also includes a temperature sensor for detecting ambient temperature; The data processing and transmission unit includes an edge computing module and a dual-path transmission module. The edge computing module is used to execute steps 2-4 of any of the algorithms described in claims 1-5 to achieve data fusion and anomaly detection. The dual-path transmission module includes a cable carrier submodule and a wireless transmission module, and the multi-terminal interaction unit includes a computer terminal, a handheld mobile terminal, and AR glasses. The computer terminal is used to display motion trajectory, vibration spectrum, temperature curve and health report; The handheld mobile terminal integrates an edge computing module and is used to control the submersible pump's submersion equipment; The AR glasses are used to overlay and display virtual models of submersible pumps and couplers with real scenes, and output directional guidance arrows and distance prompts. Handheld mobile terminals and AR glasses communicate with computer terminals.

[0019] By integrating high-precision sensing units, efficient data processing and transmission units, and diversified interactive terminals, reliable hardware support is provided for the implementation of algorithms. Dual-path transmission ensures the stability of data transmission, while multi-terminal interaction adapts to the needs of different work scenarios, further improving the flexibility and convenience of operation.

[0020] The beneficial effects of the present invention through the above technical solution are as follows: 1. This invention acquires angular velocity signals, linear acceleration signals, geomagnetic field strength signals, and ambient temperature signals. It uses error state extended Kalman filtering for multi-level filtering and data fusion to achieve accurate attitude and position calculation. Combined with 3D spatial simulation and AR virtual model overlay technology, it transforms traditional blind operation into visual guided operation, solving the underwater docking problem, significantly improving the success rate of first-time docking between the pump body and the coupler, and significantly saving installation time and equipment collision repair costs.

[0021] 2. This invention uses algorithms to perform in-depth fusion analysis of multi-dimensional signals such as vibration, attitude, and temperature collected by the sensing unit. With the help of time-domain / frequency-domain analysis, attitude threshold monitoring and other technologies, it realizes the monitoring of the submersible pump status during installation and after installation.

[0022] 3. This invention employs the error state extended Kalman filter algorithm, fusing multi-sensor data through a predictive and recursive update process to define a complete state vector containing attitude quaternions, position, velocity, and sensor zero bias. Combined with the Jacobian matrix, it achieves accurate modeling of the nonlinear system. It innovatively incorporates three anti-interference strategies: adaptive filtering based on accelerometer variance, high-pass filtering to remove extremely low-frequency drift, and zero-velocity update when the pump is momentarily stationary. These strategies effectively counteract the effects of underwater flow disturbances, inherent sensor drift, and measurement noise. Through precise quaternion-to-rotation matrix conversion and real-time updating of the homogeneous transformation matrix, it ensures the long-term stability and accuracy of core data, providing reliable support for docking guidance and status monitoring.

[0023] 4. The sensing unit of the present invention is integrated into the existing sealed wiring chamber of the submersible sewage pump, without requiring major modifications to the pump body structure. This ensures that the pump body can work normally and that the sensing unit can operate stably in the harsh underwater environment with corrosiveness and high disturbance, thus significantly improving reliability. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the steps of an intelligent monitoring and precise docking algorithm for submersible sewage pumps according to the present invention.

[0025] Figure 2 This is a schematic diagram of the intelligent monitoring and precise docking device for submersible sewage pumps of the present invention. Detailed Implementation

[0026] The present invention will be further described below with reference to the accompanying drawings and specific embodiments: Example 1 like Figure 1-2 As shown, an intelligent monitoring and precise docking algorithm for submersible sewage pumps includes the following steps: Step 1: Collect the pump body's angular velocity signal, linear acceleration signal, geomagnetic field intensity signal, and ambient temperature signal; Step 2: Use error state extended Kalman filter to perform multi-stage filtering and data fusion on the sensor parameters obtained in Step 1, and output the attitude quaternion of the submersible pump, the position vector and velocity vector in the world coordinate system. Step 3: Construct a homogeneous transformation matrix based on the attitude quaternion and position vector calculated in Step 2 to achieve real-time alignment between the virtual model of the submersible sewage pump and the actual pump body motion, and construct a virtual scene of the underwater coupler. Step 4: Analyze the temperature signal collected in Step 1 and the relevant motion parameters calculated in Step 2 to determine whether the submersible pump has abnormal vibration, attitude or temperature. If an abnormality is found, trigger an early warning. Step 5: Synchronize the 3D simulation results from Step 3 with the anomaly monitoring results from Step 4 to multiple terminals to achieve submersible pump docking guidance and visual monitoring of operating status.

[0027] Step 2 includes: Step 2.1: Define the system state vector ,in, q Let be the quaternion of the pose. p This is the position vector (three-dimensional vector) of the submersible sewage pump in the world coordinate system. v This is the velocity vector (three-dimensional vector) of the submersible sewage pump in the world coordinate system. b_ω This is the zero bias vector (three-dimensional vector) of the gyroscope. b_a This is the zero bias vector of the accelerometer (three-dimensional vector). Step 2.2: Implement using quaternion integration q The prediction, as shown in formula (1), is based on the optimal estimate at the previous moment and the current angular velocity measurement. ω_m The current state is predicted as shown in formula (2): (1); Among them, Ω( oh ) is a skew-symmetric matrix composed of angular velocity vectors; (2); in, f(...) It is the nonlinear state transition function of the system. For time step; Prediction error covariance matrix P As shown in formula (3): (3); in, F k It is a state transition function f Jacobian matrix, Q k It is the process noise covariance matrix; Step 2.3: Calculate the optimal correction weights K k As shown in formula (4): (4); in, H k It is the observation function h(x) Jacobian matrix, R kIt is the observation noise covariance matrix; Using observations , The value is the accelerometer measurement. The value measured by the magnetometer. The actual gravity direction of the temperature sensor measurement versus the predicted observation value The difference between them is used to correct the predicted state: (5); The updated error covariance matrix is: .

[0028] Step 2 also includes: Anti-disturbance strategy: Incorporate short-term high-frequency disturbances from the accelerometer into the observation noise. R k Adaptive filtering is achieved by monitoring the variance of accelerometer data. When the vibration is severe and the variance is large, the accelerometer automatically adjusts the speed. R k (This means the filter trusts the gyroscope's short-term predictions more than the noise-contaminated accelerometer observations, thus effectively filtering out vibration disturbances.) Conversely, in a steady state, lowering... R k This allows the accelerometer to fully correct for the gyroscope's drift; Relative position estimation and drift suppression (position change is estimated by double-integrating the motion acceleration obtained by subtracting the gravitational component from the accelerometer measurement). High-pass filtering: A high-pass filter is applied to the calculated velocity and translation to filter out extremely low-frequency drift signals; Zero-speed update: When the algorithm determines through variance analysis that the pump body is in a state of instantaneous relative stillness (such as brief stabilization at the moment of docking), it forcibly resets the changes in velocity and position to zero. This effectively anchors the integration path and keeps the position error within a limited range.

[0029] Step 3 includes (rotation and translation in three-dimensional space can be achieved using a 4x4 homogeneous transformation matrix. This matrix is ​​the core for achieving precise alignment between the virtual 3D model and its real-world spatial location): Step 3.1: Define the world coordinate system {W} (origin fixed at the center of the underwater coupler) and the pump coordinate system {B} (origin fixed at the center of the sensing unit, moving with the pump). The transformation matrix ᵂTᴮ from the pump coordinate system {B} to the world coordinate system {W} is: ; Where ᵂTᴮ is a 3x3 rotation matrix, which is the optimal estimated quaternion output by the Kalman filter. Transformed from, ᵂRᴮ is: ; ᵂpᴮ is a 3x1 translation vector, derived from the state variables of the Kalman filter. p ; Step 3.2: In the 3D rendering engine, set an initial transformation matrix for the pump's virtual model. Before rendering each frame, set the transformation matrix of the pump model in the engine to ᵂTᴮ. The rendering engine will automatically transform each vertex v_vertex of the pump model to the world coordinate system based on this matrix. The process of v_world=ᵂTᴮ*v_vertex is performed dozens of times per second, enabling the virtual model to track and reproduce the real pump's motion in real time and accurately.

[0030] Step 4 includes: Vibration detection: The high-frequency acceleration signal collected is analyzed in the time domain (such as RMS) and frequency domain (FFT) to monitor the vibration intensity of the pump body in real time and identify the main vibration frequency. Once the safety threshold is exceeded or abnormal characteristics of the spectrum appear, the system will immediately trigger an alarm. Attitude stability monitoring: Real-time monitoring of whether the pump body tilts or sways beyond the allowable range due to water flow impact during operation.

[0031] Temperature monitoring: Monitors temperature changes in the sealed wiring chamber to help diagnose faults such as motor overheating and seal failure.

[0032] It also includes a submersible sewage pump intelligent monitoring and precise docking device, which includes a sensing unit, a data processing and transmission unit, and a multi-terminal interaction unit. The sensing unit is installed in the sealed wiring chamber of the submersible sewage pump, close to the center of the pump body and fixedly connected to the pump body. The sensing unit includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The three-axis gyroscope is used to collect the angular velocity of the pump body, the three-axis accelerometer is used to detect the linear acceleration of the pump body, and the three-axis magnetometer is used to detect the geomagnetic field strength signal. The sensing unit also includes a temperature sensor for detecting ambient temperature; The data processing and transmission unit includes an edge computing module and a dual-path transmission module. The edge computing module is used to execute steps 2-4 of any of the algorithms described in claims 1-5 to achieve data fusion and anomaly detection. The dual-path transmission module includes a cable carrier submodule and a wireless transmission module, and the multi-terminal interaction unit includes a computer terminal, a handheld mobile terminal, and AR glasses. The computer terminal is used to display motion trajectory, vibration spectrum, temperature curve and health report; The handheld mobile terminal integrates an edge computing module and is used to control the submersible pump's submersion equipment; The AR glasses are used to overlay and display virtual models of submersible pumps and couplers with real scenes, and output directional guidance arrows and distance prompts. Handheld mobile terminals and AR glasses communicate with computer terminals.

[0033] In this embodiment, a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer are integrated into a single IMU sensor. The IMU sensors are calibrated using a six-sided method and zero-bias calibration to compensate for sensor errors. High-precision clock synchronization is employed to ensure time synchronization between the internal sensors of the IMU and the external data acquisition unit, providing accurate timestamps for Kalman filtering. . To achieve an intelligent monitoring and precise docking algorithm for submersible sewage pumps, software programs are installed on computer terminals, handheld mobile terminals, and AR glasses.

[0034] During operation, the software calculates the pump's three-dimensional attitude angles (roll, pitch, yaw) and relative position changes underwater in real time, and dynamically generates a 3D virtual scene of the underwater working environment in the interface, displaying the pump's motion trajectory and relative position relationship with the coupler in real time animation.

[0035] Operators can see virtual pump and coupler models superimposed on the real scene through AR glasses or the camera view of a handheld terminal, providing intuitive visual guidance (such as directional arrows and distance prompts) to guide operators to make precise adjustments and achieve accurate docking of the submersible pump and coupler.

[0036] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Therefore, all equivalent changes or modifications made to the structure, features and principles described in the claims of the present invention should be included within the scope of the present invention.

Claims

1. An intelligent monitoring and precise docking algorithm for submersible sewage pumps, characterized in that, Includes the following steps: Step 1: Collect the pump body's angular velocity signal, linear acceleration signal, geomagnetic field intensity signal, and ambient temperature signal; Step 2: Use error state extended Kalman filter to perform multi-stage filtering and data fusion on the sensor parameters obtained in Step 1, and output the attitude quaternion of the submersible pump, the position vector and velocity vector in the world coordinate system. Step 3: Construct a homogeneous transformation matrix based on the attitude quaternion and position vector calculated in Step 2 to achieve real-time alignment between the virtual model of the submersible sewage pump and the actual pump body motion, and construct a virtual scene of the underwater coupler. Step 4: Analyze the temperature signal collected in Step 1 and the relevant motion parameters calculated in Step 2 to determine whether the submersible pump has abnormal vibration, attitude or temperature. If an abnormality is found, trigger an early warning. Step 5: Synchronize the 3D simulation results from Step 3 with the anomaly monitoring results from Step 4 to multiple terminals to achieve submersible pump docking guidance and visual monitoring of operating status.

2. The intelligent monitoring and precise docking algorithm for submersible sewage pumps according to claim 1, characterized in that, Step 2 includes: Step 2.1: Define the system state vector ,in, q Let be the quaternion of the pose. p Let be the position vector of the submersible sewage pump in the world coordinate system. v Let V be the velocity vector of the submersible sewage pump in the world coordinate system. b_ω This is the zero bias vector of the gyroscope. b_a This is the zero bias vector of the accelerometer; Step 2.2: Implement using quaternion integration q The prediction, as shown in formula (1), is based on the optimal estimate at the previous moment and the current angular velocity measurement. ω_m The current state is predicted as shown in formula (2): （1）； Among them, Ω( ω ) is a skew-symmetric matrix composed of angular velocity vectors; （2）； in, f(...) It is the nonlinear state transition function of the system, where Δt is the time step; Prediction error covariance matrix P As shown in formula (3): （3）； in, F k It is a state transition function f Jacobian matrix, Q k It is the process noise covariance matrix; Step 2.3: Calculate the optimal correction weights K k As shown in formula (4): （4）； in, H k It is the observation function h(x) Jacobian matrix, R k It is the observation noise covariance matrix; Using observations Actual gravity direction and predicted observations The difference between them is used to correct the predicted state: （5）； The updated error covariance matrix is: .

3. The intelligent monitoring and precise docking algorithm for submersible sewage pumps according to claim 1, characterized in that, Step 2 also includes: Anti-disturbance strategy: Incorporate short-term high-frequency disturbances from the accelerometer into the observation noise. R k Adaptive filtering is achieved by monitoring the variance of accelerometer data. When the vibration is severe and the variance is large, the accelerometer automatically adjusts the speed. R k Conversely, under stable conditions, lower R k This allows the accelerometer to fully correct for the gyroscope's drift; Relative position estimation and drift suppression: A high-pass filter is applied to the calculated velocity and translation to filter out extremely low-frequency drift signals; When the algorithm determines through variance analysis that the pump body is in a state of instantaneous relative stillness, it forcibly resets the changes in velocity and position to zero.

4. The intelligent monitoring and precise docking algorithm for submersible sewage pumps according to claim 2, characterized in that, Step 3 includes: Step 3.1: Define world coordinates {W} and pump body coordinates {B}. The transformation matrix ᵂTᴮ from pump body coordinates {B} to world coordinates {W} is: ； Where ᵂTᴮ is a 3x3 rotation matrix, which is the optimal estimated quaternion output by the Kalman filter. Transformed from, ᵂRᴮ is: ； ᵂpᴮ is a 3x1 translation vector, derived from the state variables of the Kalman filter. p ; Step 3.2: In the 3D rendering engine, set an initial transformation matrix for the pump's virtual model. Before rendering each frame, set the transformation matrix of the pump model in the engine to ᵂTᴮ. The rendering engine will automatically transform each vertex v_vertex of the pump model to the world coordinate system based on this matrix. The process of v_world=ᵂTᴮ*v_vertex is performed dozens of times per second, enabling the virtual model to track and reproduce the real pump's motion in real time and accurately.

5. The intelligent monitoring and precise docking algorithm for submersible sewage pumps according to claim 1, characterized in that, Step 4 includes: Vibration detection: The high-frequency acceleration signal collected is analyzed in the time and frequency domains to monitor the vibration intensity of the pump body in real time and identify the main vibration frequency. Once the safety threshold is exceeded or abnormal characteristics of the spectrum appear, the system will immediately trigger an alarm. Attitude stability monitoring: Real-time monitoring of whether the pump body tilts or sways beyond the allowable range due to water flow impact during operation; Temperature monitoring: Monitors temperature changes in the sealed wiring chamber to help diagnose faults such as motor overheating and seal failure.

6. The intelligent monitoring and precise docking algorithm for submersible sewage pumps according to claim 1, characterized in that, It also includes a submersible sewage pump intelligent monitoring and precise docking device, which includes a sensing unit, a data processing and transmission unit, and a multi-terminal interaction unit. The sensing unit is installed in the sealed wiring chamber of the submersible sewage pump, close to the center of the pump body and fixedly connected to the pump body. The sensing unit includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The three-axis gyroscope is used to collect the angular velocity of the pump body, the three-axis accelerometer is used to detect the linear acceleration of the pump body, and the three-axis magnetometer is used to detect the geomagnetic field strength signal. It also includes a temperature sensor for detecting ambient temperature; The data processing and transmission unit includes an edge computing module and a dual-path transmission module. The edge computing module is used to execute steps 2-4 of any of the algorithms described in claims 1-5 to achieve data fusion and anomaly detection. The dual-path transmission module includes a cable carrier submodule and a wireless transmission module, and the multi-terminal interaction unit includes a computer terminal, a handheld mobile terminal, and AR glasses. The computer terminal is used to display motion trajectory, vibration spectrum, temperature curve and health report; The handheld mobile terminal integrates an edge computing module and is used to control the submersible pump's submersion equipment; The AR glasses are used to overlay and display virtual models of submersible pumps and couplers with real scenes, and output directional guidance arrows and distance prompts. Handheld mobile terminals and AR glasses communicate with computer terminals.

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