A modular prefabrication method for electrical equipment

Abstract

The present application relates to the technical field of modular installation, in particular to a kind of electrical equipment modular prefabrication method, comprising the following steps: through digital twin setting docking target pose, acquisition multi-source sensing data constructs real-time pose, deviation is calculated to generate closed-loop instruction drive equipment automatic alignment, after completion trigger locking and electrical self-checking, and integration data generates authentication.In the present application, introduce digital twin to carry out virtual rehearsal, fuse laser distance sensor and inertial measurement unit and other multi-source sensor data real-time accurate perception relative pose, and adopt kinematics decoupling algorithm to construct closed-loop control instruction and other technical means, realize the intelligent docking of electrical equipment module from extensive to accurate, from human control to automatic, and through the automatic detection and data authentication of locking process and electrical connectivity, improve the precision and efficiency of field installation, reduce the dependence on the skills of operating personnel, provide data support for the whole process quality traceability of modular installation.

Description

Technical Field

[0001] This invention relates to the field of modular installation technology, and in particular to a modular prefabrication method for electrical equipment. Background Technology

[0002] Modular installation technology is an engineering method that decomposes complex engineering systems into multiple standardized functional modules. These modules are prefabricated and assembled in a factory before being transported to the construction site for overall assembly. Its main purpose is to improve engineering quality and installation accuracy through factory production, and to shorten project construction cycles. It is applied to the installation of large equipment and facilities in industries such as construction engineering, petrochemicals, and power construction. Specifically, the modular prefabrication method for 220kV step-up electrical equipment in wind power refers to the process of dividing electrical equipment systems used in wind farms to boost voltage to 220kV into different functional units or structural modules before on-site installation. These modules are manufactured, assembled, and initially debugged in a controlled environment such as a factory. The prefabricated modules are then transported to the wind farm site for rapid assembly and installation. This typically involves integrating key equipment such as transformers, switchgear, and control systems into standardized bases or enclosures to form independent installation units.

[0003] In traditional modular prefabrication and installation of electrical equipment, while factory prefabrication improves the production quality and efficiency of individual modules, the crucial on-site installation process suffers from several drawbacks. These include reliance on manual experience for alignment accuracy, low alignment efficiency, difficulty in handling dynamic changes in complex on-site environments, and a lack of real-time, quantitative verification methods for the final connection quality, leading to uncertainty in installation quality. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a modular prefabrication method for electrical equipment, comprising the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a modular prefabrication method for electrical equipment, comprising the following steps: S1: Obtain the unique identification code, three-dimensional geometric model data and initial state information of the two electrical equipment modules to be docked, perform digital twin docking of the three-dimensional models of the two electrical equipment modules, and set and output the docking target pose and preset error range. S2: Collect measurement data from the laser distance sensor, structured light camera, and inertial measurement unit on the interface during the hoisting and movement of the electrical equipment modules, determine the six-degree-of-freedom relationship between the two electrical equipment modules, and construct the real-time relative pose vector; S3: Compare the real-time relative pose vector with the pose of the docking target, calculate the six-degree-of-freedom alignment deviation value between the two, perform kinematic decoupling on the six-degree-of-freedom alignment deviation value, construct the loop control command to drive the lifting equipment and the hydraulic platform, and obtain the pose adjustment closed-loop control command set. S4: Based on the posture adjustment closed-loop control instruction set, perform alignment operation and continuously compare the six-degree-of-freedom alignment deviation value with the preset error range to confirm successful mechanical locking, trigger the automatic self-test sequence of high voltage, low voltage and communication circuits, and obtain the mechanical locking status confirmation result and electrical communication interface self-test data. S5: Integrate the identification code, the initial state information, the six-degree-of-freedom alignment deviation value, and the mechanical locking state confirmation result with the electrical communication interface self-test data to obtain the modular prefabrication docking certification data of electrical equipment.

[0005] As a further aspect of the present invention, during the comparison of the six-degree-of-freedom alignment deviation value with the preset error range: When the six-degree-of-freedom alignment deviation value is less than the preset error range, the locking mechanism of the electrical equipment module interface is triggered, and the torque sensor reading, the interface sealing ring pressure sensor reading, and the locking pin limit switch signal are recorded during the locking process. When the torque sensor reading is greater than a preset torque threshold, the pressure sensor reading is greater than a preset pressure threshold, and the limit switch signal is in a closed state, the mechanical locking is confirmed to be successful.

[0006] As a further aspect of the present invention, during the kinematic decoupling process, the six-degree-of-freedom alignment deviation value is decomposed into the crane pose adjustment amount corresponding to the motion degree of the lifting equipment and the hydraulic platform pose adjustment amount corresponding to the motion degree of the hydraulic platform.

[0007] As a further aspect of the present invention, the real-time relative pose vector includes a relative translation component and a relative rotation component; the pose adjustment closed-loop control instruction set includes lifting equipment drive instructions and hydraulic platform drive instructions; the mechanical locking state confirmation result and electrical communication interface self-test data include locking torque compliance confirmation, sealing pressure compliance confirmation, locking pin in-place status confirmation, high-voltage circuit continuity test results, low-voltage circuit continuity test results, and communication link quality report; the modular prefabrication docking certification data of electrical equipment includes module identity information, initial working condition record, alignment accuracy data, and interface function verification report.

[0008] As a further aspect of the present invention, step S1 is as follows: S111: Obtain the unique identification code, three-dimensional geometric model data and initial state information of each of the two electrical equipment modules to be docked; render the three-dimensional geometric model data and the initial state information in the virtual space to establish a simulation scene containing the three-dimensional models of the two electrical equipment modules, and obtain the digital twin docking scene. S112: Based on the digital twin docking scenario, the three-dimensional models of the two electrical equipment modules are virtually assembled to simulate the ideal state of complete interface alignment and engagement, and the translation and rotation transformation matrices between the coordinate systems of the two model interfaces are extracted to generate a six-degree-of-freedom docking reference. S113: Based on the six-degree-of-freedom docking reference, combined with the installation process requirements and allowable tolerances of the electrical equipment module, the maximum allowable deviations in the six degrees of freedom of translation and rotation are set, and docking judgment conditions composed of the target state and tolerance range are established to obtain the docking target pose and preset error range.

[0009] As a further aspect of the present invention, step S2 is as follows: S211: During the hoisting and movement of electrical equipment modules, distance data measured by laser distance sensors deployed on module interfaces, 3D point cloud data captured by structured light cameras, and angular velocity and acceleration data output by inertial measurement units are collected simultaneously to obtain multi-source heterogeneous sensor data streams. S212: Based on the multi-source heterogeneous sensing data stream, the motion trend of the electrical equipment module is predicted using the data from the inertial measurement unit, and the prediction is corrected using the data from the laser distance sensor and the structured light camera. Multi-sensor data fusion processing is then performed to obtain the statistical results of the six-degree-of-freedom relationship state. S213: Based on the statistical results of the six-degree-of-freedom relationship, convert them into a standardized vector describing the relative position and attitude of the two electrical equipment modules in three-dimensional space, which includes three translational components and three rotational components, and construct a real-time relative pose vector.

[0010] As a further aspect of the present invention, step S3 is as follows: S311: Based on the docking target pose and the docking target pose within the preset error range, perform matrix operations with the real-time relative pose vector to calculate the difference between the two in each of the six degrees of freedom, and statistically analyze the alignment deviation value of the six degrees of freedom. S312: Based on the six-degree-of-freedom alignment deviation value, and according to the kinematic model of the lifting equipment and the hydraulic platform, the overall deviation is decomposed into the corresponding posture adjustment amounts of the lifting equipment and the hydraulic platform, and the posture adjustment amounts of the crane and the hydraulic platform are obtained. S313: Based on the crane posture adjustment amount and the hydraulic platform posture adjustment amount, respectively convert them into control voltage or current signals of the corresponding drive unit, and integrate them into a set of collaborative closed-loop control command sequences to construct a posture adjustment closed-loop control command set.

[0011] As a further aspect of the present invention, step S4 is as follows: S411: Based on the pose adjustment closed-loop control instruction set, drive the device to perform alignment, and continuously compare the updated six-degree-of-freedom alignment deviation value with the preset error range. When the deviation value is less than the error range, trigger the locking mechanism, and record the sensor readings during the operation to obtain the interface locking process sensing signal. S412: Monitor the sensor signal during the locking process of the interface, determine whether the torque sensor reading is greater than the preset torque threshold, whether the pressure sensor reading is greater than the preset pressure threshold, and whether the limit switch signal is in a closed state. When all three conditions are met, confirm that the locking operation is successful and generate a mechanical locking state confirmation result. S413: Based on the mechanical locking status confirmation result, upon successful confirmation, the automatic self-test sequence of the high voltage, low voltage and communication circuits is immediately triggered, and the circuit insulation resistance value and communication link quality parameters are collected to obtain the mechanical locking status confirmation result and electrical communication interface self-test data.

[0012] As a further aspect of the present invention, step S5 is as follows: S511: Obtain the unique identification code and initial status information of the electrical equipment module, integrate the six-degree-of-freedom alignment deviation value at the moment of docking completion, compile the information into a structured dataset that records the docking process and accuracy, and establish a docking process status data set; S512: Based on the mechanical locking status confirmation result and the electrical communication interface self-test data, the mechanical locking confirmation status and various self-test parameters of the electrical and communication interfaces are formatted to establish an interface status verification data packet; S513: Integrate the docking process status data set with the interface status verification data package, merge all information into a unique and tamper-proof authentication record, and obtain the modular prefabricated docking authentication data of electrical equipment.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention utilizes digital twins for virtual pre-simulation, integrates multi-source sensor data such as laser distance sensors and inertial measurement units for real-time and accurate perception of relative pose, and employs kinematic decoupling algorithms to construct closed-loop control commands. These technologies enable intelligent docking of electrical equipment modules, moving from extensive to precise control and from manual to automated operation. Furthermore, by automating the detection and data authentication of the locking process and electrical connectivity, the invention improves the accuracy and efficiency of on-site installation, reduces reliance on operator skills, ensures the mechanical and electrical reliability of the final interface, and provides data support for the full-process quality traceability of modular installation. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a flowchart of step S1 of the present invention; Figure 3 This is a flowchart of step S2 of the present invention; Figure 4 This is a flowchart of step S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of step S5 of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0017] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0018] Please see Figure 1 This invention provides a modular prefabrication method for electrical equipment, comprising the following steps: S1: Obtain the unique identification code, three-dimensional geometric model data and initial state information of the two electrical equipment modules to be docked, perform digital twin docking of the three-dimensional models of the two electrical equipment modules in virtual space, and set and output the docking target pose and preset error range; S2: Collect measurement data from the laser distance sensor, structured light camera, and inertial measurement unit on the interface during the hoisting and movement of the electrical equipment modules, determine the six-degree-of-freedom relationship between the two electrical equipment modules, and construct the real-time relative pose vector; S3: Compare the real-time relative pose vector with the pose of the docking target, calculate the six-degree-of-freedom alignment deviation value between the two, perform kinematic decoupling on the six-degree-of-freedom alignment deviation value, decompose the six-degree-of-freedom alignment deviation value into the crane pose adjustment amount corresponding to the motion degree of the lifting equipment and the hydraulic platform pose adjustment amount corresponding to the motion degree of the hydraulic platform, construct the loop control command to drive the lifting equipment and the hydraulic platform, and obtain the pose adjustment closed-loop control command set; S4: Based on the pose adjustment closed-loop control instruction set, perform alignment operation and continuously compare the six-degree-of-freedom alignment deviation value with the preset error range. When the six-degree-of-freedom alignment deviation value is less than the preset error range, trigger the locking mechanism of the electrical equipment module interface and record the torque sensor reading, the interface sealing ring pressure sensor reading, and the locking pin limit switch signal during the locking process. When it is determined that the torque sensor reading is greater than the preset torque threshold, the pressure sensor reading is greater than the preset pressure threshold, and the limit switch signal is in the closed state, the mechanical locking is confirmed to be successful, trigger the automatic self-test sequence of the high voltage, low voltage, and communication circuits, and obtain the mechanical locking status confirmation result and the electrical communication interface self-test data. S5: Integrate the identification code, initial state information, six-degree-of-freedom alignment deviation value, and mechanical locking state confirmation result with the electrical communication interface self-test data to obtain the modular prefabrication docking certification data of electrical equipment.

[0019] The real-time relative pose vector includes relative translation and relative rotation components; the pose adjustment closed-loop control command set includes lifting equipment drive commands and hydraulic platform drive commands; the mechanical locking status confirmation results and electrical communication interface self-test data include locking torque compliance confirmation, sealing pressure compliance confirmation, locking pin in-position status confirmation, high-voltage circuit continuity test results, low-voltage circuit continuity test results, and communication link quality report; the modular prefabrication docking certification data for electrical equipment includes module identity information, initial working condition records, alignment accuracy data, and interface function verification report.

[0020] Please see Figure 2 The specific steps of S1 are as follows: S111: Obtain the unique identification code, three-dimensional geometric model data and initial state information of each of the two electrical equipment modules to be docked. Based on the three-dimensional geometric model data and initial state information, render in virtual space to establish a simulation scene containing the three-dimensional models of the two electrical equipment modules, and obtain the digital twin docking scene. First, obtain the unique identification code "A-MOD-20231027-001" for electrical equipment module A and the unique identification code "B-MOD-20231027-002" for electrical equipment module B. These identification codes are assigned to each independent module unit during the factory prefabrication stage for full lifecycle traceability. Retrieve the corresponding 3D geometric model data and initial state information from the product lifecycle management system using these identification codes. The 3D geometric model data is stored in STEPAP242 format. This model data is a direct product of modular design and includes information such as the module's precise shape, interface features, material properties, and mass distribution. For example, the interface flange of module A has an outer diameter of 500 mm and contains eight 20 mm diameter positioning holes. The initial state information records the initial pose of the prefabricated modules in the factory coordinate system. Module A's initial position coordinates are (1000.0, 2500.0, 5000.0) mm, and its initial pose is represented by the quaternion (0.707, 0, 0, 0.707), corresponding to a 90-degree rotation around the Z-axis. Module B's position coordinates are (1000.0, 4500.0, 5000.0) mm, and its pose quaternion is (0.707, 0, 0, 0.707). Subsequently, in a physics-based virtual simulation environment, the 3D geometric model data of these two prefabricated modules are analyzed to extract topological information such as vertices, edges, and faces. Based on material properties, the models are assigned corresponding physical characteristics; for example, the shell material of module A is SS304 stainless steel, with a density set to 7.93 g / cm³. Using the initial state information, the two 3D models are placed in their corresponding poses in virtual space. During scene rendering, the ambient light intensity was set to 50,000 lux, and three virtual cameras at different angles were configured to provide a comprehensive monitoring perspective. The main monitoring camera was located 5,000 mm directly in front of the scene, with a 30-degree downward angle. This process reproduced the physical entities and their environment of the two modular prefabricated units in virtual space, ultimately generating a digital twin docking scene containing complete 3D models of module A and module B and their initial poses.

[0021] S112: Based on the digital twin docking scenario, the three-dimensional models of two electrical equipment modules are virtually assembled to simulate the ideal state of complete interface alignment and engagement, and the translation and rotation transformation matrices between the coordinate systems of the two model interfaces are extracted to generate a six-degree-of-freedom docking reference. In a digital twin docking scenario, a virtual assembly operation is performed on the 3D models of module A and module B. This operation aims to digitally rehearse the splicing process of prefabricated modules on the construction site. The operation first locks the 3D model of module B as a static reference, then sets the 3D model of module A as a movable body. Using a built-in collision detection algorithm, the minimum distance between module A and module B during movement is continuously monitored with a detection accuracy of 0.1 mm. By simulating a frictionless, weightless ideal environment, a virtual driving force is applied to module A towards the interface of module B, causing it to slowly approach. When the standardized module interface features of the two models, such as the locating pins of module A and the locating holes of module B, initial contact occurs, a constraint-based precise alignment algorithm is initiated. This algorithm identifies four predefined pairs of key matching points on the interfaces of the two models and adjusts the pose of module A through iterative calculation, aiming to minimize the sum of the Euclidean distances between these four pairs of matching points. After 35 iterations, the distance between all matching point pairs was less than 0.01 mm. At this point, the interfaces of the two electrical equipment modules were determined to be fully aligned and engaged, achieving the theoretically optimal alignment state for modular design. Under this ideal alignment state, the transformation relationship between the coordinate system of module A and the coordinate system of module B was extracted. This relationship is described by a 4x4 homogeneous transformation matrix, which includes translation and rotation information. Specifically, the translation transformation vector is [2000.0, 0.0, -15.5] mm, indicating that module A needs to move 2000.0 mm in the positive X-axis direction and 15.5 mm in the negative Z-axis direction of the module B coordinate system to achieve alignment. The rotation transformation is represented by a 3x3 rotation matrix, corresponding to a composite rotation of -3.5 degrees around the Y-axis and 0.2 degrees around the Z-axis. Integrating this translation vector and rotation matrix generates a six-degree-of-freedom docking datum describing the ideal docking state.

[0022] S113: Based on the six-degree-of-freedom docking reference, combined with the installation process requirements and allowable tolerances of the electrical equipment module, the maximum allowable deviations in the six degrees of freedom of translation and rotation are set, and docking judgment conditions composed of the target state and tolerance range are established to obtain the docking target pose and preset error range. Based on the generated six-degree-of-freedom (DOF) docking reference, docking judgment conditions were set in conjunction with the actual installation process requirements and allowable tolerances of the modular prefabrication of electrical equipment. Referring to the interface installation process specifications in the module design manual, a key technical document ensuring seamless and reliable connections between prefabricated modules, it stipulates that to ensure sealing and electrical connection reliability, the gap between docking planes must not exceed 0.5 mm at any position, and the radial gap between the locating pin and the locating hole is 1.0 mm. Based on this, the maximum permissible deviations for translational degrees of freedom were set as follows: the maximum permissible deviation along the docking direction (Z-axis) is set to ±0.5 mm; in the plane perpendicular to the docking direction (X-axis and Y-axis), considering the guiding effect of the locating pin, the maximum permissible deviation is set to ±1.5 mm. For rotational degrees of freedom, to prevent damage to connector pins and uneven stress on the sealing gaskets, maximum permissible rotational deviations were specified. The rotational deviation about the docking direction axis (Z-axis), i.e., planar torsion, has a maximum permissible deviation of ±0.2 degrees. The rotational deviation about the X-axis and Y-axis, i.e., the tilt of the docking plane, has a maximum permissible deviation of ±0.1 degrees. To verify the rationality of these tolerance values ​​derived from prefabrication precision, a series of finite element analyses were conducted to simulate the stress on the module interface under maximum deviation. The analysis results show that when the deviation is within the aforementioned set range, the maximum stress on the interface structure is 150 MPa, which is less than 30% of the material's yield strength, and the uniformity of the compression of the sealing gasket exceeds 95%. Based on the above analysis, the final docking judgment conditions are composed of the target state and the tolerance range: the target state is the precise pose defined by the six-degree-of-freedom docking datum; the tolerance range is that the translational deviation (ΔX, ΔY, ΔZ) is within [±1.5, ±1.5, ±0.5] mm, and the rotational deviation (ΔRoll, ΔPitch, ΔYaw) is within [±0.1, ±0.1, ±0.2] degrees. This yields a complete docking standard that includes the precise docking target pose and the quantified preset error range, providing a clear acceptance basis for on-site modular rapid assembly.

[0023] Please see Figure 3 The specific steps of S2 are as follows: S211: During the hoisting and movement of electrical equipment modules, distance data measured by laser distance sensors deployed on module interfaces, 3D point cloud data captured by structured light cameras, and angular velocity and acceleration data output by inertial measurement units are collected simultaneously to obtain multi-source heterogeneous sensor data streams. During the physical process of Module A, a modular prefabricated unit of electrical equipment, being hoisted by a crane and moved towards Module B, heterogeneous data streams are synchronously acquired by multi-source sensors deployed at key locations of the standardized interface. Specifically, four Panasonic HG-C1400 laser distance sensors are installed at the four corners of the Module A interface flange. These sensors have a measurement range of 400±100 mm, a repeatability of 0.1 mm, and continuously output distance data to the Module B interface surface at a frequency of 1000 Hz, in 16-bit unsigned integer format. A structured light camera, Orbbec FemtoMega, is installed 200 mm above the center of the Module A interface. It captures images of the Module B interface area at a rate of 30 frames per second, outputting 3D point cloud data with a resolution of 1280x720. Each frame contains approximately 920,000 data points, each with 3D spatial coordinates (x, y, z). Simultaneously, an industrial-grade inertial measurement unit (IMU), model ADIS16470, is fixed at the center of mass of module A. This unit synchronously outputs triaxial angular velocity (in radians per second) and triaxial acceleration (in meters per second²) at a frequency of 500 Hz. To ensure data synchronization, all sensors are timestamped using a Network Time Protocol (NTP)-based time synchronization server, achieving a timestamping accuracy of 1 millisecond. This series of sensor deployments is a key step in realizing intelligent on-site construction of modular prefabricated equipment. At the start of hoisting and movement, all sensors activate simultaneously, continuously collecting data to obtain a multi-source heterogeneous sensor data stream composed of millimeter-level precise distance data from laser distance sensors, dense 3D point cloud data from structured light cameras, and high-frequency motion attitude data from the IMU.

[0024] S212: Based on multi-source heterogeneous sensor data streams, the motion trend of electrical equipment modules is predicted using data from the inertial measurement unit, and the prediction is corrected using data from the laser distance sensor and structured light camera. Multi-sensor data fusion processing is then performed to obtain the statistical results of the six-degree-of-freedom relationship state. Based on the acquired multi-source heterogeneous sensor data stream, multi-sensor data fusion processing is performed to provide real-time and reliable pose calculation for the precise guidance of large prefabricated modules. This process employs an extended Kalman filter algorithm framework. First, in the prediction phase, using the angular velocity and acceleration data of the inertial measurement unit at time t-1, the pose state of module A at the current time t is predicted through kinematic equation integration. This state vector includes position, velocity, attitude (represented by quaternions), and angular velocity. For example, if the Z-axis acceleration at time t-1 is -0.1 m / s², the predicted Z-axis velocity at time t will be correspondingly reduced. Subsequently, in the update and correction phase, the prediction results are corrected using data from laser distance sensors and structured light cameras. Distance data acquired from four laser distance sensors, combined with the known installation positions of the sensors, calculates the precise distances of four points on the interface plane of module A relative to the interface plane of module B, thereby solving for the relative tilt angle and average distance of the interface planes. Simultaneously, the 3D point cloud data acquired by the structured light camera is processed: first, outlier noise points are removed using a radius filtering algorithm; then, the Iterative Closest Point (ICP) algorithm is used to register the point cloud of the current frame with the pre-stored 3D standard model of module B interface, which serves as the modular design benchmark, to calculate the complete 6-DOF pose measurement value of module A relative to module B at the current moment. The high-precision distance and attitude information calculated by the laser sensor is weighted and fused with the complete pose measurement value calculated by the structured light camera. The weighting coefficient for the laser sensor data is set to 0.7 based on its noise level, while the weighting coefficient for the structured light camera data is set to 0.3. The fused measurement value is used to correct the Kalman filter prediction state, ultimately outputting a fused 6-DOF relational state statistical result. This result includes an optimally estimated relative pose matrix and a covariance matrix describing the uncertainty, providing accurate input for subsequent automated alignment control of the prefabricated modules.

[0025] S213: Based on the statistical results of the six-degree-of-freedom relationship state, it is converted into a standardized vector describing the relative position and attitude of the two electrical equipment modules in three-dimensional space, containing three translational components and three rotational components, to construct a real-time relative pose vector; Based on the statistical results of the output six-degree-of-freedom relational states, a standardized vector transformation and construction are performed. The core of this statistical result is a 4x4 homogeneous transformation matrix, which accurately describes the real-time relative pose of the modular prefabricated unit A of the electrical equipment relative to the reference module B in three-dimensional space. To facilitate subsequent deviation calculation and control, this matrix-formed pose information needs to be converted into a standardized six-dimensional vector. The conversion process is as follows: First, the first three elements are extracted from the last column of the 4x4 homogeneous transformation matrix. These three elements directly constitute the translation components describing the relative position, i.e., the translation amounts along the X, Y, and Z axes. For example, if the corresponding value in the matrix is ​​[12.5, -8.2, 101.3], then the translation components are (12.5 mm, -8.2 mm, 101.3 mm). Next, rotation information is extracted from the 3x3 submatrix in the upper left corner of the matrix. This 3x3 rotation matrix is ​​decomposed into three independent rotation components according to the rotation order ZYX using the Euler angle transformation algorithm: roll (around the X-axis), pitch (around the Y-axis), and yaw (around the Z-axis). For example, by performing an arctangent function operation on the rotation matrix, the roll angle is calculated to be 0.3 degrees, the pitch angle to be -0.5 degrees, and the yaw angle to be 1.2 degrees. Finally, these three translational components and three rotational components are combined into a six-dimensional vector in a predetermined order (e.g., X translation, Y translation, Z translation, X rotation, Y rotation, Z rotation). Combining the values ​​from the above example, the real-time relative pose vector [12.5, -8.2, 101.3, 0.3, -0.5, 1.2] can be constructed. This vector intuitively describes the six-degree-of-freedom relative relationship between the two prefabricated modules.

[0026] Please see Figure 4 The specific steps of S3 are as follows: S311: Based on the docking target pose and the docking target pose within the preset error range, perform matrix operations with the real-time relative pose vector to calculate the difference between the two in each of the six degrees of freedom and statistically analyze the alignment deviation values ​​of the six degrees of freedom. Based on the established target pose and the real-time relative pose vector constructed in step S213, matrix operations are performed to calculate the six-degree-of-freedom alignment deviation. First, the target pose, i.e., the theoretical installation pose of the modular design, which is the six-degree-of-freedom docking reference, is transformed from its translation vector [2000.0, 0.0, -15.5] and rotation description (-3.5 degrees around the Y-axis, 0.2 degrees around the Z-axis) back into a 4x4 target homogeneous transformation matrix. Simultaneously, the real-time relative pose vector, for example [1988.2, 7.6, -14.8, 0.1, -3.7, 0.3], is also transformed back into a 4x4 real-time homogeneous transformation matrix. Then, the inverse of the target homogeneous transformation matrix is ​​calculated. Next, matrix multiplication is performed between the real-time homogeneous transformation matrix and the inverse of the target homogeneous transformation matrix. Ideally, if the on-site pose of the prefabricated module is completely consistent with the target pose, the result should be an identity matrix. However, due to the inherent bias, the result is a bias matrix that approximates the identity matrix. From this bias matrix, translation and rotation components are extracted using the same method as in step S213. For example, the resulting bias matrix has translation components of [-11.8, 7.6, 0.7] and rotation components of [0.1, -0.2, 0.1] (units: millimeters and degrees). These six values ​​represent the differences in each of the six degrees of freedom at the current moment. Combining these values ​​yields the following six-degree-of-freedom alignment biases: X-axis translation bias -11.8 mm, Y-axis translation bias 7.6 mm, Z-axis translation bias 0.7 mm, rotation bias around the X-axis 0.1 degrees, rotation bias around the Y-axis -0.2 degrees, and rotation bias around the Z-axis 0.1 degrees. These bias values ​​quantify the difference between the on-site installation accuracy of the prefabricated module and the design baseline.

[0027] S312: Based on the six-degree-of-freedom alignment deviation value, and according to the kinematic model of the lifting equipment and the hydraulic platform, the overall deviation is decomposed into the corresponding posture adjustment amounts of the lifting equipment and the hydraulic platform, and the posture adjustment amounts of the crane and the hydraulic platform are obtained. Based on the statistically calculated six-degree-of-freedom alignment deviation values ​​and the kinematic models of the lifting equipment and hydraulic platform, the attitude adjustment amounts are decomposed. This decomposition strategy is designed to address the typically large size and heavy weight of modular prefabricated electrical equipment units, aiming to achieve effective coordination between coarse and fine adjustments. The kinematic model of the lifting equipment shows that it is primarily responsible for large-range X, Y, and Z-axis translation, with relatively low control accuracy of approximately 5 mm. The kinematic model of the hydraulic platform, on the other hand, demonstrates its proficiency in small-range, high-precision Z-axis fine-tuning and attitude adjustment around the X, Y, and Z axes, achieving a translational accuracy of 0.1 mm and a rotational accuracy of 0.05 degrees. The decomposition strategy is as follows: First, the large-range translational deviation from the six-degree-of-freedom alignment deviation values ​​is allocated to the lifting equipment. For example, for the deviation values ​​(X: -11.8 mm, Y: 7.6 mm, Z: 0.7 mm, Roll: 0.1 degrees, Pitch: -0.2 degrees, Yaw: 0.1 degrees), the deviations of the X and Y axes exceed the crane's accuracy threshold of 5 mm. Therefore, the adjustment amounts of -11.8 mm for the X-axis and 7.6 mm for the Y-axis are all allocated to the crane. The deviation of 0.7 mm for the Z-axis is less than the adjustment range of the hydraulic platform, and the attitude adjustment requirement is higher. Therefore, the adjustment tasks for the Z-axis and all rotational deviations are allocated to the hydraulic platform. After this decomposition, the obtained crane posture adjustment amounts are: X-axis translation -11.8 mm, Y-axis translation 7.6 mm, and the remaining four degrees of freedom adjustment amounts are 0. The obtained hydraulic platform posture adjustment amounts are: Z-axis translation 0.7 mm, rotation around the X-axis 0.1 degrees, rotation around the Y-axis -0.2 degrees, rotation around the Z-axis 0.1 degrees, and the remaining two degrees of freedom adjustment amounts are 0. This decomposition process ensures that different actuators perform the adjustment tasks they are best at, thereby operating the prefabricated modules efficiently and safely.

[0028] S313: Based on the crane posture adjustment amount and the hydraulic platform posture adjustment amount, they are converted into control voltage or current signals of the corresponding drive units, and integrated into a set of collaborative closed-loop control command sequences to construct a posture adjustment closed-loop control command set. Based on the acquired crane and hydraulic platform posture adjustment values, these are converted into control signals for the corresponding drive units, forming the underlying commands for precise movement of the prefabricated modules. For the crane's posture adjustment (X-axis -11.8 mm, Y-axis 7.6 mm), the inverse kinematics model of its motion controller calculates the number of pulses and frequency required to drive the X-axis and Y-axis variable frequency motors. For example, if the X-axis motor moves 10 mm per revolution and the encoder resolution is 10,000 pulses / revolution, then an adjustment of -11.8 mm requires -11,800 pulses. The frequency of these pulse signals is generated based on a preset acceleration curve to ensure smooth start-stop. For the hydraulic platform's posture adjustment (Z-axis 0.7 mm, 0.1 degrees around the X-axis, -0.2 degrees around the Y-axis, 0.1 degrees around the Z-axis), the inverse kinematics model of its six-degree-of-freedom parallel mechanism calculates the extension / retraction length required to drive each of the six hydraulic cylinders. For example, calculations show that hydraulic cylinder #1 needs to extend by 0.52 mm, and hydraulic cylinder #2 needs to shorten by 0.31 mm. Then, based on the servo valve characteristic curve of each hydraulic cylinder, these length changes are converted into corresponding control voltage signals. For instance, the servo valve of hydraulic cylinder #1 requires a 3.5-volt control voltage input to achieve an extension of 0.52 mm. Finally, the pulse signal sequence of the crane motor and the voltage signal sequences of the six servo valves of the hydraulic platform are integrated synchronously in time to form a set of collaborative closed-loop control command sequences, thereby constructing a posture adjustment closed-loop control command set adapted to the installation requirements of modular prefabricated units.

[0029] Please see Figure 5 The specific steps of S4 are as follows: S411: Based on the pose adjustment closed-loop control instruction set, the device is driven to perform alignment, and the updated six-degree-of-freedom alignment deviation value is continuously compared with the preset error range. When the deviation value is less than the error range, the locking mechanism is triggered, and the readings of various sensors during the operation process are recorded to obtain the interface locking process sensing signal. Based on the constructed pose adjustment closed-loop control instruction set, the lifting equipment and hydraulic platform are driven to perform alignment operations in coordination. The control system cyclically executes the "sensing-decision-control" process at a frequency of 100 Hz: it acquires sensor data in real time and calculates new six-degree-of-freedom alignment deviation values, then compares them with the preset error range based on the pre-fabrication process set in step S113, i.e., translational deviations (ΔX, ΔY, ΔZ) within [±1.5, ±1.5, ±0.5] mm, and rotational deviations (ΔRoll, ΔPitch, ΔYaw) within [±0.1, ±0.1, ±0.2] degrees. In one adjustment cycle, the measured deviation values ​​are (X: -1.2 mm, Y: 0.8 mm, Z: 0.3 mm, Roll: 0.05 degrees, Pitch: -0.08 degrees, Yaw: 0.1 degrees). The comparison shows that the deviation values ​​of all six degrees of freedom have fallen within the preset error range. The system continuously monitors for five control cycles (50 milliseconds). If the deviation remains within the error range, alignment is considered successful, and the locking mechanism is immediately triggered to complete the physical hard connection between the two prefabricated modules. The drive motor of the locking mechanism receives a 24-volt DC start signal and begins the locking operation. Throughout the alignment and locking process, the system records the readings of all key sensors at a frequency of 1000 Hz, including the distance value from the laser distance sensor, the raw point cloud data from the structured light camera, the angular velocity and acceleration data from the inertial measurement unit, and the readings from the torque and pressure sensors on the locking mechanism. These data, along with timestamps, are stored in the log database, ultimately obtaining a complete sensor signal archive of the interface locking process.

[0030] S412: Monitor the interface locking process sensor signal, determine whether the torque sensor reading is greater than the preset torque threshold, whether the pressure sensor reading is greater than the preset pressure threshold and whether the limit switch signal is in the closed state. When all three conditions are met, the locking operation is confirmed to be successful and a mechanical locking status confirmation result is generated. After the locking mechanism is triggered, the sensor signals during the locking process are continuously monitored to determine whether the locking operation is successful. The determination process is based on three parallel condition checks. The first condition is to check the torque sensor reading deployed on the output shaft of the locking mechanism drive motor. A preset torque threshold is set for this sensor, based on the following: data collected from 100 prefabricated module docking experiments conducted in the factory revealed that the torque value at full locking is distributed between 45 Nm and 55 Nm, with a mean of 50 Nm and a standard deviation of 2 Nm. To ensure the reliability of locking, the preset torque threshold is set to the mean plus 3 standard deviations, i.e., 50 plus 3 multiplied by 2, which equals 56 Nm. Therefore, one of the determination conditions is whether the torque sensor reading is greater than 56 Nm. The second condition is to check the pressure sensor reading installed between the sealing surfaces of the two module interfaces. The preset pressure threshold is set with reference to the sealing ring design requirements of the modular interface, i.e., a pressure of 120 kPa is required to guarantee the IP67 protection rating. Considering safety margins, the preset pressure threshold is set to 150 kPa. Therefore, the second criterion is whether the pressure sensor reading is greater than 150 kPa. The third criterion is to check the status of the limit switch signal installed at the end of the locking pin. This switch closes when the locking pin is fully extended, outputting a high-level signal; therefore, the third criterion is whether the limit switch signal is closed. In actual operation, when the torque sensor reading is 58 Nm, the pressure sensor reading is 162 kPa, and the limit switch signal is closed, since all three conditions are met, the system confirms that the locking operation is successful and generates a mechanical locking status confirmation result of "success," signifying that the mechanical structure of the modular unit has been integrated.

[0031] S413: Based on the mechanical locking status confirmation result, upon successful confirmation, the automatic self-test sequence of the high voltage, low voltage and communication circuits is immediately triggered, and the circuit insulation resistance value and communication link quality parameters are collected to obtain the mechanical locking status confirmation result and electrical communication interface self-test data. Based on the mechanical locking status confirmation result, when the confirmation result is "successful," the control system immediately triggers the subsequent automated self-test sequence of electrical and communication interfaces. This is a key step in verifying the advantages of modular prefabrication—"plug and play." First, the high-voltage circuit self-test is initiated. The system automatically connects a built-in 5000V DC insulation tester to measure the insulation resistance values ​​of the A-phase to ground, B-phase to ground, C-phase to ground, and phase-to-phase (AB, BC, CA) of the three-phase high-voltage cable. For example, the measured insulation resistance value of the A-phase to ground is 1.5 gigaohms, far exceeding the specification requirement of 20 megohms. Next, the low-voltage control circuit self-test is initiated. By applying a 5V test voltage to each control pin and detecting the circuit current, it is confirmed that all control lines are conducting normally and without short circuits. Then, the communication interface self-test is initiated. For the gigabit Ethernet communication link, the self-test program automatically performs a network connectivity test, sending 1000 ICMP data packets to the peer module and calculating the packet loss rate and average round-trip delay. For example, the test result is a packet loss rate of 0% and an average round-trip delay of 0.8 milliseconds. All key measurement parameters during the self-test process, including insulation resistance values ​​for each circuit (e.g., 1.5 gigohms) and communication link quality parameters (0% packet loss rate, 0.8 millisecond latency), were recorded in detail. Finally, the "successful" mechanical locking status confirmation result was integrated with the self-test data from these electrical communication interfaces to form a comprehensive docking status report, functionally confirming the successful docking of the prefabricated modules.

[0032] Please see Figure 6 The specific steps of S5 are as follows: S511: Obtain the unique identification code and initial status information of the electrical equipment module, integrate the six-degree-of-freedom alignment deviation value at the moment of docking completion, compile the information into a structured dataset that records the docking process and accuracy, and establish a docking process status data set; Obtain the unique identification code "A-MOD-20231027-001" for electrical equipment module A and the unique identification code "B-MOD-20231027-002" for module B, and retrieve their initial state information, including initial pose and factory test data completed during the prefabrication stage. Simultaneously, extract the six-degree-of-freedom alignment deviation values ​​from the log data recorded in step S411, specifically the six-degree-of-freedom alignment deviation values ​​just before the locking mechanism is triggered at the moment of docking completion. These values ​​are: X-axis translation -1.2 mm, Y-axis translation 0.8 mm, Z-axis translation 0.3 mm, rotation around the X-axis 0.05 degrees, rotation around the Y-axis -0.08 degrees, and rotation around the Z-axis 0.1 degrees. Compile this information to create a structured dataset. This dataset constitutes the quality profile for the on-site installation of the modular prefabricated units. The dataset is in JSON format, and its fields include "Module A Identifier", "Module B Identifier", "Dock Start Time", "Dock End Time", "Final X-axis Translation Deviation", "Final Y-axis Translation Deviation", "Final Z-axis Translation Deviation", "Final X-axis Rotation Deviation", "Final Y-axis Rotation Deviation", and "Final Z-axis Rotation Deviation". The specific values ​​obtained above are filled into the corresponding fields to form a complete docking process record. For example, the record content is {"Module A Identifier": "A-MOD-20231027-001", ..., "Final Z-axis Translation Deviation": 0.3, ...}. By collecting key data throughout the docking process, a detailed docking process status data set is finally established, recording the docking event participants, time points, and final docking accuracy.

[0033] S512: Based on the mechanical locking status confirmation result and the electrical communication interface self-test data, the mechanical locking confirmation status and various self-test parameters of the electrical and communication interfaces are formatted to establish an interface status verification data packet; Based on the obtained mechanical locking status confirmation results and electrical communication interface self-test data, this information is formatted. First, the final confirmation result of the mechanical locking status, i.e., the Boolean value "Success", is extracted. Then, the values ​​of various key self-test parameters are extracted, including the peak torque of 58 Nm and the final sealing surface pressure of 162 kPa during the mechanical locking process; the minimum insulation resistance value of the high-voltage circuit of 1.5 GΩ; the self-test status of the low-voltage circuit "All Passed"; and the quality parameters of the communication link, with a packet loss rate of 0% and an average round-trip delay of 0.8 ms. These heterogeneous data are uniformly converted into a standardized key-value pair format for easy storage and parsing. This quantifies the complex interface status into traceable data indicators. For example, "Mechanical Locking Status": "Success", "Peak Torque (Nm): 58", "Sealing Surface Pressure (kPa): 162", "Minimum Insulation Resistance (GΩ): 1.5", "Communication Packet Loss Rate (Percentage): 0", "Communication Average Delay (ms): 0.8". All these formatted key-value pairs are encapsulated into an independent data unit, which clearly reflects the functional integrity and performance indicators of the modular interface at the mechanical, electrical, and communication levels after the docking is completed, thus establishing a well-structured and comprehensive interface status verification data packet.

[0034] S513: Integrate the docking process status data set and the interface status verification data package, merge all information into a unique and tamper-proof authentication record, and obtain the modular prefabricated docking authentication data of electrical equipment. The process integrates the docking process status data set and the interface status verification data package. This integration process merges the two data structures into a unified, richer authentication record, forming the final digital authentication for this modular prefabricated unit docking work of electrical equipment. Specifically, a new top-level data structure is created, with the docking process status data set as the content of its "Process Information" field and the interface status verification data package as the content of its "Status Verification" field. Furthermore, a globally unique timestamp, accurate to the nanosecond level, is added to this record, for example, "2023-10-27-T14:30:55.123456789Z". To ensure the immutability of the record, the entire merged data structure is serialized into a byte stream, and then the SHA-256 hash algorithm is used to calculate a 256-bit hash value from this byte stream. For example, the calculated hash value is "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855". Finally, this hash value is used as the unique identifier for this authentication record, stored along with the merged data content and timestamp. This final record contains precise data and comprehensive verification of the interface status throughout the entire docking process, and its integrity and authenticity are guaranteed through cryptographic means. This results in a complete set of prefabricated modular docking authentication data for electrical equipment, providing a reliable basis for subsequent operation, maintenance, and asset management.

[0035] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. A modular prefabrication method for electrical equipment, characterized in that, Includes the following steps: S1: Obtain the unique identification code, three-dimensional geometric model data and initial state information of the two electrical equipment modules to be docked, perform digital twin docking of the three-dimensional models of the two electrical equipment modules, and set and output the docking target pose and preset error range. S2: Collect measurement data from the laser distance sensor, structured light camera, and inertial measurement unit on the interface during the hoisting and movement of the electrical equipment modules, determine the six-degree-of-freedom relationship between the two electrical equipment modules, and construct the real-time relative pose vector; S3: Compare the real-time relative pose vector with the pose of the docking target, calculate the six-degree-of-freedom alignment deviation value between the two, perform kinematic decoupling on the six-degree-of-freedom alignment deviation value, construct the loop control command to drive the lifting equipment and the hydraulic platform, and obtain the pose adjustment closed-loop control command set. S4: Based on the posture adjustment closed-loop control instruction set, perform alignment operation and continuously compare the six-degree-of-freedom alignment deviation value with the preset error range to confirm successful mechanical locking, trigger the automatic self-test sequence of high voltage, low voltage and communication circuits, and obtain the mechanical locking status confirmation result and electrical communication interface self-test data. S5: Integrate the identification code, the initial state information, the six-degree-of-freedom alignment deviation value, and the mechanical locking state confirmation result with the electrical communication interface self-test data to obtain the modular prefabrication docking certification data of electrical equipment.

2. The modular prefabrication method for electrical equipment according to claim 1, characterized in that: During the comparison of the six-degree-of-freedom alignment deviation value with the preset error range: When the six-degree-of-freedom alignment deviation value is less than the preset error range, the locking mechanism of the electrical equipment module interface is triggered, and the torque sensor reading, the interface sealing ring pressure sensor reading, and the locking pin limit switch signal are recorded during the locking process. When the torque sensor reading is greater than a preset torque threshold, the pressure sensor reading is greater than a preset pressure threshold, and the limit switch signal is in a closed state, the mechanical locking is confirmed to be successful.

3. The modular prefabrication method for electrical equipment according to claim 1, characterized in that: During the kinematic decoupling process, the six-degree-of-freedom alignment deviation value is decomposed into the crane pose adjustment amount corresponding to the motion degree of the lifting equipment and the hydraulic platform pose adjustment amount corresponding to the motion degree of the hydraulic platform.

4. The modular prefabrication method for electrical equipment according to claim 1, characterized in that: The real-time relative pose vector includes relative translation and relative rotation components; the pose adjustment closed-loop control command set includes lifting equipment drive commands and hydraulic platform drive commands; the mechanical locking state confirmation result and electrical communication interface self-test data include locking torque compliance confirmation, sealing pressure compliance confirmation, locking pin in-position status confirmation, high-voltage circuit continuity test results, low-voltage circuit continuity test results, and communication link quality report; the modular prefabrication docking certification data of electrical equipment includes module identity information, initial working condition records, alignment accuracy data, and interface function verification report.

5. The modular prefabrication method for electrical equipment according to claim 1, characterized in that, The S1 step is as follows: S111: Obtain the unique identification code, three-dimensional geometric model data and initial state information of each of the two electrical equipment modules to be docked; render the three-dimensional geometric model data and the initial state information in the virtual space to establish a simulation scene containing the three-dimensional models of the two electrical equipment modules, and obtain the digital twin docking scene. S112: Based on the digital twin docking scenario, the three-dimensional models of the two electrical equipment modules are virtually assembled to simulate the ideal state of complete interface alignment and engagement, and the translation and rotation transformation matrices between the coordinate systems of the two model interfaces are extracted to generate a six-degree-of-freedom docking reference. S113: Based on the six-degree-of-freedom docking reference, combined with the installation process requirements and allowable tolerances of the electrical equipment module, the maximum allowable deviations in the six degrees of freedom of translation and rotation are set, and docking judgment conditions composed of the target state and tolerance range are established to obtain the docking target pose and preset error range.

6. The modular prefabrication method for electrical equipment according to claim 1, characterized in that, The S2 step is as follows: S211: During the hoisting and movement of electrical equipment modules, distance data measured by laser distance sensors deployed on module interfaces, 3D point cloud data captured by structured light cameras, and angular velocity and acceleration data output by inertial measurement units are collected simultaneously to obtain multi-source heterogeneous sensor data streams. S212: Based on the multi-source heterogeneous sensing data stream, the motion trend of the electrical equipment module is predicted using the data from the inertial measurement unit, and the prediction is corrected using the data from the laser distance sensor and the structured light camera. Multi-sensor data fusion processing is then performed to obtain the statistical results of the six-degree-of-freedom relationship state. S213: Based on the statistical results of the six-degree-of-freedom relationship, convert them into a standardized vector describing the relative position and attitude of the two electrical equipment modules in three-dimensional space, which includes three translational components and three rotational components, and construct a real-time relative pose vector.

7. The modular prefabrication method for electrical equipment according to claim 1, characterized in that, The S3 step is as follows: S311: Based on the docking target pose and the docking target pose within the preset error range, perform matrix operations with the real-time relative pose vector to calculate the difference between the two in each of the six degrees of freedom, and statistically analyze the alignment deviation value of the six degrees of freedom. S312: Based on the six-degree-of-freedom alignment deviation value, and according to the kinematic model of the lifting equipment and the hydraulic platform, the overall deviation is decomposed into the corresponding posture adjustment amounts of the lifting equipment and the hydraulic platform, and the posture adjustment amounts of the crane and the hydraulic platform are obtained. S313: Based on the crane posture adjustment amount and the hydraulic platform posture adjustment amount, respectively convert them into control voltage or current signals of the corresponding drive unit, and integrate them into a set of collaborative closed-loop control command sequences to construct a posture adjustment closed-loop control command set.

8. The modular prefabrication method for electrical equipment according to claim 1, characterized in that, The S4 step is as follows: S411: Based on the pose adjustment closed-loop control instruction set, drive the device to perform alignment, and continuously compare the updated six-degree-of-freedom alignment deviation value with the preset error range. When the deviation value is less than the error range, trigger the locking mechanism, and record the sensor readings during the operation to obtain the interface locking process sensing signal. S412: Monitor the sensor signal during the locking process of the interface, determine whether the torque sensor reading is greater than the preset torque threshold, whether the pressure sensor reading is greater than the preset pressure threshold, and whether the limit switch signal is in a closed state. When all three conditions are met, confirm that the locking operation is successful and generate a mechanical locking state confirmation result. S413: Based on the mechanical locking status confirmation result, upon successful confirmation, the automatic self-test sequence of the high voltage, low voltage and communication circuits is immediately triggered, and the circuit insulation resistance value and communication link quality parameters are collected to obtain the mechanical locking status confirmation result and electrical communication interface self-test data.

9. The modular prefabrication method for electrical equipment according to claim 1, characterized in that, The S5 step is as follows: S511: Obtain the unique identification code and initial status information of the electrical equipment module, integrate the six-degree-of-freedom alignment deviation value at the moment of docking completion, compile the information into a structured dataset that records the docking process and accuracy, and establish a docking process status data set; S512: Based on the mechanical locking status confirmation result and the electrical communication interface self-test data, the mechanical locking confirmation status and various self-test parameters of the electrical and communication interfaces are formatted to establish an interface status verification data packet; S513: Integrate the docking process status data set with the interface status verification data package, merge all information into a unique and tamper-proof authentication record, and obtain the modular prefabricated docking authentication data of electrical equipment.

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