Virtual reality based transmission dense corridor visualization management and control system

Abstract

The present application relates to the field of virtual reality, specifically to a power transmission dense channel visualization management and control system based on virtual reality, comprising a data acquisition and transmission module, a data processing and fusion module, a three-dimensional modeling update module, a real-time visualization feedback module and a path planning optimization module.In the present application, the running state of the power transmission line and equipment and the external environmental parameters are collected in real time, and the timeliness confirmation and consistency verification are performed to form a unified and effective data set, and a sustainable three-dimensional power transmission channel model is constructed in the virtual reality environment, so that the device state change and the spatial position relationship are synchronously presented, the three-dimensional display of the conductor sag vegetation distribution and the abnormal area is realized by combining dynamic rendering, the inspection path and the state information are updated in the same three-dimensional scene, the correlation expression ability between the environmental elements and the equipment running condition is enhanced, the artificial comparison intensity is reduced, and the comprehensive judgment accuracy and the continuity of the management and control record in the complex scene are improved.

Description

Technical Field

[0001] This invention relates to the field of virtual reality technology, and in particular to a virtual reality-based visualization and control system for dense power transmission channels. Background Technology

[0002] The field of virtual reality technology mainly involves the acquisition, mapping, polygon mesh rendering, and real-time viewpoint tracking and feedback of three-dimensional spatial coordinate data. By constructing independent three-dimensional digital scenes, continuous calculations are performed on the point cloud data, material texture, and lighting of spatial geometry. Combined with head-mounted display devices, binocular parallax images with depth information are output to the user. At the same time, the user's head and limb movement displacement data are continuously captured by gyroscopes and inertial sensors, thereby driving the corresponding geometric models in the digital scene to produce corresponding viewpoint changes or spatial displacements. The Dense Transmission Corridor Visual Management and Control System refers to the physical status verification and environmental monitoring of towers, conductors, insulators, and surrounding terrain and vegetation distribution within the corridors of cross-regional high-voltage transmission lines. Typically, visible light cameras and infrared thermal imagers are directly bolted to the metal crossarms of the transmission towers along the line. Two-dimensional on-site planar images and thermal maps are transmitted frame-by-frame via fiber optic cables laid along the towers to a computer monitor in a remote monitoring room for grid-based split-screen display. Staff use a mouse and keyboard to switch between different camera lenses' real-time feeds, while simultaneously visually inspecting the vegetation height, conductor sag, and foreign objects on the screen against a paper coordinate log, and filling out paper operation logs to complete daily status verification and monitoring.

[0003] In the current management and control of dense power transmission channels, the main reliance is on fixed camera equipment to collect two-dimensional images and thermal images, which are then transmitted via fiber optics to monitoring terminals for split-screen display. Staff need to frequently switch between multiple screens and manually compare and record data using paper ledgers. This operation method is based on planar images and lacks an overall representation of the relationship between spatial structure and equipment status. As a result, it is difficult to intuitively present the relationship between conductor sag and surrounding terrain. The judgment of the relationship between environmental changes and equipment anomalies depends on human experience. The information is scattered and lacks continuity. When facing complex terrain and multi-equipment scenarios, it is easy to make judgment errors and it is also difficult to form a traceable dynamic three-dimensional management and control record. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a virtual reality-based visualization and control system for dense power transmission channels.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a virtual reality-based visualization and control system for dense power transmission channels includes: The data acquisition and transmission module acquires sensor data deployed on power transmission lines and power equipment, collects equipment operating status and external environmental parameters in real time, and outputs equipment status and environmental data. Based on the device status and environmental data, the data processing and fusion module verifies the timeliness of sensor data from different power devices, checks the consistency of various types of data, and obtains a valid data set. The 3D modeling and updating module constructs a 3D environment model of the power transmission channel on the virtual reality platform based on the effective data set. When the state of the power equipment changes, the 3D model is updated in real time, and the relevant areas are dynamically rendered in the virtual reality environment to generate a real-time updated 3D visualization model. The real-time visualization feedback module, based on the real-time updated 3D visualization model, renders the real-time environment of the power transmission channel using virtual reality technology, and obtains real-time feedback information on power equipment status, line changes, and external environmental parameters to obtain real-time visualization information. Based on the real-time visualization information, the path planning and optimization module updates the status information of the power transmission channel in real time on the virtual reality platform, dynamically displays the 3D model and inspection path, and responds to power equipment faults and environmental changes. This results in a visualized management and control record of densely populated power transmission channels.

[0006] As a further aspect of the present invention, the verification of the consistency of various types of data specifically refers to confirming the consistency of various power equipment operating status data and external environmental parameters in time and space.

[0007] As a further aspect of the present invention, the equipment status and environmental data include the operating status of power equipment and external environmental parameters; the effective data set includes power equipment status data, environmental data, consistent data, and inconsistent data; the real-time updated three-dimensional visualization model includes a three-dimensional environmental model of the transmission channel and an updated three-dimensional model; the real-time visualization information includes power equipment status information, line change information, external environmental parameter feedback information, and dynamically rendered three-dimensional environmental information; the visualization management and control record of dense transmission channels includes updated transmission channel status information, inspection paths, three-dimensional models, fault response information, and environmental change response information.

[0008] As a further aspect of the present invention, the data acquisition and transmission module includes: The multi-source sensing and acquisition submodule acquires sensor data deployed on power transmission lines and power equipment, detects current signals, temperature signals, vibration signals, wind speed signals, and humidity signals, aligns each signal according to timestamps and writes it into a unified data frame structure to generate an environmental operation data frame. The state quantity construction submodule extracts current value, temperature value, vibration amplitude, wind speed value and humidity value based on the environmental operation data frame, performs field mapping and interval calibration, and obtains the equipment operation status code. The wireless transmission control submodule calculates the number of transmitted bits based on the device operating status code and the length of the environmental operating data frame, compares them with a preset channel bandwidth threshold to determine the transmission rate parameter, encapsulates the device operating status code and the environmental operating data frame into a transmission data packet and sends it to the data processing center to generate device status and environmental data.

[0009] As a further aspect of the present invention, the data processing and fusion module includes: The timeliness verification submodule reads the timestamps of sensor data from each power equipment based on the equipment status and environmental data, calculates the difference between the timestamp and the current acquisition time, compares it with the preset acquisition cycle threshold and marks the records that exceed the limit, and generates a timeliness valid identification result. The spatiotemporal consistency determination submodule filters corresponding records based on the time validity identification results, extracts spatial coordinate codes and timestamp sequences, calculates the time difference and spatial coordinate difference of similar power equipment, marks records with time differences greater than the time tolerance threshold or spatial coordinate differences greater than the spatial distance threshold as abnormal, and obtains the consistency determination result. The anomaly removal submodule filters the records marked as anomalies based on the consistency determination result, retains the remaining records as valid marked records, and reassembles them to generate a valid data set.

[0010] As a further aspect of the present invention, the three-dimensional modeling update module includes: The scene construction submodule extracts the power transmission channel coordinate sequence and equipment identification code based on the effective data set, reads the coordinate system parameters of the virtual reality platform, converts the coordinate sequence into three-dimensional vertex data and writes it into the scene cache area to establish a three-dimensional environment model of the power transmission channel.

[0011] The state mapping submodule reads the equipment identification code and equipment state value based on the three-dimensional environment model of the power transmission channel, calculates the product of the equipment state difference and spatial coordinate, and accumulates them to form the state space superposition amount. It then calculates and obtains the rendering intensity value and establishes a state mapping matrix in combination with material parameters. The dynamic rendering submodule updates the 3D vertex buffer and refreshes the pixel shading parameters according to the state mapping matrix, reads the mesh index of the 3D environment model of the power transmission channel for frame-level rendering, and generates a real-time updated 3D visualization model.

[0012] As a further aspect of the present invention, the formula for calculating the rendering intensity value is as follows: ; in, Represents the rendering intensity value. This represents the status value of the i-th device. Represents the baseline state value. This represents the spatial coordinates of the i-th device. Represents the number of devices indexed. Represents the channel length parameter. Represents the channel height parameter. Represents the current device offset. Represents the reference offset. This represents the state gradient coefficient per unit length.

[0013] As a further aspect of the present invention, the real-time visualization feedback module includes: The environment rendering submodule reads the 3D vertex data and material parameters based on the real-time updated 3D visualization model, detects the frame refresh cycle parameters and calculates the pixel fill rate, writes the 3D vertex data into the graphics buffer and outputs the stereoscopic image stream to generate a virtual environment frame sequence. The interactive access submodule establishes a viewpoint matrix based on the virtual environment frame sequence and synchronizes the head display pose parameters, collects the virtual reality device position coordinates and posture angle data, calculates the viewpoint offset and updates the view frustum clipping parameters, and generates an immersive viewpoint matrix. The information generation submodule extracts device status values, line displacement, and environmental parameter values ​​based on the immersive view matrix and the virtual environment frame sequence, calculates the status change rate, and combines it with the displacement change to obtain real-time visualization information.

[0014] As a further aspect of the present invention, the path planning optimization module includes: The status synchronization submodule reads the device status value, line displacement and environmental parameter value based on the real-time visualization information, detects the virtual reality platform timestamp and calculates the status time difference, determines that the record with the status time difference less than or equal to the preset synchronization time threshold is a valid record, deletes the record with the status time difference greater than the preset synchronization time threshold, and generates channel status record data. The path update submodule calculates the state change of each inspection node based on the channel status record data, sorts them in descending order according to the state change, rearranges the node access order, reconstructs the three-dimensional path coordinate sequence, and generates the inspection path sequence. The record generation submodule constructs a time index table based on the inspection path sequence and the channel status record data, counts the number of node visits and the number of fault responses, and writes them into the management and control log to generate a visual management and control record for dense power transmission channels.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by collecting data on the operating status of transmission lines and equipment and external environmental parameters in real time and verifying their timeliness and consistency, a unified and effective data set is formed. A continuously updated three-dimensional transmission channel model is constructed in a virtual reality environment, so that changes in equipment status and spatial position are presented synchronously. Combined with dynamic rendering, a three-dimensional display of conductor sag vegetation distribution and abnormal areas is achieved. At the same time, the inspection path and status information are updated in a linked manner within the same three-dimensional scene, which enhances the ability to express the correlation between environmental elements and equipment operating status, reduces the intensity of manual comparison, and improves the accuracy of comprehensive judgment and the continuity of control records in complex scenarios. Attached Figure Description

[0016] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the data acquisition and transmission module of the present invention; Figure 3 This is a flowchart of the data processing and fusion module of the present invention; Figure 4 This is a flowchart of the 3D modeling and updating module of the present invention; Figure 5 This is a flowchart of the real-time visual feedback module of the present invention; Figure 6 This is a flowchart of the path planning optimization module of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] Please see Figure 1 The virtual reality-based visualization and control system for dense power transmission corridors includes: The data acquisition and transmission module acquires sensor data deployed on power transmission lines and power equipment, collects equipment operating status (such as current, temperature, vibration, etc.) and external environmental parameters (such as wind speed, humidity, etc.) in real time, and transmits them to the data processing center via wireless communication to output equipment status and environmental data. The data processing and fusion module verifies the timeliness of sensor data from different power equipment based on equipment status and environmental data. It confirms the consistency of operating status data of various power equipment and external environmental parameters in time and space, eliminates inconsistent data, and obtains a valid data set. The 3D modeling and updating module constructs a 3D environment model of the power transmission channel on the virtual reality platform based on the effective data set. When the status of the power equipment changes (such as power equipment failure, current abnormality, etc.), the 3D model is updated in real time, and the relevant areas are dynamically rendered in the virtual reality environment to generate a real-time updated 3D visualization model. The real-time visualization feedback module is based on a real-time updated 3D visualization model. It renders the real-time environment of the power transmission channel through virtual reality technology. Inspection personnel enter the real-time environment through virtual reality equipment to obtain real-time feedback information on the status of power equipment, line changes and external environmental parameters, and obtain real-time visualization information. The path planning and optimization module, based on real-time visualization information, updates the status information of power transmission channels in real time on a virtual reality platform, dynamically displays 3D models and inspection paths, and responds to power equipment faults and environmental changes. This results in a visualized management and control record of densely populated power transmission channels.

[0020] Equipment status and environmental data include the operating status of power equipment and external environmental parameters; the effective data set includes power equipment status data, environmental data, consistent data, and inconsistent data; the real-time updated 3D visualization model includes the 3D environmental model of the transmission channel and the updated 3D model; real-time visualization information includes power equipment status information, line change information, external environmental parameter feedback information, and dynamically rendered 3D environmental information; the visualization management and control record of dense transmission channels includes updated transmission channel status information, inspection paths, 3D models, fault response information, and environmental change response information.

[0021] Please see Figure 2 The data acquisition and transmission module includes: The multi-source sensing and acquisition submodule acquires sensor data deployed on power transmission lines and power equipment, detects current signals, temperature signals, vibration signals, wind speed signals, and humidity signals, aligns each signal according to timestamps and writes it into a unified data frame structure to generate an environmental operation data frame. The internal interrupt controller is activated by a sensing hardware terminal array deployed at key nodes of transmission lines and power equipment (including insulator strings, fittings, conductor splices, and tower bodies), which wakes up the built-in multi-channel analog-to-digital converter. The sensing hardware terminal array includes a high-frequency zero-flux current transformer, a platinum resistance temperature sensor, a piezoelectric triaxial accelerometer, an ultrasonic anemometer, and a polymer capacitive humidity sensor. Each sensor acquires data according to an independent hardware sampling clock. The sampling frequency of the high-frequency zero-flux current transformer is set to 10000Hz to capture high-frequency transient current signals from the conductors; the sampling frequency of the platinum resistance temperature sensor is set to 1Hz; the sampling frequency of the piezoelectric triaxial accelerometer is set to 500Hz to capture weak mechanical vibrations of the tower body and conductors; and the sampling frequencies of the ultrasonic anemometer and humidity sensor are both set to 2Hz. The acquired multi-source raw analog electrical signals are converted into a 16-bit precision digital matrix by the analog-to-digital converter after passing through an impedance matching circuit and a low-pass anti-aliasing filter. Random impulse noise is removed from the acquired digital matrix by a sliding window filter within the hardware field-programmable gate array. Due to significant differences in the sampling frequencies of the various sensors, the data stream exhibits asynchronous characteristics in the time dimension. To address this, a circular buffer with timestamps is created in memory. Using 100Hz as the baseline resampling frequency, low-frequency sampled temperature, wind speed, and humidity data are upsampled using Lagrange polynomial interpolation. High-frequency sampled current and vibration data undergo a digital low-pass filtering followed by downsampling to combat aliasing. This ensures that the digital outputs of all sensors are aligned to the same absolute time axis with microsecond-level precision. The timestamps are derived from an internally integrated real-time clock module calibrated using the BeiDou Navigation Satellite System. The aligned digital values ​​are concatenated according to a preset binary data structure. The frame header is set to a specific synchronization word 0x5A5A. Then, a precise timestamp of 4 bytes, a current effective value of 2 bytes, a temperature measurement value of 2 bytes, a triaxial vibration acceleration value of 6 bytes, a wind speed value of 2 bytes, and a relative humidity value of 2 bytes are written sequentially. A cyclic redundancy check code of 2 bytes is appended to the end of the frame, thus forming an environmental operation data frame with a fixed length of 22 bytes, which is then pushed into the transmit first-in-first-out queue of the direct memory access controller.

[0022] The state quantity construction submodule extracts current, temperature, vibration amplitude, wind speed and humidity values ​​from the environmental operation data frame, performs field mapping and interval calibration, and obtains the equipment operation status code. Based on the environment running data frame residing in the memory buffer, the raw values ​​of each physical quantity in the load segment are extracted through addressing operations. For the current value, its corresponding 16-bit unsigned integer field is read and multiplied by the factory calibration factor of 0.05 to convert it into the actual ampere value; for the temperature value, the signed integer field is read, the zero-point offset is subtracted, and multiplied by 0.01 to obtain the actual temperature in degrees Celsius; for the vibration amplitude, the root mean square of the acceleration values ​​in the X, Y, and Z dimensions is calculated to obtain the comprehensive vibration intensity quantification value; for wind speed and humidity values, they are also converted into meters per second and percentage relative humidity according to the linear mapping formula. The obtained values ​​of the five actual physical quantities are sent to a multi-dimensional interval matching dictionary for interval calibration. The multi-dimensional interval matching dictionary presets safety boundary conditions under different environments and load conditions. Taking temperature as an example, it is divided into four intervals with different levels of severity: when the temperature is less than or equal to 70.0℃, it is determined to be in the normal interval, with a state weight of 0; when the temperature is greater than 70.0℃ but less than or equal to 90.0℃, it is determined to be in the warning interval, with a state weight of 1; when the temperature is greater than 90.0℃ but less than or equal to 110.0℃, it is determined to be in the alarm interval, with a state weight of 2; and when the temperature is greater than 110.0℃, it is determined to be in the danger interval, with a state weight of 3. For vibration amplitude, a baseline threshold of 0.5g is set; values ​​higher than this value are assigned a weight of 2, otherwise 0. A boundary comparison logic is used to obtain the current state weight corresponding to each physical quantity. The state weights corresponding to each physical quantity are then bitwise ORed according to a weighted concatenation method, where the current state occupies the high 4 bits, the temperature occupies the next 4 bits, and so on, finally concatenating them to generate a 16-bit binary device operating status code. For example, when the temperature of a node reaches 95.5℃ and the vibration is 0.6g, the temperature bit is assigned a value of 2, the vibration bit is assigned a value of 2, and after integrating the codes of other normal physical quantities, the generated equipment operation status code is 0x0220.

[0023] The wireless transmission control submodule calculates the number of transmitted bits based on the device operating status code and the length of the environmental operating data frame, compares them with the preset channel bandwidth threshold and determines the transmission rate parameter, encapsulates the device operating status code and the environmental operating data frame into a transmission data packet and sends it to the data processing center to generate device status and environmental data. The number of non-zero weight bits in the device status code is analyzed as a quantitative indicator of the severity of the anomaly. Simultaneously, the byte length of the environmental operation data frame in the first-in-first-out queue is read. Assuming the environmental operation data frame length is 22 bytes and the device status code length is 2 bytes, the two are directly added together, and the underlying network layer header (including a 20-byte Internet Protocol header and an 8-byte User Datagram Protocol header) and the Media Access Control (MAC) delimiter are appended, resulting in a basic byte count of 52 bytes for a single transmission, equivalent to 416 bits. Considering the redundancy of forward error correction coding in wireless transmission, the basic bit count is multiplied by a redundancy factor of 1.5, calculating the actual required transmission bits to be 624 bits. The underlying channel state information interface is called to obtain the current signal-to-interference-plus-noise ratio (SNR) and multipath delay spread parameters of the wireless radio frequency channel, and the currently available instantaneous channel bandwidth threshold is evaluated based on Shannon's theorem. The preset stable channel bandwidth threshold is set to 2,000,000 bits per second. When the calculated available channel bandwidth is higher than 2,000,000 bits per second, the radio frequency channel is determined to be in a high-quality state. A 64th-order quadrature amplitude modulation (QAM) mode is adopted, and the transmission rate parameter is configured to a higher level of 1,500,000 bits per second to minimize transmission latency. When the available channel bandwidth is lower than or equal to 2,000,000 bits per second but higher than 500,000 bits per second, the radio frequency channel is determined to be in a moderately congested state. The channel switches to a 16th-order QAM mode, and the transmission rate parameter is lowered to 800,000 bits per second. When the available channel bandwidth is lower than or equal to 500,000 bits per second, quadrature phase shift keying (QPSK) modulation is forcibly enabled to ensure anti-interference capability, and the transmission rate parameter is reduced to 250,000 bits per second. After configuring the baseband processing chip of the RF front end, the device operation status code is embedded as additional metadata into the application layer header area of ​​the environmental operation data frame. The whole is encapsulated into a standard user datagram protocol transmission data packet, and the RF antenna is driven to radiate high-frequency electromagnetic waves into space according to the determined transmission rate parameters. The signal is directionally transmitted to the socket listening port of the remote data processing center, and parsed and restored into a device status and environmental data structure array in the memory of the receiving end.

[0024] Please see Figure 3 The data processing and fusion module includes: The timeliness verification submodule reads the timestamps of sensor data from each power equipment based on equipment status and environmental data, calculates the difference between the timestamp and the current acquisition time, compares it with the preset acquisition cycle threshold and marks records that exceed the limit, and generates a timeliness valid identification result. After the network interface card receives and reassembles the device status and environment data structure array, it reads the header feature field of each record through a multi-threaded parsing module, locking the 4-byte integer precise timestamp located in bytes 4 to 7. This timestamp records the absolute number of microseconds in which the sensing hardware terminal array completed analog-to-digital conversion. The system's high-precision clock interface of the data processing center's operating system kernel is called to obtain the absolute time in microseconds of the current central processing unit's timeliness verification. The absolute value of the difference between the two is calculated to determine the end-to-end transmission time difference from front-end acquisition, network transmission, to central receiving and processing. The preset acquisition cycle threshold specified in the system configuration file is read; this threshold is set to 500,000 microseconds based on the tolerance of real-time power equipment status monitoring. The calculated transmission time difference is then compared with 500,000 microseconds using a floating-point operation. If the transmission time difference is less than or equal to 500,000 microseconds, it indicates that the timeliness of the record meets the requirements of dynamic monitoring. A 1 is written to the Boolean flag at the end of the data structure to represent validity. If the transmission time difference is greater than 500,000 microseconds, it indicates that the record has experienced a severe delay during network queuing or retransmission, and the physical quantity state it contains can no longer accurately reflect the current transient characteristics of the power equipment. A 0 is written to the corresponding Boolean flag to represent invalidity. The process iterates through and calculates the 1000 records arriving in the current batch, outputting all structures containing Boolean flags as the timeliness validity identifiers to the shared memory area for subsequent filtering.

[0025] The spatiotemporal consistency determination submodule filters corresponding records based on the time validity identification results, extracts spatial coordinate codes and timestamp sequences, calculates the time difference and spatial coordinate difference of similar power equipment, marks records with time differences greater than the time tolerance threshold or spatial coordinate differences greater than the spatial distance threshold as abnormal, and obtains the consistency determination result. The system reads the valid time stamp results from the shared memory area and initiates a memory iterator to scan the result set containing valid stamps row by row. It filters out structure records with a Boolean flag of 1 using bitmasking operations and discards records with a flag of 0, thereby freeing up some heap memory space. For the filtered valid structures, it uses regular expressions to match the device's static archive database and extracts their spatial coordinate codes (including longitude, latitude, and elevation in three-dimensional geographic space) based on their unique media access control addresses. The extracted spatial coordinate codes are combined with existing precise timestamps to form a spatiotemporal feature matrix. For adjacent records belonging to the same type of power equipment (such as multiple tower nodes on a 220kV transmission line), the absolute time difference between their precise timestamps is calculated, and the spatial geometric distance difference between the spatial coordinate codes of the two nodes is calculated using the three-dimensional Euclidean distance formula. The system's preset expert knowledge base constants are invoked, with the extraction time tolerance threshold set to 100,000 microseconds and the spatial distance threshold set to 50 meters. A dual logical judgment is performed: if the time difference between two adjacent records is greater than 100,000 microseconds, it indicates a temporal break in the data stream; or if the spatial geometric distance difference between them is greater than 50 meters, it indicates an unreasonable spatial jump within the same micro-monitoring batch (e.g., data from remote devices being mixed in due to incorrect sensor identification codes). If either of these conditions is met, a one-byte exception flag (0xFF) is appended to that row of the spatiotemporal feature matrix. If neither condition is met, a normal flag (0x00) is appended. This outputs a two-dimensional relationship table covering all filtered records, serving as the consistency judgment result.

[0026] The anomaly removal submodule filters records marked as anomalies based on the consistency determination result, retains the remaining records as valid marked records, and reassembles them to generate a valid data set. The asynchronous purging thread in the data cleaning engine is activated, directly mapping the memory region containing the consistency judgment result to a directly manipulated array. The array is traversed and scanned to identify and locate all memory row pointers ending with a 0xFF exception marker. The underlying memory reallocation function is called to directly mark and erase the physical memory blocks occupied by these records with 0xFF markers, preventing them from entering the subsequent high-level computation pool. After erasure, for the remaining valid marked records with 0x00 normal markers, a memory contiguous processing operation is initiated, concatenating the addresses of fragmented valid data blocks. A new 16-byte full-segment synchronization check header is inserted at the beginning of the concatenated valid contiguous data block, and a hash value (calculated using the SHA-256 algorithm) for integrity verification is appended to the end. This re-encapsulates the data into a continuous and compact binary stream of valid data, which is then written to the cache of the solid-state drive, establishing an index barrier for subsequent high-concurrency read requests.

[0027] Please see Figure 4 The 3D modeling update module includes: The scene construction submodule extracts the power transmission channel coordinate sequence and equipment identification code based on the valid data set, reads the coordinate system parameters of the virtual reality platform, converts the coordinate sequence into three-dimensional vertex data and writes it into the scene cache area to establish a three-dimensional environment model of the power transmission channel.

[0028] The graphics processor's compute shader pipeline is initiated, directly reading the valid data set binary stream from the SSD cache. Bitwise unpacking instructions are used to strip away any initial and final information, and the spatial coordinate sequence along with its associated device identifier is extracted iteratively. Using an application programming interface (API), the scene manager of the virtual reality engine platform is connected to obtain the base scaling, origin offset, and rotation quaternion parameters of the 3D Cartesian coordinate system used by the current rendering pipeline. A 4x4 3D affine transformation matrix is ​​constructed, and the 3D latitude and longitude spatial coordinate sequence obtained from the geographic information system is input into this matrix for matrix multiplication. During the calculation, a translation operation is first performed to align the local coordinate system origin to the center of the world coordinate system. Then, uniform scaling is applied to map the latitude and longitude values, originally in degrees, to floating-point viewport coordinates in meters. Finally, a rotation transformation is performed to make it orthogonal to the initial orientation of the virtual camera. The calculated result is an array of 3D vertex data containing X, Y, and Z components. For each vertex data point, following standard 3D model vertex buffer layout rules, normal vectors and texture coordinate placeholders are added, and the data is uniformly packaged, transmitted via a high-speed peripheral component interconnect standard bus, and written to the scene buffer in video memory. The scene manager's topology generation function is triggered, constructing a polygonal mesh surface based on the spatial connectivity of the 3D vertex data, thereby instantiating a 3D environment model of a power transmission channel that is mapped to the physical world at a 1:1 scale within the virtual video memory space.

[0029] The state mapping submodule reads the equipment identification code and equipment status value based on the three-dimensional environment model of the power transmission channel, calculates the product of the equipment status difference and the spatial coordinate, and accumulates them to form the state space superposition quantity, using the formula: ; The rendering intensity value is calculated and combined with material parameters to establish a state mapping matrix; where... Represents the rendering intensity value. This represents the status value of the i-th device. Represents the baseline state value. This represents the spatial coordinates of the i-th device. Represents the number of devices indexed. Represents the channel length parameter. Represents the channel height parameter. Represents the current device offset. Represents the reference offset. Represents the state gradient coefficient per unit length; Based on the three-dimensional environment model of the power transmission channel, the corresponding equipment status values ​​are searched and read from the memory retrieval tree according to the equipment identification code.

[0030] Obtain the reference state value of the reference device This value is determined by averaging sensor data over a historical period of trouble-free operation; in this embodiment, it is set to 20. This is the first device status value currently read. The value is 45, the second device status value. The value is 60 (taking temperature as an example); extract the spatial coordinates of the first device. The spatial coordinates of the second device are 15.0. 25.0; Total number of devices index The value is 2.

[0031] Measure the absolute geometric length of the channel mesh in the 3D environment model and set the channel length parameter. The height is 100.0 meters; measure the vertical distance of the channel grid from the ground to the highest point, and set the channel height parameter. It is 30.0 meters.

[0032] By comparing laser point cloud data, the current equipment offset relative to the coordinates on the design drawings is calculated. It is 1.2 meters, reference offset. The upper limit of the allowable error for safe construction is set at 0.5 meters.

[0033] State gradient coefficient per unit length The sensitivity of physical state decay to changes in spatial distance is used to characterize the value. It is derived from a priori thermodynamic distribution experimental fitting, and a reasonable value of 2.5 is taken in this embodiment.

[0034] Substituting the obtained parameters (values ​​of 45, 60, 20, 15.0, 25.0, 2, 100.0, 30.0, 1.2, 0.5, 2.5) into the above formula, the final rendering intensity value is calculated. The result is 11.3. This indicates that the combined effect of the device's current state and spatial deformation reaches a medium-to-high intensity level. The calculated rendering intensity value of 11.3 is used as a normalization input factor and substituted into the graphics processor's programmable fragment shader material property pipeline. A color mapping function is set: when the rendering intensity value is between 0 and 5.0, the diffuse coefficient uses RGB(0, 255, 0) to represent green safety; when it is between 5.0 and 10.0, it gradually transitions to RGB(255, 255, 0) to represent yellow warning; when it is greater than 10.0 (such as 11.3 in this example), the base luminescence coefficient of the model surface is forcibly overwritten, and the self-illumination color is set to RGB(255, 0, 0) pure red warning state. The overwritten color channel parameters, roughness parameters, and metallicity parameters are combined and written into a pre-allocated memory matrix block to complete the generation of the state mapping matrix. This result not only determines the final pixel color but also affects the normal deflection amount in subsequent lighting calculations in the shader.

[0035] Table 1 Verification table for state mapping matrix calculation parameters: ; As shown in Table 1, the effective distribution of the established rendering intensity value formula was verified by repeatedly measuring and comparing the values ​​of different device states and offset parameters.

[0036] The dynamic rendering submodule updates the 3D vertex buffer and refreshes the pixel shading parameters according to the state mapping matrix, reads the mesh index of the 3D environment model of the power transmission channel for frame-level rendering, and generates a real-time updated 3D visualization model. Using the underlying graphics application programming interface (API), state update commands are issued. Based on the generated state mapping matrix containing vertex colors and normal perturbations, the existing 3D vertex buffer in video memory is directly overwritten, and the old material parameter pointers are pointed to the newly generated mapping matrix block. Simultaneously, the register reloading action inside the fragment shader is triggered, injecting the new emissivity and specular index as pixel shading parameters into the rendering pipeline. The mesh index array composed of triangle faces generated during the construction phase of the power transmission channel 3D environment model is read. The instantiation drawing command of the graphics interface is called, triggering vertex shading and rasterization operations on a frame-by-frame basis according to the arrangement order of the mesh index array. During the rasterization phase, occlusion culling is performed based on the view depth buffer, retaining only primitives within the visible domain. The interpolated fragment data is then mixed with the color values ​​corresponding to the state mapping matrix and output to the final frame buffer. This high-frequency repetition generates a continuous frame rate of 60 frames per second, thus outputting a real-time updated 3D visualization model dataset with accurate physical state reflection capabilities in memory.

[0037] Please see Figure 5 The real-time visual feedback module includes: The environment rendering submodule reads 3D vertex data and material parameters based on the real-time updated 3D visualization model, detects frame refresh cycle parameters and calculates pixel fill rate, writes 3D vertex data into the graphics buffer and outputs stereoscopic image stream to generate virtual environment frame sequence. In the main rendering loop of the virtual reality engine, the root node of the data block of the real-time updated 3D visualization model in video memory is locked. Its child nodes are traversed using a depth-first search algorithm to extract all 3D vertex data and high-order material parameters such as ambient occlusion and subsurface scattering bound to the vertices. The operating system's display service manager is detected to obtain the frame refresh rate parameters reported by the display terminal hardware (e.g., locked at 16.67 milliseconds to match a 60Hz refresh rate). Combined with the viewport resolution (e.g., 3840 multiplied by 2160), a pixel fill rate multiplication calculation is performed to determine the total number of pixels required to render per second by the current hardware: 497,664,000. The rendering load is assessed based on the calculated pixel fill rate. If the load exceeds the graphics card's safe processing limit, the anti-aliasing sampling rate is automatically reduced to ensure the frame refresh rate parameter requirements are met. The valid 3D vertex data after frustum culling, along with the configured lighting probe array and texture sampler status, are submitted to the off-screen rendering target attachment in the graphics buffer. After the graphics card hardware performs rasterization and post-processing pixel mixing, it parses and downsamples the multisampled image data into a standard color space. Through the high-definition multimedia interface physical layer protocol, it converts the pixel matrix into a differential signal voltage stream and continuously outputs a continuous stereoscopic image stream. This image stream is strictly arranged in time according to the frame rate, forming a virtual environment frame sequence, which resides in the output scan buffer of the graphics card and waits to be read.

[0038] The interactive access submodule establishes a viewpoint matrix based on the virtual environment frame sequence and synchronizes the head display pose parameters, collects the virtual reality device position coordinates and posture angle data, calculates the viewpoint offset and updates the frustum clipping parameters, and generates an immersive viewpoint matrix. By calling the open application programming interface of the virtual reality headset, the six-degree-of-freedom tracking data stream uploaded from the device's hardware driver layer is intercepted. First, a sensor fusion algorithm is used to analyze the quaternion data stream generated by the headset's built-in inertial measurement unit and the three-dimensional spatial coordinates (X-axis, Y-axis, Z-axis accurate to millimeters) calculated by an external infrared tracking base station. The attitude angle data reflecting pitch, yaw, and roll are combined with the spatial coordinates to form a 4x4 inverse matrix. Using the origin of the global world coordinate system set by the virtual environment frame sequence as a reference, the inverse matrix is ​​multiplied by the global camera position matrix to calculate the viewpoint offset matrix relative to the absolute coordinate system of the transmission channel model. The default near clipping plane (e.g., 0.1 meters) and far clipping plane (e.g., 1000 meters) distance parameters of the graphics pipeline are read, and the translation and rotation components of the viewpoint offset matrix are superimposed onto the camera's projection matrix. The six clipping plane equation parameters of the view frustum are dynamically adjusted to remove the model culling planes located behind the observer's head and at the outer edge of the visual field after the viewpoint offset. After inverse coordinate system transformation and rewriting of the clipping plane equation, an immersive view matrix that can accurately map the user's current head pose changes is solidified in the video memory for use in the vertex position transformation of the next rendering frame.

[0039] The information generation submodule extracts device status values, line displacement and environmental parameter values ​​based on the immersive view matrix and virtual environment frame sequence, calculates the state change rate and combines it with the displacement change to obtain real-time visual information. A separate double-buffered information extraction thread is established to synchronously extract the translation components of the immersive view matrix from video memory. It also reverse-parses the device status values ​​bound by physical mapping (e.g., temperature 90℃), line displacement values ​​derived from spatial coordinate system comparison (e.g., 0.8-meter displacement due to wind deflection of conductors), and environmental parameter values ​​(e.g., relative humidity 65%) from the metadata area of ​​the virtual environment frame sequence. The cached device status values ​​with the same name from the previous rendering cycle (e.g., temperature 88℃) are retrieved. The state change rate is calculated using the difference formula: subtracting the previous cycle's temperature of 88℃ from the current temperature of 90℃ yields a difference of 2℃. Dividing this difference by the time interval of 0.01667 seconds gives a state change rate of 119.97 degrees per second. The obtained rate of change of state is formatted as a floating-point character, converted into a text string, and then merged with the displacement change (0.8 meters) representing the severity of physical deformation using a string concatenation command, with a specific separator "|" added to form a structured phrase such as "TempRate:119.97|Disp:0.8". This structured phrase is submitted to the 2D user interface layer engine, converted into a texture map, and overlaid and rendered at a fixed offset position directly in front of the viewport defined by the immersive viewpoint matrix. The final output is a real-time visualization information data packet containing the fusion of holographic physical quantity numbers and 3D images.

[0040] Please see Figure 6 The path planning optimization module includes: The status synchronization submodule reads device status values, line displacement and environmental parameter values ​​based on real-time visual information, detects virtual reality platform timestamps and calculates status time difference, determines records with status time difference less than or equal to preset synchronization time threshold as valid records, deletes records with status time difference greater than preset synchronization time threshold, and generates channel status record data. The system continuously monitors the arrival of real-time visualization information data packets on the main control server's memory data bus. Once a data packet is captured, it is broken down using a built-in regular expression matching segmenter to extract device status values ​​(e.g., 90), line displacement (e.g., 0.8), and environmental parameter values ​​(e.g., 65). The system then calls the underlying time manager of the virtual reality engine to read the system's current absolute time as the virtual reality platform's timestamp. Simultaneously, it reads the sensor acquisition timestamps accompanying the extracted data. Through simple subtraction, the system calculates the state time difference between the platform's presentation time and the physical data occurrence time. The system has a strict preset synchronization time threshold parameter; to ensure absolute real-time accuracy—what the operator sees is exactly what they are in—this threshold is set to 200 milliseconds. The calculated state time difference is then compared to this 200-millisecond threshold for verification. If the state time difference is less than or equal to 200 milliseconds, the system determines that the currently extracted numerical sequence has not experienced significant network lag or rendering stutter, and treats the group of device status values, line displacements, etc., as valid records, pushing them into a persistent queue with a doubly linked list structure. If the state time difference is greater than 200 milliseconds, it indicates that the data is expired and dirty, posing a risk of misleading operators' judgments. The system directly triggers a memory release function to destroy and delete the data, preventing it from polluting the state analysis pool. After performing the same time-based filtering on 1000 consecutively arriving packets, all qualified objects retained in the persistent queue are encapsulated into a standard communication generic sequence record set, thereby generating and distributing channel status record data.

[0041] Table 2. Analysis of the Influence of State Changes and Ranking Reconstruction: ; The path update submodule calculates the status change of each inspection node based on the channel status record data, sorts them in descending order according to the status change, rearranges the node access order, reconstructs the three-dimensional path coordinate sequence, and generates the inspection path sequence. As shown in Table 2, the data of Node-003, which had timed out, was excluded based on timestamps. For the retained channel status records, the spatial routing solver was activated. The differences in multidimensional physical quantities between the previous and current time windows for each inspection node were read. To quantify the alarm urgency of each node, a state change calculation model was introduced: the temperature change rate was multiplied by a weighting coefficient of 10, and the displacement change was multiplied by a weighting coefficient of 100; the two were then added together to obtain the node's comprehensive state change score. After calculating the scores for each node, the core function of the quicksort algorithm was called to sort them in descending order based on the comprehensive state change score. The original order from Node-001 to Node-005, based on geographical straight-line distance, was disrupted. The system extracts the three-dimensional spatial coordinate sequence corresponding to the rearranged nodes, adopts the core idea of ​​ant colony optimization algorithm with time window constraints, and uses the rearranged sequence as a strong constraint condition that must be accessed. The system then performs path insertion and smoothing again in the virtual three-dimensional model grid space, bypassing the three-dimensional voxel blocks of the restricted area, and reconstructs a new inspection path sequence matrix controlled by the node sequence and connected by three-dimensional polyline segments, which is then loaded into the navigation memory of the maintenance personnel's head-mounted display.

[0042] The record generation submodule constructs a time index table based on the inspection path sequence and channel status record data, counts the number of node visits and fault response times and writes them into the management and control log, generating a visual management and control record of dense power transmission channels; A built-in relational database connection instance is constructed, and a time index table with absolute time in seconds as the unique primary key is created using structured query language commands. The generated inspection path sequence matrix is ​​extracted, and the node number required for each stop is written into this index table as a foreign key. The raw values ​​of temperature, displacement, etc., of the corresponding node at that moment are extracted from the channel status record data as auxiliary fields. A background statistical algorithm process is started, and by executing aggregate queries (such as the SQL COUNT command) on the historical log table, the number of planned visits to each specific node within a natural month is counted. Simultaneously, fault repair confirmation identifiers are searched to count the number of fault responses for that node. After formatting the node visit count and fault response count as integers, they are written in batches to the management log storage area deployed on the disk array via asynchronous input / output streams. A tabulation tool is used to extract and format the multi-dimensional fields from the database into a standard portable document format, generating a four-dimensional visualization management record file of dense power transmission channels containing time, space, frequency, and physical characteristics. This provides basic fine-tuning data and long-term traceability evidence for subsequent large-scale power grid model training. This record fully replicates the entire mapping chain from the multimodal physical changes at the front end of the dense power transmission channel to the high-value scheduling at the back end, thus possessing extremely high operational repeatability and guiding significance.

[0043] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A virtual reality-based visualization and control system for dense power transmission channels, characterized in that, include: The data acquisition and transmission module acquires sensor data deployed on power transmission lines and power equipment, collects equipment operating status and external environmental parameters in real time, and outputs equipment status and environmental data. Based on the device status and environmental data, the data processing and fusion module verifies the timeliness of sensor data from different power devices, checks the consistency of various types of data, and obtains a valid data set. The 3D modeling and updating module constructs a 3D environment model of the power transmission channel on the virtual reality platform based on the effective data set. When the state of the power equipment changes, the 3D model is updated in real time, and the relevant areas are dynamically rendered in the virtual reality environment to generate a real-time updated 3D visualization model. The real-time visualization feedback module, based on the real-time updated 3D visualization model, renders the real-time environment of the power transmission channel using virtual reality technology, and obtains real-time feedback information on power equipment status, line changes, and external environmental parameters to obtain real-time visualization information. Based on the real-time visualization information, the path planning and optimization module updates the status information of the power transmission channel in real time on the virtual reality platform, dynamically displays the three-dimensional model and inspection path, and obtains a visual management and control record of dense power transmission channels in response to power equipment failures and environmental changes. The 3D modeling update module includes: The scene construction submodule extracts the power transmission channel coordinate sequence and equipment identification code based on the effective data set, reads the coordinate system parameters of the virtual reality platform, converts the coordinate sequence into three-dimensional vertex data and writes it into the scene cache area to establish a three-dimensional environment model of the power transmission channel. The state mapping submodule reads the equipment identification code and equipment state value based on the three-dimensional environment model of the power transmission channel, calculates the product of the equipment state difference and spatial coordinate, and accumulates them to form the state space superposition amount. It then calculates and obtains the rendering intensity value and establishes a state mapping matrix in combination with material parameters. The dynamic rendering submodule updates the 3D vertex buffer and refreshes the pixel shading parameters according to the state mapping matrix, reads the mesh index of the 3D environment model of the power transmission channel for frame-level rendering, and generates a real-time updated 3D visualization model.

2. The virtual reality-based visualization and control system for dense power transmission channels according to claim 1, characterized in that: Specifically, verifying the consistency of various data types refers to confirming the consistency of the operating status data of various power equipment and the external environmental parameters in terms of time and space.

3. The virtual reality-based visualization and control system for dense power transmission channels according to claim 1, characterized in that: The equipment status and environmental data include the operating status of power equipment and external environmental parameters; the effective data set includes power equipment status data, environmental data, consistent data, and inconsistent data; the real-time updated 3D visualization model includes a 3D environmental model of the transmission channel and the updated 3D model; the real-time visualization information includes power equipment status information, line change information, external environmental parameter feedback information, and dynamically rendered 3D environmental information; the visualization management and control record of dense transmission channels includes updated transmission channel status information, inspection paths, 3D models, fault response information, and environmental change response information.

4. The virtual reality-based visualization and control system for dense power transmission channels according to claim 1, characterized in that, The data acquisition and transmission module includes: The multi-source sensing and acquisition submodule acquires sensor data deployed on power transmission lines and power equipment, detects current signals, temperature signals, vibration signals, wind speed signals, and humidity signals, aligns each signal according to timestamps and writes it into a unified data frame structure to generate an environmental operation data frame. The state quantity construction submodule extracts current value, temperature value, vibration amplitude, wind speed value and humidity value based on the environmental operation data frame, performs field mapping and interval calibration, and obtains the equipment operation status code. The wireless transmission control submodule calculates the number of transmitted bits based on the device operating status code and the length of the environmental operating data frame, compares them with a preset channel bandwidth threshold to determine the transmission rate parameter, encapsulates the device operating status code and the environmental operating data frame into a transmission data packet and sends it to the data processing center to generate device status and environmental data.

5. The virtual reality-based visualization and control system for dense power transmission channels according to claim 1, characterized in that, The data processing and fusion module includes: The timeliness verification submodule reads the timestamps of sensor data from each power equipment based on the equipment status and environmental data, calculates the difference between the timestamp and the current acquisition time, compares it with the preset acquisition cycle threshold and marks the records that exceed the limit, and generates a timeliness valid identification result. The spatiotemporal consistency determination submodule filters corresponding records based on the time validity identification results, extracts spatial coordinate codes and timestamp sequences, calculates the time difference and spatial coordinate difference of similar power equipment, marks records with time differences greater than the time tolerance threshold or spatial coordinate differences greater than the spatial distance threshold as abnormal, and obtains the consistency determination result. The anomaly removal submodule filters the records marked as anomalies based on the consistency determination result, retains the remaining records as valid marked records, and reassembles them to generate a valid data set.

6. The virtual reality-based visualization and control system for dense power transmission channels according to claim 1, characterized in that, The real-time visualization feedback module includes: The environment rendering submodule reads the 3D vertex data and material parameters based on the real-time updated 3D visualization model, detects the frame refresh cycle parameters and calculates the pixel fill rate, writes the 3D vertex data into the graphics buffer and outputs the stereoscopic image stream to generate a virtual environment frame sequence. The interactive access submodule establishes a viewpoint matrix based on the virtual environment frame sequence and synchronizes the head display pose parameters, collects the virtual reality device position coordinates and posture angle data, calculates the viewpoint offset and updates the view frustum clipping parameters, and generates an immersive viewpoint matrix. The information generation submodule extracts device status values, line displacement, and environmental parameter values ​​based on the immersive view matrix and the virtual environment frame sequence, calculates the status change rate, and combines it with the displacement change to obtain real-time visualization information.

7. The virtual reality-based visualization and control system for dense power transmission channels according to claim 1, characterized in that, The path planning optimization module includes: The status synchronization submodule reads the device status value, line displacement and environmental parameter value based on the real-time visualization information, detects the virtual reality platform timestamp and calculates the status time difference, determines that the record with the status time difference less than or equal to the preset synchronization time threshold is a valid record, deletes the record with the status time difference greater than the preset synchronization time threshold, and generates channel status record data. The path update submodule calculates the state change of each inspection node based on the channel status record data, sorts them in descending order according to the state change, rearranges the node access order, reconstructs the three-dimensional path coordinate sequence, and generates the inspection path sequence. The record generation submodule constructs a time index table based on the inspection path sequence and the channel status record data, counts the number of node visits and the number of fault responses, and writes them into the management and control log to generate a visual management and control record for dense power transmission channels.

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