Pump truck electricity approaching early warning device based on RTK and point cloud data and control method

Abstract

The invention relates to a pump truck electricity approaching early warning device based on RTK and point cloud data and a control method, and belongs to the technical field of electricity approaching early warning. The device comprises a main machine and slave machines 1... n, the main machine is installed on a suspension arm head, the slave machine 1 is installed in a cab, the slave machines 2... n are installed at suspension arm joints, and n is an integer larger than or equal to 2. The control method comprises the following steps: S1, data acquisition and preprocessing: acquiring and exporting point cloud data; s2, secondary processing of the point cloud data: carrying out preliminary inspection, segmentation and twice sampling processing on the point cloud data; s3, point cloud data coordinate system conversion and storage: analyzing the point cloud data and storing the point cloud data; s4, RTK positioning information acquisition and distance calculation: acquiring positioning data in real time, and calculating the shortest distance; s5, distance comparison and early warning are carried out; accurate distance measurement and real-time early warning of a special vehicle and a power line are realized in a complex environment.

Description

Technical Field

[0001] The invention relates to a pump truck near-electricity warning device and a control method based on RTK and point cloud data, belonging to the technical field of near-electricity warning. Background Art

[0002] Heavy machinery and equipment such as cement pump trucks have been widely used in many fields such as construction, transportation and energy. Despite this, these special vehicles still face many safety hazards during operation, especially the risk of electric shock caused by being too close to high-voltage wires, which will not only cause power system failures, but may also damage equipment and even cause casualties, posing a threat to the stability of society and the power grid.

[0003] In order to prevent such accidents, a variety of near-electricity warning systems have been launched on the market. These systems mainly rely on technologies such as light, ultrasound, infrared, and electromagnetic induction to monitor the distance between special vehicles and power lines and the strength of electromagnetic fields in real time and issue warnings. However, these technologies still have room for improvement in terms of accuracy, stability, and real-time response.

[0004] Optical ranging technology: Sensitivity to environmental conditions such as light, temperature and humidity may result in unstable measurement results; accuracy is often sacrificed in pursuit of measurement range, making it difficult to meet the needs of both large range and high precision at the same time; limited application in special environments such as strong magnetic fields and strong electric fields may affect measurement accuracy; and the signal processing process is complex, requiring powerful computing power and efficient algorithm support.

[0005] LiDAR ranging technology: LiDAR ranging is greatly affected by weather; traditional mechanical LiDARs have high manufacturing costs. Although the research and development and mass production of solid-state LiDARs are gradually reducing costs, they still face the problem of how to further optimize cost performance; there are privacy and security concerns; data processing is complex and requires high computing power; and there are potential risks to personnel safety.

[0006] For example, Chinese patent publication number CN112785455A discloses a live wire monitoring and early warning system and method based on multiple laser radars, including several laser radars installed on different sides of a boom truck, and several laser radars are respectively connected to several data processing platforms; there is no line of sight between the several laser radars, and each laser radar senses information in one direction; the data processing platform collects laser radar information, and performs multi-laser radar point cloud fusion according to the calibrated spatial position relationship, and returns the collected point cloud to a coordinate system with the center point of the boom truck as the origin; multiple laser radars obtain conversion parameters through external parameter calibration to achieve spatial synchronization of multiple laser radars. In the live working environment of 10kv transmission lines, it can measure and warn the safe distance of live working of workers, monitor the safety of the working environment, and visually monitor the working environment, so as to realize real-time control of the working site and safety protection of workers, and improve the safety level of live working. However, there is a problem of being greatly affected by weather. Summary of the invention

[0007] The purpose of the present invention is to provide a pump truck near-electricity warning device and control method based on RTK and point cloud data, so as to realize accurate distance measurement and real-time warning between special vehicles and power lines in complex environments, thereby improving operation safety.

[0008] The pump truck near-electricity warning device based on RTK and point cloud data of the present invention comprises a host and slaves 1...n, wherein the host is installed at the boom head, the slave 1 is installed at the cab, and the slaves 2...n are installed at the boom joint, where n is an integer greater than or equal to 2;

[0009] Slave 2...n: used to collect the real-time geographical location of the pump truck, the spatial location of the operating part of the slave boom, and the electric field induction data to be transmitted to the host boom;

[0010] Host: used for edge computing, converting the data collected by slaves 2…n into the robot model in the ROS system, performing collision detection calculations, and obtaining near-electricity warning calculation results;

[0011] Slave 1: used to display the near-electricity warning calculation results in the cab, and realize the display and warning of the boom.

[0012] Preferably, each of the slaves 2...n comprises:

[0013] Slave real-time dynamic carrier phase differential module: used to obtain the pump truck’s geographical location;

[0014] Slave angle detection sensor: used to monitor the angle change of the boom in real time, and simulate the real-time posture of the boom in combination with the coordinates of the boom end points;

[0015] Slave temperature and humidity sensor: with temperature compensation function, used to ensure the calculation of the distance between the boom and the line;

[0016] Slave electromechanical field sensing module: used to collect electric field sensing data;

[0017] Slave wireless communication module: used for wireless communication between devices;

[0018] Slave controller: used to activate external sensors and collect data.

[0019] Preferably, the host comprises:

[0020] Host real-time dynamic carrier phase differential module: used to obtain the pump truck's geographic location;

[0021] Host angle detection sensor: used to monitor the angle change of the boom in real time, and simulate the real-time posture of the boom in combination with the coordinates of the boom endpoints;

[0022] Host temperature and humidity sensor: with temperature compensation function, used to ensure the calculation of the distance between the boom and the line;

[0023] Host electric field sensing module: used to collect electric field sensing data;

[0024] Host wireless communication module: used for wireless communication between devices;

[0025] Host controller: used for coordinate point calculation;

[0026] Host 4G communication module: used to establish network connection.

[0027] Preferably, the slave machine 1 comprises:

[0028] Wireless communication module: used to communicate with the host;

[0029] Controller: used to control the operation of the wireless communication module and display module;

[0030] Display module: used to display the near-electric warning calculation results in the cab.

[0031] The present invention provides a control method for a pump truck near-electricity warning device based on RTK and point cloud data, comprising the following steps:

[0032] Step S1, data collection and preprocessing: collecting point cloud data and exporting;

[0033] Step S2, secondary processing of point cloud data: preliminary inspection, segmentation and secondary processing of point cloud data;

[0034] Step S3, point cloud data coordinate system conversion and storage: parsing the point cloud data and storing it in the database;

[0035] Step S4, RTK positioning information acquisition and distance calculation: real-time acquisition of positioning data, calculation of the shortest distance;

[0036] Step S5, distance comparison and early warning: Calculate the shortest distance between the boom of the cement pump truck and the power line, and compare it with the threshold value corresponding to the early warning level.

[0037] Preferably, the step S1 specifically includes the following steps:

[0038] Step S11, data collection: using a drone carrying a laser radar device to collect point cloud data images and GPS data of power lines, and importing the collected image data and GPS information into the surveying and mapping software;

[0039] Step S12, data processing: performing image matching through surveying and mapping software, integrating the collected images and GPS data, generating preliminary point cloud data, and processing and optimizing the generated point cloud data;

[0040] Step S13, exporting point cloud data: selecting to export point cloud data in the surveying and mapping software, setting export parameters, and exporting to generate a point cloud data file.

[0041] Preferably, the step S2 specifically includes the following steps:

[0042] Step S21, data integrity and accuracy check: perform a preliminary check on the point cloud data to confirm that the point cloud data covers the target area;

[0043] Step S22, segmenting and removing the ground and tree parts: select the point cloud data to be processed, circle the point cloud data of the power line part, segment the selected power line part, and save the processed point cloud data for the next step of processing;

[0044] Step S23, two sampling processes: perform coarse sampling to generate coarsely sampled point cloud data, which is used to determine which power line the device is close to; perform fine sampling to generate finely sampled point cloud data, which is used to calculate the precise distance between the cement pump truck and the power line and the three-dimensional display.

[0045] Preferably, the step S3 specifically includes the following steps:

[0046] Step S31, uploading point cloud files: uploading point cloud files to the system;

[0047] Step S32, parsing point cloud data: reading the scaling factor and offset of the X, Y, and Z coordinates from the point cloud file header, and converting the coordinates (X, Y, Z) of each point into a global coordinate system;

[0048] Step S33, storing point cloud data: storing the coarsely sampled point cloud data in the database, generating a unique file ID, and then storing the finely sampled point cloud data in the database, generating a unique file ID, and associating the coarse sampling file ID.

[0049] Preferably, the step S4 specifically includes the following steps:

[0050] Step S41, installing RTK equipment: installing RTK equipment at the boom head and boom joints;

[0051] Step S42, real-time acquisition of positioning data: using RTK equipment to obtain the three-dimensional positioning data of the cement pump truck in real time, including latitude, longitude and altitude coordinates, and merging the coordinate data of the boom head and boom joint positions for edge computing and then transmitting them to the background;

[0052] Step S43, calculating the shortest distance: using the initial coarse sampling data to calculate the shortest distance from the two line segments formed at the boom head and the boom joint to the power line;

[0053] Step S44, calculating the precise shortest distance: using the precise point cloud coordinate data to calculate the shortest distance from the two line segments formed at the boom head and the boom joint to the power line.

[0054] Preferably, the step S43 specifically includes the following steps:

[0055] Step S431, convert the latitude, longitude and elevation coordinates into a spatial rectangular coordinate system, and the conversion formula is as follows:

[0056] X=(R+h)*cos(lat)*cos(lon)

[0057] Y=(R+h)*cos(lat)*sin(lon)

[0058] Z=(R+h)*sin(lat)

[0059] Where lat and lon are the radians of latitude and longitude respectively, h is the altitude, and R is the equatorial radius of the Earth;

[0060] Step S432, calculating the shortest distance from a point to a line segment in 3D space, including calculating the distance between the projection point and the projection point and the target point:

[0061] (1) Calculate the parameter t of the projection of the target point P onto the line segment AB:

[0062]

[0063] Where A, B, and P are the coordinates of the starting point, end point, and target point of the line segment respectively;

[0064] Limit the parameter t to the interval [0,1]:

[0065] t=max(0,min(1,t))

[0066] (2) Calculate the coordinates of the projection point P1:

[0067] P1=A+t*(BA)

[0068] (3) Calculate the distance from the target point to the projection point:

[0069]

[0070] Step S433: Compare the distances and take the shortest distance.

[0071] Compared with the existing technology, the pump truck near-electricity warning device and control method based on RTK and point cloud data of the present invention have the following beneficial effects:

[0072] The present invention proposes a pump truck near-electricity warning device and control method that combines RTK technology and point cloud data. It integrates point cloud data processing, high-precision RTK positioning, electric field sensing technology, real-time distance calculation and other technologies, and uses the high-precision measurement function of RTK equipment to achieve accurate distance measurement and real-time warning between special vehicles and power lines in complex environments, thereby improving operational safety. It can monitor the spatial position of the remote operating part of the cement pump truck and its distance from the live wire in real time, and issue an alarm when the distance is less than the safe value, effectively preventing the boom from contacting the live wire. It aims to improve the safety awareness and operating skills of cement pump truck drivers, strengthen the perception and judgment of the surrounding environment, reduce the risk of electric shock for construction personnel, ensure construction safety, and reduce the probability of safety accidents during power construction.

[0073] The specific beneficial effects are as follows:

[0074] 1. Use spatial position coordinate calculation technology to detect the distance between special vehicles and live wires, successfully avoiding the problem of inaccurate distance measurement caused by voltage and current fluctuations induced by electric fields in traditional equipment.

[0075] 2. It includes functions such as angle detection and temperature compensation, which fully considers the influence of the boom angle change and temperature (such as slight changes in the spatial position of the wire caused by thermal expansion and contraction) on the distance calculation, so that the distance calculation between the boom and the wire is more accurate.

[0076] 3. It integrates multiple alarm methods such as sound and intuitive display to effectively remind operators to always pay attention to the safe distance and promptly warn them to maintain an appropriate safe distance.

[0077] 4. High reliability: The use of high-precision RTK positioning technology and laser radar point cloud data ensures high accuracy of positioning and ranging. At the same time, with the help of real-time data upload and multi-level early warning mechanism, the false alarm rate is effectively reduced, ensuring that the system can maintain high reliability in various complex environments.

[0078] 5. Multi-point positioning technology: Multi-point positioning is achieved by installing RTK equipment on the boom head, boom joint and cab, thereby improving the stability and accuracy of positioning and effectively eliminating single-point positioning errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a structural block diagram of a pump truck near-electricity warning device based on RTK and point cloud data according to the present invention;

[0080] Figure 2 A structural block diagram of a host according to the present invention;

[0081] Figure 3 This is a structural block diagram of a slave machine 1 according to the present invention;

[0082] Figure 4 It is a structural block diagram of a slave 2 ... n according to the present invention;

[0083] Figure 5 This is a functional logic block diagram of host initialization and data acquisition according to the present invention;

[0084] Figure 6 This is a data acquisition block diagram of a slave 2...n according to the present invention.

[0085] Figure 7 A data acquisition block diagram of a slave device 1 according to the present invention;

[0086] Figure 8 This is a functional logic block diagram of a slave 2 ... n according to the present invention;

[0087] Fig. 9 This is a functional logic block diagram of a slave 1 according to the present invention;

[0088] Fig.10 It is a logic block diagram of a control method of a pump truck near-electricity warning device based on RTK and point cloud data according to the present invention;

[0089] Fig.11 This is a schematic diagram of the installation of a pump truck near-electricity warning device based on RTK and point cloud data described in the present invention. DETAILED DESCRIPTION

[0090] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0091] Example 1

[0092] like Figure 1 and Fig.11 As shown, this embodiment discloses a pump truck near-electricity warning device based on RTK and point cloud data, including a host and slaves 1...n, wherein the host is installed at the boom head, the slave 1 is installed at the cab, and the slaves 2...n are installed at the boom joint, where n is an integer greater than or equal to 2;

[0093] Slave 2...n: used to collect the real-time geographical location of the pump truck, the spatial location of the operating part of the slave boom, and the electric field induction data to be transmitted to the host boom;

[0094] Host: used for edge computing, converting the data collected by slaves 2…n into the robot model in the ROS system, performing collision detection calculations, and obtaining near-electricity warning calculation results;

[0095] Slave 1: used to display the near-electricity warning calculation results in the cab, and realize the display and warning of the boom.

[0096] like Figure 2 As shown, the host includes:

[0097] Host real-time dynamic carrier phase differential module: used to obtain the pump truck's geographic location;

[0098] Host angle detection sensor: used to monitor the angle change of the boom in real time, and simulate the real-time posture of the boom in combination with the coordinates of the boom endpoints;

[0099] Host temperature and humidity sensor: with temperature compensation function, used to ensure the calculation of the distance between the boom and the line;

[0100] Host electric field sensing module: used to collect electric field sensing data;

[0101] Host wireless communication module: used for wireless communication between devices;

[0102] Host controller: used for coordinate point calculation;

[0103] Host 4G communication module: used to establish network connection.

[0104] like Figure 3 As shown, the slave 1 includes:

[0105] Wireless communication module: used to communicate with the host;

[0106] Controller: used to control the operation of the wireless communication module and display module;

[0107] Display module: used to display the near-electric warning calculation results in the cab.

[0108] like Figure 4 As shown, each of the slaves 2 ... n includes:

[0109] Slave real-time dynamic carrier phase differential module: used to obtain the pump truck’s geographical location;

[0110] Slave angle detection sensor: used to monitor the angle change of the boom in real time, and simulate the real-time posture of the boom in combination with the coordinates of the boom end points;

[0111] Slave temperature and humidity sensor: with temperature compensation function, used to ensure the calculation of the distance between the boom and the line;

[0112] Slave electromechanical field sensing module: used to collect electric field sensing data;

[0113] Slave wireless communication module: used for wireless communication between devices;

[0114] Slave controller: used to activate external sensors and collect data.

[0115] The controller, host controller and slave controller in the device are all Air780E microcontrollers, whose key functions are to activate external sensors, collect data and calculate coordinate points.

[0116] In order to build network connection, a 4G communication module is used, which is achieved by working together with the Air780E microcontroller and 4G card.

[0117] In terms of wireless communication, ESP8266 was selected as the wireless communication module, and Wifi technology was used to achieve wireless communication between devices.

[0118] For the real-time kinematic carrier phase differential (RTK) technology, the LC29HEA module is used, which can effectively obtain the longitude and latitude coordinates in the GNSS positioning data.

[0119] The role of the angle detection sensor is played by the LIS2DH12TR, which can not only monitor the angle changes of the boom in real time, but also help to improve the accuracy of coordinate calculation, thereby improving the measurement accuracy of the distance between the boom and the surrounding lines. In addition, the sensor can also simulate the real-time posture of the boom by combining the coordinates of the boom endpoints.

[0120] In terms of environmental perception, the AHT10 temperature and humidity sensor is deployed. It has a temperature compensation function and can adapt to the changes in wire length in summer and winter, ensuring more accurate calculation of the distance between the boom and the line.

[0121] In terms of display device selection, we used a 3.5-inch TFT-LCD display screen, whose high brightness allows it to display content clearly even in direct sunlight or strong light environments. Compared with OLED screens, LCD screens are less likely to have screen burn-in problems, especially when displaying static images (such as information panels, navigation systems, etc.), their stability is superior.

[0122] The pump truck near-electricity warning device based on RTK and point cloud data has the following functional logic:

[0123] Host function logic:

[0124] (1) Initialization and data acquisition

[0125] like Figure 5 As shown in the figure, after the host is connected to the 4G network, it connects to the remote cloud server and uses the mobile RTK account to connect to the Zhicun Space CORS service to obtain centimeter-level high-precision positioning. Then it obtains data from the electric field sensing device, the angle of the angle detection sensor, and the temperature and humidity from the temperature and humidity sensor. The above process is the positioning data obtained by the host itself and the auxiliary positioning data.

[0126] (2) Data acquisition from slave 2 to slave n

[0127] like Figure 6 As shown, the host sends a command to slave 2 to obtain slave 2 RTK and other data. After obtaining the RTK and other data of slave 2, the host calculates the coordinates of slave 2 in combination with the centimeter-level high-precision positioning obtained from the Village Space CORS service. After that, the host performs edge computing through its own data, slave 2 coordinates and auxiliary calculation data to calculate the distance between the boom and the line.

[0128] (3) Data transmission from slave 1

[0129] like Figure 7 As shown, the host obtains the distance data between the boom and the line calculated by the cloud server, and sends the data to the display module via wireless communication.

[0130] Slave 2...n functional logic (taking slave 2 as an example):

[0131] like Figure 8 As shown, the slave 2 obtains the electric field sensing device data, positioning, angle, temperature and humidity data, automatically connects to the host network and waits for the command from the host to obtain data. After receiving the command from the host to obtain data, it sends its own data to the host, and repeats this process to perform real-time distance calculation and acquisition.

[0132] Slave 1 functional logic:

[0133] like Fig. 9As shown in the figure, after power is turned on, the display screen is first initialized, then establishes a network connection with the host, and waits to receive data from the host. Once the data is obtained, the display screen will display information including distance, temperature and humidity. When the distance between the boom and the line approaches the warning value, the system will immediately trigger an audible alarm and flash an alarm light on the display screen to remind the operator to keep a safe distance.

[0134] The above content is an overview of the functional logic of each part within the device, and the specific details of the relevant sensors and other functions are not expanded here.

[0135] Example 2

[0136] like Fig.10 As shown, this embodiment discloses a control method for a pump truck near-electricity warning device based on RTK and point cloud data, including data acquisition and preprocessing, secondary processing of point cloud data, point cloud data coordinate system conversion and storage, RTK positioning information acquisition and distance calculation, distance comparison and warning.

[0137] 1 Data collection and preprocessing

[0138] 1.1 Data Collection

[0139] Use a DJI drone carrying a lidar device to collect point cloud data images and GPS data of power lines, and import the collected image data and GPS information into the DJI Terra software.

[0140] 1.2 Data processing

[0141] DJI Terra will automatically perform image matching, integrate the collected images and GPS data, generate preliminary point cloud data, and use software to process and optimize the generated point cloud data, including noise removal, filtering, reconstruction and other steps.

[0142] 1.3 Export point cloud data

[0143] In DJI Terra, select Export point cloud data, select LAS format, set export parameters, including coordinate system, resolution, file naming, etc., and export to generate LAS file.

[0144] 2. Secondary processing of point cloud data

[0145] 2.1 Data integrity and accuracy check

[0146] Open the las file with CloudCompare and perform a preliminary check on the point cloud data using CloudCompare's basic browsing tools such as rotate, pan, zoom, etc. Confirm that the point cloud data covers the target areas such as power lines, ground, and trees.

[0147] 2.2 Removing the ground and trees in sections

[0148] Select the point cloud data to be processed, select the "Segment" tool and use the rectangular or polygonal selection tool to circle the point cloud data of the power line part, segment the selected power line part, and save the processed point cloud data for the next step.

[0149] 2.3 Two sampling processes

[0150] Coarse sampling processing: Select the processed point cloud data, select the "Subsample" tool to set the sampling type to "Spatial", and set the minimum point distance to a larger value (such as 1 meter) for coarse sampling to generate coarsely sampled point cloud data. The coarsely sampled data is used to determine which power line the device is close to.

[0151] Fine sampling processing: Select the processed point cloud data, select the "Subsample" tool to set the sampling type to "Spatial", and set the minimum point distance to a smaller value (such as 0.1 meters) for fine sampling to generate finely sampled point cloud data. The finely sampled data is used to accurately calculate the distance between the cement pump truck and the power line and for three-dimensional display.

[0152] 3. Point cloud data coordinate system conversion and storage

[0153] 3.1 Upload LAS point cloud file

[0154] Upload LAS point cloud files to the system. Usually, a power line is stored in one or more LAS files, which need to be parsed and processed.

[0155] 3.2 Calculation formula and algorithm for analyzing point cloud data

[0156] 1. Read LAS file header information:

[0157] Open the LAS file and read the scaling factor and offset of the X, Y, Z coordinates from the point cloud file header.

[0158] 2. Convert Mercator projection coordinates to UTM coordinates:

[0159] Convert each point's Mercator projection coordinates (X, Y, Z) to UTM coordinates.

[0160] formula:

[0161] X UTM =X*scale_factor X +offset X

[0162] Y UTM=Y*scale_factor Y +offset Y

[0163] Z UTM =Z*scale_factor Z +offset Z

[0164] 3. UTM coordinates are converted to the global coordinate system (WGS84):

[0165] Get UTM zone code: According to the division of UTM coordinate system, every 6 degrees of longitude is a UTM zone. The UTM zone number corresponding to the longitude of the area where the point cloud data is located (such as Linyi, Shandong) can be determined by the following calculation:

[0166] UTM area code calculation method:

[0167]

[0168] The longitude of Linyi, Shandong is approximately 118 degrees, so we put it into the calculation and round it up:

[0169]

[0170] Therefore, the UTM zone number for Linyi, Shandong, China is 50.

[0171] UTM to latitude and longitude conversion: Use a projection conversion library, such as Proj4 or GeoTools, to convert UTM coordinates to latitude and longitude coordinates.

[0172] The core program is as follows:

[0173]

[0174] 3.3 Point cloud data storage

[0175] The coarsely sampled point cloud data is stored in the database, a unique file ID is generated, and then the finely sampled point cloud data is stored in the database, a unique file ID is generated, and the coarse sampling file ID is associated.

[0176] 4RTK positioning information acquisition and distance calculation

[0177] 4.1 Install RTK equipment

[0178] Install the RTK equipment at the boom head and boom joints and ensure that the equipment can work properly.

[0179] 4.2 Obtaining positioning data in real time

[0180] Use RTK equipment to obtain the three-dimensional positioning data of the cement pump truck in real time, including longitude, latitude and altitude coordinates, and merge the coordinate data of the boom head and boom joints for edge computing and then transmit them to the background.

[0181] 4.3 Calculating the shortest distance

[0182] Rough sampling data calculation: Use the initial rough sampling data to calculate the shortest distance from the two line segments formed by the boom head and the boom joint to the power line. The specific calculation steps are as follows:

[0183] 1. Convert latitude, longitude and elevation coordinates into a spatial rectangular coordinate system

[0184] The conversion formula is as follows:

[0185] X=(R+h)*cos(lat)*cos(lon)

[0186] Y=(R+h)*cos(lat)*sin(lon)

[0187] Z=(R+h)*sin(lat)

[0188] Among them, lat and lon are the radians of latitude and longitude respectively, h is the altitude, and R is the equatorial radius of the earth (about 6378137.0 meters).

[0189] 2. Calculate the distance from a point to a line segment

[0190] Calculate the shortest distance from a point to a line segment in 3D space, including calculating the projection point and the distance between the projection point and the target point:

[0191] The calculation formula of the projection point is:

[0192] (1) Calculate the parameter t of the projection of the target point P onto the line segment AB:

[0193]

[0194] Where A, B, and P are the coordinates of the starting point, end point, and target point of the line segment, respectively.

[0195] Limit the parameter t to the interval [0,1]:

[0196] t=max(0,min(1,t))

[0197] (2) Calculate the coordinates of the projection point P1:

[0198] P1=A+t*(BA)

[0199] (3) Calculate the distance from the target point to the projection point:

[0200]

[0201] The core program is as follows:

[0202]

[0203] 3. Compare distances and take the shortest distance

[0204] Obtain the ID of the coarse sampling data point cloud file: obtain and record the ID of the coarse sampling data point cloud file where the shortest distance is located.

[0205] Get the ID of the fine sampling data point cloud file: Get the ID of the fine sampling data point cloud file associated with the coarse sampling data point cloud file through file association.

[0206] Get point cloud coordinate data: Get point cloud coordinate data from the database or file system according to the ID of the fine sampling data point cloud file

[0207] 4.4 Calculate the exact shortest distance

[0208] The shortest distance from the two line segments formed at the boom head and the boom joint to the power line is calculated using the precise point cloud coordinate data in the above method of calculating the shortest distance.

[0209] 5Distance comparison and early warning

[0210] The shortest distance between the boom of the cement pump truck and the power line is calculated and compared with the threshold corresponding to the warning level (red, orange, yellow). When the shortest distance is less than the threshold, the warning information is stored and displayed, and a safety warning is provided to the backend and the driver through message prompts, text messages or sound and light warning devices.

[0211] The specific formula is as follows:

[0212] set up:

[0213] D represents the shortest distance between the boom of the cement pump truck and the power line.

[0214] T red Indicates the red warning threshold.

[0215] T orange Indicates the orange warning threshold.

[0216] T yellow Indicates the yellow warning threshold.

[0217] 1. Red Alert:

[0218] If D <T red , a red alert is triggered

[0219] 2. Orange warning:

[0220] If T red ≤D <T orange , then an orange warning is triggered

[0221] 3. Yellow warning:

[0222] If T orange ≤D <T yellow , a yellow warning is triggered

[0223] Through the above formulas and operating steps, the proximity warning between cement pump trucks and power lines can be effectively realized, thereby improving the safety of operations.

[0224] The above description is only a preferred specific implementation manner of this embodiment, but the protection scope of this embodiment is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of this embodiment within the technical scope disclosed in this embodiment, and they should be covered by the protection scope of this embodiment.

Claims

1. A pump truck near-electricity warning device based on RTK and point cloud data, characterized in that: It includes a main machine and slaves 1...n, wherein the main machine is installed at the boom head, the slave 1 is installed at the cab, and the slaves 2...n are installed at the boom joint, where n is an integer greater than or equal to 2; Slave 2...n: used to collect the real-time geographical location of the pump truck, the spatial location of the operating part of the slave boom, and the electric field induction data to be transmitted to the host boom; Host: used for edge computing, converting the data collected by slaves 2…n into the robot model in the ROS system, performing collision detection calculations, and obtaining near-electricity warning calculation results; Slave 1: used to display the near-electricity warning calculation results in the cab, and realize the display and warning of the boom.

2. According to the RTK and point cloud data based pump truck near electric warning device of claim 1, it is characterized in that: Each of the slaves 2 ... n comprises: Slave real-time dynamic carrier phase differential module: used to obtain the pump truck’s geographical location; Slave angle detection sensor: used to monitor the angle change of the boom in real time, and simulate the real-time posture of the boom in combination with the coordinates of the boom end points; Slave temperature and humidity sensor: with temperature compensation function, used to ensure the calculation of the distance between the boom and the line; Slave electromechanical field sensing module: used to collect electric field sensing data; Slave wireless communication module: used for wireless communication between devices; Slave controller: used to activate external sensors and collect data.

3. The pump truck near-electricity warning device based on RTK and point cloud data according to claim 1 is characterized in that: The host comprises: Host real-time dynamic carrier phase differential module: used to obtain the pump truck's geographic location; Host angle detection sensor: used to monitor the angle change of the boom in real time, and simulate the real-time posture of the boom in combination with the coordinates of the boom endpoints; Host temperature and humidity sensor: with temperature compensation function, used to ensure the calculation of the distance between the boom and the line; Host electric field sensing module: used to collect electric field sensing data; Host wireless communication module: used for wireless communication between devices; Host controller: used for coordinate point calculation; Host 4G communication module: used to establish network connection.

4. The pump truck near-electricity warning device based on RTK and point cloud data according to claim 3 is characterized in that: The slave 1 comprises: Wireless communication module: used to communicate with the host; Controller: used to control the operation of the wireless communication module and display module; Display module: used to display the near-electric warning calculation results in the cab.

5. A control method for a pump truck near-electricity warning device based on RTK and point cloud data according to any one of claims 1 to 4, characterized in that: The steps include: Step S1, data collection and preprocessing: collecting point cloud data and exporting; Step S2, secondary processing of point cloud data: preliminary inspection, segmentation and secondary processing of point cloud data; Step S3, point cloud data coordinate system conversion and storage: parsing the point cloud data and storing it in the database; Step S4, RTK positioning information acquisition and distance calculation: real-time acquisition of positioning data, calculation of the shortest distance; Step S5, distance comparison and early warning: Calculate the shortest distance between the boom of the cement pump truck and the power line, and compare it with the threshold value corresponding to the early warning level.

6. The control method of the pump truck near-electricity warning device based on RTK and point cloud data according to claim 5 is characterized in that: The step S1 specifically includes the following steps: Step S11, data collection: using a drone carrying a laser radar device to collect point cloud data images and GPS data of power lines, and importing the collected image data and GPS information into the surveying and mapping software; Step S12, data processing: performing image matching through surveying and mapping software, integrating the collected images and GPS data, generating preliminary point cloud data, and processing and optimizing the generated point cloud data; Step S13, exporting point cloud data: selecting to export point cloud data in the surveying and mapping software, setting export parameters, and exporting to generate a point cloud data file.

7. The control method of the pump truck near-electricity warning device based on RTK and point cloud data according to claim 5 is characterized in that: The step S2 specifically includes the following steps: Step S21, data integrity and accuracy check: perform a preliminary check on the point cloud data to confirm that the point cloud data covers the target area; Step S22, segmenting and removing the ground and tree parts: select the point cloud data to be processed, circle the point cloud data of the power line part, segment the selected power line part, and save the processed point cloud data for the next step of processing; Step S23, double sampling processing: performing coarse sampling processing to generate coarsely sampled point cloud data, the coarsely sampled data is used to determine which power line the device is close to; Fine sampling processing is performed to generate finely sampled point cloud data, and the finely sampled data is used to calculate the precise distance between the cement pump truck and the power line and to display it in three dimensions.

8. The control method of the pump truck near-electricity warning device based on RTK and point cloud data according to claim 5 is characterized in that: The step S3 specifically includes the following steps: Step S31, uploading point cloud files: uploading point cloud files to the system; Step S32, parsing point cloud data: reading the scaling factor and offset of the X, Y, and Z coordinates from the point cloud file header, and converting the coordinates (X, Y, Z) of each point into a global coordinate system; Step S33, storing point cloud data: storing the coarsely sampled point cloud data in the database, generating a unique file ID, and then storing the finely sampled point cloud data in the database, generating a unique file ID, and associating the coarse sampling file ID.

9. The control method of the pump truck near-electricity warning device based on RTK and point cloud data according to claim 5 is characterized in that: The step S4 specifically includes the following steps: Step S41, installing RTK equipment: installing RTK equipment at the boom head and boom joints; Step S42, real-time acquisition of positioning data: using RTK equipment to obtain the three-dimensional positioning data of the cement pump truck in real time, including latitude, longitude and altitude coordinates, and merging the coordinate data of the boom head and boom joint positions for edge computing and then transmitting them to the background; Step S43, calculating the shortest distance: using the initial coarse sampling data to calculate the shortest distance from the two line segments formed at the boom head and the boom joint to the power line; Step S44, calculating the precise shortest distance: using the precise point cloud coordinate data to calculate the shortest distance from the two line segments formed at the boom head and the boom joint to the power line.

10. The control method of the pump truck near-electricity warning device based on RTK and point cloud data according to claim 9 is characterized in that: The step S43 specifically includes the following steps: Step S431, convert the latitude, longitude and elevation coordinates into a spatial rectangular coordinate system, and the conversion formula is as follows: X=(R+h)*cos(lat)*cos(lon) Y=(R+h)*cos(lat)*sin(lon) Z=(R+h)*sin(lat) Where lat and lon are the radians of latitude and longitude respectively, h is the altitude, and R is the equatorial radius of the Earth; Step S432, calculating the shortest distance from a point to a line segment in 3D space, including calculating the distance between the projection point and the projection point and the target point: (1) Calculate the parameter t of the projection of the target point P onto the line segment AB: Where A, B, and P are the coordinates of the starting point, end point, and target point of the line segment respectively; Limit the parameter t to the interval [0,1]: t=max(0,min(1,t)) (2) Calculate the coordinates of the projection point P1: P1=A+t*(BA) (3) Calculate the distance from the target point to the projection point: Step S433: Compare the distances and take the shortest distance.

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