Lidar-based water sampling machine internal environment detection method, system and product

Abstract

The application discloses a kind of water sampling machine internal environment detection method, system and product based on laser radar, it is related to three-dimensional change detection field.The method includes using laser radar to collect the original point cloud data of water sampling machine internal environment;Create the queue of point cloud data type, and receive the original point cloud data in time sequence;Extract the first frame point cloud in the queue as template frame, and extract current frame point cloud according to the interval set;The first frame point cloud and the current frame point cloud are divided into multiple key monitoring areas respectively;According to the monitoring object, the first frame point cloud feature in the first frame point cloud key monitoring area and the current frame point cloud feature in the current frame point cloud key monitoring area are extracted respectively;According to the first frame point cloud feature and the current frame point cloud feature, the internal environment of water sampling machine is dynamically monitored.The application can monitor the various abnormal changes of water sampling machine internal environment in real time, improve the accuracy of monitoring result.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional change detection, and in particular to a method, system, and product for detecting the internal environment of a water sampling machine based on lidar. Background Technology

[0002] The Lop Nur salt field uses wet mining machines for production. Currently, all of Lop Nur Potash's wet mining machines have basically achieved remote automated control. Only a few employees are assigned to the machines on a rotating shift system to deal with emergencies and take corresponding measures based on operating procedures and experience to prevent the situation from escalating and endangering production safety.

[0003] However, the level of intelligence of water sampling machines is still insufficient, and they still rely heavily on manual monitoring and intervention. In particular, the monitoring of the internal working environment of water sampling machines, including the salt accumulation in the bottom tank, the operation of the tracks, and the operation of components, is currently carried out by using multiple cameras to check at irregular intervals and from different directions. Due to the low resolution of the camera images and the fact that dirt often adheres to the camera surface, the range and distance that the naked eye can see through the camera are very limited. In addition, the cameras are also very susceptible to changes in lighting conditions. Dark or bright lighting environments will reduce the accuracy and real-time performance of monitoring. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and product for detecting the internal environment of a water sampling machine based on lidar, addressing the problems of camera-based monitoring of the internal working environment of a water sampling machine being susceptible to environmental influences and exhibiting poor accuracy and real-time performance.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for detecting the internal environment of a water sampling machine based on lidar includes:

[0007] Raw point cloud data of the internal environment of the water sampling machine is collected using lidar; the raw point cloud data includes the interior of the water sampling machine hull, the floating pipe on the lake surface, the shoreline, and the buildings on the shore.

[0008] Create a queue of point cloud data types and receive the raw point cloud data in chronological order;

[0009] Extract the first frame point cloud from the queue as the template frame, and extract the current frame point cloud at a set interval;

[0010] The first frame point cloud and the current frame point cloud are each divided into multiple key monitoring areas; the key monitoring area is either the key monitoring area of ​​the first frame point cloud or the key monitoring area of ​​the current frame point cloud; the key monitoring area of ​​the first frame point cloud and the key monitoring area of ​​the current frame point cloud are exactly the same.

[0011] Based on the monitored objects, extract the first frame point cloud features within the key monitoring area of ​​the first frame point cloud and the current frame point cloud features within the key monitoring area of ​​the current frame point cloud; the monitored objects include the salt mine accumulation amount, the mechanical component position offset amount, and the mechanical component attitude change amount.

[0012] The internal environment of the water sampling machine is dynamically monitored based on the point cloud features of the first frame and the point cloud features of the current frame.

[0013] Optionally, the process of creating a queue for point cloud data types and receiving the raw point cloud data in chronological order further includes:

[0014] The original point cloud data is cropped along the X-axis, Y-axis and Z-axis to generate cropped original point cloud data;

[0015] The cropped original point cloud data is filtered to generate processed original point cloud data.

[0016] Optionally, dividing the first frame point cloud and the current frame point cloud into multiple key monitoring areas specifically includes:

[0017] The point cloud of the first frame and the point cloud of the current frame are rasterized on the XY plane to generate several grids.

[0018] Based on the grid, the point cloud of the first frame and the point cloud of the current frame are respectively divided into multiple key monitoring areas.

[0019] Optionally, the step of dynamically monitoring the internal environment of the water sampling machine based on the point cloud features of the first frame and the point cloud features of the current frame specifically includes:

[0020] Calculate the feature difference between the point cloud features of the first frame and the point cloud features of the current frame in each of the key monitoring areas;

[0021] When the feature difference exceeds the set difference, an alarm is issued, and the point cloud and coordinate position of the key monitoring area corresponding to the feature difference are output.

[0022] A lidar-based water sampling machine internal environment detection system includes:

[0023] The raw point cloud data acquisition module is used to acquire raw point cloud data of the internal environment of the water sampling machine using lidar; the raw point cloud data includes the interior of the water sampling machine hull, the floating pipe on the lake surface, the shoreline, and the buildings on the shore.

[0024] The queue creation and raw point cloud data receiving module is used to create queues of point cloud data types and receive the raw point cloud data in chronological order.

[0025] The current frame point cloud extraction module is used to extract the first frame point cloud in the queue as a template frame, and extract the current frame point cloud at a set interval.

[0026] The key monitoring area division module is used to divide the first frame point cloud and the current frame point cloud into multiple key monitoring areas; the key monitoring area is either the key monitoring area of ​​the first frame point cloud or the key monitoring area of ​​the current frame point cloud; the key monitoring area of ​​the first frame point cloud and the key monitoring area of ​​the current frame point cloud are exactly the same.

[0027] The point cloud feature extraction module is used to extract the first frame point cloud features within the key monitoring area of ​​the first frame point cloud and the current frame point cloud features within the key monitoring area of ​​the current frame point cloud, respectively, based on the monitored objects; the monitored objects include the salt mine accumulation amount, the mechanical component position offset amount, and the mechanical component attitude change amount.

[0028] The dynamic monitoring module is used to dynamically monitor the internal environment of the water sampling machine based on the point cloud features of the first frame and the point cloud features of the current frame.

[0029] Optional, also includes:

[0030] The cropping module is used to crop the original point cloud data along the X-axis, Y-axis and Z-axis to generate cropped original point cloud data;

[0031] The filtering module is used to filter the cropped original point cloud data to generate processed original point cloud data.

[0032] Optionally, the key monitoring area delineation module specifically includes:

[0033] The rasterization processing unit is used to rasterize the first frame point cloud and the current frame point cloud on the XY plane respectively to generate a number of grids;

[0034] The key monitoring area division unit is used to divide the first frame point cloud and the current frame point cloud into multiple key monitoring areas based on the grid.

[0035] Optionally, the dynamic monitoring module specifically includes:

[0036] The feature difference calculation unit is used to calculate the feature difference between the first frame point cloud feature and the current frame point cloud feature in each of the key monitoring areas;

[0037] An alarm unit is used to issue an alarm when the feature difference exceeds a set difference, and to output the point cloud and coordinate position of the key monitoring area corresponding to the feature difference.

[0038] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to enable the electronic device to perform the aforementioned lidar-based water sampling machine internal environment detection method.

[0039] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for detecting the internal environment of a water sampling machine based on lidar.

[0040] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention provides a method, system, and product for detecting the internal environment of a water sampling machine based on lidar. It utilizes lidar to collect raw point cloud data of the internal environment of the water sampling machine, divides key monitoring areas, extracts the first frame point cloud features and the current frame point cloud features within the key monitoring areas based on the monitoring object, and compares them to dynamically monitor the internal environment of the water sampling machine. Even in environments with poor lighting conditions and blurry cameras, it can monitor various abnormal changes in the internal environment of the water sampling machine in real time, thereby improving the accuracy of the monitoring results. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 The flowchart of the water sampling machine internal environment detection method based on lidar provided by the present invention;

[0043] Figure 2 This is a flowchart of the method for detecting the internal environment of a water sampling machine based on lidar provided in Embodiment 2 of the present invention;

[0044] Figure 3 This is a schematic diagram of the installation position of the lidar sensor provided in Embodiment 2 of the present invention;

[0045] Figure 4 This is a diagram of the central computing processing system provided in Embodiment 2 of the present invention;

[0046] Figure 5 This is a schematic diagram of the key monitoring area division provided in Embodiment 2 of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] The purpose of this invention is to provide a method, system, and product for detecting the internal environment of a water sampling machine based on lidar, which can monitor various abnormal changes in the internal environment of the water sampling machine in real time and improve the accuracy of the monitoring results.

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Example 1

[0051] Figure 1 The flowchart of the water sampling machine internal environment detection method based on lidar provided by the present invention is as follows: Figure 1 As shown, this invention provides a method for detecting the internal environment of a water sampling machine based on lidar, comprising:

[0052] Step 101: Use lidar to collect raw point cloud data of the internal environment of the water sampling machine; the raw point cloud data includes the interior of the water sampling machine, the floating pipe on the lake surface, the shoreline, and the buildings on the shore.

[0053] Taking Lop Nur Salt Lake as an example, the lake surface cannot reflect laser beams, so the point cloud in this environment is particularly sparse. The places where point clouds exist include the inside of the water sampling machine, the floating pipes on the lake surface, the shoreline and the buildings on the shore. The scanned point cloud data is the internal environment of the water sampling machine. The lidar is stationary relative to the water sampling machine, and the point cloud data is a continuous input.

[0054] Step 102: Create a queue of point cloud data types and receive the raw point cloud data in chronological order.

[0055] In practical applications, although the input point cloud data is already relatively sparse, in order to reduce the false detection rate, the original point cloud data is preprocessed by cropping, filtering and other operations according to the actual internal environment of the water sampling machine, which improves the monitoring efficiency and accuracy.

[0056] In practical applications, a queue of point cloud data of a specified length is created, and the raw point cloud data is sent into the queue to achieve a "first-in, first-out" data extraction order. Processing the point cloud data in chronological order conforms to the logic of change detection.

[0057] When the system starts at a certain time, the first piece of raw point cloud data entering the queue is regarded as the first frame of point cloud, which is the initial value of a certain monitoring. The point cloud at the end of the queue is extracted as the point cloud data for each current frame, with the queue length as the interval. If the queue is not full, it returns to the beginning and waits for the input of raw point cloud data again.

[0058] Step 103: Extract the first frame point cloud from the queue as the template frame, and extract the current frame point cloud according to the set interval.

[0059] In practical applications, the first frame of point cloud in the queue is extracted as the template frame. The current frame of point cloud is extracted at a set interval, and the current frame is continuously updated. The interval is the length of the queue, which can be controlled by the frequency of point cloud data reception.

[0060] Step 104: Divide the first frame point cloud and the current frame point cloud into multiple key monitoring areas; the key monitoring area is either the key monitoring area of ​​the first frame point cloud or the key monitoring area of ​​the current frame point cloud; the key monitoring area of ​​the first frame point cloud is exactly the same as the key monitoring area of ​​the current frame point cloud.

[0061] In practical applications, if the three-dimensional coordinates of the original point cloud are used as the positioning information, the amount of point cloud data is large when locating a certain object, and it is impossible to classify many point clouds into the same object. Therefore, rasterization is performed on the XY plane to obtain several grids with controllable size and indexable position. Dividing the point cloud into several grids is beneficial for accurate positioning of the monitored object.

[0062] The internal environment of the water sampling machine is filled with numerous machines and has many interference factors. However, not all areas need to be monitored frequently during the production process. Therefore, in order to reduce the impact of interference factors, the key areas to be monitored are delineated based on the grid drawn by rasterization, making the calculation more focused and efficient. The key monitoring areas of the current frame and the first frame are completely consistent and overlap.

[0063] Step 105: Extract the first frame point cloud features within the key monitoring area of ​​the first frame point cloud and the current frame point cloud features within the key monitoring area of ​​the current frame point cloud, respectively, based on the monitored objects; the monitored objects include the amount of salt deposits, the positional offset of mechanical parts, and the change in the attitude of mechanical parts. The monitored objects also include the presence of workers, etc.

[0064] In practical applications, within the defined key monitoring areas, point cloud features are extracted from each monitoring area according to the different monitored objects. The point cloud feature types extracted from each key monitoring area in the first frame and the current frame are exactly the same, thus obtaining the point cloud features of each key monitoring area in the point clouds of the first frame and the current frame.

[0065] The objects of interest differ in each key monitoring area, such as the amount of salt accumulation, the displacement of the lifting column, the attitude changes of components, and the presence of workers. The point cloud features used for comparison are also different, such as the number of point clouds within the grid, height difference, normal, and reflection intensity. The feature types extracted in the current frame and the first frame are completely consistent. Therefore, extracting matching point cloud features in each region increases the accuracy of change detection.

[0066] Step 106: Dynamically monitor the internal environment of the water sampling machine based on the point cloud features of the first frame and the point cloud features of the current frame.

[0067] In practical applications, step 106 specifically includes: calculating the feature difference between the first frame point cloud feature and the current frame point cloud feature in each of the key monitoring areas; when the feature difference exceeds a set difference, issuing an alarm and outputting the point cloud and coordinate position in the key monitoring area corresponding to the feature difference.

[0068] In practical applications, based on the characteristics of the object of interest, a threshold (i.e., a set difference) is set for each key monitoring area to calculate the point cloud features. The point cloud features of each key monitoring area in the first frame point cloud and the current frame point cloud are calculated by subtracting the difference. When the result obtained in a certain key monitoring area exceeds the set threshold, the point cloud data and coordinate position of that monitoring area are output, thus completing the dynamic change detection. This indicates that the dynamic change of a certain object in the key monitoring area exceeds the normal state, reminding staff to take timely action on the changes in that area to maintain the normal operation of production.

[0069] Example 2

[0070] Taking the Tenglong No. 10 water sampling machine in the Lop Nur salt field of Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region as an example, the method of this study was used to detect the dynamic changes of the internal environment of the water sampling machine.

[0071] Figure 2 This is a flowchart of the water sampling machine internal environment detection method based on lidar provided in Embodiment 2 of the present invention, as shown below. Figure 2 As shown.

[0072] A) LiDAR data acquisition: Utilizing the existing LeiShen C32-line LiDAR (such as...) Figure 3 As shown, the point cloud data obtained by scanning the inner side wall of the water sampling machine's cabin is input to the central processing unit (such as...) via a wired connection. Figure 4 As shown in the figure, the scanning speed is 10Hz. The coverage of the point cloud is the internal environment of the water sampling machine and a small number of objects on the shore. There is no point cloud data on the surface of the salt lake. The overall point cloud data is relatively sparse.

[0073] B) Creation of point cloud data queue: Based on the frequency of dynamic changes in the internal environment of the water sampling machine, the size of the point cloud data queue is selected as 10, that is, 10 frames of point cloud data are stored in the queue, which is the point cloud data scanned by the lidar in one second. The interval is equivalent to matching and comparing the internal environment of the water sampling machine once every second, that is, dynamic change detection.

[0074] C) Preprocessing of point cloud data: Filtering is performed on areas of no concern in the internal environment of the water sampler and objects in the external environment of the water sampler (such as floating pipes, work boats, etc.). Then, the point clouds of other places inside the water sampler are cropped along the X, Y, and Z axes to select the point cloud that best fits the monitoring range, which reduces the amount of calculation and improves the detection accuracy.

[0075] D) The first frame of point cloud scanned by the lidar at the moment of system operation is taken as the first frame of point cloud data for the dynamic change detection method. The point cloud data of subsequent frames are taken from the tail of the queue, and the interval is the length of the queue, that is, ten frames of point cloud data are separated.

[0076] E) Point cloud rasterization: In the XY plane of the point cloud in the first and subsequent frames, i.e., from the bird's-eye view, divide it into a grid of size 1*1 and number 50*50. The grid can cover the range to be monitored, and the grid index is used to achieve accurate positioning of each monitoring target, while also facilitating the aggregation of similar targets.

[0077] F) Generation of Key Monitoring Areas: Within the internal environment of the water sampling machine, dynamic changes take various forms, such as increases in quantity, changes in displacement, and alterations in pose. The point cloud features extracted differ depending on the monitoring object. In this example, key monitoring areas were primarily defined, including the ship's bottom plate, pump body, and lifting column. The first frame and subsequent frames were divided in identical ways. Figure 5 As shown, the left side of the box represents the ship's bottom plate, and the right side represents the pump body.

[0078] G) Extraction of point cloud features in key areas: Based on the division of key areas, the dynamic changes of each key area are analyzed, and point cloud features that match them are extracted. For the interior of the ship's cabin, the main scene to be monitored is the accumulation of salt, which can hinder the normal operation of the machine and needs to be cleaned in a timely manner. The point cloud features extracted in this area are the quantity and height features. For the lifting column, the main scene to be detected is its displacement change in the vertical direction. Since underwater tracks often slip, the displacement of the movable rod at the top of the lifting column can be used for detection. The point cloud features extracted in this area are the height and normal features. Among them, the point cloud feature types extracted from each key area in the first frame and subsequent frames are completely consistent.

[0079] H) Dynamic Change Detection: By extracting point cloud features from key areas, including quantity features, height features, and normal features, the first frame is matched and compared with subsequent frames. A threshold is set according to the actual situation. If a feature exceeds the threshold, it is considered that a dynamic change has occurred in a designated area inside the water sampling machine that requires human attention, thereby issuing an alarm to remind monitoring and inspection personnel, and handling it in a timely manner according to relevant procedures and experience.

[0080] Example 3

[0081] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a water sampling machine internal environment detection system based on lidar is provided below.

[0082] A lidar-based water sampling machine internal environment detection system includes:

[0083] The raw point cloud data acquisition module is used to collect raw point cloud data of the internal environment of the water sampling machine using lidar; the raw point cloud data includes the interior of the water sampling machine hull, the floating pipe on the lake surface, the shoreline, and the buildings on the shore.

[0084] The queue creation and raw point cloud data receiving module is used to create queues of point cloud data types and receive the raw point cloud data in chronological order.

[0085] The present invention further includes: a cropping module for cropping the original point cloud data along the X-axis, Y-axis and Z-axis to generate cropped original point cloud data; and a filtering module for filtering the cropped original point cloud data to generate processed original point cloud data.

[0086] The current frame point cloud extraction module is used to extract the first frame point cloud in the queue as a template frame, and extract the current frame point cloud according to a set interval.

[0087] The key monitoring area division module is used to divide the first frame point cloud and the current frame point cloud into multiple key monitoring areas; the key monitoring area is either the key monitoring area of ​​the first frame point cloud or the key monitoring area of ​​the current frame point cloud; the key monitoring area of ​​the first frame point cloud is exactly the same as the key monitoring area of ​​the current frame point cloud.

[0088] In practical applications, the key monitoring area division module specifically includes: a rasterization processing unit, used to rasterize the first frame point cloud and the current frame point cloud on the XY plane to generate several grids; and a key monitoring area division unit, used to divide the first frame point cloud and the current frame point cloud into multiple key monitoring areas based on the grids.

[0089] The point cloud feature extraction module is used to extract the first frame point cloud features within the key monitoring area of ​​the first frame point cloud and the current frame point cloud features within the key monitoring area of ​​the current frame point cloud, respectively, based on the monitored objects; the monitored objects include the amount of salt deposits, the positional offset of mechanical parts, and the attitude change of mechanical parts.

[0090] The dynamic monitoring module is used to dynamically monitor the internal environment of the water sampling machine based on the point cloud features of the first frame and the point cloud features of the current frame.

[0091] In practical applications, the dynamic monitoring module specifically includes: a feature difference calculation unit, used to calculate the feature difference between the first frame point cloud feature and the current frame point cloud feature in each of the key monitoring areas; and an alarm unit, used to issue an alarm when the feature difference exceeds a set difference, and output the point cloud and coordinate position in the key monitoring area corresponding to the feature difference.

[0092] This invention monitors various abnormal changes in the internal environment of a water sampling machine under poor lighting conditions and with blurry cameras, eliminating the need for manual, intermittent camera adjustments. It also provides accurate calculations and results, preventing safety accidents and ensuring the normal operation of the water sampling machine.

[0093] Example 4

[0094] This invention provides an electronic device including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the lidar-based water sampling machine internal environment detection method provided in Embodiment 1.

[0095] In practical applications, the aforementioned electronic devices can be servers.

[0096] In practical applications, electronic devices include: at least one processor, memory, bus, and communication interface.

[0097] The processor, communication interface, and memory communicate with each other via a communication bus.

[0098] A communication interface is used to communicate with other devices.

[0099] The processor is used to execute programs, specifically the methods described in the above embodiments.

[0100] Specifically, the program may include program code, which includes computer operation instructions.

[0101] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The electronic device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0102] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0103] Based on the description of the above embodiments, this application provides a storage medium storing computer program instructions thereon, which can be executed by a processor to implement the methods described in any embodiment.

[0104] The lidar-based water sampling machine internal environment detection system provided in this application exists in various forms, including but not limited to:

[0105] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include: smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones, etc.

[0106] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access capabilities. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0107] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0108] (4) Other electronic devices with data interaction functions.

[0109] Specific embodiments of the subject matter have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0110] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0111] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components. Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0116] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0117] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, and CD-ROM.

[0118] Digital multifunction optical disc (DVD) or other optical storage, magnetic cassette tape, magnetic tape, disk storage or other magnetic storage devices

[0119] Or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0120] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0121] This application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific transactions or implement specific abstract data types. This application can also be practiced in distributed computing environments where transactions are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0122] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0123] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for detecting the internal environment of a water sampling machine based on lidar, characterized in that, include: Raw point cloud data of the internal environment of the water sampling machine is collected using lidar; the raw point cloud data includes the interior of the water sampling machine hull, the floating pipe on the lake surface, the shoreline, and the buildings on the shore. Create a queue of point cloud data types and receive the raw point cloud data in chronological order; Extract the first frame point cloud from the queue as the template frame, and extract the current frame point cloud at a set interval; The first frame point cloud and the current frame point cloud are respectively divided into multiple key monitoring areas; the key monitoring area is either the key monitoring area of ​​the first frame point cloud or the key monitoring area of ​​the current frame point cloud. The key monitoring area of ​​the point cloud in the first frame is exactly the same as the key monitoring area of ​​the point cloud in the current frame. Based on the monitored objects, extract the first frame point cloud features within the key monitoring area of ​​the first frame point cloud and the current frame point cloud features within the key monitoring area of ​​the current frame point cloud; the monitored objects include the salt mine accumulation amount, the mechanical component position offset amount, and the mechanical component attitude change amount. The internal environment of the water sampling machine is dynamically monitored based on the point cloud features of the first frame and the point cloud features of the current frame.

2. The method for detecting the internal environment of a water sampling machine based on lidar according to claim 1, characterized in that, The process of creating a queue for point cloud data types and receiving the raw point cloud data in chronological order further includes: The original point cloud data is cropped along the X-axis, Y-axis and Z-axis to generate cropped original point cloud data; The cropped original point cloud data is filtered to generate processed original point cloud data.

3. The method for detecting the internal environment of a water sampling machine based on lidar according to claim 2, characterized in that, The step of dividing the first frame point cloud and the current frame point cloud into multiple key monitoring areas specifically includes: The point cloud of the first frame and the point cloud of the current frame are rasterized on the XY plane to generate several grids. Based on the grid, the point cloud of the first frame and the point cloud of the current frame are respectively divided into multiple key monitoring areas.

4. The method for detecting the internal environment of a water sampling machine based on lidar according to claim 1, characterized in that, The dynamic monitoring of the internal environment of the water sampling machine based on the point cloud features of the first frame and the point cloud features of the current frame specifically includes: Calculate the feature difference between the point cloud features of the first frame and the point cloud features of the current frame in each of the key monitoring areas; When the feature difference exceeds the set difference, an alarm is issued, and the point cloud and coordinate position of the key monitoring area corresponding to the feature difference are output.

5. A water sampling machine internal environment detection system based on lidar, characterized in that, include: The raw point cloud data acquisition module is used to acquire raw point cloud data of the internal environment of the water sampling machine using lidar. The raw point cloud data includes the interior of the water sampling machine hull, the floating pipes on the lake surface, the shoreline, and the buildings on the shore. The queue creation and raw point cloud data receiving module is used to create queues of point cloud data types and receive the raw point cloud data in chronological order. The current frame point cloud extraction module is used to extract the first frame point cloud in the queue as a template frame, and extract the current frame point cloud at a set interval. The key monitoring area division module is used to divide the first frame point cloud and the current frame point cloud into multiple key monitoring areas; the key monitoring area is either the key monitoring area of ​​the first frame point cloud or the key monitoring area of ​​the current frame point cloud. The key monitoring area of ​​the point cloud in the first frame is exactly the same as the key monitoring area of ​​the point cloud in the current frame. The point cloud feature extraction module is used to extract the first frame point cloud features within the key monitoring area of ​​the first frame point cloud and the current frame point cloud features within the key monitoring area of ​​the current frame point cloud, respectively, based on the monitored objects; the monitored objects include the salt mine accumulation amount, the mechanical component position offset amount, and the mechanical component attitude change amount. The dynamic monitoring module is used to dynamically monitor the internal environment of the water sampling machine based on the point cloud features of the first frame and the point cloud features of the current frame.

6. The water sampling machine internal environment detection system based on lidar according to claim 5, characterized in that, Also includes: The cropping module is used to crop the original point cloud data along the X-axis, Y-axis and Z-axis to generate cropped original point cloud data; The filtering module is used to filter the cropped original point cloud data to generate processed original point cloud data.

7. The water sampling machine internal environment detection system based on lidar according to claim 6, characterized in that, The key monitoring area delineation module specifically includes: The rasterization processing unit is used to rasterize the first frame point cloud and the current frame point cloud on the XY plane respectively to generate a number of grids; The key monitoring area division unit is used to divide the first frame point cloud and the current frame point cloud into multiple key monitoring areas based on the grid.

8. The water sampling machine internal environment detection system based on lidar according to claim 5, characterized in that, The dynamic monitoring module specifically includes: The feature difference calculation unit is used to calculate the feature difference between the first frame point cloud feature and the current frame point cloud feature in each of the key monitoring areas; An alarm unit is used to issue an alarm when the feature difference exceeds a set difference, and to output the point cloud and coordinate position of the key monitoring area corresponding to the feature difference.

9. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to enable the electronic device to perform the lidar-based water sampling machine internal environment detection method as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the method for detecting the internal environment of a water sampling machine based on lidar as described in any one of claims 1-4.

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