A system and method for measuring grain storage quantity based on lidar

Abstract

This application relates to the field of measurement technology, and in particular to a system and method for measuring the quantity of grain storage in a grain warehouse based on lidar. The system includes a pan-tilt unit, a lidar unit, a computing server, and a grain warehouse SaaS system. The pan-tilt unit controls the lidar unit to scan the grain warehouse and sends the collected point cloud data to the computing server. The computing server receives the point cloud data, calculates the relevant results, and sends the results to the grain warehouse SaaS system. This system enables rapid scanning of grain piles, and the entire process is automated, requiring no manual intervention. This not only saves manpower but also avoids human measurement errors.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of measurement, in particular to a system and method for measuring the quantity of grain storage in a grain depot based on a laser radar. BACKGROUND

[0002] Traditional methods for calculating the quantity of grain in a grain depot generally measure the length, width or diameter of the grain depot according to the shape of the grain accumulation, and then use mathematical volume formulas for calculation. This method not only wastes a lot of manpower and material resources, but also cannot guarantee the regularity of the shape of the grain pile, and the measurement error will be larger. SUMMARY

[0003] In view of the above problems, the present application provides a system for measuring the quantity of grain storage in a grain depot based on a laser radar, to solve the technical problem that the existing method for measuring the quantity of grain in a grain depot not only wastes a lot of manpower and material resources, but also cannot guarantee the regularity of the shape of the grain pile, and the measurement error will be larger. The specific technical solutions are as follows:

[0004] A system for measuring the quantity of grain storage in a grain depot based on a laser radar, comprising: a holder, a laser radar, a computing server and a grain depot SAAS system;

[0005] The holder is configured to control the laser radar to scan the grain depot and send the collected point cloud data to the computing server;

[0006] The computing server is configured to receive the point cloud data and calculate the relevant results, and send the relevant results to the grain depot SAAS system.

[0007] Further, the computing server is further configured to receive the point cloud data and convert the point cloud data to a first specific format file through a preset system command;

[0008] Run a mapping algorithm to construct a grain depot scan map;

[0009] Store the construction result in a second specific format file;

[0010] Load the construction result of the second specific format file and calculate the relevant results.

[0011] Further, the computing server module is further configured to determine whether the deviation of the relevant results and the last relevant results conforms to a preset rule, and if not, issue a warning or perform re-computation.

[0012] Further, the grain depot SAAS system is further configured to display the relevant results and the warning results.

[0013] Further, the holder is further configured to control the rotation of the holder through a PELCO-D protocol.

[0014] The laser radar comprises a high-performance radar.

[0015] To solve the above technical problems, a method for measuring the quantity of grain storage in a grain depot based on a laser radar is also provided, and the specific technical solutions are as follows:

[0016] A method for measuring the quantity of grain storage in a grain depot based on a laser radar comprises the following steps:

[0017] The gimbal controls the laser radar to scan the grain depot and sends the collected point cloud data to a computing server.

[0018] The computing server receives the point cloud data, calculates the relevant results, and sends the relevant results to a grain depot SAAS system.

[0019] Further, the computing server receives the point cloud data and calculates the relevant results, and the method further comprises the following steps: receiving the point cloud data and converting the point cloud data into a first specific format file through a preset system command.

[0020] A mapping algorithm is run to construct a grain depot scan map.

[0021] The constructed map result is stored in a second specific format file.

[0022] The constructed map result of the second specific format file is loaded, and the relevant results are calculated.

[0023] Further, the computing server receives the point cloud data and calculates the relevant results, and the method further comprises the following steps: judging whether the deviation between the current relevant results and the last relevant results meets a preset rule, and if not, issuing a warning or performing recalculation.

[0024] Further, the method further comprises the following steps: the grain depot SAAS system displays the relevant results and the warning results.

[0025] Further, the gimbal is further used to control the rotation of the gimbal through a PELCO-D protocol.

[0026] The laser radar comprises a high-performance radar.

[0027] The beneficial effects of the present application are: a system for measuring the quantity of grain storage in a grain warehouse based on a laser radar, comprising: a holder, a laser radar, a computing server and a grain warehouse SAAS system; the holder is used to control the laser radar to scan the grain warehouse and send the collected point cloud data to the computing server; the computing server is used to receive the point cloud data and calculate the relevant results and send the relevant results to the grain warehouse SAAS system. Through the above system, the grain pile can be quickly scanned, and the whole process is automatically calculated, without the need for manual participation, not only saving human resources, but also avoiding human measurement errors.

[0028] The above summary of the invention is only a summary of the technical solutions of the present application. In order to enable those skilled in the art to more clearly understand the technical solutions of the present application, and then can be implemented according to the content of the description and the drawings, and in order to let the above-mentioned purposes and other purposes, characteristics and advantages of the present application can be more easily understood, the following is described in combination with the specific embodiments of the present application and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0029] The drawings are only used to show the principles, implementation manners, applications, characteristics and effects of the specific embodiments of the present application and other related contents, and cannot be considered as limitations of the present application.

[0030] In the drawings of the specification:

[0031] Figure 1 The module schematic diagram of the system for measuring the quantity of grain storage in a grain warehouse based on a laser radar described in the specific embodiments;

[0032] Figure 2 The schematic diagram of the system for measuring the quantity of grain storage in a grain warehouse based on a laser radar described in the specific embodiments;

[0033] Figure 3 The flowchart of the method for measuring the quantity of grain storage in a grain warehouse based on a laser radar described in the specific embodiments;

[0034] Figure 4 The flowchart of the computing server receiving the point cloud data and calculating the relevant results described in the specific embodiments;

[0035] Figure 5 The schematic diagram of the method for measuring the quantity of grain storage in a grain warehouse based on a laser radar described in the specific embodiments.

[0036] The reference signs involved in the above drawings are explained as follows:

[0037] 100. A system for measuring the quantity of grain storage in a grain warehouse based on a laser radar,

[0038] 101. a holder,

[0039] 102. Laser radar,

[0040] 103. Computing server,

[0041] 104. Granary SAAS system. DETAILED DESCRIPTION

[0042] To explain possible application scenarios, technical principles, specific schemes that can be implemented, purposes and effects that can be achieved, etc. of the present application in detail, the following will be described in detail in combination with specific embodiments listed and with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and therefore only serve as examples, and cannot limit the protection scope of the present application.

[0043] In this article, the term "embodiment" means that the specific features, structures or characteristics described in combination with the embodiment can be included in at least one embodiment of the present application. The term "embodiment" appearing at various positions in the specification does not necessarily refer to the same embodiment, and does not particularly limit the independence or association between other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, each technical feature mentioned in each embodiment can be combined in any way to form a corresponding implementable technical solution.

[0044] Unless otherwise defined, the meanings of the technical terms used herein are the same as those commonly understood by those skilled in the art to which the present application belongs; the use of related terms in this article is only for the purpose of describing specific embodiments, and is not intended to limit the present application.

[0045] In the description of the present application, the phrase "and / or" is a description of the logical relationship between objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally represents that the associated objects before and after are a "or" logical relationship.

[0046] In the present application, terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual quantity, primary and secondary or order relationship between the entities or operations.

[0047] In the present application, the terms "comprise", "contain", "have" or other similar expressions used in the statements are intended to cover the non-exclusive inclusion, and the expressions do not exclude the presence of other elements in the process, method or product comprising the elements, so that the process, method or product comprising a series of elements can not only include those limited elements, but also include other elements not explicitly listed, or also include the elements inherent to such process, method or product.

[0048] In the present application, the expressions "greater than", "less than", "exceed" and the like are understood as not including the number itself; the expressions "above", "below", "within" and the like are understood as including the number itself. In addition, in the description of the embodiments of the present application, the meaning of "multiple" is more than two (including two), and similar expressions related to "multiple" are also understood in this way, for example, "multiple groups", "multiple times" and the like, unless otherwise explicitly specified.

[0049] As mentioned in the background of the present application, the existing method of calculating the amount of grain in the warehouse not only wastes a lot of manpower and material resources, but also makes it difficult to ensure the regularity of the grain pile shape, and the measurement error will be larger. Therefore, in the present application, a laser radar is selected, and a high-performance laser radar is used to realize rapid grain pile scanning, and the number of point clouds collected is ten times that of the early method, combined with a composition algorithm and a volume calculation method, to solve the technical problems faced by the early manual measurement and the early automatic measurement.

[0050] First, some terms of the present application are explained as follows:

[0051] 1、Lvx; point cloud file format collected by laser radar.

[0052] 2、ROS: ROS (Robot Operating System) is a robot software platform that can provide operating system-like functions for a heterogeneous computer cluster.

[0053] 3、BAG: a point cloud format supported by the ROS system (i.e. the first specific format file mentioned below).

[0054] 4、PCD: a file format for storing point clouds.

[0055] 5、BIN: a file format for storing point clouds (i.e. the second specific format file mentioned below).

[0056] 6、Warehouse SAAS system: a supporting management system designed by the present application, which can be deployed locally or in the cloud, and the main functions include: warehouse management, alarm management, system management and data statistics, etc.

[0057] The following describes specific technical solutions:

[0058] Figure 1 is a module schematic diagram of a system 100 for measuring the quantity of grain storage based on laser radar. As Figure 1 indicated, the system 100 for measuring the quantity of grain storage based on laser radar includes a holder 101, a laser radar 102, a computing server 103, and a grain storage SAAS system 104. The holder 101 is configured to control the laser radar 102 to scan a grain storage and send collected point cloud data to the computing server 103. The computing server 103 is configured to receive the point cloud data and calculate a relevant result and send the relevant result to the grain storage SAAS system 104.

[0059] The application schematic diagram is shown in Figure 2 . In actual application, the holder 101 drives the laser radar 102 to realize 360-degree dead-angle-free scanning of the grain storage. The collected point cloud data is transmitted to the computing server 103 through a data line. The computing server 103 completes calculation of the relevant result (in this embodiment, mainly calculation of volume and area). The calculated relevant result is transmitted to the grain storage SAAS system 104 through a network.

[0060] In some embodiments, the holder 101 is further configured to control rotation of the holder 101 through a PELCO-D protocol. Mainly, the holder 101 is configured to complete control of horizontal rotation speed and angle and vertical rotation speed and angle of the holder 101.

[0061] In some embodiments, preferably, the laser radar 102 adopts a high-performance radar, which can realize scanning of a grain pile within five minutes, collect more than 20 million point clouds, and report the point clouds to the computing server 103 through a data line. Because the laser radar 102 collects a large amount of point cloud data, this is beneficial to accuracy of subsequent data calculation.

[0062] In some embodiments, preferably, the computing server 103 is further configured to receive the point cloud data, convert the point cloud data into a first specific format file through a preset system command, run a mapping algorithm to construct a grain storage scanning map, store a construction result in a second specific format file, and load the construction result of the second specific format file to calculate the relevant result.

[0063] It is determined whether the deviation of the relevant result from a last relevant result meets a preset rule. If not, a warning is issued or recalculation is performed.

[0064] The following describes a mapping algorithm used in this embodiment:

[0065] Loam_livox is a fast, robust, and high-precision radar odometry and mapping algorithm for lidar with a small field of view (FoV). Traditional LOAM algorithms are designed for mechanical lidar, and their performance is unsatisfactory when directly applied to small FoV and non-repetitive lidar. Therefore, in this implementation, Loam_livox builds upon the LOAM algorithm by improving both the front-end and back-end, addressing several core issues encountered when using the LOAM algorithm with solid-state lidar.

[0066] The algorithm execution steps include:

[0067] 1. Feature point extraction and selection in cases of very small Fov values.

[0068] 2. Robust outlier removal and moving target filtering: Remove the 20% of points with the largest errors each time.

[0069] 3. Motion distortion correction: A piecewise parallel processing method is used, which is more effective and efficient than interpolation.

[0070] 4. Eliminate certain points, such as edges, points with excessive or insufficient intensity, and points behind objects, to improve accuracy.

[0071] 5. Use point-to-line and point-to-surface ICP (nonlinear optimization) methods.

[0072] 6. Add reflection intensity as a fourth dimension to identify edges formed by different materials.

[0073] The basic version of the loam_livox algorithm uses random values ​​to control the parameters for filtering outliers in step 4. Different random values ​​may filter out different outliers, affecting computational accuracy and robustness. This application modifies step 4 by replacing the random value with a fixed value after training on a large amount of data, ensuring the idempotency of the filtering step and further improving computational accuracy and robustness.

[0074] The grain warehouse SaaS system 104 is also used to display relevant results and alarm results. The results are displayed directly for easy viewing by users, thus facilitating the maintenance of the grain warehouse.

[0075] The system 100, which measures the quantity of grain storage based on lidar, can quickly scan grain piles and automatically calculate the results throughout the entire process without human intervention. This not only saves manpower but also avoids human measurement errors.

[0076] in Figure 5 A schematic diagram of the execution process of a system 100 for measuring the quantity of grain storage in a grain warehouse based on lidar is shown.

[0077] likeFigure 5 As shown, the execution process of a system 100 for measuring the quantity of grain storage based on lidar in practical applications includes:

[0078] Step 1: Activate the PTZ 101 and radar to acquire point cloud data: This step mainly involves acquiring point cloud data. Specifically, it includes:

[0079] Start the gimbal 101; the gimbal 101 returns to the preset initial coordinate position.

[0080] Activate the radar and begin collecting point cloud data;

[0081] The PTZ 101 selects the preset angular velocity to complete a 360-degree scan of the grain silo without blind spots.

[0082] Step 2: Convert point cloud LVX files to BAG files: Use ROS system commands to convert LVX files into BAG files.

[0083] Step 3: Run the mapping algorithm to complete the mapping: Run the mapping algorithm to complete the mapping of the grain warehouse scan map.

[0084] Step 4: Merge PCD files: The mapping results are stored as PCD files. Merge the PCD files into a single BIN file to facilitate calculations in subsequent steps.

[0085] Step 5: Load the BIN file and complete the volume calculation: The calculation method is to divide the bottom surface of the 2.5D point cloud into discrete grids, calculate the volume and area of ​​the corresponding unit of each grid, and sum them up to complete the volume and area calculation of the entire scan space.

[0086] Step Six: Automatic Result Verification: If the deviation between the calculated result and the previous calculation result exceeds 1%, and both the volume and area have changed simultaneously, the result is considered reliable; otherwise, the result is considered potentially abnormal, and steps one through five are repeated. The result is then the average of the two calculations. This step performs automatic verification to ensure the accuracy of the calculation results.

[0087] Step 7: Does the result exceed the threshold? Determine whether the settlement result exceeds the set threshold. If it does, generate the corresponding quantity or shape alarm.

[0088] In some embodiments, preferably, when a shape alarm occurs, the result of the shape alarm is sent to the corresponding administrator immediately, so that the administrator can process or review it as soon as possible.

[0089] See below Figures 3 to 5 This paper describes a specific implementation of a method for measuring the quantity of grain stored in a grain warehouse based on lidar. This method can be applied to the aforementioned system for measuring the quantity of grain stored in a grain warehouse based on lidar.

[0090] like Figure 3 As shown, a method for measuring the quantity of grain storage in a grain warehouse based on lidar includes steps S301 and S302.

[0091] In step S301, the gimbal controls the lidar to scan the grain silo and sends the collected point cloud data to the computing server.

[0092] In step S302, the computing server receives the point cloud data, calculates the relevant results, and sends the relevant results to the grain warehouse SAAS system.

[0093] In practical applications, the gimbal drives the lidar to achieve 360-degree scanning of the grain warehouse without blind spots. The collected point cloud data is transmitted to the computing server via data cable. The computing server completes the calculation of relevant results (in this embodiment, mainly the calculation of volume and area). The calculation results are transmitted to the grain warehouse SaaS system via the network.

[0094] In some embodiments, the gimbal is further used to control the rotation of the gimbal via the PELCO-D protocol. This mainly includes controlling the horizontal rotation speed and angle, and the vertical rotation speed and angle of the gimbal.

[0095] In some embodiments, preferably, the lidar is a high-performance radar capable of scanning the grain pile within five minutes, collecting more than 20 million point cloud data, and reporting it to the computing server via a data line. Because of the large amount of point cloud data collected, this is beneficial to the accuracy of subsequent data calculations.

[0096] In some embodiments, preferably, such as Figure 4 As shown, the computing server receives the point cloud data and calculates the relevant results, and also includes steps S401 to S404.

[0097] In step S401, the point cloud data is received and converted to a first specific format file using a preset system command.

[0098] In step S402, a mapping algorithm is run to construct a scan map of the grain warehouse.

[0099] In step S403, the drawing results are stored in a second specific format file.

[0100] In step S404, the composition result of the second specific format file is loaded, and relevant results are calculated.

[0101] In some embodiments, the system further includes: determining whether the deviation between the current correlation result and the previous correlation result conforms to a preset rule; if not, issuing an early warning or recalculating. The grain warehouse SaaS system displays the correlation results and alarm results. The results are displayed directly for easy viewing by users, thus facilitating the maintenance of the grain warehouse.

[0102] The above method for measuring the quantity of grain storage in a grain warehouse based on lidar can quickly scan grain piles, and the entire process is automated, requiring no manual intervention. This not only saves manpower but also avoids human measurement errors.

[0103] In practical applications, it can be like Figure 5 As shown, it includes the following steps:

[0104] Step 1: Activate the gimbal and radar to acquire point cloud data: This step mainly involves acquiring point cloud data. Specifically, it includes:

[0105] Activate the gimbal; the gimbal will return to the preset initial coordinate position.

[0106] Activate the radar and begin collecting point cloud data;

[0107] The gimbal selects the preset angular velocity to complete a 360-degree scan of the grain silo without blind spots.

[0108] Step 2: Convert point cloud LVX files to BAG files: Use ROS system commands to convert LVX files into BAG files.

[0109] Step 3: Run the mapping algorithm to complete the mapping: Run the mapping algorithm to complete the mapping of the grain warehouse scan map.

[0110] Step 4: Merge PCD files: The mapping results are stored as PCD files. Merge the PCD files into a single BIN file to facilitate calculations in subsequent steps.

[0111] Step 5: Load the BIN file and complete the volume calculation: Load the BIN file, and use the calculation method of dividing the bottom surface of the 2.5D point cloud into discrete grids, calculating the volume and area of ​​the corresponding unit of each grid and summing them to complete the volume and area calculation of the overall scan space.

[0112] Step Six: Automatic Result Verification: If the deviation between the calculated result and the previous calculation result exceeds 1%, and both the volume and area have changed, the result is considered reliable; otherwise, the result is considered to be abnormal, and steps one through five are repeated. The result is then the average of the two calculations.

[0113] Step 7: Does the result exceed the threshold? Determine whether the settlement result exceeds the set threshold. If it does, generate the corresponding quantity or shape alarm.

[0114] In some embodiments, preferably, when a shape alarm occurs, the result of the shape alarm is sent to the corresponding administrator immediately, so that the administrator can process or review it as soon as possible.

[0115] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. A system for measuring the quantity of grain stored in a silo based on laser radar, characterized in that, Comprise: A gimbal, a laser radar, a computing server and a granary SAAS system; The gimbal is used for controlling the laser radar to scan the granary and sending the collected point cloud data to the computing server; The computing server is used for receiving the point cloud data, calculating relevant results and sending the relevant results to the granary SAAS system; The computing server is further used for receiving the point cloud data and calculating relevant results when receiving the point cloud data and calculating relevant results: Receiving the point cloud data; Converting the point cloud LVX file into a BAG file: using the ROS system command to pack the LVX file into a BAG file; Running a mapping algorithm to complete the mapping: running a mapping algorithm to complete the mapping of the granary scan map; Merging PCD files: the mapping results are stored in PCD files, and the PCD files are merged into a BIN file for easy calculation in subsequent steps; Loading the BIN file to complete the volume calculation: the 2.5D point cloud bottom surface is divided into discrete grids, the volume and area of each grid corresponding unit are calculated and summed up to complete the volume and area calculation of the overall scanning space; The computing server module is further used for judging whether the deviation of the relevant results and the last relevant results meets the preset rule, if both the area and the volume change, the preset rule is met, otherwise the preset rule is not met, if not, an early warning is issued or a recalculation is performed, and the average value of the two calculations is taken as the settlement result; Judging whether the settlement result exceeds the set threshold, if yes, generating a corresponding quantity or shape warning, and sending the generated shape warning result to the corresponding administrator.

2. The system for measuring the quantity of grain stored in a silo based on laser radar according to claim 1, characterized in that, The granary SAAS system is further used for displaying the relevant results and the warning results.

3. The system for measuring the quantity of granary storage based on the laser radar according to any one of claims 1 to 2, characterized in that, The gimbal is further used for controlling the rotation of the gimbal through the PELCO-D protocol; The laser radar comprises a high-performance radar.

4. A method for measuring the quantity of grain stored in a silo based on a laser radar, characterized in that, Comprise steps: The gimbal controls the laser radar to scan the granary and sends the collected point cloud data to the computing server; The computing server receives the point cloud data, calculates relevant results and sends the relevant results to the granary SAAS system; The computing server receives the point cloud data and calculates relevant results, further comprising: Receiving the point cloud data; Converting the point cloud LVX file into a BAG file: using the ROS system command to pack the LVX file into a BAG file; Running a mapping algorithm to complete the mapping: running a mapping algorithm to complete the mapping of the granary scan map; Merging PCD files: the mapping results are stored in PCD files, and the PCD files are merged into a BIN file for easy calculation in subsequent steps; Loading the BIN file to complete the volume calculation: the 2.5D point cloud bottom surface is divided into discrete grids, the volume and area of each grid corresponding unit are calculated and summed up to complete the volume and area calculation of the overall scanning space; The computing server receives the point cloud data and calculates a correlation result, and further comprises: judging whether the deviation between the current correlation result and the last correlation result conforms to a preset rule, wherein if both the area and the volume change, the preset rule is met, otherwise, the preset rule is not met; if the preset rule is not met, an early warning is sent or a recalculation is performed, and the average of the two calculations is taken as the settlement result; judging whether the settlement result exceeds a set threshold, and if so, generating a corresponding quantity or shape warning, and sending the result of the generated shape warning to the corresponding administrator.

5. A method of measuring the quantity of grain stored in a silo based on laser radar as claimed in claim 4, characterized in that, Further comprising: The granary SAAS system displays the correlation result and the warning result.

6. The method of claim 4 or 5, wherein the cloud platform is further configured to: The pan-tilt head is further configured to control the rotation of the pan-tilt head through a PELCO-D protocol. The laser radar comprises a high-performance radar.

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