CN111174699A - Bulk cargo quality measuring method - Google Patents
Bulk cargo quality measuring method Download PDFInfo
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- CN111174699A CN111174699A CN202010020085.5A CN202010020085A CN111174699A CN 111174699 A CN111174699 A CN 111174699A CN 202010020085 A CN202010020085 A CN 202010020085A CN 111174699 A CN111174699 A CN 111174699A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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Abstract
The bulk cargo quality measurement method provided by the embodiment of the invention relates to the technical field of data processing, and improves the measurement accuracy by acquiring each piece point cloud data of bulk cargo, splicing each piece point cloud data into complete point cloud data, correcting the complete point cloud data, and eliminating noise data in the complete point cloud data according to a formula
The volume of the bulk cargo is calculated, and the mass of the bulk cargo is calculated according to the formula m ═ pV, so that the measurement efficiency is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method for measuring the quality of bulk cargoes.
Background
At present, the volume/weight of bulk cargo is measured in two conventional ways, and the volume and the weight of the bulk cargo (such as coal piles) stacked in an open field are calculated after the bulk cargo is decomposed into regular geometric bodies according to the stacking form; secondly, the surface of the goods is arranged into a plane, and the volume and the weight of the bulk goods (such as grain) stored in the closed regular warehouse are calculated according to the known warehouse area and the stacking height obtained by measurement.
The existing bulk cargo quality measurement method has the following defects:
(1) due to the randomness of the goods form and the difference of the service capability of the measuring personnel, the possibility of error exists in the measured data, so that the measuring accuracy is not high;
(2) the field manual measurement operation and the grain surface leveling operation are needed, and a large amount of time and human resources are needed to be invested, so that the measurement efficiency is not high.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides a method for measuring the quality of bulk cargos, which comprises the following steps:
acquiring point cloud data of each fragment of the bulk cargo;
splicing the fragment point cloud data into complete point cloud data according to the serial numbers of the fragment point cloud data;
correcting the complete point cloud data;
clearing noise data in the complete point cloud data;
according to the formula
Calculating the volume of the bulk cargo, wherein xiIs the abscissa, x, of the ith point cloud datai+1Is the abscissa, y, of the i +1 st point cloud dataiIs the ordinate, y, of the ith point cloud datai+1Is the ordinate, z, of the i +1 st point cloud dataiThe height of the point cloud data is i, and n is the number of the point cloud data;
calculating the mass of the bulk cargo according to the formula of m-rho V, wherein rho is the average density of the bulk cargo, and V is the volume of the bulk cargo.
Preferably, the modifying the complete point cloud data comprises:
and fitting and registering the splicing surfaces of the complete point cloud data according to the bulk cargo stacking angle characteristics, and correcting splicing errors.
Preferably, the cleaning of the noise data in the complete point cloud data comprises:
acquiring neighborhood data of each point cloud data in the complete point cloud data according to a K nearest neighbor algorithm, and generating a neighborhood data set;
acquiring the number of adjacent points of each point cloud data from the neighborhood data set;
and judging whether the number is larger than a set threshold value, and if not, removing the point cloud data corresponding to the number as noise data.
Preferably, according to the sequence number of each piece point cloud data, before the piece point cloud data is spliced into complete point cloud data, the method further includes:
and clearing the repeated point cloud data in each piece of point cloud data.
The method for measuring the quality of the bulk cargo provided by the embodiment of the invention has the following beneficial effects:
(1) the point cloud data is corrected and denoised, so that the measurement accuracy is improved;
(2) under the unattended condition of a measurement site, the measurement of the bulk cargo volume/mass can be automatically and quickly completed, and the measurement efficiency is improved.
Drawings
FIG. 1 is a schematic flow chart of a bulk cargo quality measurement method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating the effect of acquiring point cloud data of each piece of bulk cargo according to an embodiment of the present invention;
FIG. 3a is a schematic diagram of the complete point cloud data before correction;
FIG. 3b is a schematic diagram of the corrected complete point cloud data;
FIG. 4a is a schematic diagram of complete point cloud data before denoising;
FIG. 4b is a schematic diagram of the denoised complete point cloud data.
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
Referring to fig. 1, the method for measuring the quality of bulk cargo provided by the embodiment of the invention comprises the following steps:
s101, acquiring point cloud data of each piece of bulk cargo.
As a specific example, as shown in fig. 2, measurement target point cloud data is acquired using a three-dimensional coordinate measuring apparatus (three-dimensional laser scanner). And acquiring n pieces of sliced point cloud data according to the acquisition capacity of the three-dimensional coordinate measuring equipment. Point cloud data of the detected bulk surface is acquired using a three-dimensional coordinate measuring device 5. The three-dimensional coordinate measuring equipment is arranged above bulk goods and respectively collects piece point cloud data with the sequence from 1 to 4.
The three-dimensional laser scanning scans point location data at high speed, can acquire dense point cloud data and generates a high-precision measurement result.
And S102, splicing the sliced point cloud data into complete point cloud data according to the serial numbers of the sliced point cloud data.
S103, correcting the complete point cloud data.
And S104, eliminating noise data in the complete point cloud data.
S105, according to the formula
Calculating the volume of the bulk cargo, wherein xiIs the abscissa, x, of the ith point cloud datai+1Is the abscissa, y, of the i +1 st point cloud dataiIs the ordinate, y, of the ith point cloud datai+1Is the ordinate, z, of the i +1 st point cloud dataiThe height of the point cloud data is i, and n is the number of the point cloud data;
and S106, calculating the mass of the bulk cargo according to a formula m-rho V, wherein rho is the average density of the bulk cargo, and V is the volume of the bulk cargo.
Optionally, the modifying the complete point cloud data comprises:
and fitting and registering the splicing surfaces of the complete point cloud data according to the bulk cargo stacking angle characteristic, and correcting splicing errors.
As a specific example, fig. 3a is the complete point cloud data before correction, and fig. 3b is the complete point cloud data after correction.
Optionally, removing noise data in the complete point cloud data comprises:
acquiring neighborhood data of each point cloud data in the complete point cloud data according to a K nearest neighbor algorithm, and generating a neighborhood data set;
acquiring the number of adjacent points of each point cloud data from a neighborhood data set;
and judging whether the quantity is greater than a set threshold value, and if not, rejecting the point cloud data corresponding to the quantity as noise data.
As a specific example, fig. 4a is the complete point cloud data before denoising, and fig. 4b is the complete point cloud data after denoising.
Optionally, before the segment point cloud data are spliced into complete point cloud data according to the sequence number of the segment point cloud data, the method further includes:
and clearing the repeated point cloud data in each piece of point cloud data.
According to the bulk cargo quality measurement method provided by the embodiment of the invention, the measurement accuracy is improved by acquiring each piece point cloud data of the bulk cargo, splicing each piece point cloud data into complete point cloud data and correcting the complete point cloud data, and eliminating noise data in the complete point cloud data according to a formula
The volume of the bulk cargo is calculated, and the mass of the bulk cargo is calculated according to the formula m ═ rho V, so that the measurement efficiency is improved.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In addition, the memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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 means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, 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 technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The use of the phrase "including an" as used herein does not exclude the presence of other, identical elements, components, methods, articles, or apparatus that may include the same, unless expressly stated otherwise.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (5)
1. A method of bulk cargo quality measurement, comprising:
acquiring point cloud data of each fragment of the bulk cargo;
splicing the fragment point cloud data into complete point cloud data according to the serial numbers of the fragment point cloud data;
correcting the complete point cloud data;
clearing noise data in the complete point cloud data;
according to the formula
Calculating the volume of the bulk cargo, wherein xiIs the abscissa, x, of the ith point cloud datai+1Is the abscissa, y, of the i +1 st point cloud dataiIs the ordinate, y, of the ith point cloud datai+1Is the ordinate, z, of the i +1 st point cloud dataiThe height of the point cloud data is i, and n is the number of the point cloud data;
calculating the mass of the bulk cargo according to the formula of m-rho V, wherein rho is the average density of the bulk cargo, and V is the volume of the bulk cargo.
2. The method of bulk cargo quality measurement according to claim 1, wherein correcting the complete point cloud data comprises:
and fitting and registering the splicing surfaces of the complete point cloud data according to the bulk cargo stacking angle characteristics, and correcting splicing errors.
3. The method of bulk quality measurement according to claim 1, wherein cleaning noise data in the complete point cloud data comprises:
acquiring neighborhood data of each point cloud data in the complete point cloud data according to a K nearest neighbor algorithm, and generating a neighborhood data set;
acquiring the number of adjacent points of each point cloud data from the neighborhood data set;
and judging whether the number is larger than a set threshold value, and if not, removing the point cloud data corresponding to the number as noise data.
4. The method of measuring the quality of bulk cargo according to claim 1, wherein the method further comprises, before splicing the respective sliced point cloud data into complete point cloud data according to the serial numbers of the respective sliced point cloud data:
and clearing the repeated point cloud data in each piece of point cloud data.
5. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of claims 1-4 are implemented when the computer program is executed by the processor.
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| CN111861941A (en) * | 2020-07-31 | 2020-10-30 | 富德康(北京)科技股份有限公司 | Compensation algorithm for three-dimensional space measurement result data |
| CN116834026A (en) * | 2023-09-04 | 2023-10-03 | 广州侨益科技有限公司 | Automatic inventory monitoring method, robot and network system for warehouse bulk material pile |
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