CN115127856B - Method and device for sampling and identifying concrete test block compression test robot - Google Patents
Method and device for sampling and identifying concrete test block compression test robot Download PDFInfo
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- 238000012360 testing method Methods 0.000 title claims abstract description 66
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- 239000004567 concrete Substances 0.000 title claims abstract description 44
- 238000005070 sampling Methods 0.000 title claims abstract description 43
- 238000012669 compression test Methods 0.000 title claims abstract description 33
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 17
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- 238000009749 continuous casting Methods 0.000 claims description 3
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- 238000010561 standard procedure Methods 0.000 description 1
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G06K17/0022—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device
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Abstract
The invention discloses a method and a device for sampling and identifying a concrete test block compression test robot, which comprise the steps of sampling a sample to be tested by using the concrete test block compression test robot; numbering and generating the sampled samples based on a random forest algorithm to obtain a unique two-dimensional code electronic tag; placing the sample with the two-dimensional code electronic tag on the allocated sample car to obtain an associated sample serial number; and constructing an identification model by combining a multichannel algorithm, carrying out identification processing operation on the sample, the sample vehicle and the sample serial number, outputting an identification result and feeding back to an identification device to complete a sampling identification self-adaptive test. According to the method, accurate sample test block positions and characteristics are obtained through a decision tree strategy, the unique electronic two-dimensional code is generated by combining the serial numbers, the influence of damage of paper labels on a sample sampling test is avoided, and the sample self-adaptive sampling recognition efficiency and accuracy of the concrete test block are further improved according to the recognition of the multichannel model.
Description
Technical Field
The invention relates to the technical field of sampling and identifying of a concrete test block compression test robot, in particular to a method and a system for sampling and identifying of a concrete test block compression test robot.
Background
The existing house construction and civil engineering in China mainly takes reinforced concrete structures as main materials, and in engineering project construction engineering, the concrete engineering is the engineering with the largest quantity, the largest grade and the most complex raw material varieties, and the quality of the concrete engineering construction quality directly influences the quality of the whole engineering. In order to effectively improve the construction quality of engineering, the construction department, the railway department, the traffic department and related government departments go out of the platform to a series of regulations and policies specially aiming at quality management, and the quality inspection work of engineering construction is required to be greatly enhanced by all levels of departments and construction units. However, due to the characteristics of engineering construction, due to the lack of necessary technical means, the quality of concrete still has management holes even under the multi-party supervision and inspection of government supervision departments, owners, supervision units, construction units and the like.
Sample feeding detection of concrete test blocks is an important component of civil engineering experiments. The concrete structure engineering construction quality acceptance criterion and the concrete strength inspection evaluation standard are required for the problems in the concrete test block sample feeding process. The test of the compressive strength of the cubes is carried out by adopting 28d or designing standard curing test pieces with specified age when the strength test is carried out to evaluate the strength of the concrete. The standard value of the compressive strength of the cube refers to the compressive strength with 95% guarantee rate measured by a standard test method at the 28d age by manufacturing a cube test piece with a side length of 150mm which is maintained according to a standard method. The concrete strength grade is determined according to the standard value of the cube compressive strength and is divided into 19 strength grades from C10 to C100.
By using the robot to perform the square compressive strength test, the working efficiency is improved, the influence of working noise on operators is reduced, the working strength of testers is greatly reduced, and the fairness and scientificity of the test are ensured.
In order to ensure the smooth proceeding of the full-automatic test process, the efficient and correct identification of the sample code plays a key role in the continuity of the whole automatic process in the whole test process, and the efficiency and experience of using the full-automatic equipment by a user are greatly affected because the whole automatic running process is interrupted due to various non-equipment reasons such as sample code damage and the like.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the technical problems solved by the invention are as follows: the method aims at solving the problems of low label identification precision, low sample identification precision and low clamping efficiency in the prior art.
In order to solve the technical problems, the invention provides the following technical scheme: sampling a sample to be tested by using a concrete test block compression test robot; numbering and generating the sampled samples based on a random forest algorithm to obtain a unique two-dimensional code electronic tag; placing the sample with the two-dimensional code electronic tag on an allocated sample car to obtain an associated sample serial number; and constructing an identification model by combining a multichannel algorithm, carrying out identification processing operation on the sample, the sample vehicle and the sample serial number, outputting an identification result and feeding back to an identification device to complete a sampling identification self-adaptive test.
As a preferable scheme of the method for sampling and identifying the concrete block compression test robot, the invention comprises the following steps: the sampling comprises the steps of setting the continuous casting quantity to be 1000 square; three groups of test groups with different periods of 3 days, 7 days and 28 days are set; sampling a group of execution every 200 sides; each group consisted of 3 sample coupons; and respectively clamping and detecting the sample test blocks of each group by using the concrete test block compression test robot.
As a preferable scheme of the method for sampling and identifying the concrete block compression test robot, the invention comprises the following steps: the two-dimensional code electronic tag comprises that the concrete test block compression test robot performs view scanning processing on the clamped sample test block to obtain imaging characteristic data; the imaging characteristic data are transmitted to a recognition device processing center through a spp protocol stack to be specially processed, and regression coding is carried out on the imaging characteristic data based on the random forest algorithm; the imaging features gradually generate different tip nodes along the root of the decision tree to each crotch node in the operation process; the peripheral nodes are connected with each other through tree nerves, and the positions of the peripheral nodes in the decision tree are output by utilizing nerve feedback; the position is the number of the sample test block; and calling a two-dimensional code generation packet, and performing two-dimensional code generation processing on the number to obtain the unique two-dimensional code electronic tag.
As a preferable scheme of the method for sampling and identifying the concrete block compression test robot, the invention comprises the following steps: the association comprises that the sample vehicles are provided with unique license plate numbers, the two-dimensional code electronic tags and the license plate numbers are scanned, and system records are generated; positioning and calculating the position of the object by using a space coordinate algorithm; binding the two-dimensional code electronic tag of the sample placed on the sample vehicle with the license plate number of the sample vehicle to obtain the unique associated sample serial number.
As a preferable scheme of the method for sampling and identifying the concrete block compression test robot, the invention comprises the following steps: the sample serial number comprises the sample serial number = the license plate number of the sample vehicle + the two-dimensional code electronic tag of the sample; two-dimensional code electronic tag = number of sample + two-dimensional code generation time.
As a preferable scheme of the method for sampling and identifying the concrete block compression test robot, the invention comprises the following steps: the construction of the identification model includes the steps of,
Where L cv denotes the classifier loss function, by classifying the visible classes so that the network M can preserve the variability between the visible classes, ns denotes the number of visible class samples,Tag representing the ith sample,/>The visual characteristics of the i-th sample are represented, i being a constant.
As a preferable scheme of the method for sampling and identifying the concrete block compression test robot, the invention comprises the following steps: the recognition model needs to be subjected to precision training in advance, and comprises the steps of training the network M in an assisted mode by using a classifier and a relational network, and storing similarity and difference before classification; training the recognition model by utilizing a multichannel GAN network structure, and converting semantic features into visual features; the semantic feature is the sample serial number; the visual features were used to train a softmax classifier and tested according to GZSL standards.
As a preferable scheme of the method for sampling and identifying the concrete block compression test robot, the invention comprises the following steps: the identification result is obtained by inputting the shot pictures of the sample and the sample vehicle into Imagenet pre-training network M for extraction, so as to obtain visual characteristics of visible categories; inputting the visual features into the multi-channel recognition model for training to obtain fusion features of visible categories; training and generating the softmax classifier by utilizing the visual features and the fusion features; and performing recognition test on the trained softmax classifier to obtain a recognition result.
As a preferable scheme of the device for sampling and identifying the concrete block compression test robot, the invention comprises the following steps: the system comprises an information acquisition module, a data processing module and a data processing module, wherein the information acquisition module is used for acquiring sampling data, shooting picture information, and carrying out data preprocessing and picture cleaning operation on the sampling data and the picture information; the data processing center is connected with the information acquisition module and comprises a calculation body, a database and a decoding body, wherein the calculation body is used for receiving data information transmitted by the information acquisition module, the calculation body carries a random forest algorithm and an identification model running program, the calculation body calls the running program to calculate and feeds back calculation results to the database to store and divide and manage, and the decoding body is used for decoding characteristics, serial numbers, differences and similarities which appear in the calculation process of the calculation body so as to ensure the efficient running of the calculation body; the data input/output module is connected with each module and is used for providing data transmission service for each module; the identification module is connected with the database and is used for receiving the related data information stored in the database, analyzing the related data information in combination with the calculation result and outputting an identification result.
The invention has the beneficial effects that: according to the method, accurate sample test block positions and characteristics are obtained through a decision tree strategy, the unique electronic two-dimensional code is generated by combining the serial numbers, the influence of damage of paper labels on a sample sampling test is avoided, and the sample self-adaptive sampling recognition efficiency and accuracy of the concrete test block are further improved according to the recognition of the multichannel model.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of a method for sample identification of a concrete block compression test robot according to a first embodiment of the present invention;
FIG. 2 is a schematic illustration of another method for sample identification by a concrete block compression test robot according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a decision tree network topology of a method for sample identification of a concrete block compression test robot according to a first embodiment of the present invention;
Fig. 4 is a schematic distribution diagram of a module structure of a device for sampling and identifying a concrete block compression test robot according to a second embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1 to 3, for a first embodiment of the present invention, there is provided a method for sampling and identifying a concrete test block compression test robot, including:
S1: and sampling the sample to be tested by using the concrete test block compression test robot. Wherein, it should be noted that the sampling includes:
Setting the continuous casting quantity as 1000 square;
Three groups of test groups with different periods of 3 days, 7 days and 28 days are set;
Sampling a group of execution every 200 sides;
each group consisted of 3 sample coupons;
and respectively clamping and detecting the sample test blocks of each group by using a concrete test block compression test robot.
S2: and numbering and generating the sampled samples based on a random forest algorithm to obtain the unique two-dimensional code electronic tag. Referring to fig. 3, the step of obtaining the two-dimensional code electronic tag includes:
the concrete test block compression test robot performs view scanning treatment on the clamped sample test block to obtain imaging characteristic data;
the imaging characteristic data are transmitted to a recognition device processing center through the spp protocol stack to be specially processed, and regression coding is carried out on the imaging characteristic data based on a random forest algorithm;
The imaging features gradually generate different peripheral nodes along the root of the decision tree to each crotch node in the operation process;
the peripheral nodes are connected with each other through tree nerves, and the positions of the peripheral nodes in the decision tree are output by utilizing nerve feedback;
the position is the number of the sample test block;
and calling the two-dimensional code generation package, and carrying out two-dimensional code generation processing on the number to obtain the unique two-dimensional code electronic tag.
S3: and placing the sample with the two-dimensional code electronic tag on the allocated sample car to obtain the associated sample serial number. It should be further noted that, the association includes:
The sample vehicle is provided with a unique license plate number, and a two-dimensional code electronic tag and the license plate number are scanned to generate a system record;
positioning and calculating the position of the object by using a space coordinate algorithm;
binding a two-dimensional code electronic tag of a sample placed on the sample vehicle with a license plate number of the sample vehicle to obtain a unique associated sample serial number.
Specifically, the sample serial number includes:
sample serial number = license plate number of sample car + two-dimensional code electronic label of sample;
two-dimensional code electronic tag = number of sample + two-dimensional code generation time.
S4: and constructing an identification model by combining a multichannel algorithm, carrying out identification processing operation on the sample, the sample vehicle and the sample serial number, outputting an identification result and feeding back to an identification device to complete a sampling identification self-adaptive test. The step also needs to be described as that the construction of the identification model includes:
Where L cv denotes the classifier loss function, by classifying the visible classes so that the network M can preserve the variability between the visible classes, ns denotes the number of visible class samples, Tag representing the ith sample,/>The visual characteristics of the i-th sample are represented, i being a constant.
Furthermore, the recognition model needs to be trained in advance, which comprises the following steps:
training the network M with the aid of a classifier and a relational network, and storing the similarity and the difference before classification;
Training an identification model by utilizing a multichannel GAN network structure, and converting semantic features into visual features;
Semantic features are sample serial numbers;
the softmax classifier was trained with visual features and tested according to GZSL standards.
Still further, obtaining the identification result includes:
inputting the photographed pictures of the sample and the sample vehicle into Imagenet pre-training network M for extraction to obtain visual characteristics of visible categories;
Inputting the visual characteristics into a multi-channel recognition model for training to obtain fusion characteristics of the visual categories;
Training and generating a softmax classifier by utilizing visual features and fusion features;
and performing recognition test on the trained softmax classifier to obtain a recognition result.
Referring to fig. 2, when samples are stacked on a trolley, one surface containing two-dimensional codes faces the same side surface of the trolley, a group of three sample blocks are placed on the same row, and two-dimensional code marks exposed on the side surfaces represent three sample codes in one group; after the samples are stacked on the trolley, the mobile phone APP is used for integrally photographing the side surface of the sample trolley, the photographs are uploaded to a computer, the computer performs visual identification on the photographs, the number of layers and the number of each layer of the samples are determined, and the identified sample two-dimensional code numbers correspond to the sample positions of the test blocks; for unidentified codes, manually inputting the positions of the group of test blocks in the n-m grid by a tester, and after the treatment of the vehicle sample is finished, storing the treatment result of each vehicle test block into a database according to the vehicle number for the full-automatic tester to call; the process is a front part of a full-automatic test, and the pretreatment identification is carried out on the test block codes on the sample car, so that the test is ensured not to be interrupted due to various reasons such as mark breakage and the like in the test process.
After sample identification pretreatment is completed, pushing a sample car into a sample area in front of a testing machine, carrying out sample positioning identification by an industrial camera above the sample area, obtaining xy coordinates of each sample, selecting a car number which is pretreated from a list during testing, carrying out a cube compression-resistant full-automatic test on a test block on the car, and identifying codes of the samples in the test process.
Example 2
Referring to fig. 4, a second embodiment of the present invention, which is different from the first embodiment, is a device for sampling and identifying a concrete test block compression test robot, specifically including:
The information acquisition module 100 is used for acquiring sampling data, shooting picture information, and performing data preprocessing and picture cleaning operations on the sampling data and the picture information.
The data processing center 200 is connected with the information acquisition module 100, the data processing center 200 comprises a computing body 201, a database 202 and a decoding body 203, the computing body 201 is used for receiving data information transmitted by the information acquisition module 100, the computing body 201 carries a random forest algorithm and an identification model running program, the computing body 201 calls the running program to calculate, the calculation result is fed back to the database 202 to be stored and classified and managed, and the decoding body 203 is used for decoding characteristics, serial numbers, differences and similarities which appear in the operation process of the computing body 201 so as to ensure efficient running of the computing body 201.
The data input/output module 300 is connected to each module for providing data transmission services to each module.
The recognition module 400 is connected to the database 202, and is configured to receive related data information stored in the database 202, analyze the computing result, and output a recognition result.
It should be appreciated that embodiments of the invention may be implemented or realized by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer readable storage medium configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, in accordance with the methods and drawings described in the specific embodiments. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes (or variations and/or combinations thereof) described herein may be performed under control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications), by hardware, or combinations thereof, collectively executing on one or more processors. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable computing platform, including, but not limited to, a personal computer, mini-computer, mainframe, workstation, network or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and so forth. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and/or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, which when read by a computer, is operable to configure and operate the computer to perform the processes described herein. Further, the machine readable code, or portions thereof, may be transmitted over a wired or wireless network. When such media includes instructions or programs that, in conjunction with a microprocessor or other data processor, implement the steps described above, the invention described herein includes these and other different types of non-transitory computer-readable storage media. The invention also includes the computer itself when programmed according to the methods and techniques of the present invention. The computer program can be applied to the input data to perform the functions described herein, thereby converting the input data to generate output data that is stored to the non-volatile memory. The output information may also be applied to one or more output devices such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on a display.
As used in this disclosure, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, the components may be, but are not limited to: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. Furthermore, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (4)
1. A method for sampling and identifying a concrete test block compression test robot is characterized by comprising the following steps: comprising the steps of (a) a step of,
Sampling a sample to be tested by using a concrete test block compression test robot;
numbering and generating the sampled samples based on a random forest algorithm to obtain a unique two-dimensional code electronic tag;
Placing the sample with the two-dimensional code electronic tag on an allocated sample car to obtain an associated sample serial number;
Constructing an identification model by combining a multichannel algorithm, carrying out identification processing operation on the sample, the sample vehicle and the sample serial number, outputting an identification result and feeding back to an identification device to complete a sampling identification self-adaptive test;
the sampling comprises the steps of setting the continuous casting quantity to be 1000 square;
Three groups of test groups with different periods of 3 days, 7 days and 28 days are set;
Sampling a group of execution every 200 sides;
each group consisted of 3 sample coupons;
The concrete test block compression test robots are utilized to clamp and detect the sample test blocks of each group respectively;
The method comprises the steps that the unique two-dimensional code electronic tag comprises the step that the concrete test block compression test robot performs view scanning processing on the clamped sample test block to obtain imaging characteristic data;
the imaging characteristic data are transmitted to a recognition device processing center through a spp protocol stack to be specially processed, and regression coding is carried out on the imaging characteristic data based on the random forest algorithm;
the imaging features gradually generate different tip nodes along the root of the decision tree to each crotch node in the operation process;
The peripheral nodes are connected with each other through tree nerves, and the positions of the peripheral nodes in the decision tree are output by utilizing nerve feedback;
the position is the number of the sample test block;
a two-dimensional code generation packet is called, and two-dimensional code generation processing is carried out on the number to obtain a unique two-dimensional code electronic tag;
the association may comprise a combination of the following,
The sample vehicle is provided with a unique license plate number, and the two-dimensional code electronic tag and the license plate number are scanned to generate a system record;
positioning and calculating the position of the object by using a space coordinate algorithm;
binding the two-dimensional code electronic tag of the sample placed on the sample vehicle with the license plate number of the sample vehicle to obtain a unique associated sample serial number;
The sample serial number comprises the sample serial number = the license plate number of the sample vehicle + the two-dimensional code electronic tag of the sample;
The two-dimensional code electronic tag=the number of the sample+the two-dimensional code generation time;
the construction of the identification model includes the steps of,
Where L cv denotes the classifier loss function, by classifying the visible classes so that the network M can preserve the variability between the visible classes, ns denotes the number of visible class samples,Tag representing the ith sample,/>The visual characteristics of the i-th sample are represented, i being a constant.
2. The method for sampling and identifying the concrete block compression test robot according to claim 1, wherein the method comprises the following steps: the recognition model needs to be trained in advance in precision, including,
Training the network M with the aid of a classifier and a relational network, and storing similarity and difference before classification;
Training the recognition model by utilizing a multichannel GAN network structure, and converting semantic features into visual features;
the semantic feature is the sample serial number;
the visual features were used to train a softmax classifier and tested according to GZSL standards.
3. The method for sampling and identifying the concrete block compression test robot according to claim 2, wherein the method comprises the following steps: the obtaining of the result of the identification includes,
Inputting the shot pictures of the sample and the sample vehicle into Imagenet pre-training network M for extraction to obtain visual characteristics of visible categories;
Inputting the visual features into the multi-channel recognition model for training to obtain fusion features of visible categories;
training and generating the softmax classifier by utilizing the visual features and the fusion features;
And performing recognition test on the trained softmax classifier to obtain a recognition result.
4. A sample recognition device applied to the sample recognition method of the concrete test block compression test robot according to any one of claims 1 to 3, characterized in that: comprising the steps of (a) a step of,
The information acquisition module (100) is used for acquiring sampling data, shooting picture information, and carrying out data preprocessing and picture cleaning operation on the sampling data and the shot picture information;
The data processing center (200) is connected with the information acquisition module (100), the data processing center (200) comprises a calculation body (201), a database (202) and a decoding body (203), the calculation body (201) is used for receiving data information transmitted by the information acquisition module (100), the calculation body (201) is loaded with a random forest algorithm and an identification model running program, the calculation body (201) calls the running program to calculate, the calculation result is fed back to the database (202) to store and manage the division, and the decoding body (203) is used for decoding characteristics, serial numbers, differences and similarities which appear in the calculation process of the calculation body (201) so as to ensure efficient running of the calculation body (201).
The data input/output module (300) is connected with each module and is used for providing data transmission service for each module;
the identification module (400) is connected with the database (202) and is used for receiving relevant data information stored in the database (202), analyzing by combining with a calculation result and outputting an identification result.
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