CN115127856A - 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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Abstract
The invention discloses a method and a device for sampling and identifying a concrete test block compression test robot, which comprises the following steps of sampling a sample to be tested by using the concrete test block compression test robot; the sampled samples are numbered and generated based on a random forest algorithm, and a unique two-dimensional code electronic tag is obtained; placing the sample with the two-dimensional code electronic tag on the distributed sample car to obtain a sample serial number after correlation; and (3) constructing an identification model by combining a multi-channel algorithm, identifying and processing the sample, the sample car and the sample serial number, outputting an identification result and feeding back the identification result to the identification device to finish a sampling identification self-adaptive test. According to the method, the accurate position and the characteristics of the sample test block are obtained through a decision tree strategy, the unique electronic two-dimensional code is generated by combining the serial number, the influence of the damage of a paper label on a sample sampling test is not needed to be worried about, and the sample self-adaptive sampling identification efficiency and the sample self-adaptive sampling identification precision of the concrete test block are further improved according to the identification of a multi-channel model.
Description
Technical Field
The invention relates to the technical field of sampling identification of concrete test block compression test robots, in particular to a method and a system for sampling identification of a concrete test block compression test robot.
Background
The current house construction and civil engineering in China mainly adopt a reinforced concrete structure, in the construction engineering of engineering projects, the concrete engineering is the engineering with the largest quantity, the largest grade and the most complicated raw material varieties, and the quality of the construction quality of the concrete engineering directly influences the quality of the whole engineering. In order to effectively improve the construction quality of the engineering, a series of regulations and policies specially aiming at quality management are issued by the construction department, the railway department, the traffic department and related government departments, and the quality inspection work of the engineering construction is required to be strengthened by all levels of departments and construction units. However, due to the characteristics of the engineering construction, due to the lack of necessary technical means, the quality of the concrete still has management holes even under the supervision and inspection of multiple parts such as government supervision departments, owner units, supervision units, construction units and the like.
The sample sending detection of the concrete test block is an important component of civil engineering tests. The 'inspection and acceptance criteria for construction quality of concrete structural engineering' and 'inspection and evaluation standards for concrete strength' have requirements for problems occurring in the process of delivering a concrete test block. When the strength of the concrete is tested and evaluated, a standard curing test piece with 28d or a specified design age is adopted to perform a cubic compressive strength test. The standard value of the cubic compressive strength refers to the compressive strength of a cubic test piece with the side length of 150mm, which is manufactured and cured according to a standard method and is measured by a standard test method in the 28d age period and has 95% guarantee rate. The concrete strength grade is determined according to a cubic compression strength standard value and is divided into 19 strength grades from C10 to C100.
The robot is used for carrying out the cube compressive strength test, so that the working efficiency is improved, the influence of working noise on operators is reduced, the working strength of the testers is greatly reduced, and the fairness and the scientificity of the test are ensured.
In order to ensure the smooth progress of the process of the full-automatic test, in the whole test process, the efficient and correct identification of the sample codes plays a key role in the continuity of the whole automation process, and the efficiency and the experience of a user using full-automatic equipment are greatly influenced because the whole automation process is interrupted due to various non-equipment reasons such as sample code damage and the like.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: the problems that in the prior art, label identification and sample identification are low in precision and clamping efficiency is too low are solved.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of sampling a sample to be tested by using a concrete test block compression test robot; the sampled samples are numbered and generated based on a random forest algorithm, and a unique two-dimensional code electronic tag is obtained; placing the sample with the two-dimensional code electronic tag on a distributed sample car to obtain a sample serial number after correlation; and constructing an identification model by combining a multi-channel algorithm, carrying out identification processing operation on the sample, the sample vehicle and the sample serial number, outputting an identification result and feeding back the identification result to an identification device to finish a sampling identification self-adaptive test.
As a preferable scheme of the method for sampling and identifying the concrete test block compression test robot, the method comprises the following steps: the sampling comprises setting the continuous pouring quantity to be 1000 squares; three different period test groups of 3 days, 7 days and 28 days are set; performing one group of sampling according to every 200 squares; each group consists of 3 sample test blocks; and 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 test block compression test robot, the method comprises the following steps: the step of obtaining the two-dimension code electronic tag comprises the step of carrying out view scanning processing on the clamped sample test block by the concrete test block compression resistance test robot to obtain imaging characteristic data; the imaging characteristic data are transmitted to a recognition device processing center through an spp protocol stack for special processing, and regression coding is carried out on the imaging characteristic data based on the random forest algorithm; the imaging characteristics gradually generate different tip nodes to each branch node along the root of the decision tree 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 using nerve feedback; the position is the serial number of the sample test block; and calling a two-dimension code generation packet, and performing two-dimension code generation processing on the number to obtain the unique two-dimension code electronic tag.
As a preferable scheme of the method for sampling and identifying the concrete test block compression test robot, the method comprises the following steps: the association comprises that the sample vehicles are all 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 calculation is carried out on the obtained object by utilizing a space coordinate algorithm; and binding the two-dimensional code electronic tag of the sample placed on the sample car with the license plate number of the sample car to obtain the unique associated sample serial number.
As an optimal scheme of the method for sampling and identifying the concrete test block compression test robot, the method comprises the following steps: the sample serial number comprises the license plate number of the sample vehicle and the two-dimensional code electronic tag of the sample; the two-dimension code electronic tag is the number of the sample plus the two-dimension code generation time.
As a preferable scheme of the method for sampling and identifying the concrete test block compression test robot, the method comprises the following steps: constructing the recognition model includes the steps of,
wherein L is cv Representing classifier penalty function by classifying visible classes such that the network M preserves the variability between visible classes, Ns representing samples of a visible classThe number of the first and second groups is,
a label representing the ith sample is shown,
the visual characteristics of the ith sample are shown, i being a constant.
As an optimal scheme of the method for sampling and identifying the concrete test block compression test robot, the method comprises the following steps: the identification model needs to be accurately trained in advance, and the method comprises the steps of utilizing a classifier and a relation network to assist in training the network M, and storing the similarity and difference before classification; training the recognition model by using a multi-channel GAN network structure, and converting semantic features into visual features; the semantic feature is the sample sequence number; and training a softmax classifier by using the visual features and testing according to GZSL standards.
As a preferable scheme of the method for sampling and identifying the concrete test block compression test robot, the method comprises the following steps: the step of obtaining the identification result comprises the step of inputting the shot pictures of the sample and the sample car into an Imagenet pre-training network M for extraction to obtain visual features of visible categories; inputting the visual features into the recognition models of the multiple channels for training to obtain fusion features of visible categories; training and generating the softmax classifier by using the visual features and the fusion features; and performing identification test on the trained softmax classifier to obtain an identification result.
As a preferred scheme of the device for sampling and identifying the concrete test block compression test robot, the device comprises: the system comprises an information acquisition module, a data processing module and a picture cleaning module, wherein the information acquisition module is used for acquiring sampling data, shooting picture information and carrying out data preprocessing and picture cleaning operations on the sampling data and the shooting picture information; the data processing center is connected with the information acquisition module and comprises a calculator, a database and a decoder, wherein the calculator is used for receiving data information transmitted by the information acquisition module, the calculator is loaded with a random forest algorithm and an identification model operation program, the calculator calls the operation program to calculate and feeds a calculation result back to the database to be stored and subjected to admittance management, and the decoder is used for decoding features, serial numbers, differences and similarities appearing in the calculation process of the calculator so as to ensure the efficient operation of the calculator; the data input and 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 used for receiving the related data information stored in the database, analyzing the related data information by combining with the calculation result and outputting the identification result.
The invention has the beneficial effects that: according to the method, the accurate position and the characteristics of the sample test block are obtained through a decision tree strategy, the unique electronic two-dimensional code is generated by combining the serial numbers, the influence of the damage of the paper label on the sample sampling test is not needed to be worried about, and the sample self-adaptive sampling identification efficiency and the sample self-adaptive sampling identification accuracy of the concrete test block are further improved according to the identification of the multi-channel model.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of a method for sampling and identifying a concrete test block compression test robot according to a first embodiment of the invention;
fig. 2 is a schematic sampling diagram illustrating a method for identifying a concrete block compression test robot according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a network topology of a decision tree of a method for sampling and identifying a concrete test block compression test robot according to a first embodiment of the present invention;
fig. 4 is a schematic diagram of the distribution of the module structures of the device for sampling and identifying the concrete test block compression test robot according to the second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection 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 than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is 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.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Also in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are only for convenience of description and simplification of description, but do not indicate or imply that the device or element 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 connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 3, for a first embodiment of the present invention, a method for sampling and identifying a concrete test block compression test robot is provided, including:
s1: and sampling the sample to be tested by using the concrete test block compression test robot. It should be noted that the sampling includes:
setting the continuous casting quantity to be 1000 squares;
three different period test groups of 3 days, 7 days and 28 days are set;
performing one group of sampling according to every 200 squares;
each group consists of 3 sample test blocks;
and clamping and detecting the sample test blocks of each group by using the 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, in this step, it should be noted that obtaining the two-dimensional code electronic tag includes:
the concrete test block compression test robot carries out view scanning processing on the clamped sample test block to obtain imaging characteristic data;
the imaging characteristic data are transmitted to a processing center of the recognition device through an spp protocol stack for special processing, and regression coding is carried out on the imaging characteristic data based on a random forest algorithm;
the imaging characteristics gradually generate different tip nodes to each branch node along the root of the decision tree 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-dimension code to generate a package, and performing two-dimension code generation processing on the serial number to obtain the unique two-dimension code electronic tag.
S3: and placing the sample with the two-dimensional code electronic tag on the distributed sample car to obtain the associated sample serial number. It should be further noted that the association includes:
the sample vehicles are all 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 calculation is carried out on the obtained object by utilizing a space coordinate algorithm;
and binding the two-dimensional code electronic tag of the sample placed on the sample car with the license plate number of the sample car to obtain the unique associated sample serial number.
Specifically, the sample serial number includes:
the sample serial number is the license plate number of the sample car plus the two-dimensional code electronic tag of the sample;
the two-dimension code electronic tag is the number of the sample and the generation time of the two-dimension code.
S4: and (3) constructing an identification model by combining a multi-channel algorithm, identifying and processing the sample, the sample car and the sample serial number, outputting an identification result and feeding back the identification result to the identification device to finish a sampling identification self-adaptive test. It should be further noted that, the constructing the recognition model includes:
wherein L is cv Representing a classifier loss function that allows the network M to preserve the variability between visible classes by classifying the visible classes, Ns representing the number of visible class samples,
label for ith sampleThe label is a paper label with a color,
the visual characteristics of the ith sample are shown, i being a constant.
Furthermore, the recognition model needs to be accurately trained in advance, and the method comprises the following steps:
training the network M by using a classifier and a relationship network to assist, and storing the similarity and difference before classification;
training an identification model by using a multi-channel GAN network structure, and converting semantic features into visual features;
the semantic features are sample serial numbers;
and training a softmax classifier by using visual features and testing according to GZSL standards.
Still further, obtaining the recognition result includes:
inputting the shot images of the sample and the sample car into an Imagenet pre-training network M for extraction to obtain visual characteristics of visible categories;
inputting the visual features into a multi-channel recognition model for training to obtain fusion features of visible categories;
training and generating a softmax classifier by using the visual features and the fusion features;
and performing identification test on the trained softmax classifier to obtain an identification result.
Referring to fig. 2, when samples are stacked on a trolley, one surface containing the two-dimensional codes faces to the same side surface of the trolley, three test blocks of a group of samples are placed on the same line, and the two-dimensional code marks exposed on the side surface represent three sample codes in a group; after the samples are stacked on the trolley, the samples are integrally shot on the side face of the sample trolley by using a mobile phone APP, the pictures are uploaded to a computer, the computer visually identifies the pictures, the number of layers and the number of each layer of the samples are determined, and the number of the two-dimensional codes of the identified samples corresponds to the position of the sample of the test block; for the unidentified codes, the positions of the test blocks in the n x m grid are manually input by a tester, and after the vehicle sample is processed, the processing result of each test block is stored in a database according to the vehicle number for the full-automatic testing machine to call; the process is a front-end part of the full-automatic test, and the test block codes on the sample car are preprocessed and identified, so that test interruption caused by various reasons such as identification damage and the like can be avoided in the test process.
After the sample identification pretreatment is completed, a sample vehicle is pushed into a sample area before a testing machine, an industrial camera above the sample area is used for positioning and identifying samples, xy coordinates of each sample are obtained, a preprocessed trolley number is selected from a list during testing, a full-automatic test of cube compression resistance is carried out on a test block on the vehicle, and codes of the samples are not identified any more in the testing process.
Example 2
Referring to fig. 4, a second embodiment of the present invention is different from the first embodiment in that a sampling recognition device for a concrete block compression test robot is provided, which specifically includes:
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 calculator 201, a database 202 and a decoder 203, the calculator 201 is used for receiving data information transmitted by the information acquisition module 100, the calculator 201 carries a random forest algorithm and an identification model operation program, the calculator 201 calls the operation program to perform calculation, and feeds calculation results back to the database 202 for storage and admittance management, and the decoder 203 is used for decoding features, serial numbers, differences and similarities appearing in the calculation process of the calculator 201 so as to guarantee efficient operation of the calculator 201.
The data input/output module 300 is connected to each module, and is configured to provide data transmission services for each module.
The recognition module 400 is connected to the database 202, and is configured to receive the relevant data information stored in the database 202, perform analysis in combination with the calculation result, and output a recognition result.
It should be recognized that embodiments of the present invention can be realized and implemented 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 the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. 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.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the 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) collectively executed on one or more processors, by hardware, or combinations thereof. 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 connection, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, or the like. Aspects of the invention may be embodied 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, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to 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 particular visual depictions of physical and tangible objects produced on a display.
As used in this application, 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, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, 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-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, 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 modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (9)
1. A sampling and identifying method of a concrete test block compression test robot is characterized by comprising the following steps: comprises the steps of (a) preparing a substrate,
sampling a sample to be tested by using a concrete test block compression test robot;
the sampled samples are numbered and generated based on a random forest algorithm, and a unique two-dimensional code electronic tag is obtained;
placing the sample with the two-dimension code electronic tag on a distributed sample car to obtain a correlated sample serial number;
and constructing an identification model by combining a multi-channel algorithm, carrying out identification processing operation on the sample, the sample car and the sample serial number, outputting an identification result and feeding back the identification result to an identification device to finish a sampling identification self-adaptive test.
2. The method for sampling and identifying the concrete test block compression test robot according to claim 1, wherein the method comprises the following steps: the sampling comprises the steps of sampling the sample,
setting the continuous pouring amount to be 1000 squares;
three different period test groups of 3 days, 7 days and 28 days are set;
performing one group of sampling according to every 200 squares;
each group consists of 3 sample test blocks;
and clamping and detecting the sample test blocks of each group by using the concrete test block compression test robot.
3. The method for sampling and identifying the concrete test block compression test robot according to claim 2, wherein: the two-dimension code electronic tag is obtained by the steps of,
the concrete test block compression test robot carries out view scanning processing on the clamped sample test block to obtain imaging characteristic data;
the imaging characteristic data is transmitted to a processing center of a recognition device through an spp protocol stack for special processing, and regression coding is carried out on the imaging characteristic data based on the random forest algorithm;
the imaging characteristics gradually generate different tip nodes to each branch node along the root of the decision tree 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 using nerve feedback;
the position is the serial number of the sample test block;
and calling a two-dimension code generation packet, and performing two-dimension code generation processing on the number to obtain the unique two-dimension code electronic tag.
4. The method for sampling and identifying the concrete test block compression test robot as recited in claim 1 or 3, wherein: the association includes the association of the received data with the selected data,
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 calculation is carried out on the obtained object by utilizing a space coordinate algorithm;
and binding the two-dimensional code electronic tag of the sample placed on the sample car with the license plate number of the sample car to obtain the unique associated sample serial number.
5. The method for sampling and identifying the concrete test block compression test robot according to claim 4, wherein the method comprises the following steps: the sample serial number includes a serial number of the sample,
the sample serial number is the license plate number of the sample car plus the two-dimensional code electronic tag of the sample;
the two-dimension code electronic tag is the number of the sample plus the two-dimension code generation time.
6. The method for sampling and identifying the concrete test block compression test robot according to claim 5, wherein: constructing the recognition model includes the steps of,
wherein L is cv Representing a classifier loss function that allows the network M to preserve the variability between visible classes by classifying the visible classes, Ns representing the number of visible class samples,
a label representing the ith sample,
the visual characteristics of the ith sample are shown, i being a constant.
7. The method for sampling and identifying the concrete test block compression test robot according to claim 6, wherein: the recognition model needs to be accurately trained in advance, including,
training the network M by using a classifier and a relation network in an auxiliary manner, and storing the similarity and difference before classification;
training the recognition model by using a multi-channel GAN network structure, and converting semantic features into visual features;
the semantic features are the sample serial numbers;
and training a softmax classifier by using the visual features and testing according to GZSL standards.
8. The method for sampling and identifying the concrete test block compression test robot according to claim 7, wherein: obtaining the result of the recognition may include obtaining,
inputting the shot pictures of the sample and the sample car into an Imagenet pre-training network M for extraction to obtain visual features of visible categories;
inputting the visual features into the recognition models of the multiple channels for training to obtain fusion features of visible categories;
training and generating the softmax classifier by using the visual features and the fusion features;
and performing identification test on the trained softmax classifier to obtain an identification result.
9. A sampling identification device applied to the concrete test block compression test robot sampling identification method according to claims 1-3 and 5-8 is characterized in that: comprises the steps of (a) preparing a substrate,
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 picture information;
the data processing center (200) is connected with the information acquisition module (100), the data processing center (200) comprises a calculator (201), a database (202) and a decoder (203), the calculator (201) is used for receiving data information transmitted by the information acquisition module (100), the calculator (201) carries a random forest algorithm and an identification model operation program, the calculator (201) calls the operation program to calculate, and feeds a calculation result back to the database (202) for storage and storage management, and the decoder (203) is used for decoding features, serial numbers, differences and similarities appearing in the operation process of the calculator (201) so as to guarantee efficient operation of the calculator (201);
the data input and 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 the related data information stored in the database (202), analyzing the related data information by combining the calculation result and outputting the identification result.
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