Sample grading method and device and electronic equipment

Abstract

The invention provides a sample grading method and device and electronic equipment. The target sample is graded by means of cooperation of a multi-mode imaging mode, namely, the X-ray image obtained based on X-ray scanning of the target sample and the at least one sample surface image, collected based on the at least one data collection module, of the surface of the target sample, so that the grading accuracy of the target sample is improved. Furthermore, the grading evaluation results of different surface areas in the target sample, such as the fat distribution characteristics of one piece of meat, are visually displayed, so that the grading evaluation results of different surface areas in the target sample, such as the fat distribution characteristics of one piece of meat, are visually displayed; and thus, finer quality evaluation of target samples such as meat is realized.

Description

Technical Field

[0001] The present application relates to data processing technologies, and particularly to a method and apparatus for sample grading and an electronic device. Background Art

[0002] In specific applications, samples are often graded to facilitate standardized management of the samples. Taking meat samples as an example, meat grading aims to standardize meat quality, facilitating market transactions and consumer selection.

[0003] When grading samples, it often depends on the characteristic data of the samples. Still taking meat samples as an example, for meat samples, their characteristic data is, for example, fat content. Currently, manual visual inspection is often relied on to grade samples such as meat samples. However, due to the relatively large volume of the samples and the uneven distribution of their characteristics, there is often a risk of inaccurate grading with the manual visual inspection method. Still taking meat samples as an example, for example, when grading a piece of meat, since the piece of meat has a relatively large volume and is streaky pork with a very uneven fat and lean distribution, there is often a risk of inaccurate grading of the piece of meat with the manual visual inspection method. Summary of the Invention

[0004] The present application provides a method and apparatus for sample grading and an electronic device, so as to grade the sample by cooperating with the data of the sample collected in a multi-modal manner, and improve the accuracy of sample grading.

[0005] An embodiment of the present application provides a method for sample grading, the method including: Determining the characteristic data of each image unit in the X-ray image; the X-ray image is an image obtained by scanning a target sample with X-rays of at least two energies; the characteristic data of any image unit is used to represent the component information of the position corresponding to the image unit in the target sample; Performing image registration on at least one sample surface image and the X-ray image, and performing a grading evaluation process on different surface regions of the target sample according to the registration result and the characteristic data of each image unit; the at least one sample surface image is an image obtained by using at least one data acquisition module to collect the surface of the target sample; the grading evaluation result of any surface region is determined based on the predicted component information of the surface region and the region block characteristic data of the region block corresponding to the surface region; the region block corresponding to any surface region refers to a region block from the surface region to the bottom of the target sample, and the region block characteristic data of any region block is determined based on the characteristic data of each image unit in the region block, and the region block characteristic data of any region block characterizes the component information of the region block; Visually displaying the grading evaluation results of different surface regions in the target sample.

[0006] A sample grading apparatus, the apparatus including: A determination module, configured to determine the characteristic data of each image unit in the X-ray image; the X-ray image is an image obtained by scanning a target sample with X-rays of at least two energies; the characteristic data of any image unit is used to represent the composition information of the position corresponding to the image unit in the target sample. A processing module, configured to perform image registration on at least one sample surface image and the X-ray image, and perform hierarchical evaluation processing on different surface regions of the target sample according to the registration result and the characteristic data of each image unit; the at least one sample surface image is an image obtained by using at least one data acquisition module to acquire the surface of the target sample; the hierarchical evaluation result of any surface region is determined based on the predicted composition information of the surface region and the region block characteristic data of the region block corresponding to the surface region; the region block corresponding to any surface region refers to a region block from the surface region to the bottom of the target sample in the target sample, the region block characteristic data of any region block is determined according to the characteristic data of each image unit in the region block, and the region block characteristic data of any region block characterizes the composition information of the region block. A display module, configured to visually display the hierarchical evaluation results of different surface regions of the target sample.

[0007] An electronic device, which includes a processor and a machine-readable storage medium; several computer instructions are stored on the machine-readable storage medium, and when the computer instructions are executed by the processor, the steps in the above method are implemented.

[0008] It can be seen from the above technical solutions that in this embodiment, by means of a multi-modal imaging method, that is, the X-ray image obtained by scanning the target sample with X-rays and at least one sample surface image of the surface of the target sample collected by at least one data acquisition module are combined to classify the target sample, so as to improve the accuracy of the classification of the target sample.

[0009] Furthermore, in this embodiment, by visually displaying the hierarchical evaluation results of different surface regions of the target sample, such as the fat distribution characteristics of a piece of meat, the hierarchical evaluation results of different surface regions of the target sample, such as the fat distribution characteristics of a piece of meat, are intuitively displayed, and then a more refined quality evaluation of the target sample, such as meat, is realized. Description of the Drawings

[0010] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0011] Figure 1 It is a flowchart of the method provided by the embodiment of the present application; Figure 2 It is a flowchart for implementing step 102 provided by the embodiment of the present application; Figure 3 Schematic diagram of the device provided by an embodiment of the present application; Figure 4 Structural diagram of the electronic device provided by an embodiment of the present application. Detailed implementation manners

[0012] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of the devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0013] The terms used in the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said", and "the" used in the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0014] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application, and to make the above objects, features, and advantages of the embodiments of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0015] See Figure 1 , Figure 1 which is a flowchart of the method provided by an embodiment of the present application. This method is applied to an electronic device. As Figure 1 shown, the process may include the following steps: Step 101, determining the characteristic data of each image unit in the X-ray image; the X-ray image is an image obtained by scanning a target sample with X-rays of at least two energies; the characteristic data of any image unit is used to represent the composition information of the position corresponding to the image unit in the target sample.

[0016] As an embodiment, the target sample here may be a piece of meat.

[0017] In specific implementation, this embodiment will use a direct digital radiography system (DR: DigitRadiography), or use a CT to emit X-rays of at least two energies to the target sample to scan and obtain a tomographic image of the target sample (i.e., the above-mentioned X-ray image). Optionally, the DR may be a multi-energy or dual-energy DR. The CT may be a multi-energy or dual-energy CT.

[0018] In this embodiment, the structure of the X-ray image can be a two-dimensional or three-dimensional image structure. For example, the X-ray image obtained by scanning with DR-emitted X-rays has a two-dimensional image structure, and the X-ray image obtained by scanning with CT-emitted X-rays has a three-dimensional image structure. If the structure of the X-ray image is different, the image units in the X-ray image are different. For example, for a two-dimensional X-ray image, its image unit is a single pixel point, while for a three-dimensional X-ray image, its image unit is a single volume element (abbreviated as voxel).

[0019] In this embodiment, the characteristic data of any image unit is used to represent the composition information of the position in the target sample corresponding to this image unit. Still taking the target sample as a piece of meat as an example, the characteristic data of any image unit is used to represent the composition information of the position in this piece of meat corresponding to this image unit, such as fat data or muscle data.

[0020] In specific implementation, the determination method of the characteristic data of any image unit in the X-ray image is related to the imaging mode of the X-ray image. If the imaging mode is different, the above determination method is also different. For example, if the imaging mode of the X-ray image is the DR mode (for example, the X-ray image at this time is obtained by scanning with DR-emitted X-rays), the basis material decomposition algorithm is often used to determine the characteristic data of any image unit in the X-ray image, such as the fat ratio. For another example, if the imaging mode of the X-ray image is the CT mode (for example, the X-ray image at this time is obtained by scanning with CT-emitted X-rays), the material recognition and segmentation algorithm is often used to determine the characteristic data of any image unit in the X-ray image, such as the fat density value.

[0021] That is to say, in this embodiment, the relatively mature basis material decomposition algorithm or material recognition and segmentation algorithm that was not originally applied to the meat scenario can be applied to the meat grading scenario. Taking the basis material decomposition algorithm as an example, in meat detection, when dual-energy or multi-energy X-rays pass through a piece of meat, such as a piece of meat with fat and muscle superimposed, the signal received by the detector is:

[0022] ; Among them, is the signal emission intensity, represents the signal reception intensity received by the detector. is the mass attenuation coefficient of fat and muscle in the th energy interval, and this value can be obtained according to theory or experiment.

[0023] Suppose the attenuation coefficient matrix is , with a size of , is the total energy interval number, where , . If the image matrix is , with a size of , ; if the mass thickness matrix is , with a size of , , and the definition of mass thickness is density multiplied by thickness. Based on the basis material decomposition algorithm, is solved to obtain , then the fat mass , muscle mass , and total mass at the position corresponding to each pixel point in the target sample can be obtained. Correspondingly, the fat percentage at this position is .

[0024] Here, taking the fat percentage as an example of the characteristic data, in this embodiment, the characteristic data of each image unit, that is, the fat percentage, will be integrated in the spatial domain, and finally a full-field quantization map containing fat distribution characteristics will be generated.

[0025] Step 102, perform image registration on at least one sample surface image and the above X-ray image, and perform hierarchical evaluation processing on different surface regions of the target sample according to the registration result and the characteristic data of each image unit.

[0026] In specific applications, since the X-ray image lacks the depth information of each position in the target sample, relying solely on the X-ray image to grade the target sample may also have the risk of inaccurate grading. Based on this, this embodiment will also use at least one data acquisition module, such as at least one of a visible light imaging device, a multispectral sensor, and a hyperspectral acquisition device, to acquire images of the surface of the target sample to obtain at least one sample surface image. Still taking the target sample as a piece of meat as an example, the obtained sample surface images characterize the surface information of the piece of meat.

[0027] As an embodiment, after obtaining at least one sample surface image, necessary preprocessing, such as at least one of image correction, image denoising, and principal component analysis, can be performed on each sample surface image to reduce the data volume and remove noise data.

[0028] After that, the sample surface images can be registered with the above X-ray image. Optionally, in this embodiment, the sample surface images can be aligned with the X-ray image according to a set alignment method to make the sample surface images and the X-ray image spatially aligned. As an embodiment, the set alignment method can be at least one of methods such as feature matching algorithm and scale-invariant feature transform (SIFT).

[0029] After that, grading and evaluation processing is performed on different surface regions of the target sample according to the registration result and the characteristic data of each image unit. Here, the grading and evaluation result of any surface region is determined based on the predicted component information of the surface region and the region block characteristic data of the region block corresponding to the surface region; the region block corresponding to any surface region refers to a region block from the surface region to the bottom of the target sample in the target sample, the region block characteristic data of any region block is determined based on the characteristic data of each image unit in the region block, and the region block characteristic data of any region block characterizes the component information of the region block. How to perform grading and evaluation processing on different surface regions of the target sample according to the registration result and the characteristic data of each image unit will be described by way of example below.

[0030] Step 103, visually display the grading and evaluation results of different surface regions of the target sample.

[0031] For example, the grading and evaluation result is projected onto the interactive screen in real time through a projection display device to form a dynamic visualization interface for real-time quality grade determination of the target sample such as meat based on the display result such as the fat distribution characteristics, which is applied to the meat grading scenario, and this visual display can be used to guide subsequent operations such as cutting the meat.

[0032] So far, the Figure 1 shown process is completed.

[0033] Through Figure 1 the shown process, it can be seen that in this embodiment, the target sample is graded by means of a multimodal imaging method, that is, an X-ray image obtained by scanning the target sample based on X-rays and at least one sample surface image of the surface of the target sample collected by at least one data acquisition module are combined to improve the accuracy of grading the target sample.

[0034] Furthermore, in this embodiment, by visually displaying the grading and evaluation results of different surface regions of the target sample such as the fat distribution characteristics of a piece of meat, the grading and evaluation results of different surface regions of the target sample such as the fat distribution characteristics of a piece of meat are intuitively displayed, and further, a more refined quality evaluation of the target sample such as meat is realized.

[0035] The following describes how to perform grading and evaluation processing on different surface regions of the target sample according to the registration result and the characteristic data of each image unit in step 102: Optionally, the grading and evaluation result of any surface region indicates the consistency judgment result of the predicted component information of the surface region and the component information of the region block corresponding to the surface region. The following is a description: Refer to Figure 2 , Figure 2 which is the flowchart of step 102 provided by the embodiment of the present application. As Figure 2As shown, the process may include the following steps: Step 201: Obtain the predicted component information of each different surface area on the upper surface of the target sample.

[0036] It should be noted that in this embodiment, the predicted component information of each different surface area on the upper surface of the target sample can be implemented based on an image classification module. Taking the target sample as a piece of meat as an example, the image classification module is used to distinguish whether the component information of each area (denoted as the surface area) on the upper surface of the piece of meat is muscle or fat, so as to distinguish the components (muscle or fat) of each different surface area on the upper surface of the piece of meat. The image classification module can be a region component recognition model constructed by at least one of SVM, convolutional neural network, etc.

[0037] Step 202: For each surface area, based on the above registration result, determine the region block corresponding to the surface area from the target sample, and determine the region block feature data of the region block according to the feature data of each image unit in the region block. The region block feature data characterizes the component information of the region block; judge the consistency between the region block feature data and the predicted component information of the surface area.

[0038] For example, if the target sample is a piece of meat, assuming a surface area is determined, the region block corresponding to the surface area is a small piece of meat framed from top to bottom along the region edge of the surface area.

[0039] After determining the region block, determine the region block feature data of the region block according to the feature data of each image unit in the region block. For example, taking the target sample as a piece of meat and the feature data of the image unit as the fat ratio, calculate the average value of the fat ratios of each image unit in the region block to obtain the average fat ratio, which is the region block feature data of the region block and characterizes the components of the entire region block. For example, if the average fat ratio of the region block is greater than or equal to the set fat threshold, determine that the entire region block is fat; otherwise, if the average fat ratio of the region block is less than the set fat threshold, determine that the entire region block is lean meat.

[0040] After that, a consistency judgment is made on the region block feature data and the predicted component information of the surface region obtained in the above step 201 (such as the component information of the surface region predicted based on the above image classification module). For example: Compare whether the region block feature data and the predicted component information of the surface region obtained in the above step 201 (such as the component information of the surface region predicted based on the above image classification module) meet the set consistency requirements. If the set consistency requirements are met, it is determined that the surface region is a region with good consistency (at this time, this region can have a corresponding identification value such as 1, indicating that the set consistency requirements are met). At this time, the component of the surface region can be the predicted component information of the surface region. Otherwise, it is determined that the surface region is a region with poor consistency (at this time, this region can have a corresponding identification value such as 0, indicating that the set consistency requirements are not met). At this time, the component of the surface region can be marked as uncertain or may be different from the predicted component information of the surface region).

[0041] Taking the target sample as a piece of meat and the region block feature data as the average fat percentage of the region block as an example, the above region block feature data and the predicted component information of the surface region meeting the set consistency requirements means that: if the predicted component information of the surface region is fat and the average fat percentage is greater than or equal to the set fat threshold, it is determined that the set consistency requirements are met. Vice versa.

[0042] Optionally, in this embodiment, for each surface region, it can have a corresponding consistency degree to characterize the degree to which the region block feature data of the surface region and the predicted component information of the surface region (such as the component information of the surface region predicted based on the above image classification module) meet the set consistency requirements. For example, the consistency degree of the surface region can be represented by the residual between the characteristic value corresponding to the predicted component information of the surface region (used to represent the component information) and the region block feature data of the surface region. Optionally, in this embodiment, the characteristic value corresponding to the predicted component information of any surface region is related to the component information. For example, when the component information is fat, the characteristic value is 0, and when the component information is muscle, the characteristic value is 1, etc. This embodiment does not specifically limit this.

[0043] So far, the Figure 2 shown process is completed.

[0044] Through the Figure 2 shown process, how to perform hierarchical evaluation processing on different surface regions of the target sample is realized.

[0045] Optionally, in this embodiment, the characteristic data of each image unit in the above X-ray image can also be subjected to component analysis, and the spatial distribution characteristics of the target sample can be visually displayed; the spatial distribution characteristics are represented by the quantization indexes of each image unit; the quantization index of any image unit is the mass of the position corresponding to the image unit in the target sample, and / or the component distribution information determined based on the characteristic data of the image unit. Preferably, three-dimensional stereoscopic rendering technology or pseudo-three-dimensional projection technology and other visual presentation means can be used to visually display the spatial distribution characteristics of the target sample. Additionally, if the target sample is a piece of meat, the component distribution information determined based on the characteristic data of the image unit is: the fat percentage and / or muscle percentage, and the fat and / or muscle mass distribution.

[0046] It should be noted that the above spatial distribution characteristics can be at least one of a heat map, a contour map, a two-dimensional histogram, etc.

[0047] Optionally, in this embodiment, by configuring a dynamic rendering engine, the rendering parameters can be adjusted in real time according to user interaction instructions to achieve intuitive display. For example, by configuring a dynamic rendering engine, the rendering parameters can be adjusted in real time according to user interaction instructions, so as to intuitively display the spatial distribution characteristics and quantization ratios of different tissue components in the target sample, thereby helping users to perform a more refined quality assessment of the target sample such as meat.

[0048] Furthermore, in this embodiment, a digital identification code can be generated based on the grading evaluation results of different surface areas of the target sample; the digital identification code is represented by a quality parameter matrix, and the quality parameter matrix is determined based on the grading evaluation results of different surface areas of the target sample.

[0049] As an alternative implementation, based on the description of the above grading evaluation process, optionally, the quality parameter matrix here can be the identification values (whether the set consistency requirements are met) corresponding to different surface areas of the target sample. Table 1 below exemplifies the quality parameter matrix: Table 1

[0050] As another alternative implementation, based on the description of the above grading evaluation process, optionally, the quality parameter matrix here can be the degree of consistency corresponding to different surface areas of the target sample. Table 2 below exemplifies the quality parameter matrix: Table 2

[0051] Optionally, in this embodiment, the digital identification code is attached to the packaging surface of the target sample. For example, the digital identification code is printed on a waterproof material substrate through a thermal transfer device to form a traceable physical label and attached to the product packaging surface of the target sample.

[0052] The method provided by the embodiments of the present application has been described above. Next, the device provided by the embodiments of the present application will be described: Refer to Figure 3 , Figure 3 , which is the structural diagram of the device provided by the embodiments of the present application. As Figure 3 shown, the device includes: A determination module, configured to determine the feature data of each image unit in the X-ray image; the X-ray image is an image obtained by scanning a target sample with X-rays of at least two energies; the feature data of any image unit is used to represent the component information of the position corresponding to the image unit in the target sample; A processing module, configured to perform image registration on at least one sample surface image and the X-ray image, and perform hierarchical evaluation processing on different surface regions of the target sample according to the registration result and the feature data of each image unit; the at least one sample surface image is an image obtained by using at least one data acquisition module to acquire the surface of the target sample; the hierarchical evaluation result of any surface region is determined based on the predicted component information of the surface region and the region block feature data of the region block corresponding to the surface region; the region block corresponding to any surface region refers to a region block from the surface region to the bottom of the target sample, and the region block feature data of any region block is determined according to the feature data of each image unit in the region block, and the region block feature data of any region block characterizes the component information of the region block; A display module, configured to visually display the hierarchical evaluation results of different surface regions of the target sample.

[0053] Optionally, the performing image registration on at least one sample surface image and the X-ray image includes: Aligning the sample surface image and the X-ray image according to a set alignment method so that the sample surface image and the X-ray image are spatially aligned.

[0054] Optionally, the performing hierarchical evaluation processing on different surface regions of the target sample according to the registration result and the feature data of each image unit includes: Obtaining the predicted component information of different surface regions in the upper surface of the target sample; For each surface region, determining the region block corresponding to the surface region from the target sample based on the registration result, and determining the region block feature data of the region block according to the feature data of each image unit in the region block, where the region block feature data indicates the component information of the region block; performing a consistency judgment on the region block feature data and the predicted component information of the surface region; Among them, the grading evaluation result of any surface area indicates the consistency judgment result between the predicted component information of the surface area and the component information of the regional block corresponding to the surface area.

[0055] Optionally, the display module is configured to visually display the spatial distribution characteristics of the target sample; the spatial distribution characteristics are represented by the quantization indexes of each image unit; the quantization index of any image unit includes mass and / or component distribution information determined based on the characteristic data of the image unit.

[0056] Optionally, the target sample is a piece of meat; the component distribution information determined based on the characteristic data of the image unit is: fat ratio and / or muscle ratio.

[0057] Optionally, the processing module is configured to generate a digital identification code based on the grading evaluation results of different surface areas of the target sample; the digital identification code is represented by a quality parameter matrix, and the quality parameter matrix is determined based on the grading evaluation results of different surface areas of the target sample; the digital identification code is attached to the packaging surface of the target sample.

[0058] Optionally, the target sample is a piece of meat, and the characteristic data of any image unit is used to represent the fat data or muscle data at the position corresponding to the image unit in the piece of meat; The predicted component information of any surface area is muscle or fat; The component information represented by the regional block is determined based on the fat data or muscle data of each image unit in the regional block.

[0059] Optionally, the at least one sample surface image includes an image of the surface of the target sample collected by at least one of a visible light imaging device, a multispectral sensor, and a hyperspectral acquisition device.

[0060] Thus far, the Figure 3 structural description of the illustrated device is completed.

[0061] Correspondingly, an embodiment of the present application further provides Figure 3 the hardware structure of the illustrated device. Refer to Figure 4 , Figure 4 which is the hardware structure diagram provided by an embodiment of the present application. As Figure 4 shown, the hardware structure includes a processor and a machine-readable storage medium; a number of computer instructions are stored on the machine-readable storage medium, and when the computer instructions are executed by the processor, the steps in the above method are implemented.

[0062] Based on the same application concept as the above method, an embodiment of the present application further provides a machine-readable storage medium, on which several computer instructions are stored. When the computer instructions are executed by a processor, the method disclosed in the above examples of the present application can be implemented.

[0063] Exemplarily, the above machine-readable storage medium can be any electronic, magnetic, optical or other physical storage device that can contain or store information, such as executable instructions, messages, and so on. For example, the machine-readable storage medium can be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, storage drives (such as hard disk drives), solid state drives, any type of storage disk (such as optical discs, DVDs, etc.), or similar storage media, or a combination thereof.

[0064] The systems, devices, modules or units illustrated in the above embodiments can be specifically implemented by a computer processing unit or entity, or by a product with certain functions. A typical implementation device is a computer, and the specific form of the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver device, a game console, a tablet computer, a wearable device, or a combination of any several of these devices.

[0065] For the convenience of description, when describing the above devices, they are described as various units according to functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0066] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable message processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable message processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0068] Moreover, these computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable message processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one flow Figure 1 or multiple flows and / or blocks Figure 1 or the functions specified in multiple blocks.

[0069] These computer program instructions can also be loaded onto a computer or other programmable message processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 or multiple flows and / or blocks Figure 1 or the functions specified in multiple blocks.

[0070] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A sample classification method, characterized in that: The method includes: Determine characteristic data of each image unit in an X-ray image; the X-ray image is an image obtained by scanning a target sample using X-rays of at least two energies; the characteristic data of any image unit is used to represent component information of a position in the target sample corresponding to the image unit; Performing image registration on at least one sample surface image and the X-ray image, and performing graded evaluation processing on different surface areas in the target sample according to the registration result and the characteristic data of each image unit; the at least one sample surface image is an image obtained by collecting the surface of the target sample using at least one data acquisition module; the graded evaluation result of any surface area is determined based on the predicted component information of the surface area and the regional block characteristic data of the regional block corresponding to the surface area; the regional block corresponding to any surface area refers to a regional block in the target sample from the surface area to the bottom of the target sample, the regional block characteristic data of any regional block is determined based on the characteristic data of each image unit in the regional block, and the regional block characteristic data of any regional block represents the component information of the regional block; The graded evaluation results of different surface areas in the target sample are visually displayed.

2. The method according to claim 1, characterized in that The performing image registration on at least one sample surface image and the X-ray image comprises: The sample surface image is aligned with the X-ray image according to a set alignment method, so that the sample surface image is aligned with the X-ray image in space.

3. The method according to claim 1, characterized in that: The step of performing a hierarchical evaluation process on different surface areas in the target sample according to the registration result and the feature data of each image unit comprises: Obtaining predicted composition information of different surface areas on the upper surface of the target sample; For each surface area, a region block corresponding to the surface area is determined from the target sample based on the registration result, and region block feature data of the region block is determined according to feature data of each image unit in the region block, wherein the region block feature data indicates component information of the region block; and consistency judgment is performed on the region block feature data and the predicted component information of the surface area; The hierarchical evaluation result of any surface area indicates the consistency judgment result between the predicted component information of the surface area and the component information of the area block corresponding to the surface area.

4. The method according to claim 1, characterized in that: The method further comprises: The spatial distribution characteristics of the target sample are displayed visually; the spatial distribution characteristics are represented by quantitative indicators of each image unit; the quantitative indicators of any image unit include quality, and / or component distribution information determined based on characteristic data of the image unit.

5. The method according to claim 4, characterized in that The target sample is a piece of meat; The component distribution information determined based on the characteristic data of the image unit is: fat percentage and / or muscle percentage.

6. The method according to claim 1, characterized in that The method further comprises: Based on the graded evaluation results of different surface areas in the target sample, a digital identification code is generated; the digital identification code is represented by a quality parameter matrix, and the quality parameter matrix is ​​determined based on the graded evaluation results of different surface areas in the target sample; the digital identification code is attached to the packaging surface of the target sample.

7. The method according to any one of claims 1 to 6, characterized in that: The target sample is a piece of meat, and the feature data of any image unit is used to represent the fat data or muscle data of the position corresponding to the image unit in the piece of meat; The predicted composition information of any surface area is muscle or fat; The component information represented by the region block is determined based on the fat data or muscle data of each image unit in the region block.

8. The method according to any one of claims 1 to 6, characterized in that: The at least one sample surface image includes an image of the surface of the target sample acquired by at least one of a visible light imaging device, a multispectral sensor, and a hyperspectral acquisition device.

9. A sample grading device, characterized in that: The device includes: A determination module, used to determine characteristic data of each image unit in an X-ray image; the X-ray image is an image obtained by scanning a target sample using X-rays of at least two energies; the characteristic data of any image unit is used to represent component information of a position in the target sample corresponding to the image unit; A processing module, used for performing image registration between at least one sample surface image and the X-ray image, and performing graded evaluation processing on different surface areas in the target sample according to the registration result and the characteristic data of each image unit; the at least one sample surface image is an image obtained by collecting the surface of the target sample using at least one data acquisition module; the graded evaluation result of any surface area is determined based on the predicted component information of the surface area and the regional block characteristic data of the regional block corresponding to the surface area; the regional block corresponding to any surface area refers to a regional block in the target sample from the surface area to the bottom of the target sample, the regional block characteristic data of any regional block is determined based on the characteristic data of each image unit in the regional block, and the regional block characteristic data of any regional block represents the component information of the regional block; The display module is used to visually display the graded evaluation results of different surface areas in the target sample.

10. An electronic device, characterized in that: The electronic device comprises a processor and a machine-readable storage medium; the machine-readable storage medium stores a plurality of computer instructions, and when the computer instructions are executed by the processor, the steps in any one of the methods of claims 1 to 8 are implemented.

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