Preserved meat production quality detection method and system based on characteristic analysis

Abstract

The invention discloses a preserved meat production quality detection method and system based on feature analysis, and belongs to the field of feature analysis. The preserved meat smell change abnormity is evaluated based on the change speed of the smell emitted by the preserved meat, and the implicit features reflecting the quality of the produced preserved meat in the preserved meat image and the smell are extracted; the preserved meat image change abnormity and the preserved meat smell change abnormity are respectively analyzed through the obtained implicit features, and the quality of the preserved meat placed after production is accurately evaluated by integrating the preserved meat image change abnormity and the preserved meat smell change abnormity, so that the accuracy and the efficiency of preserved meat quality evaluation are improved; the invention further provides quantitative analysis of the preserved meat placement environment data, so that the influence of environment change on preserved meat production evaluation in the evaluation process is eliminated, and the accuracy of preserved meat quality evaluation is further improved.

Description

Technical Field

[0001] The present application belongs to the field of feature analysis, and more specifically, is a method and system for detecting the quality of bacon production based on feature analysis. Background Art

[0002] Bacon refers to meat that has been cured and then baked or dried in the sun (baked or naturally dried products). It is usually made of pork, with a dry, firm surface, bright color, and a bright red color. Bacon has a strong aroma, mainly because of the addition of appropriate amounts of spices and seasonings. After the production of bacon, its production quality often needs to be checked. The existing technology usually tests the production quality of bacon manually, which is time-consuming and labor-intensive. Therefore, a system that can automatically test the quality of bacon is needed; The existing bacon quality detection system usually detects the quality of bacon by collecting pictures of bacon in real time. This detection method is relatively one-sided and cannot extract implicit features reflecting the quality of bacon produced in bacon images and odors, resulting in the inability to analyze abnormal changes in bacon images and bacon odors separately, and thus the inability to comprehensively evaluate the quality of bacon placed after production based on abnormal changes in bacon images and bacon odors. At the same time, environmental changes during the evaluation process have a greater impact on the evaluation of bacon production. These factors lead to low efficiency and accuracy of bacon quality evaluation in the existing technology, and most of the existing technologies have the above problems; In order to solve the problems raised by this background technology, the present application designs a bacon production quality detection method and system based on feature analysis. Summary of the invention

[0003] In order to address the deficiencies in the prior art mentioned in the background technology, the present application proposes a bacon production quality detection method and system based on feature analysis, extracts implicit features in bacon images and odors that reflect the quality of the produced bacon, and analyzes the acquired implicit features of abnormal bacon image changes and abnormal bacon odor changes separately, and comprehensively evaluates the quality of the bacon placed after production by accurately evaluating the abnormal bacon image changes and the abnormal bacon odor changes, thereby improving the accuracy of bacon quality assessment.

[0004] To achieve the above objectives, the present application provides the following technical solutions: In the first aspect, the present application provides a method for detecting the quality of bacon production based on characteristic analysis, which comprises the following specific steps: Step 1, obtaining the production image data of bacon and the odor data emitted by the bacon, and obtaining the placement environment data of the bacon; Step 2, evaluating abnormal changes in the bacon image based on the change speed and change uniformity of the bacon production image; Step 3, evaluating the abnormal change of the bacon odor based on the change speed of the odor emitted by the bacon; Step 4, analyzing the bacon production quality by integrating the bacon image change abnormality assessment result, the bacon odor change abnormality assessment result, and the bacon placement environment data; Step 5: Provide bacon quality warning based on the bacon production quality analysis results.

[0005] As a preferred technical solution of the bacon production quality detection method based on feature analysis, the specific content of obtaining the bacon production image data and the bacon odor data, and simultaneously obtaining the bacon placement environment data is: Step 11: during the production process of bacon, the production image data of the bacon is obtained through an image acquisition terminal, and the production image data of the bacon is imported into an image processing software to obtain pixel value data of each pixel point on the surface of the bacon; Step 12: During the bacon production process, the gas type and emission amount data emitted by the bacon production are obtained through a gas sensor, wherein the gas type here is mainly hydrogen sulfide and other gases with a rancid odor; Step 13, collecting temperature and humidity data of the environment in which the bacon is placed through an environmental data collector; Step 14: Store the acquired data in a memory.

[0006] As a preferred technical solution of the bacon production quality detection method based on feature analysis, the evaluation of abnormal changes in bacon images based on the change speed and change uniformity of bacon production images includes the following specific steps: Step 21, obtaining pixel value data of image pixel points at the monitoring time corresponding to the production of bacon; Step 22, performing abnormal analysis on the change speed of the bacon image based on the pixel value data of the image pixels at the monitoring time after the bacon production; The specific steps are as follows: S221, obtaining pixel value data of image pixel points at the monitoring time corresponding to the production of bacon, and importing the pixel value data of image pixel points at the monitoring time corresponding to the production of bacon into the pixel abnormal value calculation formula to calculate the pixel abnormal value, wherein the pixel abnormal value calculation formula is: , where N is the number of pixels, xi is the pixel value of the i-th pixel at the monitoring time, xis is the average pixel value of the corresponding bacon that has been produced, xim is the average pixel value of the pixel that is judged to be spoiled bacon, and Xc is the pixel abnormal value; S222, substituting the calculated pixel abnormal value at the corresponding monitoring moment and the previous monitoring moment into the pixel change speed abnormal value calculation formula to calculate the pixel change speed abnormal value, wherein the pixel change speed abnormal value calculation formula is: , where Xct is the pixel abnormal value at the corresponding monitoring moment, Xc(t-1) is the pixel abnormal value at the previous monitoring moment, T is the monitoring moment interval, Vm is the standard value of the change speed, and Vx is the pixel change speed abnormal value; Step 23, performing abnormal analysis on the uniformity of bacon image changes based on the pixel value data of the image pixels at the monitoring time corresponding to the bacon production; The specific content of step 23 is: obtain the calculated pixel abnormal value and the pixel value of the image pixel point at the monitoring time, substitute them into the image change uniformity abnormal value calculation formula to calculate the image change uniformity abnormal value, wherein the image change uniformity abnormal value calculation formula is: , Px is the abnormal value of image uniformity; Step 24, based on the abnormal analysis results of the bacon image change speed and the abnormal analysis results of the bacon image change uniformity, perform abnormal evaluation of the bacon image change; The specific content of step 24 is: obtaining the calculated pixel change speed abnormal value and image change uniformity abnormal value and importing them into the image change abnormal evaluation value calculation formula to calculate the image change abnormal evaluation value, wherein the image change abnormal evaluation value calculation formula is: , where a is the image change abnormality weight and Tx is the image change abnormality assessment value.

[0007] As a preferred technical solution of the bacon production quality detection method based on feature analysis, the evaluation of abnormal bacon odor changes based on the change rate of the odor emitted by the bacon includes the following specific steps: Obtain the data of the generation rate of the relevant odor gas emitted by the bacon between the two monitoring moments, and import the data of the generation rate of the relevant odor gas emitted by the bacon between the two monitoring moments into the calculation formula of the odor change abnormal value to calculate the odor change abnormal value, wherein the calculation formula of the odor change abnormal value is: , where vq is the average generation rate data of the relevant odor gases emitted by bacon between two monitoring moments, vm is the average value of the generation rate of the relevant odor gases emitted by normal bacon, and Mq is the abnormal value of odor change.

[0008] As a preferred technical solution of the bacon production quality detection method based on feature analysis, the bacon production quality analysis based on the comprehensive bacon image change abnormality evaluation results, bacon odor change abnormality evaluation results and bacon placement environment data includes the following specific contents: Step 41, obtaining the storage environment data of the bacon, substituting the storage environment data into the environmental preservation difficulty calculation formula to calculate the environmental preservation difficulty, wherein the environmental preservation difficulty calculation formula is: , where Kh is the difficulty of environmental preservation, m is the type of environmental data, bj is the weight of the j-th environmental data, T is the test time, s is the proportion of environmental anomalies, 1-s is the proportion of environmental change frequency, cjt is the specific value of the j-th environmental data at time t, cjm is the median of the safety range of the j-th environmental data, cjmax is the maximum value of the safety range of the j-th environmental data, cjmin is the minimum value of the safety range of the j-th environmental data, dt is the time integral, and cj(t-1) is the specific value of the j-th environmental data at time t-1; Step 42: Obtain the calculated image change abnormality assessment value, odor change abnormality value, and environmental preservation difficulty, and import the acquired data into the production quality assessment value calculation formula to calculate the production quality assessment value, wherein the production quality assessment value calculation formula is: in, is the image change ratio weight, is the weight of odor change, and Z is the production quality assessment value. In this formula, the storage quality change of bacon is analyzed by the changes in the image and odor of bacon during storage, while taking into account the difficulty of environmental preservation to eliminate the impact of environmental changes on bacon production assessment during the assessment process; Step 43, compare the calculated production quality assessment value with the set production quality assessment threshold. If the obtained production quality assessment value is greater than or equal to the set production quality assessment threshold, it means that the bacon production quality is qualified and the next inspection is carried out. If the obtained production quality assessment value is less than the set production quality assessment threshold, it means that the bacon production quality is unqualified and a bacon quality warning is issued.

[0009] In a second aspect, the present application provides a bacon production quality detection system based on feature analysis, which is implemented based on the above-mentioned bacon production quality detection method based on feature analysis, and specifically includes a data acquisition module, a bacon image change analysis module, a bacon odor analysis module, a bacon production quality analysis module and a bacon quality early warning module; Among them, the data acquisition module is used to obtain the production image data of bacon and the odor data emitted by the bacon, and at the same time obtain the placement environment data of the bacon; the bacon image change analysis module is used to evaluate the abnormal bacon image change based on the change speed and change uniformity of the bacon production image; the bacon odor analysis module is used to evaluate the abnormal bacon odor change based on the change speed of the odor emitted by the bacon; the bacon production quality analysis module is used to comprehensively analyze the bacon production quality by integrating the bacon image change abnormality evaluation results, the bacon odor abnormality evaluation results and the placement environment data of the bacon; the bacon quality early warning module is used to issue a bacon quality early warning based on the bacon production quality analysis results; and it can also include a control module, which is used to control the operation of the data acquisition module, the bacon image change analysis module, the bacon odor analysis module, the bacon production quality analysis module and the bacon quality early warning module.

[0010] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned bacon production quality detection method based on feature analysis by calling the computer program stored in the memory.

[0011] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for detecting the quality of bacon production based on feature analysis.

[0012] Compared with the prior art, the beneficial effects of the present application are as follows: the present application evaluates abnormal changes in bacon images based on the change speed and change uniformity of images produced by bacon, evaluates abnormal changes in bacon odor based on the change speed of the odor emitted by the bacon, extracts implicit features in the bacon images and odors that reflect the quality of the produced bacon, analyzes the abnormal changes in bacon images and bacon odors respectively through the acquired implicit features, and accurately evaluates the quality of bacon placed after production based on the abnormal changes in bacon images and bacon odors, thereby improving the accuracy and efficiency of bacon quality evaluation; The present application also proposes to conduct a quantitative analysis of the bacon storage environment data to eliminate the impact of environmental changes during the evaluation process on the bacon production evaluation, thereby further improving the accuracy of the bacon quality evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings; Figure 1 This is a schematic diagram of the overall process of the bacon production quality detection method based on feature analysis in this application; Figure 2 This is a flow chart of step 2 of the method for detecting the quality of bacon production based on feature analysis in this application; Figure 3 This is a flow chart of step 4 of the method for detecting the quality of bacon production based on feature analysis in this application; Figure 4 This is a schematic diagram of the framework of the bacon production quality detection system based on feature analysis in this application. DETAILED DESCRIPTION

[0014] In order to better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application and do not limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0015] In the accompanying drawings, the size, dimensions and shape of the elements have been slightly adjusted for ease of explanation. The accompanying drawings are only examples and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms to indicate approximation, not as terms to indicate degree, and are intended to illustrate the inherent deviations in measured or calculated values ​​that will be recognized by those of ordinary skill in the art. In addition, in the present application, the order in which the steps are processed does not necessarily represent the order in which these processes occur in actual operation, unless otherwise clearly defined or can be derived from the context. It should also be understood that expressions such as "including", "including", "having", "including" and / or "including" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, rather than just modifying the individual elements in the list. In addition, when describing the embodiments of the present application, "may" is used to represent "one or more embodiments of the present application". Furthermore, the term "exemplary" is intended to refer to an example or illustration. Unless otherwise specified, all words used herein (including engineering terms and scientific and technological terms) have the same meaning as those commonly understood by those of ordinary skill in the art to which this application belongs. It should also be understood that, unless otherwise clearly stated in this application, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0016] Example 1

[0017] In order to solve the technical problems raised in the background technology, the present application provides a preferred embodiment: Figure 1-Figure 3 As shown, the bacon production quality detection method based on feature analysis includes the following specific steps: Step 1, obtaining the production image data of bacon and the odor data emitted by the bacon, and obtaining the placement environment data of the bacon; In one specific embodiment, the specific contents of obtaining the production image data of bacon and the odor data emitted by the bacon, and obtaining the placement environment data of the bacon at the same time are: Step 11: during the production process of bacon, the production image data of the bacon is obtained through an image acquisition terminal, and the production image data of the bacon is imported into an image processing software to obtain pixel value data of each pixel point on the surface of the bacon; Step 12: During the bacon production process, the gas type and emission amount data emitted by the bacon production are obtained through a gas sensor, wherein the gas type here is mainly hydrogen sulfide and other gases with a rancid odor; Step 13, collecting temperature and humidity data of the environment in which the bacon is placed through an environmental data collector; Step 14: storing the acquired data in a storage device; Step 2, evaluating abnormal changes in the bacon image based on the change speed and change uniformity of the bacon production image; In one specific embodiment, evaluating the abnormal change of the bacon image based on the change speed and change uniformity of the bacon production image includes the following specific steps: Step 21, obtaining pixel value data of image pixel points at the monitoring time corresponding to the production of bacon; Step 22, performing abnormal analysis on the change speed of the bacon image based on the pixel value data of the image pixels at the monitoring time after the bacon production; The specific steps are as follows: S221, obtaining pixel value data of image pixel points at the monitoring time corresponding to the production of bacon, and importing the pixel value data of image pixel points at the monitoring time corresponding to the production of bacon into the pixel abnormal value calculation formula to calculate the pixel abnormal value, wherein the pixel abnormal value calculation formula is: , where N is the number of pixels, xi is the pixel value of the i-th pixel at the monitoring time, xis is the average pixel value of the corresponding bacon that has been produced, xim is the average pixel value of the pixel that is judged to be spoiled bacon, and Xc is the pixel abnormality value. In this embodiment, it should be explained that in this formula, the image abnormality of the bacon at the corresponding time is comprehensively analyzed by the pixel changes of each pixel, and divided by Standardize the parameters by setting the values; S222, substituting the calculated pixel abnormal value at the corresponding monitoring moment and the previous monitoring moment into the pixel change speed abnormal value calculation formula to calculate the pixel change speed abnormal value, wherein the pixel change speed abnormal value calculation formula is: , where Xct is the pixel abnormal value at the corresponding monitoring moment, Xc(t-1) is the pixel abnormal value at the previous monitoring moment, T is the monitoring moment interval, Vm is the standard value of the change speed, and Vx is the pixel change speed abnormal value. In this way, the abnormal pixel change speed is analyzed by calculating the ratio of the pixel abnormal value change speed to the standard value of the change speed within the monitoring time period; Step 23, performing abnormal analysis on the uniformity of bacon image changes based on the pixel value data of the image pixels at the monitoring time corresponding to the bacon production; The specific content of step 23 is: obtain the calculated pixel abnormal value and the pixel value of the image pixel point at the monitoring time, substitute them into the image change uniformity abnormal value calculation formula to calculate the image change uniformity abnormal value, wherein the image change uniformity abnormal value calculation formula is: , it should be explained in this embodiment that, in this formula, the image change uniformity abnormality is analyzed by analyzing the uniform phase difference of each pixel point, and Px is the image change uniformity abnormality value; Step 24, based on the abnormal analysis results of the bacon image change speed and the abnormal analysis results of the bacon image change uniformity, perform abnormal evaluation of the bacon image change; The specific content of step 24 is: obtaining the calculated pixel change speed abnormal value and image change uniformity abnormal value and importing them into the image change abnormal evaluation value calculation formula to calculate the image change abnormal evaluation value, wherein the image change abnormal evaluation value calculation formula is: , where a is the image change abnormality weight, and Tx is the image change abnormality assessment value; Step 3, evaluating the abnormal change of the bacon odor based on the change speed of the odor emitted by the bacon; In one specific embodiment, evaluating the abnormal change of bacon odor based on the change rate of the odor emitted by the bacon includes the following specific steps: Obtain the data of the generation rate of the relevant odor gas emitted by the bacon between the two monitoring moments, and import the data of the generation rate of the relevant odor gas emitted by the bacon between the two monitoring moments into the calculation formula of the odor change abnormal value to calculate the odor change abnormal value, wherein the calculation formula of the odor change abnormal value is: , where vq is the average generation rate data of the relevant odor gas emitted by the bacon between two monitoring moments, vm is the average generation rate of the relevant odor gas emitted by the normal bacon, and it should be explained here that the average generation rate of the relevant odor gas emitted by the normal bacon is obtained by placing the normal bacon in a storage environment and conducting an experiment, and the volume of the bacon blocks is basically the same, and Mq is the abnormal value of the odor change; Step 4, analyzing the bacon production quality by integrating the bacon image change abnormality assessment result, the bacon odor change abnormality assessment result, and the bacon placement environment data; In one specific embodiment, the bacon production quality analysis based on the abnormal evaluation results of bacon image changes, the abnormal evaluation results of bacon odor changes, and the bacon placement environment data includes the following specific contents: Step 41, obtaining the storage environment data of the bacon, substituting the storage environment data into the environmental preservation difficulty calculation formula to calculate the environmental preservation difficulty, wherein the environmental preservation difficulty calculation formula is: , where Kh is the difficulty of environmental preservation, m is the type of environmental data, bj is the weight of the proportion of the j-th environmental data, T is the test time, s is the proportion of environmental anomalies, 1-s is the proportion of environmental change frequency, cjt is the specific value of the j-th environmental data at time t, cjm is the median of the safety range of the j-th environmental data, cjmax is the maximum value of the safety range of the j-th environmental data, cjmin is the minimum value of the safety range of the j-th environmental data, dt is the time integral, cj(t-1) is the specific value of the j-th environmental data at time t-1. In this embodiment, it should be explained that the safety range of the j-th environmental data is the appropriate range of the j-th environmental data for the preservation of bacon. For example, the temperature range of the bacon storage environment is generally between 5°C and 10°C, and the relative humidity should be maintained at about 75%-85%. In this formula, is the difference between the specific value of the j-th environmental data at time t and the median of the safety range of the j-th environmental data, that is, the offset of the j-th environmental data at time t. In order to standardize the offset, it is divided by ,and This is the data of the fluctuation range of the environment, because bacon needs a relatively stable environment to maintain its quality and flavor. If the environmental fluctuation range is too large, such as drastic fluctuations in temperature and humidity, the bacon may deteriorate or lose its original flavor. Step 42: Obtain the calculated image change abnormality assessment value, odor change abnormality value, and environmental preservation difficulty, and import the acquired data into the production quality assessment value calculation formula to calculate the production quality assessment value, wherein the production quality assessment value calculation formula is: ,in, is the weight of image change ratio, is the weight of odor change, and Z is the production quality assessment value. In this formula, the storage quality change of bacon is analyzed by the changes in the image and odor of bacon during storage, while taking into account the difficulty of environmental preservation to eliminate the impact of environmental changes on bacon production assessment during the assessment process; Step 43, comparing the calculated production quality assessment value with the set production quality assessment threshold, if the obtained production quality assessment value is greater than or equal to the set production quality assessment threshold, it means that the bacon production quality is qualified, and the next inspection is carried out; if the obtained production quality assessment value is less than the set production quality assessment threshold, it means that the bacon production quality is unqualified, and a bacon quality warning is issued; It should be explained in this embodiment that the setting parameters in this embodiment (such as the standard value of the change speed, the weight of the abnormal image change, the proportion of environmental abnormalities, the proportion of environmental change frequency, the proportion weight of the j-th type of environmental data, the weight of the image change proportion, the weight of the odor change proportion and the production quality assessment threshold, etc.) are obtained by experiments by those skilled in the art. The preferred acquisition method is: obtain 500 sets of production image data of bacon and odor data emitted by bacon, and at the same time obtain the placement environment data of bacon, import them into each algorithm in this embodiment for calculation, and finally substitute them into the production quality assessment value calculation formula to calculate the production quality assessment value, obtain the expert's judgment results on whether these bacon are qualified, import the production quality assessment value and the bacon qualified judgment results into the fitting software (such as MATLAB software), and output the values ​​of the setting parameters that meet the maximum judgment accuracy; Step 5: Based on the bacon production quality analysis results, a bacon quality warning is issued, and the corresponding bacon number and quality inspection results are transmitted to the management end.

[0018] It should be noted in this example that this embodiment has the following advantages over the prior art: implicit features reflecting the quality of the produced bacon are extracted from the bacon image and odor, abnormal bacon image changes and abnormal bacon odor changes are analyzed separately through the acquired implicit features, and the quality of the bacon placed after production is accurately evaluated based on the abnormal bacon image changes and abnormal bacon odor changes, thereby improving the accuracy and efficiency of bacon quality evaluation, and proposing a quantitative analysis of the bacon placement environment data to eliminate the impact of environmental changes during the evaluation process on the bacon production evaluation, thereby further improving the accuracy of bacon quality evaluation.

[0019] Example 2

[0020] like Figure 4As shown, a bacon production quality detection system based on feature analysis is implemented based on the above-mentioned bacon production quality detection method based on feature analysis, and specifically includes a data acquisition module, a bacon image change analysis module, a bacon odor analysis module, a bacon production quality analysis module and a bacon quality early warning module; wherein the data acquisition module is used to obtain the production image data of the bacon and the odor data emitted by the bacon, and simultaneously obtain the placement environment data of the bacon; the bacon image change analysis module is used to evaluate the abnormal change of the bacon image based on the change speed and change uniformity of the bacon production image; the bacon odor analysis module is used to evaluate the abnormal change of the bacon image based on the change speed and change uniformity of the bacon production image; the bacon odor analysis module is used to evaluate the abnormal change of the bacon image based on the change speed and change uniformity of the bacon production image; the bacon odor analysis module is used to evaluate the abnormal change of the bacon image based on the change speed and change uniformity of the bacon production image; the bacon odor analysis module is used to evaluate the abnormal change of the bacon odor ... The bacon smell change abnormality is evaluated by the change speed of the smell emitted by the meat; a bacon production quality analysis module is used to analyze the bacon production quality by integrating the bacon image change abnormality evaluation results, the bacon smell change abnormality evaluation results and the bacon placement environment data; a bacon quality early warning module is used to make a bacon quality early warning based on the bacon production quality analysis results; it can also include a control module, the control module is used to control the operation of the data acquisition module, the bacon image change analysis module, the bacon smell analysis module, the bacon production quality analysis module and the bacon quality early warning module; at the same time, the data transmission direction of each module in this embodiment is as follows Figure 4 As shown by the arrow direction in the figure, the specific steps of each module in this embodiment have been described in detail in the above method embodiment and will not be repeated here.

[0021] Example 3

[0022] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned bacon production quality detection method based on feature analysis by calling the computer program stored in the memory.

[0023] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the bacon production quality detection method based on feature analysis provided in the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface, so as to input and output data. This embodiment will not be described in detail here.

[0024] Example 4

[0025] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program runs on a computer device, the computer device executes the above-mentioned bacon production quality detection method based on feature analysis.

[0026] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0027] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0028] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for detecting the quality of bacon production based on characteristic analysis, characterized in that: It includes the following specific steps: Step 1, obtaining the production image data of bacon and the odor data emitted by the bacon, and obtaining the placement environment data of the bacon; Step 2, evaluating abnormal changes in the bacon image based on the change speed and change uniformity of the bacon production image; Step 3, evaluating the abnormal change of the bacon odor based on the change speed of the odor emitted by the bacon; Step 4, analyzing the bacon production quality by integrating the bacon image change abnormality assessment result, the bacon odor change abnormality assessment result, and the bacon placement environment data; Step 5: Provide bacon quality warning based on the bacon production quality analysis results.

2. The method for detecting the quality of bacon production based on characteristic analysis according to claim 1, characterized in that: The evaluation of abnormal changes in bacon images based on the change speed and change uniformity of bacon production images includes the following specific steps: Step 21, obtaining pixel value data of image pixel points at the monitoring time corresponding to the production of bacon; Step 22, performing abnormal analysis on the change speed of the bacon image based on the pixel value data of the image pixels at the monitoring time after the bacon production; Step 23, performing abnormal analysis on the uniformity of bacon image changes based on the pixel value data of the image pixels at the monitoring time corresponding to the bacon production; Step 24: perform an abnormal assessment of the bacon image change based on the abnormal analysis results of the bacon image change speed and the abnormal analysis results of the bacon image change uniformity.

3. The method for detecting the quality of bacon production based on characteristic analysis according to claim 2, characterized in that: The specific content of step 22 is: S221, obtaining pixel value data of image pixel points at the monitoring time corresponding to the production of bacon, and importing the pixel value data of image pixel points at the monitoring time corresponding to the production of bacon into the pixel abnormal value calculation formula to calculate the pixel abnormal value, wherein the pixel abnormal value calculation formula is: , where N is the number of pixels, xi is the pixel value of the i-th pixel at the monitoring time, xis is the average pixel value of the corresponding bacon that has been produced, xim is the average pixel value of the pixel that is judged to be spoiled bacon, and Xc is the pixel abnormal value; S222, substituting the calculated pixel abnormal value at the corresponding monitoring moment and the previous monitoring moment into the pixel change speed abnormal value calculation formula to calculate the pixel change speed abnormal value, wherein the pixel change speed abnormal value calculation formula is: , where Xct is the pixel abnormal value at the corresponding monitoring moment, Xc(t-1) is the pixel abnormal value at the previous monitoring moment, T is the interval between monitoring moments, Vm is the standard value of the change speed, and Vx is the abnormal value of the pixel change speed.

4. The method for detecting the quality of bacon production based on characteristic analysis according to claim 3, characterized in that: The specific content of step 23 is: obtain the calculated pixel abnormal value and the pixel value of the image pixel point at the monitoring time, substitute them into the image change uniformity abnormal value calculation formula to calculate the image change uniformity abnormal value, wherein the image change uniformity abnormal value calculation formula is: .

5. The method for detecting the quality of bacon production based on characteristic analysis according to claim 4, characterized in that: The specific content of step 24 is: obtaining the calculated pixel change speed abnormal value and image change uniformity abnormal value and importing them into the image change abnormal evaluation value calculation formula to calculate the image change abnormal evaluation value, wherein the image change abnormal evaluation value calculation formula is: , where a is the image change abnormality weight and Tx is the image change abnormality assessment value.

6. The method for detecting the quality of bacon production based on characteristic analysis according to claim 5, characterized in that: The evaluation of the abnormal change of bacon odor based on the change rate of the odor emitted by the bacon includes the following specific steps: obtaining the generation rate data of the relevant odor gas emitted by the bacon between two monitoring moments, and importing the generation rate data of the relevant odor gas emitted by the bacon between the two monitoring moments into the odor change abnormal value calculation formula to calculate the odor change abnormal value, wherein the odor change abnormal value calculation formula is: , where vq is the average generation rate data of the relevant odor gases emitted by bacon between two monitoring moments, vm is the average value of the generation rate of the relevant odor gases emitted by normal bacon, and Mq is the abnormal value of odor change.

7. The method for detecting the quality of bacon production based on characteristic analysis according to claim 6, characterized in that: The bacon production quality analysis based on the abnormal evaluation results of bacon image changes, abnormal evaluation results of bacon odor changes and bacon placement environment data includes the following specific contents: Step 41, obtaining the storage environment data of the bacon, substituting the storage environment data into the environmental preservation difficulty calculation formula to calculate the environmental preservation difficulty, wherein the environmental preservation difficulty calculation formula is: , where Kh is the difficulty of environmental preservation, m is the type of environmental data, bj is the weight of the j-th environmental data, T is the test time, s is the proportion of environmental anomalies, 1-s is the proportion of environmental change frequency, cjt is the specific value of the j-th environmental data at time t, cjm is the median of the safety range of the j-th environmental data, cjmax is the maximum value of the safety range of the j-th environmental data, cjmin is the minimum value of the safety range of the j-th environmental data, dt is the time integral, and cj(t-1) is the specific value of the j-th environmental data at time t-1; Step 42: Obtain the calculated image change abnormality assessment value, odor change abnormality value, and environmental preservation difficulty, and import the acquired data into the production quality assessment value calculation formula to calculate the production quality assessment value, wherein the production quality assessment value calculation formula is: ,in, is the weight of image change ratio, is the weight of odor change proportion, Z is the production quality assessment value; Step 43, compare the calculated production quality assessment value with the set production quality assessment threshold. If the obtained production quality assessment value is greater than or equal to the set production quality assessment threshold, it means that the bacon production quality is qualified and the next inspection is carried out. If the obtained production quality assessment value is less than the set production quality assessment threshold, it means that the bacon production quality is unqualified and a bacon quality warning is issued.

8. A bacon production quality detection system based on feature analysis, which is implemented based on the bacon production quality detection method based on feature analysis as claimed in any one of claims 1 to 7, characterized in that: It specifically includes a data acquisition module, a bacon image change analysis module, a bacon odor analysis module, a bacon production quality analysis module and a bacon quality early warning module; Among them, the data acquisition module is used to obtain the production image data of bacon and the odor data emitted by the bacon, and at the same time obtain the placement environment data of the bacon; the bacon image change analysis module is used to evaluate the abnormal bacon image change based on the change speed and change uniformity of the bacon production image; the bacon odor analysis module is used to evaluate the abnormal bacon odor change based on the change speed of the odor emitted by the bacon; the bacon production quality analysis module is used to comprehensively analyze the bacon production quality by integrating the bacon image change abnormality evaluation results, the bacon odor change abnormality evaluation results and the bacon placement environment data; the bacon quality warning module is used to issue a bacon quality warning based on the bacon production quality analysis results.

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