Method for detecting and processing freshness of baby cabbage

Abstract

The invention discloses a baby cabbage freshness detection processing method, and relates to the technical field of baby cabbages, and the method comprises the following steps: S1, receiving a baby cabbage freshness detection request, and reading the input information of baby cabbages through a reader; s2, carrying out freshness detection on the baby cabbages to obtain detection data corresponding to the baby cabbages; s3, based on the input information of the baby cabbages, combined with the detection data corresponding to the baby cabbages, evaluating total freshness constraint parameters of the baby cabbages; s4, current storage condition parameters of the baby cabbages are obtained, proper storage adjustment condition parameters of the baby cabbages are obtained after adjustment processing, and the continuous storage duration of the baby cabbages is obtained through synchronous processing; and S5, importing the suitable storage condition parameters and the storage continuous duration of the baby cabbages into a preset display interface for visual display. According to the method, the accuracy of freshness analysis of the baby cabbages is improved, the quality and safety of the baby cabbages are guaranteed, the preservation duration of the baby cabbages is optimized, and the quality of the baby cabbages is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of baby cabbages, and particularly relates to a method for detecting and processing the freshness of baby cabbages. Background Art

[0002] In today's society, the issue of food safety has become increasingly prominent, and people have higher and higher requirements for the quality and safety of food. As a common vegetable, the freshness of baby cabbages directly affects the taste and health of consumers. When detecting the freshness of traditional baby cabbages, the freshness is usually judged by senses such as appearance and smell. However, this method has certain limitations and is easily affected by subjective factors, resulting in inaccurate judgment results. By conducting scientific and objective detection and analysis of the freshness of baby cabbages, the quality status of baby cabbages can be more accurately understood. Therefore, the detection and analysis of the freshness of baby cabbages are particularly important and necessary.

[0003] The prior art, such as an invention patent application with the publication number of CN110020604B, discloses a non-destructive detection method for discriminating the freshness of baby cabbages, including: obtaining an image of the vegetable to be measured captured by the image acquisition device; extracting the texture features, shape features, and color features of the image; analyzing the texture features, shape features, and color features to determine the variety of the vegetable to be measured; according to the texture features of the vegetables of the variety, dividing the texture into pleated wrinkle types and self-pattern types; using the ratio of the number of textures of the pleated wrinkle type to the total number of textures to determine the water content of the vegetable to be measured; obtaining the smell of the vegetable to be measured collected by the electronic nose; comparing the smell with the standard smell of the vegetable to be measured to determine the degree of rot of the vegetable to be measured; obtaining the mass spectrum obtained by detecting the vegetable to be measured by the mass spectrometer; comparing the mass spectrum with the pesticide standard mass spectrum to determine the pesticide residue amount of the vegetable to be measured; comprehensively determining whether the quality of the vegetable to be measured is qualified based on the water content, the degree of rot, and the pesticide residue amount. The disclosed vegetable quality detection method and system comprehensively detect the quality of vegetables by integrating the water content, the degree of rot, and the pesticide residue amount of the vegetables, achieving a comprehensive detection of the vegetable quality, improving the reliability of the detection results, and improving the detection accuracy.

[0004] Combining the above solutions, it is found that in the prior art, little attention has been paid to the impact of the initial storage and storage during transportation of baby bok choy on freshness, and the data sources are relatively single, resulting in inaccurate analysis of the freshness of baby bok choy, reducing the quality and safety of baby bok choy. On the other hand, there is also a lack of subsequent utilization of the results of the freshness detection and analysis of baby bok choy. The overall freshness of baby bok choy can be used to judge the suitability of the current storage conditions and determine whether adjustment is needed. The neglect of this aspect in the prior art makes it difficult to ensure the suitability of the current storage conditions of baby bok choy, unable to optimize the freshness preservation duration of baby bok choy, thus reducing the quality of baby bok choy and affecting subsequent sales. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for detecting and processing the freshness of baby bok choy, which solves the problems existing in the background technology.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for detecting and processing the freshness of baby bok choy, including: S1. Receiving a request for detecting the freshness of baby bok choy, and reading the input information of the baby bok choy through a reader.

[0007] S2. Conducting a freshness detection on the baby bok choy to obtain the corresponding detection data of the baby bok choy.

[0008] S3. Based on the input information of the baby bok choy and in combination with the corresponding detection data of the baby bok choy, evaluating the overall freshness constraint parameters of the baby bok choy.

[0009] S4. Obtaining the current storage condition parameters of the baby bok choy, obtaining the appropriate storage adjustment condition parameters of the baby bok choy after adjustment processing, and synchronously processing to obtain the continuous storage duration of the baby bok choy.

[0010] S5. Importing the appropriate storage condition parameters and continuous storage duration of the baby bok choy into a preset display interface for visual display.

[0011] The beneficial effects of the present invention are as follows: (1) The present invention first receives a request for detecting the freshness of baby bok choy, and then reads the input information of the baby bok choy through a reader, laying a data foundation for the subsequent analysis of the overall freshness constraint parameters of the baby bok choy.

[0012] (2) The present invention conducts a freshness detection on the current baby bok choy to obtain the detection data of the baby bok choy, providing data support for the subsequent analysis of the overall freshness constraint parameters of the baby bok choy.

[0013] (3) The present invention analyzes the freshness of baby bok choy through the initial storage, storage during transportation, and the current presentation quality of the baby bok choy. The data sources are relatively rich, making up for the deficiencies in the neglect of this aspect in the prior art, improving the accuracy of the freshness analysis of baby bok choy, and ensuring the quality and safety of baby bok choy.

[0014] (4) Based on the freshness of baby Chinese cabbage and combined with the current storage condition parameters of baby Chinese cabbage, the present invention first evaluates the suitability of the current storage condition parameters of baby Chinese cabbage. If it is not suitable, it analyzes the appropriate storage adjustment condition parameters for baby Chinese cabbage, making up for the defect of low subsequent utilization rate of the detection and analysis results of the freshness of baby Chinese cabbage in the prior art, ensuring the suitability of the current storage conditions of baby Chinese cabbage, optimizing the freshness preservation duration of baby Chinese cabbage, thereby ensuring the quality of baby Chinese cabbage and avoiding affecting subsequent sales. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a schematic flow chart of the method of the present invention. Detailed Embodiments

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0018] Referring to Figure 1 As shown, the present invention provides a method for detecting and processing the freshness of baby Chinese cabbage, including:

[0019] S1. Receive a request for detecting the freshness of baby Chinese cabbage, and read the input information of the baby Chinese cabbage through a reader.

[0020] It should be noted that reading the input information of the baby Chinese cabbage through the reader is specifically done by reading the label attached to the baby Chinese cabbage.

[0021] In a specific embodiment of the present invention, the input information includes production data and traceability data. The production data specifically includes the production date, variety, each initial image, and each quality. The traceability data includes the packaging material, the interference values of each initial freshness influencing factor, and the interference values of each in-process freshness influencing factor.

[0022] It should be noted that the interference values of the initial freshness influencing factors of the baby bok choy can be read from the attached label of the baby bok choy. The interference values of the initial freshness influencing factors include, but are not limited to, influencing factors such as the average temperature, temperature difference value, average humidity, humidity difference value, and average wind speed that affect the freshness of the baby bok choy.

[0023] It should also be noted that for the in-process freshness influencing factors of the baby bok choy, the interference values of the in-process freshness influencing factors corresponding to the baby bok choy at the execution terminal can be statistically obtained from the meteorological platform, GIS geographical platform, and execution terminal platform during the transportation path.

[0024] Specifically, when the execution terminal is a refrigerated truck, the interference values of the in-process freshness influencing factors of the refrigerated truck include, but are not limited to, the average temperature inside the vehicle, the temperature difference value inside the vehicle, and the average vehicle speed, etc.

[0025] In a specific embodiment, when the execution terminal is an ordinary truck, the interference values of the in-process freshness influencing factors of the ordinary truck include, but are not limited to, the stacking height, average rainfall, and surface wind speed, etc.

[0026] In another specific embodiment, when the execution terminal is an airplane, the interference values of the in-process freshness influencing factors of the airplane include, but are not limited to, the average air flow velocity, flight trajectory curvature, and number of transfers, etc.

[0027] The present invention first receives a baby bok choy freshness detection request, and then reads the input information of the baby bok choy through a reader, laying a data foundation for the analysis of the overall freshness constraint parameters of the baby bok choy subsequently.

[0028] S2. Perform a freshness detection on the baby bok choy to obtain the corresponding detection data of the baby bok choy.

[0029] In a specific embodiment of the present invention, the method for performing a freshness detection on the baby bok choy to obtain the corresponding detection data of the baby bok choy is as follows: Randomly select several baby bok choy from this batch of baby bok choy, and perform image acquisition on them to obtain respective first images.

[0030] Weigh several baby bok choy to obtain respective masses.

[0031] Perform spectral tests on several baby bok choy to obtain respective moisture contents and respective chlorophyll contents.

[0032] The present invention performs a freshness detection on the current baby bok choy to obtain the detection data of the baby bok choy, providing data support for the analysis of the overall freshness constraint parameters of the baby bok choy subsequently.

[0033] S3. Based on the input information of the baby bok choy, combined with the corresponding detection data of the baby bok choy, evaluate the overall freshness constraint parameters of the baby bok choy.

[0034] In a specific embodiment of the present invention, the overall freshness constraint parameter for evaluating baby bok choy is evaluated as follows: According to the production date, variety, each initial image, and each quality in the baby bok choy production data, through numerical fitting and comparison processing, the first freshness constraint parameter of the baby bok choy is obtained.

[0035] In a specific embodiment of the present invention, the specific analysis method for the first freshness constraint parameter of the baby bok choy is: Obtain each volume according to each initial image of the baby bok choy, and according to each quality, process to obtain each density, and perform mean processing on them to obtain the average density of the baby bok choy.

[0036] According to each initial image of the baby bok choy, combined with the variety of the baby bok choy, evaluate the number of abnormal gray values of the baby bok choy, and combined with the production date of the baby bok choy, obtain the exposure duration of the baby bok choy.

[0037] Import the average density, the number of abnormal gray values, and the exposure duration of the baby bok choy into the first freshness constraint condition of the baby bok choy, and process to obtain the first freshness constraint parameter of the baby bok choy.

[0038] Specifically, the first freshness constraint condition of the baby bok choy is specifically In the formula, ε is the first freshness constraint parameter of the baby bok choy, χ″ is the reference density of the preset baby bok choy, M is the number of abnormal gray values of the baby bok choy, M’ is the number of normal gray values of the baby bok choy, t is the exposure duration of the baby bok choy, t’ is the normal storage duration of the baby bok choy stored in the web database, λ1, λ2, and λ3 respectively represent the constraint weight ratio coefficients corresponding to the predefined density risk, color abnormality, and exposure duration, and e is the natural constant.

[0039] It should be noted that the specific preset method for the reference density of the preset baby bok choy is: Obtain the reference density corresponding to each variety of baby bok choy from the web database, and combined with the variety of the baby bok choy, screen the reference density of the baby bok choy. The specific number of abnormal gray values of the baby bok choy is: Obtain each normal gray value of each variety of baby bok choy from the web database, combined with the variety of the baby bok choy, screen each normal gray value of the baby bok choy, and perform gray processing on each initial image of the baby bok choy to obtain a number of gray values of each initial image of the baby bok choy. Compare them with each normal gray value. If a certain gray value is different from all normal gray values, then record this gray value as an abnormal gray value to obtain each abnormal gray value of the baby bok choy, and summarize to obtain the number of abnormal gray values of the baby bok choy.

[0040] It should also be noted that the exposure duration is specifically the duration from the production date to the current date.

[0041] According to the packaging material of the baby bok choy, each initial freshness influencing factor, and each in-process freshness influencing factor, through processing, the second freshness constraint parameter of the baby bok choy is obtained.

[0042] In a specific embodiment of the present invention, the specific analysis method of the second freshness constraint parameter of the baby bok choy is as follows: Based on the packaging material of the baby bok choy, combined with the interference values corresponding to each packaging material stored in the cloud database, screen the interference value corresponding to the packaging material of the baby bok choy.

[0043] Import the interference value corresponding to the packaging material of the baby bok choy, the interference values corresponding to each initial freshness influencing factor, and the interference values corresponding to each in-process freshness influencing factor into the second freshness constraint condition of the baby bok choy, and process to obtain the second freshness constraint parameter of the baby bok choy.

[0044] It should be noted that the second freshness constraint parameter of the baby bok choy is specifically

[0045]

[0046] In the formula, μ is the second freshness constraint parameter of the baby bok choy, β is the interference value corresponding to the packaging material of the baby bok choy, c i is the interference value corresponding to the i-th initial freshness influencing factor of the baby bok choy, d m is the interference value corresponding to the m-th in-process freshness influencing factor of the baby bok choy, c i0 is the defined interference value corresponding to the i-th initial freshness influencing factor of the baby bok choy, d m0 is the defined interference value corresponding to the m-th in-process freshness influencing factor of the baby bok choy, n is the number of initial freshness influencing factors, l is the number of in-process freshness influencing factors, i is the number of each initial freshness influencing factor, i = 1, 2,..., n, n is any integer greater than 2, m is the number of each in-process freshness influencing factor, m = 1, 2,..., l, l is any integer greater than 2, and γ1, γ2, γ3 respectively represent the constraint weight ratio coefficients corresponding to the predefined packaging material, initial freshness, and in-process freshness.

[0047] According to the detection data corresponding to the baby bok choy, through numerical fitting processing, obtain the presented freshness constraint parameter corresponding to the baby bok choy, and process to obtain the overall freshness constraint parameter of the baby bok choy.

[0048] It should be noted that the specific analysis method of the presented freshness constraint parameter corresponding to the baby bok choy is as follows: Obtain each first volume according to each first image, obtain each first mass according to each mass, process to obtain each density, and perform mean processing on them to obtain the current density of the baby bok choy, and import the current density and average density of the baby bok choy into the density attenuation model to output the density attenuation degree of the baby bok choy, where the density attenuation model is specifically In the formula, U is the density attenuation degree of the baby bok choy, χ is the current density of the baby bok choy, and χ′ is the average density of the baby bok choy.

[0049] Based on each first image, identify the color difference regions corresponding to each first image, and obtain the surface areas of the color difference regions of each first image. Aggregate to obtain the total area of the color difference regions of each first image. Obtain the surface area of each first image, and divide the total area of the color difference regions of each first image by the surface area to obtain the color difference area ratio of each first image. After numerical normalization processing, obtain the presented color characteristic value of the baby Chinese cabbage. The specific implementation method of the numerical normalization processing is as follows: In the formula, Y is the presented color characteristic value of the baby Chinese cabbage, S p is the color difference area ratio of the p-th first image, p is the number of each first image, p = 1, 2,..., q, and q is any integer greater than 2.

[0050] Specifically, based on the initial images of the baby Chinese cabbage, obtain the initial images corresponding to each first image, and compare each first image with the corresponding initial image, and then identify the color difference regions between each first image and the initial image accordingly.

[0051] It should be supplemented again that each first image can be segmented into sub-regions, and the initial image corresponding to each first image can be segmented into sub-regions. Analyze the color similarity by comparing the color histograms of the sub-regions of each first image and the sub-regions of the corresponding initial image. If the color similarity between a sub-region of a certain first image and the sub-region of the initial image is less than or equal to the predefined color similarity threshold, then mark this sub-region as a color difference region, and then obtain the color difference regions corresponding to each first image. It is also possible to calculate the ΔE value of the Euclidean distance to evaluate the color difference regions corresponding to each first image.

[0052] Based on each moisture content and each chlorophyll content, construct the current moisture content interval and the current chlorophyll content interval of the baby Chinese cabbage. Combine the standard moisture content interval and the standard chlorophyll content interval of each variety of baby Chinese cabbage in the web database. Through the variety of the baby Chinese cabbage, screen the standard moisture content interval and the standard chlorophyll content interval corresponding to the baby Chinese cabbage. Through the interval overlap length calculation formula

[0053] where E.start and E.end are the starting value and the ending value of the standard moisture content interval of the baby Chinese cabbage respectively, F.start and F.end are the starting value and the ending value of the current moisture content interval of the baby Chinese cabbage respectively. Calculate the overlap length H1 between the current moisture content interval of the baby Chinese cabbage and the standard moisture content interval, and similarly calculate the overlap length H2 between the current chlorophyll content interval and the standard chlorophyll content interval. Combine the characteristic value h1 corresponding to the unit moisture content interval overlap length and the characteristic value h2 corresponding to the unit chlorophyll content interval overlap length stored in the web database. Through the calculation formula Obtain the internal content characteristic value R of the baby Chinese cabbage.

[0054] Import the density attenuation degree, the presented color characteristic value, and the internal content characteristic value of the baby Chinese cabbage into the presented freshness constraint parameter analysis model corresponding to the baby Chinese cabbage. Output the presented freshness constraint parameter corresponding to the baby Chinese cabbage.

[0055] In a specific embodiment of the present invention, the overall freshness constraint parameter of the baby Chinese cabbage is specifically obtained by jointly processing the first freshness constraint parameter, the second freshness constraint parameter, and the presented freshness constraint parameter of the baby Chinese cabbage, and is used as the influencing degree for judging the suitability of the current storage condition parameters of the baby Chinese cabbage.

[0056] It should be noted that the first freshness constraint parameter, the second freshness constraint parameter, and the presented freshness constraint parameter of the baby Chinese cabbage are averaged to obtain the overall freshness constraint parameter of the baby Chinese cabbage.

[0057] The present invention analyzes the freshness of the baby Chinese cabbage through the initial storage of the baby Chinese cabbage, the storage during transportation, and the presented quality of the current baby Chinese cabbage. The data source is relatively rich, which makes up for the deficiency of neglecting this aspect in the prior art, improves the accuracy of the freshness analysis of the baby Chinese cabbage, and ensures the quality and safety of the baby Chinese cabbage.

[0058] S4. Obtain the current storage condition parameters of the baby Chinese cabbage, obtain the appropriate storage adjustment condition parameters of the baby Chinese cabbage after adjustment processing, and synchronously process to obtain the continuous storage duration of the baby Chinese cabbage.

[0059] In a specific embodiment of the present invention, the specific analysis method of the appropriate storage adjustment condition parameter of the baby Chinese cabbage is as follows: Extract the storage condition parameters corresponding to the freshness constraint parameter intervals of each variety of the baby Chinese cabbage from the cloud database, and combine the variety and the overall freshness constraint parameter of the baby Chinese cabbage to screen the matching storage condition parameters corresponding to the baby Chinese cabbage.

[0060] All the condition data in the current storage condition parameters of the baby Chinese cabbage are sequentially retrieved and analyzed with all the condition data in the matching storage condition parameters through a data item identification model, and a first execution result is output, where the first execution result includes a numerical value of -1 or 1.

[0061] It should be noted that the current storage condition parameters include but are not limited to temperature, humidity, and wind speed, and the expression of the data item identification model is

[0062] In the formula, f is all the condition data in the current storage condition parameters of the baby Chinese cabbage, and f' is all the condition data in the matching condition parameters of the baby Chinese cabbage.

[0063] If the first execution result of the output is -1, based on the current storage condition parameters of the baby bok choy and in combination with the hindrance values corresponding to the various storage condition influencing factors of the storage terminal, the appropriate storage condition parameters for the baby bok choy are analyzed and obtained.

[0064] It should be noted that the hindrance values of the various storage condition influencing factors of the storage terminal can be obtained from the monitoring platform of the storage terminal. The hindrance values of the various storage condition influencing factors of the storage terminal include, but are not limited to, the average pedestrian flow, the average number of changes, and the average ventilation rate, etc. The average number of changes is the number of starts of the switch actuator of the storage terminal.

[0065] It also should be noted that in combination with the hindrance values corresponding to the various storage condition influencing factors of the storage terminal, the environmental constraint parameters corresponding to the storage terminal are evaluated. In the formula, g r is the hindrance value of the r-th storage condition influencing factor of the storage terminal, and g r0 is the defined value of the r-th storage condition influencing factor of the storage terminal stored in the web database. w is the number of storage condition influencing factors, and r is the number of each storage condition influencing factor. r = 1, 2,..., w, and w is any integer greater than 2.

[0066] The environmental constraint parameters corresponding to the storage terminal are compared with the environmental constraint parameter intervals corresponding to each compensation adjustment value combination stored in the web database, and each compensation adjustment value combination corresponding to the storage terminal is screened.

[0067] Based on each compensation adjustment value combination corresponding to the storage terminal, in combination with all the condition data in the current storage condition parameters of the baby bok choy, the compensation adjustment values corresponding to all the condition data in the current storage condition parameters of the baby bok choy are screened, and all the condition data of the baby bok choy are added to the compensation adjustment values to obtain the adjusted data of all the adjustment data of the baby bok choy, and the appropriate storage adjustment condition parameters of the baby bok choy are constructed.

[0068] In a specific embodiment of the present invention, for the continuous storage duration of the baby bok choy, the specific analysis method is as follows: The freshness decline rate corresponding to each first freshness constraint parameter interval of each variety of baby bok choy is extracted from the cloud database, and the first freshness decline rate of the baby bok choy is screened.

[0069] The freshness decline rate corresponding to each second freshness constraint parameter interval of each variety of baby bok choy is extracted from the cloud data, and the second freshness decline rate of the baby bok choy is screened.

[0070] The unloading parameters of the baby bok choy are obtained, and the third freshness decline rate of the baby bok choy is processed.

[0071] It should be noted that the unloading parameters can be obtained from the monitoring platform of the storage terminal, and the unloading parameters include but are not limited to unloading duration, unloading temperature, unloading storage temperature difference, and unloading storage humidity difference.

[0072] It should also be noted that based on the unloading parameters of baby bok choy, the evaluation method of the environmental constraint parameters corresponding to the storage terminal is the same. The unloading constraint parameters of baby bok choy are evaluated, and combined with the third freshness decline rate of each variety of baby bok choy in each unloading constraint parameter interval stored in the cloud database, the third freshness decline rate of baby bok choy is screened.

[0073] Evaluate the target freshness decline rate V of baby bok choy, and combined with the target overall freshness constraint parameter P of baby bok choy, analyze the continuous storage duration of baby bok choy In the formula, P′ is the overall freshness constraint parameter of baby bok choy.

[0074] It should be noted that the first freshness decline rate, the second freshness decline rate, and the third freshness decline rate of baby bok choy are averaged to obtain the target freshness decline rate of baby bok choy.

[0075] Based on the freshness of baby bok choy, combined with the current storage condition parameters of baby bok choy, the present invention first evaluates the suitability of the current storage condition parameters of baby bok choy. If it is not suitable, it analyzes the appropriate storage adjustment condition parameters of baby bok choy, making up for the defect of low subsequent utilization of the detection and analysis results of the freshness of baby bok choy in the prior art, ensuring the suitability of the current storage conditions of baby bok choy, optimizing the freshness preservation duration of baby bok choy, thereby ensuring the quality of baby bok choy and avoiding affecting subsequent sales.

[0076] S5. Import the appropriate storage condition parameters and continuous storage duration of baby bok choy into a preset display interface for visual display.

[0077] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of the present technology make various modifications or supplements or use similar methods to replace the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A method for detecting and processing the freshness of baby Chinese cabbage, characterized in that, Including: S1. Receive a fresh - degree detection request for Chinese cabbage, and read the input information of Chinese cabbage through a reader; S2. Conduct a fresh - degree detection on Chinese cabbage to obtain the corresponding detection data of Chinese cabbage; S3. Based on the input information of Chinese cabbage, combined with the corresponding detection data of Chinese cabbage, evaluate the overall fresh - degree constraint parameters of Chinese cabbage; S4. Obtain the current storage condition parameters of Chinese cabbage, obtain the appropriate storage adjustment condition parameters of Chinese cabbage after adjustment processing, and synchronously process to obtain the continuous storage duration of Chinese cabbage; S5. Import the appropriate storage condition parameters and continuous storage duration of Chinese cabbage into a preset display interface for visual display.

2. The detection and processing method for the freshness of baby Chinese cabbage according to claim 1, characterized in that, The input information includes production data and traceability data. The production data specifically includes the production date, variety, each initial image, and each quality. The traceability data includes packaging materials, interference values of each initial fresh - degree influencing factor, and interference values of each in - process fresh - degree influencing factor.

3. The detection and processing method for the freshness of baby cabbages according to claim 2, characterized in that, The method for conducting a fresh - degree detection on Chinese cabbage to obtain the corresponding detection data of Chinese cabbage is as follows: Randomly select several Chinese cabbages from this batch of Chinese cabbages, and conduct image acquisition on them to obtain each first image; Weigh several Chinese cabbages to obtain each quality; Conduct spectral tests on several Chinese cabbages to obtain each moisture content and each chlorophyll content.

4. A method for detecting and processing the freshness of baby bok choy according to claim 3, characterized in that, The specific evaluation method for evaluating the overall fresh - degree constraint parameters of Chinese cabbage is as follows: According to the production date, variety, each initial image, and each quality in the production data of Chinese cabbage, through numerical fitting and comparison processing, obtain the first fresh - degree constraint parameter of Chinese cabbage; According to the packaging materials of Chinese cabbage, each initial fresh - degree influencing factor, and each in - process fresh - degree influencing factor, through processing, obtain the second fresh - degree constraint parameter of Chinese cabbage; According to the corresponding detection data of Chinese cabbage, through numerical fitting processing, obtain the corresponding presented fresh - degree constraint parameter of Chinese cabbage, and process to obtain the overall fresh - degree constraint parameter of Chinese cabbage; Import the density attenuation degree, the presented color characteristic value, and the internal content characteristic value of the baby cabbage into the analysis model of the presented freshness constraint parameter corresponding to the baby cabbage Output the presented freshness constraint parameter corresponding to the baby cabbage. In the formula, U is the density attenuation degree of the baby cabbage, Y is the presented color characteristic value of the baby cabbage, and R is the internal content characteristic value of the baby cabbage.

5. The detection and processing method for the freshness of baby cabbages according to claim 4, characterized in that, The overall fresh - degree constraint parameter of Chinese cabbage is specifically obtained by jointly processing the first fresh - degree constraint parameter, the second fresh - degree constraint parameter, and the presented fresh - degree constraint parameter of Chinese cabbage, as the influencing degree for judging the suitability of the current storage condition parameters of Chinese cabbage.

6. The detection and processing method for the freshness of baby cabbages according to claim 4, characterized in that, The specific analysis method for the first fresh - degree constraint parameter of Chinese cabbage is as follows: Obtain each volume according to each initial image of Chinese cabbage, and according to each quality, process to obtain each density, and perform mean processing on them to obtain the average density of Chinese cabbage; According to each initial image of Chinese cabbage, combined with the variety of Chinese cabbage, evaluate the number of abnormal gray - scale values of Chinese cabbage, and combined with the production date of Chinese cabbage, obtain the exposure duration of Chinese cabbage; Import the average density, the number of abnormal gray values, and the exposure duration of baby bok choy into the first freshness constraint condition of baby bok choy, and process to obtain the first freshness constraint parameter of baby bok choy. The first freshness constraint condition of baby bok choy is specifically In the formula, ε is the first freshness constraint parameter of baby bok choy, χ″ is the reference density of the preset baby bok choy, M is the number of abnormal gray values of baby bok choy, M’ is the number of normal gray values of baby bok choy, t is the exposure duration of baby bok choy, t’ is the normal storage duration of baby bok choy stored in the web database, λ1, λ2, and λ3 respectively represent the constraint weight ratio coefficients corresponding to the predefined density risk, color abnormality, and exposure duration, and e is the natural constant.

7. A method for detecting and processing the freshness of baby Chinese cabbage according to claim 4, characterized in that, The specific analysis method for the second fresh - degree constraint parameter of Chinese cabbage is as follows: Based on the packaging materials of Chinese cabbage, combined with the interference values corresponding to each packaging material stored in the cloud database, screen the interference values corresponding to the packaging materials of Chinese cabbage; Import the interference values corresponding to the packaging materials of Chinese cabbage, the interference values corresponding to each initial fresh - degree influencing factor, and the interference values corresponding to each in - process fresh - degree influencing factor into the second fresh - degree constraint condition of Chinese cabbage, and process to obtain the second fresh - degree constraint parameter of Chinese cabbage.

8. The detection and processing method for the freshness of baby bok choy according to claim 1, characterized in that, The specific analysis method for the appropriate storage adjustment condition parameters of the baby Chinese cabbage is as follows: Extract the storage condition parameters corresponding to the freshness constraint parameter intervals of each variety of baby Chinese cabbage from the cloud database, and combine the variety and overall freshness constraint parameters of the baby Chinese cabbage to screen the matching storage condition parameters corresponding to the baby Chinese cabbage; Successively retrieve and analyze all the condition data in the current storage condition parameters of the baby Chinese cabbage and all the condition data in the matching storage condition parameters through the data item recognition model, and output the first execution result, where the first execution result includes a value of -1 or 1; If the output first execution result is -1, then based on the current storage condition parameters of the baby Chinese cabbage, combined with the obstacle values corresponding to each influencing storage condition factor of the storage terminal, analyze and obtain the appropriate adjusted storage condition parameters of the baby Chinese cabbage.

9. The detection and processing method for the freshness of baby cabbages according to claim 1, characterized in that, The specific analysis method for the continuous storage duration of the baby Chinese cabbage is as follows: Extract the freshness decline rates corresponding to each first freshness constraint parameter interval of each variety of baby Chinese cabbage from the cloud database, and screen the first freshness decline rate of the baby Chinese cabbage; Extract the freshness decline rates corresponding to each second freshness constraint parameter interval of each variety of baby Chinese cabbage from the cloud data, and screen the second freshness decline rate of the baby Chinese cabbage; Obtain the unloading parameters of the baby Chinese cabbage, and process to obtain the third freshness decline rate of the baby Chinese cabbage. Evaluate the target freshness decline rate V of baby bok choy, and analyze the continuous storage duration of baby bok choy in combination with the target overall freshness constraint parameter P of baby bok choy In the formula, P′ is the overall freshness constraint parameter of baby bok choy.

Images