A method, system, device and storage medium for monitoring freshness of fresh produce

Abstract

The present application provides a method, system, device, and storage medium for monitoring the freshness of fresh produce, relating to the field of fresh produce monitoring technology. The method includes: obtaining a surface image of a target fresh produce sent by a target sensor in a current environment, and the weight difference between the current weight and the initial weight of the target fresh produce; determining a first freshness score for the target fresh produce based on a first environmental characteristic of the current environment and the surface image; determining a second freshness score for the target fresh produce based on the weight difference and a second environmental characteristic of the current environment; determining an initial freshness level of the target fresh produce by combining the first freshness score and the second freshness score; obtaining a target gas concentration within a preset range for the target fresh produce, adjusting the initial freshness level based on the target gas concentration, and generating a target freshness level for the target fresh produce. The present application has the technical effect of improving the accuracy of fresh produce freshness assessment.

Description

Technical Field

[0001] The present application relates to the technical field of fresh produce freshness monitoring, and specifically to a fresh produce freshness monitoring method, system, device, and storage medium. Background Art

[0002] Fresh products are prone to quality deterioration during circulation and storage, leading to economic losses. Therefore, real-time monitoring and evaluation of the freshness of fresh products is necessary.

[0003] Currently, common monitoring methods assess the freshness of produce from a single perspective. For example, some rely solely on image recognition technology to analyze surface features, while others rely solely on weighing to determine moisture loss. This single-dimensional approach ignores the complex changes in the nature of produce during storage, leading to inaccurate assessment results. Summary of the Invention

[0004] The present application provides a method for monitoring the freshness of fresh produce, which is used to improve the accuracy of fresh produce freshness assessment.

[0005] In a first aspect, the present application provides a method for monitoring the freshness of fresh produce, the method comprising: obtaining a surface image of a target fresh produce sent by a target sensor in a current environment, and a weight difference between the current weight of the target fresh produce and the initial weight of the target fresh produce; determining a first freshness score of the target fresh produce based on a first environmental characteristic of the current environment and the surface image; determining a second freshness score of the target fresh produce based on the weight difference and a second environmental characteristic of the current environment, the current environment including the first environmental characteristic and the second environmental characteristic; determining an initial freshness level of the target fresh produce in combination with the first freshness score and the second freshness score; obtaining a target gas concentration within a preset range of the target fresh produce, adjusting the initial freshness level based on the target gas concentration, and generating a target freshness level of the target fresh produce.

[0006] By employing this technical solution, surface images and weight differences of the target fresh produce are collected as baseline data. This data is analyzed and processed in conjunction with current environmental characteristics to generate a primary freshness score and a secondary freshness score, reflecting the fresh produce's appearance and moisture loss, respectively. This scoring method considers the impact of environmental factors on changes in fresh produce quality, making the evaluation more objective. By combining these two scores to determine the initial freshness grade and then introducing the target gas concentration as a key indicator for grade adjustment, a comprehensive assessment of fresh produce freshness is achieved across three dimensions: appearance, weight, and gas. This multi-dimensional assessment method overcomes the limitations of single-metric evaluation and improves the accuracy of fresh produce freshness assessment.

[0007] Optionally, determining the first freshness score of the target fresh produce based on the first environmental feature of the current environment and the surface image includes: determining an environmental error coefficient of the current environment relative to the standard environment based on the first environmental feature; correcting the surface image based on the environmental error coefficient to obtain a target surface image, wherein the environmental error coefficient is used as a coefficient for correcting color parameters and brightness parameters of the surface image; matching the target surface image with a standard surface image in a pre-stored standard image library, and determining the first freshness score of the target fresh produce based on the matching result, wherein the standard image library includes standard surface images corresponding to the target fresh produce at different freshness scores.

[0008] By adopting the above-mentioned technical solution, which uses an environmental error coefficient to correct surface images, the problem of color and brightness differences in fresh produce images captured under different environmental conditions is resolved. Specifically, the environmental error coefficient is obtained by comparing the current environment with a standard environment. This coefficient is then used to correct the color and brightness parameters of the surface image to obtain a target surface image. This corrected image eliminates interference from factors such as ambient lighting, making it more similar to the imaging effect under a standard environment. When the corrected target surface image is matched with a standard surface image in a standard image library, a more accurate match is achieved, thereby improving the accuracy of the first freshness score. This image assessment method based on environmental correction ensures greater comparability and reliability in fresh produce assessment results under different environmental conditions.

[0009] Optionally, determining the second freshness score of the target fresh produce based on the weight difference and the second environmental characteristic of the current environment includes: determining the weight error coefficient of the current environment relative to the standard environment based on the second environmental characteristic; correcting the weight difference based on the weight error coefficient to obtain a target weight difference, wherein the weight error coefficient is a coefficient for correcting the deviation of the weight difference affected by the environment; matching the target weight difference with a standard weight difference in a pre-stored standard weight library, and determining the second freshness score of the target fresh produce based on the matching result, wherein the standard weight library includes the standard weight differences corresponding to the target fresh produce under different freshness scores.

[0010] By adopting the above technical solution, the current environment is compared with the standard environment to obtain a weight error coefficient, and this coefficient is used to correct the weight difference to obtain a target weight difference. This corrected weight difference can eliminate the impact of factors such as ambient temperature and humidity on the moisture loss rate of fresh produce, making it closer to the weight change under a standard environment. When the corrected target weight difference is matched with the standard weight difference in the standard weight library, a more accurate matching result can be obtained, thereby improving the accuracy of the second freshness score. This weight assessment method based on environmental correction makes the assessment results of fresh produce freshness under different environmental conditions more comparable and reliable.

[0011] Optionally, the combining of the first freshness score and the second freshness score to determine the initial freshness level of the target fresh produce includes: obtaining a first weight coefficient corresponding to the first freshness score and a second weight coefficient corresponding to the second freshness score; arithmetically multiplying the first freshness score by the first weight coefficient to obtain a first weighted score, and arithmetically multiplying the second freshness score by the second weight coefficient to obtain a second weighted score; arithmetically adding the first weighted score and the second weighted score to obtain a target score value; and determining the initial freshness level of the target fresh produce based on the target score value.

[0012] By adopting this technical solution, a weighted score is obtained by multiplying the two scores by their corresponding weight coefficients, and then adding the weighted scores to obtain the target score, achieving a reasonable weighting of different scoring indicators. This weighted calculation method allows for differentiated assessments of fresh produce appearance characteristics and weight changes based on their importance, avoiding the assessment bias that may result from simple averaging. This makes the final determined initial freshness grade more consistent with actual conditions, improving the scientific nature and accuracy of fresh produce freshness assessments.

[0013] Optionally, adjusting the initial freshness level according to the target gas concentration to generate the target freshness level of the target fresh food includes: determining a preset gas concentration threshold according to the type of the target fresh food, wherein the preset gas concentration threshold includes multiple concentration intervals, each concentration interval corresponds to the standard gas concentration of the target fresh food at different freshness levels; determining the first freshness level of the target fresh food according to the target gas concentration on the basis of the preset gas concentration threshold; adjusting the initial freshness level according to the first freshness level to generate the target freshness level of the target fresh food.

[0014] By adopting the above technical solution, and by comparing the target gas concentration with a preset gas concentration threshold, the problem of overlooking internal changes in fresh produce when relying solely on appearance and weight for evaluation is resolved. By setting preset gas concentration thresholds containing multiple concentration intervals for different types of target fresh produce, and matching the measured target gas concentration with these intervals, a first freshness grade reflecting internal changes in the fresh produce is obtained. This grade is then used to adjust the initial freshness grade, ultimately generating a target freshness grade. This gas concentration-based grade adjustment method can promptly detect potential quality changes within fresh produce, addressing the shortcomings of relying solely on appearance and weight evaluation, and making the freshness assessment results of fresh produce more comprehensive and accurate.

[0015] Optionally, the adjusting the initial freshness level according to the first freshness level to generate the target freshness level of the target fresh food includes: calculating the level difference between the initial freshness level and the first freshness level; judging whether the level difference exceeds a level difference threshold; if the level difference does not exceed the level difference threshold, determining the initial freshness level as the target freshness level of the target fresh food; if the level difference exceeds the level difference threshold, adjusting the initial freshness level according to the first freshness level to obtain the target freshness level of the target fresh food, wherein, when the first freshness level is higher than the initial freshness level, the initial freshness level is increased; when the first freshness level is lower than the initial freshness level, the initial freshness level is decreased.

[0016] By adopting the above technical solution, which sets a grade difference threshold to determine whether the initial freshness grade needs to be adjusted, the problem of how to properly adjust the grades derived from different evaluation dimensions is solved. When the difference between the initial freshness grade and the first freshness grade derived based on gas concentration is within the threshold range, the initial evaluation result remains unchanged, avoiding unnecessary grade adjustments. When the difference exceeds the threshold, the initial freshness grade is adjusted accordingly based on the first freshness grade. This threshold-based dynamic adjustment mechanism not only ensures the stability of the evaluation results, but also promptly detects and corrects significant evaluation deviations, thereby making the final target freshness grade more accurate and reliable.

[0017] Optionally, after generating the target freshness level of the target fresh food, the method further includes: obtaining a change trend of the target freshness level of the target fresh food within a first preset time period; predicting a predicted freshness level of the target fresh food after a second preset time period based on the change trend; and generating an early warning message when the predicted freshness level is lower than a preset level threshold, wherein the early warning message includes the current target freshness level of the target fresh food, the predicted freshness level, and a recommended processing method.

[0018] By employing this technical solution, the difficulty of predicting fresh produce quality deterioration is addressed by analyzing the changing trends of the target freshness level within a first preset time period and predicting the predicted freshness level after a second preset time period. When the predicted freshness level falls below a preset threshold, the system generates an early warning message containing the current target freshness level, the predicted freshness level, and a recommended treatment approach. This historical data-based prediction and early warning mechanism can identify significant deterioration in fresh produce quality in advance, enabling management to take timely action, effectively preventing fresh produce spoilage and minimizing economic losses.

[0019] In a second aspect, the present application provides a freshness monitoring system for fresh produce, the system comprising: an acquisition module, a first determination module, a second determination module, a combination module, and an adjustment module; wherein the acquisition module is configured to acquire a surface image of a target fresh produce sent by a target sensor in a current environment, as well as a weight difference between a current weight of the target fresh produce and an initial weight of the target fresh produce; the first determination module is configured to determine a first freshness score of the target fresh produce based on a first environmental feature of the current environment and the surface image; the second determination module is configured to determine a second freshness score of the target fresh produce based on the weight difference and a second environmental feature of the current environment, the current environment including the first environmental feature and the second environmental feature; the combination module is configured to determine an initial freshness level of the target fresh produce by combining the first freshness score and the second freshness score; the adjustment module is configured to acquire a target gas concentration within a preset range of the target fresh produce, adjust the initial freshness level based on the target gas concentration, and generate a target freshness level of the target fresh produce.

[0020] In a third aspect, the present application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-mentioned freshness monitoring methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned freshness monitoring methods.

[0022] In summary, this application includes at least one of the following beneficial technical effects:

[0023] 1. By collecting surface images and weight differences of target fresh produce as basic data and analyzing and processing this data in combination with current environmental characteristics, we obtain a primary freshness score and a secondary freshness score, reflecting the fresh produce's appearance and moisture loss, respectively. This scoring method considers the impact of environmental factors on fresh produce quality changes, making the evaluation results more objective.

[0024] 2. By combining these two scores to determine the initial freshness grade and then introducing the target gas concentration as a key indicator for grade adjustment, a comprehensive assessment of produce freshness is achieved across three dimensions: appearance, weight, and gas concentration. This multi-dimensional assessment method overcomes the limitations of single-metric evaluation and improves the accuracy of produce freshness assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a method for monitoring the freshness of fresh produce provided in an embodiment of the present application;

[0026] Figure 2 This is a structural diagram of a fresh produce freshness monitoring system provided in an embodiment of the present application;

[0027] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0028] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0029] In order to enable people skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0030] In the description of the embodiments of this application, words such as "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0031] Figure 1 This is a flow chart of a method for monitoring the freshness of fresh produce provided in an embodiment of the present application. Figure 1 As shown, the method includes S101-S105:

[0032] S101 , obtaining a surface image of a target fresh produce sent by a target sensor in a current environment, and a weight difference between a current weight of the target fresh produce and an initial weight of the target fresh produce.

[0033] Because the freshness of fresh produce during storage and sales is directly related to food safety, an accurate freshness monitoring method is necessary. Fresh produce can experience surface color changes and moisture loss during storage, which are important indicators of freshness. This embodiment accurately monitors freshness by acquiring two key indicators: surface images and weight differences of the target produce.

[0034] Specifically, an image acquisition device and a weight sensor are placed in the storage environment of the target fresh produce. The image acquisition device can be a CCD camera or a CMOS camera, which is used to capture real-time surface images of the target fresh produce; the weight sensor can be a high-precision electronic weighing device, which is used to measure the real-time weight of the target fresh produce. When the target fresh produce enters the warehouse, the initial weight is first captured and recorded as the initial weight. Subsequently, during the monitoring process, the current weight is captured in real time by the weight sensor, and the difference between the current weight and the initial weight is used as the weight difference. The data collected by the image acquisition device and the weight sensor is transmitted to a data processing unit via a wireless communication module or a wired connection. The image acquisition device captures surface images of the target fresh produce every preset time period (e.g., 30 minutes), while the weight sensor continuously collects weight data. This approach not only captures information on changes in the fresh produce's appearance but also monitors the degree of moisture loss, providing comprehensive data support for subsequent freshness assessment. This step enables real-time and accurate monitoring of the target fresh produce's condition, providing a reliable data basis for subsequent freshness scoring. Furthermore, the use of automated monitoring avoids the subjectivity and discontinuity of manual monitoring, improving monitoring efficiency and accuracy.

[0035] S102: Determine a first freshness score of a target fresh product based on a first environmental feature and a surface image of a current environment.

[0036] Specifically, since the surface image of the target fresh produce will be affected by factors such as ambient light, temperature, and humidity, direct freshness assessment based on the surface image may result in large errors. Therefore, it is necessary to correct the image based on environmental characteristics before scoring.

[0037] In this embodiment, the first environmental feature includes parameters such as ambient light intensity, color temperature, and ambient temperature and humidity. First, environmental monitoring equipment such as light sensors, temperature and humidity sensors are set in the storage environment of the target fresh produce to collect environmental parameters in real time. The currently collected environmental parameters are compared with the parameters under the standard environment to calculate the environmental error coefficient. Among them, the standard environment refers to an ideal storage environment with a light intensity of 500lux, a color temperature of 5000K, a temperature of 4°C, and a relative humidity of 85%. The calculation of the environmental error coefficient takes into account the weight of each environmental parameter. For example, the deviation in light intensity can be given a weight of 0.4, the color temperature deviation can be given a weight of 0.3, the temperature deviation can be given a weight of 0.2, and the humidity deviation can be given a weight of 0.1. The comprehensive environmental error coefficient is obtained through weighted calculation. After obtaining the environmental error coefficient, the surface image of the target fresh produce is corrected, mainly including the correction of color parameters and brightness parameters.

[0038] Specifically, an image processing algorithm is used to adjust image parameters such as RGB values, brightness, and contrast to restore the image characteristics to those under standard conditions. The corrected target surface image is then matched against a pre-established standard image library, which contains standard surface images of the target fresh produce at different freshness scores. These standard images were collected under standard conditions. An image feature matching algorithm is used to calculate the similarity between the target surface image and each standard surface image in the library. The freshness score corresponding to the standard surface image with the highest similarity is selected as the first freshness score.

[0039] Based on the above embodiment, as an optional implementation, in S102, determining the first freshness score of the target fresh food according to the first environmental feature and the surface image of the current environment specifically includes S201-S203:

[0040] S201: Determine an environmental error coefficient of a current environment relative to a standard environment based on a first environmental characteristic.

[0041] S202 , correcting the surface image according to an environmental error coefficient to obtain a target surface image, wherein the environmental error coefficient is used as a coefficient for correcting color parameters and brightness parameters of the surface image.

[0042] S203: Match the target surface image with standard surface images in a pre-stored standard image library, and determine a first freshness score of the target fresh produce based on the matching result, wherein the standard image library includes standard surface images corresponding to the target fresh produce at different freshness scores.

[0043] The environmental error coefficient essentially measures the degree to which current environmental conditions affect image acquisition. It is a normalized coefficient derived from a weighted combination of multiple environmental parameters. Using a standard environment (light intensity 500 lux, color temperature 5000K, temperature 4°C, relative humidity 85%) as a benchmark, the deviation between the current environmental parameters and the standard values ​​is calculated. The specific calculation process is as follows:

[0044] Light intensity deviation coefficient: Assuming the current light intensity is 600 lux, the deviation is: |(600-500)| / 500=0.2, the weight is 0.4, and the weighted value is: 0.2×0.4=0.08.

[0045] Color temperature deviation coefficient: Assuming the current color temperature is 5500K, the deviation is: |(5500-5000)| / 5000=0.1, the weight is 0.3, and the weighted value is: 0.1×0.3=0.03.

[0046] Temperature deviation coefficient: Assuming the current temperature is 6°C, the deviation is: |(6-4)| / 4 = 0.5, the weight is 0.2, and the weighted value is: 0.5×0.2=0.1.

[0047] Humidity deviation coefficient: Assuming the current humidity is 80%, the deviation is: |(80-85)| / 85=0.059, the weight is 0.1, and the weighted value is: 0.059×0.1=0.0059.

[0048] The final environmental error coefficient is the sum of all weighted values: 0.08+0.03+0.1+0.0059=0.2159.

[0049] Specifically, when evaluating the freshness of produce, image features are an important basis for judgment, but environmental factors can have a significant impact on image acquisition, so a series of processing steps are required to ensure the accuracy of the evaluation.

[0050] First, the first environmental feature mainly includes parameters such as ambient light intensity, color temperature, and temperature and humidity, which are different from the standard environment. The standard environment in this embodiment refers to an ideal storage environment with a light intensity of 500 lux, a color temperature of 5000K, a temperature of 4°C, and a relative humidity of 85%. The current environmental parameters are collected in real time by the environmental sensor, and compared with the standard environmental parameters to obtain the deviation values ​​of each parameter. When calculating the environmental error coefficient, taking into account the different degrees of influence of different environmental parameters on image quality, a weighted calculation method is adopted, and the weight of the light intensity deviation is set to 0.4, the weight of the color temperature deviation is set to 0.3, the weight of the temperature deviation is set to 0.2, and the weight of the humidity deviation is set to 0.1. The comprehensive environmental error coefficient is obtained by weighted summation.

[0051] After obtaining the environmental error coefficient, the captured surface image needs to be corrected. Specifically, the image's RGB values ​​are corrected using an image processing algorithm, using the environmental error coefficient as a correction factor to adjust the pixel values ​​of the red, green, and blue channels. Simultaneously, the image's brightness and contrast are modified accordingly to eliminate the effects of ambient lighting on the image's visual quality. This correction results in an image of the target surface that more closely resembles the characteristics of a true image under standard conditions.

[0052] Next, the corrected target surface image is matched against a pre-established standard image library. This library contains standard surface images of the target fresh produce at different freshness rating stages, captured under standardized conditions. The matching process utilizes an image feature matching algorithm, encompassing multiple dimensions such as color, texture, and shape. The similarity between the target surface image and each standard surface image in the library is calculated, and the freshness score corresponding to the standard surface image with the highest similarity is selected as the primary freshness score.

[0053] S103: Determine a second freshness score of the target fresh produce based on the weight difference and a second environmental characteristic of the current environment, where the current environment includes the first environmental characteristic and the second environmental characteristic.

[0054] Since the weight change of the target fresh produce is affected not only by its own water loss but also by factors such as ambient temperature and humidity, directly using weight difference to evaluate freshness may lead to inaccurate evaluation results.

[0055] In this embodiment, the second environmental characteristics primarily include parameters such as ambient temperature, relative humidity, and air pressure. These parameters affect the rate of moisture evaporation from fresh produce, and thus the accuracy of the weight difference. To eliminate the influence of these environmental factors, the weight error coefficient is first calculated based on the second environmental characteristics.

[0056] Specifically, temperature, humidity, and air pressure sensors collect environmental parameters in real time and compare them with those under a standard environment. In addition to maintaining a temperature of 4°C and a relative humidity of 85%, the standard atmospheric pressure (i.e., 101.325 kPa) is also required. The weight error coefficient is calculated by taking into account the impact of each environmental parameter on water loss. For example, temperature deviation can be weighted 0.5 (because temperature has the greatest impact on water evaporation), humidity deviation 0.3, and air pressure deviation 0.2. This weighted calculation yields a comprehensive weight error coefficient. After obtaining the weight error coefficient, it is corrected and compared with the measured weight difference to obtain a target weight difference under the standard environment. This corrected target weight difference is then matched against a pre-established standard weight database, which stores standard weight differences corresponding to different freshness scores for the target fresh produce. These standard data are all measured under standard conditions. A numerical matching algorithm is used to identify the standard weight difference that is closest to the target weight difference, and the corresponding freshness score is used as the secondary freshness score.

[0057] Based on the above embodiment, as an optional implementation, in S103, determining the second freshness score of the target fresh produce according to the weight difference and the second environmental characteristic of the current environment specifically includes S301-S303:

[0058] S301: Determine a weight error coefficient of the current environment relative to the standard environment based on the second environment characteristic.

[0059] S302: Correct the weight difference according to the weight error coefficient to obtain a target weight difference, wherein the weight error coefficient is a coefficient for correcting the deviation of the weight difference due to environmental influences.

[0060] S303: Match the target weight difference with standard weight differences in a pre-stored standard weight library, and determine a second freshness score of the target fresh produce based on the matching result, wherein the standard weight library includes standard weight differences corresponding to different freshness scores of the target fresh produce.

[0061] Specifically, the weight changes of fresh produce during storage are significantly affected by environmental factors. In order to accurately assess the degree of water loss in fresh produce, it is necessary to eliminate the impact of environmental factors on weight measurement.

[0062] First, the secondary environmental characteristics primarily include parameters such as ambient temperature, relative humidity, and air pressure, which directly impact the evaporation rate of fresh produce. In this embodiment, the standard environment refers to an ideal storage environment with a temperature of 4°C, a relative humidity of 85%, and an air pressure of 101.325 kPa. Environmental sensors collect current environmental parameters in real time and compare them with the standard environmental parameters. When calculating the weight error coefficient, a weighted calculation method is used to account for the varying degrees of impact of different environmental parameters on water evaporation.

[0063] For example, when the current ambient temperature is 6°C, the temperature deviation is |(6-4)| / 4=0.5, with a weight of 0.5; when the current relative humidity is 80%, the humidity deviation is |(80-85)| / 85=0.059, with a weight of 0.3; and when the current air pressure is 100 kPa, the pressure deviation is |(100-101.325)| / 101.325=0.013, with a weight of 0.2. Multiply each deviation by the corresponding weight and sum them to obtain the comprehensive weight error coefficient.

[0064] After obtaining the weight error coefficient, the measured weight difference needs to be corrected. For example, if the measured weight difference of a fresh produce item is 50g and the weight error coefficient is 0.25, the correction can be made using the formula: Target weight difference = Measured weight difference × (1 ± weight error coefficient). Whether to increase or decrease the correction depends on the deviation of the current environmental parameters from the standard environment.

[0065] If the temperature of the current environment is higher than the standard environment and the humidity is lower than the standard environment, it means that the environment will accelerate the evaporation of water. At this time, the measured weight difference should be reduced, that is, the target weight difference = 50×(1-0.25) = 37.5g. The corrected target weight difference is closer to the actual water loss under the standard environment. Next, the corrected target weight difference is matched with the pre-established standard weight library. The standard weight library stores the standard weight differences of the target fresh products at different freshness scoring stages. These standard data are all measured under a standard environment. Through the numerical matching algorithm, find the standard weight difference that is closest to the target weight difference, and use its corresponding freshness score as the second freshness score. If the target weight difference falls exactly between the two standard values, the corresponding score can be calculated by linear interpolation.

[0066] S104: Determine an initial freshness level of the target fresh produce by combining the first freshness score and the second freshness score.

[0067] In this embodiment, the weight coefficients for the two scoring indicators need to be determined first. Different types of fresh produce may exhibit different characteristic changes during storage. For example, for leafy vegetables, their surface morphology changes more significantly, while water loss is relatively slow. Therefore, the first weight coefficient can be set to 0.7 and the second weight coefficient can be set to 0.3. For fresh fruits, their water loss and surface changes are both more significant. Therefore, the first and second weight coefficients can be set to 0.5.

[0068] After determining the weight coefficients, the first freshness score is multiplied by the first weight coefficient to obtain the first weighted score, and the second freshness score is multiplied by the second weight coefficient to obtain the second weighted score. The two weighted scores are arithmetically added to obtain the target score. For example, when the first freshness score is 85 points and the second freshness score is 80 points, and the corresponding weight coefficients are 0.6 and 0.4, respectively, the final target score is 85 × 0.6 + 80 × 0.4 = 83 points. Based on the pre-set score range, the target score is converted into the initial freshness grade. Specifically, scores above 90 are classified as Grade A (very fresh), 80-90 points as Grade B (fresh), 70-80 points as Grade C (relatively fresh), 60-70 points as Grade D (slightly worse), and below 60 points as Grade E (poor).

[0069] Based on the above embodiment, as an optional implementation, in S104, combining the first freshness score and the second freshness score to determine the initial freshness level of the target fresh produce specifically includes S401-S404:

[0070] S401: Obtain a first weight coefficient corresponding to a first freshness score and a second weight coefficient corresponding to a second freshness score.

[0071] S402 arithmetically multiplies the first freshness score by the first weight coefficient to obtain a first weighted score, and arithmetically multiplies the second freshness score by the second weight coefficient to obtain a second weighted score.

[0072] S403: arithmetically add the first weighted score and the second weighted score to obtain a target score value.

[0073] S404: Determine the initial freshness level of the target fresh produce according to the target score value.

[0074] In one example, because the impact of surface characteristics and moisture loss characteristics of fresh produce on its freshness changes dynamically with changes in storage environment and time, a dynamic weighting system is needed to comprehensively evaluate the freshness level of fresh produce.

[0075] In this embodiment, the corresponding weight coefficients are first determined based on the freshness type and storage stage. For example, for leafy vegetables, in the early storage period (0-24 hours), surface characteristics change more significantly, while water loss is relatively small. In this case, the first weight coefficient (corresponding to the surface characteristic score) can be set to 0.7, and the second weight coefficient (corresponding to the weight characteristic score) can be set to 0.3. In the middle storage period (24-48 hours), the two characteristics are equally important, so both weight coefficients can be set to 0.5. In the late storage period (more than 48 hours), water loss becomes the primary influencing factor, so the first weight coefficient can be set to 0.3, and the second weight coefficient can be set to 0.7. After obtaining the weight coefficients, the first freshness score is multiplied by the corresponding first weight coefficient to obtain the first weighted score.

[0076] For example, if the first freshness score is 85 and the first weight coefficient is 0.7, the first weighted score is 85 × 0.7 = 59.5. Similarly, if the second freshness score is 80 and the second weight coefficient is 0.3, the second weighted score is 80 × 0.3 = 24. Adding the two weighted scores yields a final target score of 59.5 + 24 = 83.5. This weighted calculation method dynamically adjusts the contribution of different features to the final score based on their importance at different stages.

[0077] Based on the calculated target score and the preset scoring range, the initial freshness grade of the target fresh produce is determined. Specifically, scores above 90 are classified as A (Excellent), 80-90 as B (Good), 70-80 as C (Fair), 60-70 as D (Poor), and below 60 as E (Poor). In this example, the target score is 83.5, corresponding to an initial freshness grade of B. This dynamic weighted scoring method more accurately reflects the actual quality of fresh produce at different stages of storage, avoiding the bias that can result from a single scoring metric.

[0078] S105 , obtaining a target gas concentration within a preset range of the target freshness product, adjusting the initial freshness level according to the target gas concentration, and generating a target freshness level of the target freshness product.

[0079] Specifically, various volatile gases are produced during the storage process of fresh produce. The changes in the concentration of these gases can directly reflect the degree of corruption and freshness of the fresh produce. Therefore, it is necessary to make further grade adjustments based on the initial freshness grade combined with the gas concentration data.

[0080] In this embodiment, a gas concentration sensor array is placed within a preset range of the target fresh produce (e.g., within 10 cm of the produce surface) to detect the concentrations of multiple target gases. These target gases include, but are not limited to, typical spoilage-signaling gases such as acetaldehyde, ethanol, and ammonia. The concentrations of these gases show a significant correlation with the degree of spoilage. The gas concentration sensors acquire real-time concentration data for each target gas through periodic sampling (e.g., every 5 minutes). The collected target gas concentrations are compared with a pre-established gas concentration standard database, which stores standard concentration ranges for each target gas at different freshness levels.

[0081] For example, for fresh leafy vegetables, when the detected ammonia concentration exceeds 5ppm and the ethanol concentration exceeds 10ppm, it indicates that the fresh produce has shown obvious signs of spoilage. Based on the exceeding of the target gas concentration, the grade adjustment rules are set: if the concentration of any detected target gas exceeds the upper limit of the standard range of the corresponding grade, the initial freshness grade is lowered by one level; if the concentrations of two or more gases are detected to exceed the standard, the grade is lowered by two levels; if all gas concentrations are within the standard range, the initial grade remains unchanged. At the same time, considering the different degrees of impact of different gases on the quality of fresh produce, weighting coefficients can be set for gas concentrations, such as a weighting coefficient of 0.4 for ammonia, 0.3 for ethanol, and 0.3 for acetaldehyde. A comprehensive gas concentration evaluation index is obtained through weighted calculation, and when this index exceeds the preset threshold, the grade adjustment is triggered. The final target freshness grade is a more accurate evaluation result after gas concentration correction.

[0082] Based on the above embodiment, as an optional implementation, in S105, adjusting the initial freshness level according to the target gas concentration to generate the target freshness level of the target fresh food specifically includes S501-S503:

[0083] S501: Determine a preset gas concentration threshold according to the type of target fresh produce, wherein the preset gas concentration threshold includes a plurality of concentration intervals, each concentration interval corresponding to a standard gas concentration of the target fresh produce at a different freshness level.

[0084] S502: Determine a first freshness level of the target fresh produce based on a preset gas concentration threshold and a target gas concentration.

[0085] S503 adjusts the initial freshness level according to the first freshness level to generate a target freshness level of the target fresh food.

[0086] Specifically, fresh produce releases specific gases during storage, and the concentration changes of these gases are closely related to the freshness of the produce. Therefore, using gas concentration as an adjustment factor can further improve the accuracy of freshness ratings.

[0087] Taking fresh fruits as an example, ethylene gas is released during storage, and changes in its concentration can reflect the maturity and freshness of the fruit. In this embodiment, the corresponding preset gas concentration threshold is first determined based on the specific type of target fresh fruit. For example, for apples, the ethylene gas concentration can be divided into the following intervals: 0-0.5ppm corresponds to grade A (excellent), 0.5-1.0ppm corresponds to grade B (good), 1.0-1.5ppm corresponds to grade C (average), 1.5-2.0ppm corresponds to grade D (poor), and above 2.0ppm corresponds to grade E (poor). When the target gas concentration is detected to be 0.8ppm, the first freshness grade can be determined to be grade B by comparing with the preset gas concentration threshold. Next, the first freshness grade obtained based on the gas concentration needs to be combined with the initial freshness grade obtained based on the surface characteristics and weight characteristics to generate the final target freshness grade.

[0088] The specific adjustment rules can be set as follows: when the difference between the two levels is one, the lower level is used as the final level; when the difference between the two levels is two levels or more, the middle level is used as the final level. For example, if the initial freshness level is A and the first freshness level based on gas concentration is B, the difference between the two is one level, so the final target freshness level should be adjusted to B.

[0089] This adjustment mechanism fully considers the impact of gas concentration, a key indicator, on the freshness of fresh produce. Through a comprehensive, multi-dimensional assessment, it can more accurately reflect the actual quality of fresh produce. Furthermore, because changes in gas concentration often precede significant changes in surface and weight characteristics, this adjustment mechanism provides a warning function, enabling early detection of declining fresh produce quality. This gas concentration-based grading method not only improves the accuracy and reliability of freshness ratings but also provides warehouse managers with a more comprehensive basis for quality monitoring, enabling timely implementation of appropriate storage adjustments to extend the shelf life of fresh produce.

[0090] After generating the target freshness level of the target fresh food, the method further includes:

[0091] Obtain a change trend of the target freshness level of the target fresh produce within a first preset time period; based on the change trend, predict the predicted freshness level of the target fresh produce after a second preset time period; when the predicted freshness level is lower than a preset level threshold, generate an early warning message, wherein the early warning message includes the current target freshness level of the target fresh produce, the predicted freshness level, and a recommended handling method.

[0092] In one example, to achieve proactive management of fresh produce quality and avoid economic losses caused by fresh produce spoilage, it is necessary to establish a fresh produce quality prediction mechanism based on historical data. In this embodiment, the system first records the target freshness grade change data of the target fresh produce within a first preset time period (e.g., the last 24 hours). Taking a batch of apples as an example, the system records the freshness grade every 4 hours. In the last 24 hours, the following data was recorded: 0 hours (Grade A), 4 hours (Grade A), 8 hours (Grade A), 12 hours (Grade B), 16 hours (Grade B), 20 hours (Grade B), 24 hours (Grade B).

[0093] By analyzing these data points, we can conclude that this batch of apples has experienced a downgrade from grade A to grade B within the last 24 hours, with the downgrade occurring approximately 12 hours later. Combining current storage environment data (e.g., a temperature consistently at 6°C, 4°C above the standard storage temperature) with the characteristics of fresh produce, we can predict the grade change trend within a second preset time period (e.g., the next 12 hours).

[0094] Based on a simple linear prediction method, and taking into account the rate of change, which decreases by one grade every 12 hours, the freshness grade of this batch of apples is predicted to drop to C within the next 12 hours. Since the system's preset freshness threshold is B, an early warning message is generated. This warning message contains three key elements: the current target freshness grade (B), the predicted freshness grade (C), and the recommended treatment method.

[0095] The recommended processing method is automatically generated based on pre-set processing rules. For example, for apples that are currently grade B and are predicted to drop to grade C, the system will give the following suggestions: "Current freshness level: Grade B; expected to drop to Grade C in 12 hours; it is recommended to adjust the storage temperature to 4°C and complete the sales processing within 8 hours."

[0096] The implementation of this predictive warning mechanism has been effective: first, by regularly recording and analyzing data on changes in freshness levels, trends in quality deterioration can be detected in a timely manner; second, the warning information contains complete status information and specific treatment suggestions, facilitating managers to make quick decisions and take action; finally, historical data recorded by the system can be used to optimize preset level thresholds and treatment suggestions.

[0097] For example, after adopting this system, a fresh produce supermarket reduced its fresh produce loss rate from 8% to 3%. This is because managers can predict the risk of a significant decline in fresh produce quality and implement appropriate measures such as temperature adjustment or promotions. Furthermore, this forecasting and early warning mechanism can be integrated with the inventory management system to automatically generate inventory adjustment recommendations when impending declines in fresh produce quality are predicted, achieving efficient fresh produce management. This historical data-based forecasting and early warning method not only improves the scientific and forward-looking nature of fresh produce management, but also significantly reduces fresh produce losses and improves operational efficiency.

[0098] Based on the above method, this application also discloses a freshness monitoring system. Figure 2 As shown, Figure 2 This is a structural schematic diagram of a fresh produce monitoring system provided by an embodiment of the present application, and the system includes: an acquisition module, a first determination module, a second determination module, a combination module and an adjustment module; wherein the acquisition module is used to obtain a surface image of a target fresh produce sent by a target sensor in a current environment, as well as a weight difference between the current weight of the target fresh produce and the initial weight of the target fresh produce; the first determination module is used to determine a first freshness score of the target fresh produce based on a first environmental feature of the current environment and the surface image; the second determination module is used to determine a second freshness score of the target fresh produce based on the weight difference and a second environmental feature of the current environment, where the current environment includes the first environmental feature and the second environmental feature; the combination module is used to determine an initial freshness level of the target fresh produce by combining the first freshness score and the second freshness score; the adjustment module is used to obtain a target gas concentration within a preset range of the target fresh produce, adjust the initial freshness level according to the target gas concentration, and generate a target freshness level of the target fresh produce.

[0099] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0100] See Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0101] The communication bus 1002 is used to implement the connection and communication between these components.

[0102] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0103] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0104] Processor 1001 may include one or more processing cores. Using various interfaces and circuits, processor 1001 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in memory 1005, as well as accesses data stored in memory 1005, to perform various server functions and process data. Optionally, processor 1001 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). Processor 1001 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications. It is understood that the modem may not be integrated into processor 1001 but implemented as a separate chip.

[0105] Among them, the memory 1005 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for monitoring the freshness of fresh produce.

[0106] exist Figure 3 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program for a fresh food freshness monitoring method stored in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0107] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0108] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0109] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of the devices or units can be electrical or other forms.

[0111] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0112] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0113] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0114] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for monitoring the freshness of fresh produce, characterized in that: The method comprises: Acquire a surface image of a target fresh produce sent by a target sensor in a current environment, and a weight difference between a current weight of the target fresh produce and an initial weight of the target fresh produce; Determining a first freshness score of the target fresh produce based on a first environmental characteristic of the current environment and the surface image, including: determining an environmental error coefficient of the current environment relative to a standard environment based on the first environmental characteristic, wherein the first environmental characteristic primarily includes ambient light intensity, color temperature, and temperature and humidity; comparing the ambient light intensity, color temperature, and temperature and humidity with standard environmental parameters to obtain deviation values ​​of the respective parameters; and performing a weighted summation of the deviation values ​​of the respective parameters to obtain the environmental error coefficient; Correcting the surface image according to the environmental error coefficient includes: correcting the RGB values ​​of the image using an image processing algorithm, using the environmental error coefficient as a correction factor, and adjusting the pixel values ​​of the red, green, and blue channels respectively; correcting the brightness and contrast of the image according to the environmental error coefficient to obtain a target surface image, wherein the environmental error coefficient is used as a coefficient for correcting the color parameters and brightness parameters of the surface image; matching the target surface image with a standard surface image in a pre-stored standard image library, and determining a first freshness score of the target fresh produce based on the matching result, wherein the standard image library includes standard surface images corresponding to the target fresh produce at different freshness scores; Determining a second freshness score of the target fresh produce based on the weight difference and a second environmental characteristic of the current environment, including: the current environment including the first environmental characteristic and the second environmental characteristic; comparing and calculating the ambient temperature, relative humidity, and air pressure with standard environmental parameters based on the second environmental characteristic, which includes ambient temperature, relative humidity, and air pressure, to obtain deviation values ​​of the various parameters; performing weighted summation on the deviation values ​​of the various parameters to determine a weight error coefficient of the current environment relative to the standard environment; correcting the weight difference based on the weight error coefficient, including: correcting the weight difference using the formula: target weight difference = measured weight difference × (1±weight error coefficient) to obtain a target weight difference, wherein the weight error coefficient is a coefficient for correcting the deviation of the weight difference due to environmental influences; matching the target weight difference with standard weight differences in a pre-stored standard weight library, and determining the second freshness score of the target fresh produce based on the matching result, wherein the standard weight library includes standard weight differences corresponding to the target fresh produce at different freshness scores; Determining an initial freshness level of the target fresh produce by combining the first freshness score and the second freshness score; A target gas concentration within a preset range of the target fresh food is obtained, and the initial freshness level is adjusted according to the target gas concentration to generate a target freshness level of the target fresh food.

2. The method for monitoring the freshness of fresh produce according to claim 1, wherein: The combining of the first freshness score and the second freshness score to determine the initial freshness level of the target fresh produce includes: obtaining a first weight coefficient corresponding to the first freshness score and a second weight coefficient corresponding to the second freshness score; arithmetically multiplying the first freshness score by the first weight coefficient to obtain a first weighted score, and arithmetically multiplying the second freshness score by the second weight coefficient to obtain a second weighted score; arithmetically adding the first weighted score and the second weighted score to obtain a target score value; and determining the initial freshness level of the target fresh produce based on the target score value.

3. The method for monitoring the freshness of fresh produce according to claim 1, wherein: The adjusting the initial freshness level according to the target gas concentration to generate the target freshness level of the target fresh produce includes: determining a preset gas concentration threshold according to the type of the target fresh produce, wherein the preset gas concentration threshold includes multiple concentration intervals, each concentration interval corresponding to a standard gas concentration of the target fresh produce at a different freshness level; determining a first freshness level of the target fresh produce according to the target gas concentration on the basis of the preset gas concentration threshold; and adjusting the initial freshness level according to the first freshness level to generate the target fresh produce.

4. The method for monitoring the freshness of fresh produce according to claim 3, wherein: The adjusting the initial freshness level according to the first freshness level to generate the target freshness level of the target fresh food includes: calculating the level difference between the initial freshness level and the first freshness level; judging whether the level difference exceeds a level difference threshold; if the level difference does not exceed the level difference threshold, determining the initial freshness level as the target freshness level of the target fresh food; if the level difference exceeds the level difference threshold, adjusting the initial freshness level according to the first freshness level to obtain the target freshness level of the target fresh food, wherein when the first freshness level is higher than the initial freshness level, the initial freshness level is increased; when the first freshness level is lower than the initial freshness level, the initial freshness level is decreased.

5. The method for monitoring the freshness of fresh produce according to claim 1, wherein: After generating the target freshness level of the target fresh produce, the method further includes: obtaining a change trend of the target freshness level of the target fresh produce within a first preset time period; predicting a predicted freshness level of the target fresh produce after a second preset time period based on the change trend; and generating warning information when the predicted freshness level is lower than a preset level threshold, wherein the warning information includes the current target freshness level of the target fresh produce, the predicted freshness level, and a recommended handling method.

6. A fresh produce freshness monitoring system, characterized in that: The system includes: an acquisition module, a first determination module, a second determination module, a combination module, and an adjustment module; wherein the acquisition module is used to acquire a surface image of a target fresh produce sent by a target sensor in a current environment, and a weight difference between a current weight of the target fresh produce and an initial weight of the target fresh produce; the first determination module is used to determine a first freshness score of the target fresh produce based on first environmental characteristics of the current environment and the surface image, including: determining an environmental error coefficient of the current environment relative to a standard environment based on the first environmental characteristics, including: the first environmental characteristics mainly include ambient light intensity, color temperature, and temperature and humidity; comparing and settling the ambient light intensity, color temperature, and temperature and humidity with standard environmental parameters to obtain deviation values ​​of each parameter; and performing a weighted summation of the deviation values ​​of each parameter to obtain the environmental error coefficient; Correcting the surface image according to the environmental error coefficient includes: correcting the RGB values ​​of the image using an image processing algorithm, using the environmental error coefficient as a correction factor, and adjusting the pixel values ​​of the red, green, and blue channels respectively; correcting the brightness and contrast of the image according to the environmental error coefficient to obtain a target surface image, wherein the environmental error coefficient is used as a coefficient for correcting the color parameters and brightness parameters of the surface image; matching the target surface image with a standard surface image in a pre-stored standard image library, and determining a first freshness score of the target fresh produce based on the matching result, wherein the standard image library includes standard surface images corresponding to the target fresh produce at different freshness scores; The second determination module is used to determine the second freshness score of the target fresh food according to the weight difference and the second environmental characteristics of the current environment, including: the current environment includes the first environmental characteristics and the second environmental characteristics; according to the second environmental characteristics, the second environmental characteristics include ambient temperature, relative humidity and air pressure, the ambient temperature, relative humidity and air pressure are compared and calculated with the standard environmental parameters to obtain the deviation value of each parameter, the deviation value of each parameter is weighted and summed to determine the weight error coefficient of the current environment relative to the standard environment; the weight difference is corrected according to the weight error coefficient, including: correcting by the formula: target weight difference = measured weight difference × (1 ± weight error coefficient) to obtain the target weight difference. a target weight difference, wherein the weight error coefficient is a coefficient for correcting the deviation of the weight difference affected by the environment; matching the target weight difference with the standard weight difference in a pre-stored standard weight library, and determining the second freshness score of the target fresh food according to the matching result, wherein the standard weight library includes the standard weight difference corresponding to the target fresh food under different freshness scores; the combining module is used to combine the first freshness score and the second freshness score to determine the initial freshness level of the target fresh food; the adjusting module is used to obtain the target gas concentration within a preset range of the target fresh food, adjust the initial freshness level according to the target gas concentration, and generate the target freshness level of the target fresh food.

7. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 5.

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