Fresh food logistics cold chain dynamic monitoring method and device and collaborative monitoring system

Abstract

The invention discloses a fresh food logistics cold chain dynamic monitoring method and device and a collaborative monitoring system, and belongs to the technical field of cold chain logistics. The storage monitoring model and the logistics monitoring model are selected through the server according to the knowledge graph corresponding to the first monitoring parameter, compared with a local maintenance model, the problem that adaptability becomes poor along with use of a local maintenance processing model and an existing processing model is avoided, and the accuracy of fresh product monitoring is improved; the storage monitoring model is generated by the long and short term memory network, and the logistics monitoring model passes through the multilayer sensor, so that the control method is prevented from depending on simple rule response, the reliability of fresh product processing is improved, the safety, traceability and efficiency of fresh product distribution are improved, and the risk of food deterioration and loss is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of cold chain logistics technology, and in particular to a method, equipment and collaborative monitoring system for dynamic monitoring of the cold chain of fresh food logistics. Background Technology

[0002] With the rapid development of e-commerce and the increasing demand for fresh produce from consumers, the cold chain delivery system for fresh produce e-commerce has received widespread attention in recent years.

[0003] With advancements in refrigeration technology, large refrigerated trucks and cold storage systems have gradually become the primary means of fresh food cold chain distribution. Cold chain systems can maintain a stable low-temperature environment over long distances, significantly extending the shelf life of fresh products and expanding the delivery range. However, this method still suffers from high energy consumption and poor flexibility, particularly in the last mile of urban delivery where efficiency is low. Furthermore, the lack of monitoring during the logistics process leads to ineffective management of fresh products, resulting in losses. Summary of the Invention

[0004] To address the problems of existing technologies, embodiments of the present invention provide a method, equipment, and collaborative monitoring system for dynamic monitoring of the cold chain logistics of fresh produce, including: On the one hand, a method for dynamic monitoring of the cold chain in fresh food logistics is provided, the method comprising: After the local equipment preheats and recovers the fresh products, it calculates the usable parameter values ​​obtained from the recovery and obtains the first monitoring parameter of the fresh products based on the fresh product monitor. The server selects a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter. The warehouse monitoring model is generated through a long short-term memory network, and the logistics monitoring model is generated through a multilayer perceptron. The local device obtains image data of the fresh produce to be processed based on multiple distributed image monitors in the fresh produce storage device, and uploads the image data to the server; The server calculates the storage time, available storage locations, and storage parameters based on the image data and the storage monitoring model. The local equipment transports the fresh produce to be processed according to the storage time, the storage conditions, and the storage parameters, to obtain the transported fresh produce. The local device obtains the second monitoring parameters corresponding to the transported fresh products based on the fresh product monitor; The local device obtains image parameters of the fresh produce during the storage process after transportation based on the mobile image monitoring device in the storage device, and uploads the image parameters and the second monitoring parameters to the server; The server calculates the transportation time and transportation parameters based on the image parameters, the first monitoring parameters, and the logistics monitoring model. The local device operates the storage device according to the transportation parameters, and after the transportation time, separates the transported fresh products. The local device obtains the third monitoring parameters corresponding to the transported fresh products based on the fresh product monitor, and uploads them to the server. The server optimizes the parameters in the warehouse monitoring model and the logistics monitoring model based on the values ​​of the first monitoring parameter, the second monitoring parameter, and the third monitoring parameter. The server sets constraints based on the available parameter values, the warehousing parameters, and the transportation parameters, and performs secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model; The server calculates the processing strategy corresponding to the transported fresh produce based on the third monitoring parameter. The fresh produce monitor is a portable fresh produce monitor.

[0005] Optionally, the local device obtains the first monitoring parameters of the fresh produce based on the fresh produce monitor, including: A fresh product monitor is placed in the fresh product storage device; Based on the data collected by the fresh produce monitors, a multi-monitor distribution map of the fresh produce is estimated; The fresh produce monitor calculates the first monitoring parameter based on the multi-monitor distribution map.

[0006] Optionally, the server selects a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter, including: Based on the knowledge graph corresponding to the first monitoring parameter, the target monitoring substance is calculated, and the transportation and storage strategies are determined. Based on the first decision-making model, the warehouse monitoring model is selected according to the transportation strategy. Based on the second decision model, the logistics monitoring model is selected according to the storage strategy.

[0007] Optionally, the local device obtains image data of the fresh produce to be processed based on multiple distributed image monitors in the fresh produce storage device, including: Determine the reliable monitoring range based on the monitoring data from the distributed image monitor; Based on the trusted monitoring range, and the locations and trusted monitoring ranges of other distributed image monitors, calculate the movement path of the distributed image monitors; After all the distributed image monitors have collected monitoring data along the aforementioned movement path, the image data is calculated based on multiple monitoring data points. The image data includes the size range and distribution range of substances that characterize the freshness of the produce.

[0008] Optionally, the server calculates the storage time, available storage locations, and storage parameters based on the image data and the storage monitoring model, including: The image data is input into the warehouse monitoring model, and the warehouse time and the available warehouses are output. The reliability of the storage time and the optional storage is verified; After verification is completed, the storage parameters are calculated based on the storage time and the available storage.

[0009] Optionally, the local device obtains the second monitoring parameters corresponding to the transported fresh produce based on the fresh produce monitor, including: Set the target monitoring substance for the fresh produce monitor; Based on the concentration distribution of the target monitored substance in the fresh products after transportation, the monitoring path of the fresh product monitor in the fresh products after transportation is calculated; The fresh produce monitor obtains the first relevant monitoring parameters of the target monitored substance according to the monitoring path; The second monitoring parameter is calculated based on the first monitoring parameter and the first related monitoring parameter.

[0010] Optionally, the local device obtains image parameters of the fresh produce during the storage process based on the mobile image monitoring device in the storage device, including: After the mobile image monitoring device is placed on the transported fresh produce, the mobile image monitoring device acquires images of the fresh produce surface. Based on the fresh food surface image, multiple abnormal areas are obtained. The concentration of the food to be stored in the abnormal area is greater than a preset value, or there are large fresh food freshness characterizing substances in the abnormal area. The mobile image monitoring device moves to the multiple abnormal areas and sets a monitoring cycle based on the temperature of the transported fresh products and the ambient temperature. During the monitoring period, the trend of the concentration of the stored material in the multiple abnormal areas over time is calculated; The image parameters are obtained based on multiple trends and the quantity of substances characterized by the freshness of fresh produce.

[0011] Optionally, the local device obtains the third monitoring parameters corresponding to the transported fresh produce based on the fresh produce monitor, including: Set the target monitoring parameters for the fresh produce monitor; Based on the concentration distribution in various areas along the vertical direction within the transported fresh produce, the residence height and residence time of the fresh produce monitor in the transported fresh produce are calculated. The fresh produce monitor obtains the vertical distribution of the target monitoring parameters based on the dwell height and dwell time. The third monitoring parameter is calculated based on the second monitoring parameter and the vertical distribution.

[0012] Optionally, the server sets constraints based on the available parameter values, the warehousing parameters, and the transportation parameters to perform secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model, including: Based on the available parameter values ​​and the corresponding device parameters of the local device, predict the storage parameters and transportation parameters, and execute the parameter value allocation strategy; Based on the predicted storage parameters and the storage parameters, transportation parameter value constraints are set, and the storage monitoring model is optimized a second time. Based on the predicted transportation parameters and the transportation parameters themselves, constraints on the stored parameter values ​​are set, and the logistics monitoring model is optimized a second time.

[0013] On the other hand, a collaborative monitoring system is also provided, the system including a server and multiple local devices, the local devices including warehousing and storage devices, wherein: The local device is used to preheat and recycle fresh products, calculate the usable parameter values ​​obtained from the recycling, and obtain the first monitoring parameter of the fresh products based on the fresh product monitor. The server is used to select a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter. The warehouse monitoring model is generated through a long short-term memory network, and the logistics monitoring model is generated through a multilayer perceptron. The local device is used to obtain image data of the fresh products to be processed based on multiple distributed image monitors in the fresh product storage device, and upload the image data to the server; The server is used to calculate the storage time, available storage locations, and storage parameters based on the image data and the storage monitoring model. The local equipment is used to transport the fresh products to be processed according to the storage time, the storage, and the storage parameters, so as to obtain the transported fresh products. The local device is used to obtain the second monitoring parameters corresponding to the transported fresh products based on the fresh product monitor; The local device is used to obtain image parameters of the fresh produce during the storage process after transportation based on the mobile image monitoring device in the storage device, and upload the image parameters and the second monitoring parameters to the server; The server is used to calculate the transportation time and transportation parameters based on the image parameters, the first monitoring parameters, and the logistics monitoring model. The local device is used to operate the storage device according to the transportation parameters, and to separate the transported fresh products after the transportation time. The local device is used to obtain the third monitoring parameters corresponding to the transported fresh products based on the fresh product monitor, and upload them to the server; The server is used to optimize the parameters in the warehouse monitoring model and the logistics monitoring model based on the values ​​of the first monitoring parameter, the second monitoring parameter, and the third monitoring parameter. The server is used to set constraints based on the available parameter values, the warehousing parameters, and the transportation parameters, and to perform secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model; The server is used to calculate the processing strategy corresponding to the transported fresh products based on the third monitoring parameter. The fresh produce monitor is a portable fresh produce monitor.

[0014] On the other hand, a dynamic monitoring device for the cold chain logistics of fresh produce is also provided, the device comprising: The processing module is used to select a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter. The warehouse monitoring model is generated through a long short-term memory network, and the logistics monitoring model is generated through a multilayer perceptron. The processing module is also used to calculate storage time, available storage, and storage parameters based on image data and the storage monitoring model. The processing module is also used to calculate the transportation time and transportation parameters based on the image parameters, the first monitoring parameters, and the logistics monitoring model; An optimization module is used to optimize the parameters in the warehouse monitoring model and the logistics monitoring model based on the values ​​of the first monitoring parameter, the second monitoring parameter, and the third monitoring parameter. The optimization module is also used to set constraints based on the available parameter values, the warehousing parameters, and the transportation parameters, and to perform secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model; The strategy execution module is used to calculate the processing strategy corresponding to the transported fresh products based on the third monitoring parameter.

[0015] The beneficial effects that can be achieved by the embodiments of the present invention are: 1. By using the knowledge graph corresponding to the first monitoring parameter, the server selects the warehouse monitoring model and the logistics monitoring model. Compared with the local maintenance model, this avoids the problem of the local maintenance model becoming less adaptable with use, thus improving the accuracy of fresh product monitoring. 2. By generating the warehouse monitoring model from a long short-term memory network and the logistics monitoring model from a multilayer sensor, the control method avoids relying on simple rule responses, thereby improving the reliability of fresh product handling, enhancing the safety, traceability, and efficiency of fresh product distribution, and effectively reducing the risk of food spoilage and loss. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a method for dynamic monitoring of the cold chain logistics of fresh produce, provided by an embodiment of the present invention. Figure 2 A schematic diagram of a collaborative monitoring system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a dynamic monitoring device for the cold chain logistics of fresh produce, provided as an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0019] Reference Figure 1 As shown, a method for dynamic monitoring of the cold chain in fresh food logistics is provided, the method including: 101. The local equipment obtains the first monitoring parameters of the fresh produce based on the fresh produce monitor; In practical applications, the aforementioned fresh produce monitors include temperature and humidity monitors, cameras, and positioning devices.

[0020] 102. The server selects a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter. The warehouse monitoring model is generated through a long short-term memory network, and the logistics monitoring model is generated through a multilayer perceptron. 103. The local device obtains image data of the fresh products to be processed based on multiple distributed image monitors in the fresh product storage device, and uploads the image data to the server; 104. Based on the image data and the warehouse monitoring model, the server calculates the storage time, available storage locations, and storage parameters. 105. Local equipment transports the fresh produce to be processed based on storage time, storage conditions, and storage parameters, and obtains the transported fresh produce. 106. The local equipment obtains the second monitoring parameters corresponding to the fresh products after transportation based on the fresh product monitor; 107. The local device obtains the image parameters of the fresh produce during the storage process after transportation based on the mobile image monitoring device in the storage device, and uploads the image parameters and the second monitoring parameters to the server. 108. The server calculates the transportation time and transportation parameters based on the image parameters, the first monitoring parameters, and the logistics monitoring model; 109. The local equipment operates the storage device according to the transportation parameters, and after the transportation time, separates the transported fresh products. 110. The local equipment obtains the third monitoring parameters corresponding to the fresh products after transportation based on the fresh product monitor, and uploads them to the server; 111. The server optimizes the parameters in the warehouse monitoring model and the logistics monitoring model based on the values ​​of the first monitoring parameter, the second monitoring parameter, and the third monitoring parameter. 112. The server sets constraints based on available parameter values, warehousing parameters, and transportation parameters, and performs secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model; 113. The server calculates the processing strategy for fresh produce after transportation based on the third monitoring parameter; Among them, the fresh produce monitor is a portable fresh produce monitor.

[0021] Optionally, step 101, which involves obtaining the first monitoring parameter of the fresh produce based on the fresh produce monitor, includes: 201. Place fresh product monitoring devices in fresh product storage equipment; 202. Based on the data collected by the fresh produce monitors, estimate the multi-monitor distribution map of fresh produce; Specifically, after taking photos of the storage equipment for fresh produce, target recognition is performed on the photos; Identify the lighting parameters in the storage device and the areas of fresh produce in the photos; The above region is dimensionally expanded to obtain its spatial parameters. Based on the lighting and spatial parameters, multiple placement locations for this area are obtained; The above-mentioned placement locations are marked in the photo to generate a multi-monitor distribution map.

[0022] 203. The fresh produce monitor calculates the first monitoring parameter based on the distribution map of multiple monitors.

[0023] Optionally, in step 102, the server selects the warehouse monitoring model and the logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter, including: 301. Based on the knowledge graph corresponding to the first monitoring parameter, calculate the target monitoring substance and determine the transportation and storage strategies; The aforementioned knowledge graph includes temperature, humidity, and freshness associated with the first monitoring parameter, as well as temperature monitoring substances (such as the color of fresh produce), humidity monitoring substances (such as the water vapor condensation state and color on the surface of fresh produce), and freshness monitoring substances (such as changes in the shape and color of fresh produce) used to indicate the temperature. At the current time, for an image containing at least one fresh food item, the above process can be specifically described as follows: Get real-time images of fresh produce at the current time and the current lighting parameters; Calculate the initial image of fresh produce under the given lighting parameters; this prediction process is to obtain an image by adjusting the lighting parameters on a picture of fresh produce when it leaves the factory or warehouse. Calculate the difference between the real-time image and the initial image; Based on a preset image recognition model (such as CNN), the color change value, shape change value, and water vapor condensation parameters between the real-time image and the initial image are identified. The water vapor condensation parameter includes at least the proportion of the fresh food image that is identified as a water vapor condensation area.

[0024] The process of determining the transportation strategy based on the target monitored substance is as follows: Based on the temperature and humidity inside the transport equipment or vehicle, as well as the ambient temperature and humidity along the route, set the transport time, preferred route, and preferred transport equipment; The process of determining the storage strategy based on the target monitored substance is as follows: Determine the sensitive space for the target monitoring material (such as insufficient light, unsuitable humidity or temperature for storage, or large changes in light, humidity or temperature within a preset time period). Exclude the aforementioned sensitive spaces from the fresh produce storage equipment. For the remaining space, implement the following strategy: If the light is suitable for storage and the change in light within a preset time is less than the light threshold, the space is set as the first set; If the temperature is suitable for storage and the temperature change value within a preset time is less than the temperature threshold, the space is set as the second set; Spaces with suitable humidity for storage, and whose humidity changes less than a humidity threshold within a preset time period, are designated as the third set. The intersection of the first set, the second set, and the third set is selected as the storage space.

[0025] 302. Based on the first decision-making model, select a logistics monitoring model according to the transportation strategy; The first decision model mainly consists of: Based on the current freshness of the produce and transportation cost constraints, output the transportation time and the transportation cost threshold per unit time. The process of selecting a logistics monitoring model based on the transportation strategy can be as follows: Based on the transportation time and the transportation cost per unit time threshold, select the transportation route and transportation equipment (or transportation vehicle) from the transportation strategy. Set up a logistics monitoring model corresponding to the transportation route and the transportation equipment; The input to this logistics monitoring model is the change value of the target monitored substance, and the output is the real-time transportation time and real-time transportation cost on the transportation route and transportation equipment.

[0026] 303. Based on the second decision-making model, select a warehouse monitoring model according to the storage strategy.

[0027] The second decision model mainly consists of: Based on the current freshness of the produce and storage cost constraints, output the storage time and storage cost threshold per unit time; The process of selecting a warehouse monitoring model based on the storage strategy can be as follows: Based on the storage time and storage cost per unit time threshold, select the target storage device from the transportation strategy; Set up a warehouse monitoring model for the target storage device; The input to this warehouse monitoring model is the change value of the target monitored substance, and the output is the real-time storage time and real-time storage cost of the storage device.

[0028] Optionally, the local device obtains image data of the fresh produce to be processed from multiple distributed image monitors in the fresh produce storage device, including: Determine the reliable monitoring range based on the monitoring data from the distributed image monitor; Based on the trusted monitoring range, and the locations and trusted monitoring ranges of other distributed image monitors, calculate the movement path of the distributed image monitors; After all the distributed image monitors have collected monitoring data along their movement paths, image data is calculated based on multiple monitoring data. The image data includes the size range and distribution range of substances that characterize the freshness of the produce.

[0029] Optionally, the server calculates storage time, available storage locations, and storage parameters based on image data and a storage monitoring model, including: Input image data into the warehouse monitoring model, and output warehouse time and available warehouses; Verify the reliability of storage time and optional storage; After verification is completed, storage parameters are calculated based on storage time and available storage.

[0030] Optionally, the local device may obtain the second monitoring parameters corresponding to the transported fresh produce based on the fresh produce monitor, including: The target substances to be monitored by the fresh produce monitor; Based on the concentration distribution of the target monitored substance in the fresh products after transportation, the monitoring path of the fresh product monitor in the fresh products after transportation is calculated. The fresh produce monitor obtains the first relevant monitoring parameters of the target substance based on the monitoring path; The process involves setting up a target monitoring substance-associated substance corresponding to the target monitoring substance, and this associated substance must have at least the following characteristics: Its change over time is positively or negatively correlated with the change over time of the target monitored substance; The second monitoring parameter is calculated based on the first monitoring parameter and the first related monitoring parameter.

[0031] Optionally, the local device obtains image parameters of the fresh produce during the storage process based on the mobile image monitoring device in the storage device, including: After the mobile image monitoring equipment is deployed onto the transported fresh produce, it acquires images of the fresh produce's surface. Based on the surface image of fresh produce, multiple abnormal areas are obtained. The concentration of the food to be stored in the abnormal area is greater than the preset value, or there are large fresh produce freshness characterizing substances in the abnormal area. The mobile image monitoring equipment was moved to multiple abnormal areas, and the monitoring cycle was set according to the temperature of the fresh products after transportation and the ambient temperature. During the monitoring period, the trend of the concentration of the stored material in multiple abnormal areas over time was calculated. Image parameters are obtained based on multiple trends and the quantity of substances characterized by the freshness of fresh produce.

[0032] Optionally, the local equipment may obtain the following third monitoring parameters for the transported fresh produce based on the fresh produce monitor: Set the target monitoring parameters for the fresh produce monitor; Based on the concentration distribution in various areas along the vertical direction within the fresh produce after transportation, the residence height and residence time of the fresh produce monitor in the fresh produce after transportation are calculated. The fresh produce monitor obtains the vertical distribution of target monitoring parameters based on the dwell height and dwell time; The third monitoring parameter is calculated based on the second monitoring parameter and the vertical distribution.

[0033] Optionally, the server can set constraints based on available parameter values, warehousing parameters, and transportation parameters to perform secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model, including: Based on the available parameter values ​​and the corresponding device parameters of the local device, predict the storage parameters and transportation parameters, and execute the parameter value allocation strategy; Based on the predicted storage parameters, and the storage parameters themselves, constraints are set for the transportation parameter values, and the storage monitoring model is optimized a second time. The secondary optimization process described in this embodiment of the invention includes at least the following: The correlation function between storage parameters and transportation parameters is calculated as: Y2=F2(X2), where Y2 is the transportation parameter and X2 is the storage parameter; set up: Transportation parameters = F2 (predicted storage parameters) * 10% + F2 (storage parameters); After obtaining the new transportation parameters, these new transportation parameters are used as transportation parameter constraints, and the warehouse monitoring model is retrained.

[0034] Based on the predicted transportation parameters, and the constraints on the stored parameter values, the logistics monitoring model is optimized a second time.

[0035] The secondary optimization process described in this embodiment of the invention includes at least the following: Calculate the correlation function between transportation parameters and warehousing parameters: Y1=F1(X1), where Y1 is the warehousing parameter and X1 is the transportation parameter; set up: Warehousing parameters = F1 (predicted transportation parameters) * 15% + F1 (transportation parameters); After obtaining the new warehousing parameters, these new warehousing parameters are used as warehousing parameter constraints, and the logistics monitoring model is retrained.

[0036] In practical applications, by deploying intelligent terminal devices such as temperature and humidity sensors, cameras, and positioning devices, the system collects real-time location information of transport vehicles, environmental data of the cargo compartment, and cargo status. This data is then uploaded to a cloud platform for centralized processing and analysis using wireless communication technology. By establishing a multi-node collaborative monitoring mechanism, the system can dynamically track temperature and humidity changes in fresh produce throughout the transportation process and provide timely warnings when anomalies occur. It also optimizes delivery routes. This technology effectively solves problems such as information opacity and difficulty in supervision in traditional cold chain logistics, ensuring the quality and safety of fresh produce and reducing transportation losses.

[0037] Reference Figure 2 As shown, a collaborative monitoring system is also provided. The system includes a server and multiple local devices, including warehousing and storage devices, storage devices within the storage devices, and storage devices within transportation and distribution equipment, wherein: The local equipment obtains the first monitoring parameters of the fresh produce based on the fresh produce monitor; The server is used to select a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter. The warehouse monitoring model is generated through a long short-term memory network, and the logistics monitoring model is generated through a multilayer perceptron. The local device is used to obtain image data of the fresh products to be processed based on multiple distributed image monitors in the fresh product storage device, and upload the image data to the server; The server is used to calculate storage time, available storage locations, and storage parameters based on image data and a storage monitoring model. Local equipment is used to transport fresh produce to be processed based on storage time, storage conditions, and storage parameters, and to obtain the transported fresh produce. The local equipment is used to obtain the second monitoring parameters corresponding to the fresh products after transportation based on the fresh product monitor; The local device is used to obtain image parameters of fresh produce during storage after transportation based on the mobile image monitoring device in the storage device, and upload the image parameters and second monitoring parameters to the server; The server is used to calculate the transportation time and transportation parameters based on the image parameters, the first monitoring parameters, and the logistics monitoring model. Local equipment is used to operate storage devices according to transportation parameters and to separate the fresh products after transportation time. The local device is used to obtain the third monitoring parameters corresponding to the fresh products after transportation based on the fresh product monitor, and then upload them to the server; The server is used to optimize the parameters in the warehouse monitoring model and the logistics monitoring model based on the values ​​of the first monitoring parameter, the second monitoring parameter, and the third monitoring parameter. The server is used to set constraints based on available parameter values, warehousing parameters, and transportation parameters, and to perform secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model; The server is used to calculate the processing strategy for fresh produce after transportation based on the third monitoring parameters. Among them, the fresh produce monitor is a portable fresh produce monitor.

[0038] Optionally, the local device is used for: Place fresh product monitors in fresh product storage equipment; Based on the data collected by the fresh produce monitors, a multi-monitor distribution map of fresh produce is estimated. The fresh produce monitor calculates the first monitoring parameter based on the distribution map of multiple monitors.

[0039] Optionally, the server is used for: Based on the knowledge graph corresponding to the first monitoring parameter, the target monitoring substance is calculated, and the transportation and storage strategies are determined. Based on the first decision-making model, a logistics monitoring model is selected according to the transportation strategy; Based on the second decision-making model, a warehouse monitoring model is selected according to the storage strategy.

[0040] Optionally, the local device is used for: Determine the reliable monitoring range based on the monitoring data from the distributed image monitor; Based on the trusted monitoring range, and the locations and trusted monitoring ranges of other distributed image monitors, calculate the movement path of the distributed image monitors; After all the distributed image monitors have collected monitoring data along their movement paths, image data is calculated based on multiple monitoring data. The image data includes the size range and distribution range of substances that characterize the freshness of the produce.

[0041] Optionally, the server is used for: Input image data into the warehouse monitoring model, and output warehouse time and available warehouses; Verify the reliability of storage time and optional storage; After verification is completed, storage parameters are calculated based on storage time and available storage.

[0042] Optionally, the local device is used for: The target substances to be monitored by the fresh produce monitor; Based on the concentration distribution of the target monitored substance in the fresh products after transportation, the monitoring path of the fresh product monitor in the fresh products after transportation is calculated. The fresh produce monitor obtains the first relevant monitoring parameters of the target substance based on the monitoring path; The second monitoring parameter is calculated based on the first monitoring parameter and the first related monitoring parameter.

[0043] Optionally, the local device is used for: After the mobile image monitoring equipment is deployed onto the transported fresh produce, it acquires images of the fresh produce's surface. Based on the surface image of fresh produce, multiple abnormal areas are obtained. The concentration of the food to be stored in the abnormal area is greater than the preset value, or there are large fresh produce freshness characterizing substances in the abnormal area. The mobile image monitoring equipment was moved to multiple abnormal areas, and the monitoring cycle was set according to the temperature of the fresh products after transportation and the ambient temperature. During the monitoring period, the trend of the concentration of the stored material in multiple abnormal areas over time was calculated. Image parameters are obtained based on multiple trends and the quantity of substances characterized by the freshness of fresh produce.

[0044] Optionally, the local device is used for: Set the target monitoring parameters for the fresh produce monitor; Based on the concentration distribution in various areas along the vertical direction within the fresh produce after transportation, the residence height and residence time of the fresh produce monitor in the fresh produce after transportation are calculated. The fresh produce monitor obtains the vertical distribution of target monitoring parameters based on the dwell height and dwell time; The third monitoring parameter is calculated based on the second monitoring parameter and the vertical distribution.

[0045] Optionally, the server is used for: Based on the available parameter values ​​and the corresponding device parameters of the local device, predict the storage parameters and transportation parameters, and execute the parameter value allocation strategy; Based on the predicted storage parameters, and the storage parameters themselves, constraints are set for the transportation parameter values, and the storage monitoring model is optimized a second time. Based on the predicted transportation parameters, and the constraints on the stored parameter values, the logistics monitoring model is optimized a second time.

[0046] Reference Figure 3 As shown, a dynamic monitoring device for the cold chain logistics of fresh produce is also provided. The device includes: The processing module is used to select a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter. The warehouse monitoring model is generated through a long short-term memory network, and the logistics monitoring model is generated through a multilayer perceptron. The processing module is also used to calculate storage time, available storage, and storage parameters based on image data and the storage monitoring model; The processing module is also used to calculate the transportation time and transportation parameters based on the image parameters, the first monitoring parameters, and the logistics monitoring model; An optimization module is used to optimize the parameters in the warehouse monitoring model and the logistics monitoring model based on the values ​​of the first monitoring parameter, the second monitoring parameter, and the third monitoring parameter. The optimization module is also used to set constraints based on available parameter values, warehousing parameters, and transportation parameters, and to perform secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model; The strategy execution module is used to calculate the processing strategy for fresh produce after transportation based on the third monitoring parameters.

[0047] Optionally, the processing module is used for: Based on the knowledge graph corresponding to the first monitoring parameter, the target monitoring substance is calculated, and the transportation and storage strategies are determined. Based on the first decision-making model, a logistics monitoring model is selected according to the transportation strategy; Based on the second decision-making model, a warehouse monitoring model is selected according to the storage strategy.

[0048] Optionally, the processing module is used for: Input image data into the warehouse monitoring model, and output warehouse time and available warehouses; Verify the reliability of storage time and optional storage; After verification is completed, storage parameters are calculated based on storage time and available storage.

[0049] Optionally, the optimized module is used for: Based on the available parameter values ​​and the corresponding device parameters of the local device, predict the storage parameters and transportation parameters, and execute the parameter value allocation strategy; Based on the predicted storage parameters, and the storage parameters themselves, constraints are set for the transportation parameter values, and the storage monitoring model is optimized a second time. Based on the predicted transportation parameters, and the constraints on the stored parameter values, the logistics monitoring model is optimized a second time.

[0050] The above specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0051] The technical features of the above embodiments can be combined in any way (as long as there is no contradiction in the combination of these technical features). For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; these embodiments not explicitly written should also be considered to be within the scope of this specification.

[0052] The present invention has been described in detail above through general description and specific embodiments. It should be noted that, without departing from the concept of the present invention, various modifications and improvements can be made to these specific embodiments, all of which fall within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the appended claims.

[0053] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic monitoring of the cold chain logistics for fresh produce, characterized in that, The method includes: The local equipment obtains the first monitoring parameters of the fresh produce based on the fresh produce monitor; The server selects a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter. The local device obtains image data of the fresh produce to be processed based on multiple distributed image monitors in the fresh produce storage device, and uploads the image data to the server; The server calculates the storage time, available storage locations, and storage parameters based on the image data and the storage monitoring model. The local equipment stores the fresh produce to be processed according to the storage time, the storage conditions, and the storage parameters. The local device obtains the second monitoring parameters corresponding to the transported fresh products based on the fresh product monitor; The local device obtains image parameters of the fresh produce during the storage process after transportation based on the mobile image monitoring device in the storage device, and uploads the image parameters and the second monitoring parameters to the server; The server calculates the transportation time and transportation parameters based on the image parameters, the first monitoring parameters, and the logistics monitoring model. The local device operates the storage device according to the transportation parameters, and obtains the transported fresh products after the transportation time. The local device obtains the third monitoring parameters corresponding to the transported fresh products based on the fresh product monitor, and uploads them to the server. The warehouse monitoring model is generated using a long short-term memory network, and the logistics monitoring model is generated using a multilayer perceptron.

2. The method according to claim 1, characterized in that, The local device obtains the first monitoring parameters of the fresh produce based on the fresh produce monitor, including: A fresh product monitor is placed in the fresh product storage device; Based on the data collected by the fresh produce monitors, a multi-monitor distribution map of the fresh produce is estimated; The fresh produce monitor calculates the first monitoring parameter based on the multi-monitor distribution map; The server selects the warehouse monitoring model and the logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter, including: Based on the knowledge graph corresponding to the first monitoring parameter, the target monitoring substance is calculated, and the transportation and storage strategies are determined. Based on the first decision-making model, the warehouse monitoring model is selected according to the transportation strategy. Based on the second decision model, the logistics monitoring model is selected according to the storage strategy.

3. The method according to claim 2, characterized in that, The local device obtains image data of the fresh produce to be processed from multiple distributed image monitors in the fresh produce storage device, including: Determine the reliable monitoring range based on the monitoring data from the distributed image monitor; Based on the trusted monitoring range, and the locations and trusted monitoring ranges of other distributed image monitors, calculate the movement path of the distributed image monitors; After all the distributed image monitors have collected monitoring data along the aforementioned movement path, the image data is calculated based on multiple monitoring data points. The image data includes the size range and distribution range of substances that characterize the freshness of the produce.

4. The method according to claim 3, characterized in that, The server calculates the storage time, available storage locations, and storage parameters based on the image data and the storage monitoring model, including: The image data is input into the warehouse monitoring model, and the warehouse time and the available warehouses are output. The reliability of the storage time and the optional storage is verified; After verification is completed, the storage parameters are calculated based on the storage time and the available storage.

5. The method according to claim 4, characterized in that, The local device obtains the second monitoring parameters corresponding to the transported fresh produce based on the fresh produce monitor, including: Set the target monitoring substance for the fresh produce monitor; Based on the concentration distribution of the target monitored substance in the fresh products after transportation, the monitoring path of the fresh product monitor in the fresh products after transportation is calculated; The fresh produce monitor obtains the first relevant monitoring parameters of the target monitored substance according to the monitoring path; The second monitoring parameter is calculated based on the first monitoring parameter and the first related monitoring parameter.

6. The method according to claim 5, characterized in that, The local device obtains image parameters of the fresh produce during the storage process based on the mobile image monitoring device in the storage device, including: After the mobile image monitoring device is placed on the transported fresh produce, the mobile image monitoring device acquires images of the fresh produce surface. Based on the fresh food surface image, multiple abnormal areas are obtained. The concentration of the food to be stored in the abnormal area is greater than a preset value, or there are large fresh food freshness characterizing substances in the abnormal area. The mobile image monitoring device moves to the multiple abnormal areas and sets a monitoring cycle based on the temperature of the transported fresh products and the ambient temperature. During the monitoring period, the trend of the concentration of the stored material in the multiple abnormal areas over time is calculated; The image parameters are obtained based on multiple trends and the quantity of substances characterized by the freshness of fresh produce.

7. The method according to claim 6, characterized in that, The local device obtains the third monitoring parameters corresponding to the transported fresh produce based on the fresh produce monitor, including: Set the target monitoring parameters for the fresh produce monitor; Based on the concentration distribution in various areas along the vertical direction within the transported fresh produce, the residence height and residence time of the fresh produce monitor in the transported fresh produce are calculated. The fresh produce monitor obtains the vertical distribution of the target monitoring parameters based on the dwell height and dwell time. The third monitoring parameter is calculated based on the second monitoring parameter and the vertical distribution.

8. The method according to claim 7, characterized in that, Also includes: The server sets constraints based on the available parameter values, the warehousing parameters, and the transportation parameters, and performs secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model, including: Based on the available parameter values ​​and the corresponding device parameters of the local device, predict the storage parameters and transportation parameters, and execute the parameter value allocation strategy; Based on the predicted storage parameters and the storage parameters, transportation parameter value constraints are set, and the storage monitoring model is optimized a second time. Based on the predicted transportation parameters and the transportation parameters themselves, constraints on the stored parameter values ​​are set, and the logistics monitoring model is optimized a second time.

9. A collaborative monitoring system, characterized in that, The system includes a server and multiple local devices, including warehousing and storage devices, wherein: The local device obtains the first monitoring parameters of the fresh products based on the fresh product monitor; The server is used to select a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter; The local device is used to obtain image data of the fresh products to be processed based on multiple distributed image monitors in the fresh product storage device, and upload the image data to the server; The server is used to calculate the storage time, available storage locations, and storage parameters based on the image data and the storage monitoring model. The local equipment is used to transport the fresh produce to be processed according to the storage time, the storage, and the storage parameters, so as to obtain the transported fresh produce. The local device is used to obtain the second monitoring parameters corresponding to the transported fresh products based on the fresh product monitor; The local device is used to obtain image parameters of the fresh produce during the storage process after transportation based on the mobile image monitoring device in the storage device, and upload the image parameters and the second monitoring parameters to the server; The server is used to calculate the transportation time and transportation parameters based on the image parameters, the first monitoring parameters, and the logistics monitoring model. The local device is used to operate the storage device according to the transportation parameters, and to separate the transported fresh products after the transportation time. The local device is used to obtain the third monitoring parameters corresponding to the transported fresh products based on the fresh product monitor, and upload them to the server; The warehouse monitoring model is generated using a long short-term memory network, and the logistics monitoring model is generated using a multilayer perceptron.

10. A dynamic monitoring device for the cold chain logistics of fresh produce, characterized in that, The device includes: The processing module is used to select a warehouse monitoring model and a logistics monitoring model based on the knowledge graph corresponding to the first monitoring parameter. The warehouse monitoring model is generated through a long short-term memory network, and the logistics monitoring model is generated through a multilayer perceptron. The processing module is also used to calculate storage time, available storage, and storage parameters based on image data and the storage monitoring model. The processing module is also used to calculate the transportation time and transportation parameters based on the image parameters, the first monitoring parameters, and the logistics monitoring model. An optimization module is used to optimize the parameters in the warehouse monitoring model and the logistics monitoring model based on the values ​​of the first monitoring parameter, the second monitoring parameter, and the third monitoring parameter. The optimization module is also used to set constraints based on the available parameter values, the warehousing parameters, and the transportation parameters, and to perform secondary optimization on the optimized warehousing monitoring model and the optimized logistics monitoring model; The strategy execution module is used to calculate the processing strategy corresponding to the transported fresh products based on the third monitoring parameter.

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