CN117333094B - Simulation model-based fresh-keeping control method and system for crisp Li Lenglian logistics - Google Patents
Simulation model-based fresh-keeping control method and system for crisp Li Lenglian logistics Download PDFInfo
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Abstract
The invention belongs to the field of transportation logistics freshness control, and particularly relates to a green and crisp Li Lenglian logistics freshness control method and system based on a simulation model, which are used for collecting dynamic logistics data; importing the dynamic logistics data into a simulation model obtained by training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information; identifying fresh green and crisp Li Yangben from a reference sample, obtaining environmental information and corresponding air-conditioning ratio of the fresh green and crisp plum sample, recording the reference sample in a logistics storage space provided with the fresh green and crisp Li Yangben, and recording the same recording area corresponding to the reference sample and dynamic logistics data; and determining the green and crisp plum logistics information according to the environment information of the fresh green and crisp plum sample, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information. The invention further ensures that the quality attributes of fruits and vegetables are preserved, and quantifies the influence of other driving factors in the decay process of fresh agricultural products in the supply chain.
Description
Technical Field
The invention belongs to the field of transportation logistics freshness control, and particularly relates to a green and crisp Li Lenglian logistics freshness control method and system based on a simulation model.
Background
The cold chain technology has the limitation that the picked life of fruits and vegetables is prolonged by adopting a method of coating, soaking and the like by adding natural or chemical antistaling agents, and meanwhile, the technology has the characteristics of low cost, easy acquisition and simple operation, is an indispensable fresh-keeping technology in the fresh-keeping of fruits and vegetables at the present stage, and has been widely applied to the fresh-keeping of fruits and vegetables. The damage of fruits and vegetables in the cold chain transportation process is reduced, the application of fresh-keeping technology of the fruits and vegetables is removed, modern scientific technology is gradually utilized in recent years to monitor, control and predict the quality evolution of various fruits and vegetables in the post-harvest supply chain in a nondestructive and contactless manner, so that the quality loss in the fruit and vegetable packaging, transportation and storage processes is reduced, and the technology comprises imaging technology, sensor technology, radio frequency identification, mathematical modeling prediction and the like.
The digital modeling predicts bad factors causing loss through proper mathematical model, so that the cold chain flow can be optimized or controlled in advance. For many years, researchers have developed different mathematical models for the post-harvest supply chain of fruit including heat and mass transfer, fluid flow, mass changes, gas exchange, moisture migration, etc. within and around fresh produce to predict and gain insight into post-harvest mass changes in produce. However, the transport supply chain for fruit is not as much a modeling study, considering transport time, different environmental conditions (e.g. temperature, humidity and air flow), packaging and vehicle movement, involving multiple links, and complex operations. To gain a deeper understanding of the quality attributes of fruits and vegetables to what extent they are better preserved and to quantify the impact of other driving factors (e.g., relative humidity, light) of the decay process in fresh produce supply chains, this requires more effort by researchers.
Disclosure of Invention
According to a first aspect of the invention, the invention claims a green and crisp Li Lenglian logistics fresh-keeping control method based on a simulation model, which comprises the following steps:
collecting dynamic logistics data;
the dynamic logistics data are imported into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information;
identifying fresh green and crisp Li Yangben from a reference sample, and obtaining environmental information and corresponding air-conditioning ratio of the fresh green and crisp plum sample, wherein the reference sample is obtained by recording a logistics storage space provided with the fresh green and crisp Li Yangben, and the reference sample corresponds to the same recording area as the dynamic logistics data;
determining green and crisp Li Wuliu information according to the environment information of the fresh green and crisp plum samples, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information, wherein the green and crisp plum logistics information comprises the air-conditioning ratio;
the dynamic candidate environment information comprises dynamic label environment information, and the step of determining the air-conditioning ratio according to the environment information of the fresh green and crisp plum sample, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information comprises the following steps:
Determining the environmental information of each logistics storage space according to the dynamic tag environmental information;
extracting reference density from dynamic green and crisp plum environment information of each cold chain storage box in each logistics storage space in the dynamic logistics data under the condition that the logistics storage space is a candidate monitoring logistics storage space;
determining a reference fresh green crisp Li Yangben according to the reference density and the environmental information of the fresh green crisp plum sample;
and determining the air-conditioning ratio corresponding to the cold chain storage box according to the air-conditioning ratio corresponding to the reference fresh crisp Li Yangben.
Further, before the step of importing the dynamic logistics data into the simulation model obtained by training in advance, the method further comprises:
carrying out space correction on the dynamic logistics data so as to enable the logistics storage space in the dynamic logistics data to be in a horizontal environment; the candidate monitoring material flow storage space is obtained through the following steps:
importing standard logistics data into the simulation model to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard tag environment information and standard green and crisp plum environment information;
Determining the height of each logistics storage space according to the environmental information of the standard labels adjacent to each other;
determining the number of standard cold chain storage boxes in each logistics storage space according to the standard green and crisp plum environmental information and the standard label environmental information;
and aiming at each logistics storage space, determining the logistics storage space as a candidate monitoring logistics storage space under the condition that the height of the logistics storage space is larger than a cold chain storage box height threshold value or the number of standard cold chain storage boxes of the logistics storage space is larger than a cold chain storage box number threshold value.
Further, the method further comprises:
determining the number of dynamic cold chain storage boxes in the logistics storage space according to the dynamic green and crisp plum environment information under the condition that the logistics storage space is a candidate monitoring logistics storage space aiming at each logistics storage space in the dynamic logistics data;
and determining the number of the cold chain storage boxes without the fragile plums according to the number of the dynamic cold chain storage boxes and the number of the standard cold chain storage boxes in the logistics storage space.
Further, the standard cold chain storage box number is obtained through the following steps:
collecting standard logistics data, and importing the standard logistics data into a simulation model obtained by training in advance to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard green and crisp plum environment information;
And determining the number of standard cold chain storage boxes according to the standard green and crisp plum environment information.
Further, the simulation model is obtained through training the following steps:
deep learning is carried out through a green and crisp plum logistics data set collected in advance, so that a simulation model is obtained, and each green and crisp Li Wuliu data in the green and crisp plum logistics data set is marked with green and crisp plum environment information and/or label environment information;
the fresh crisps Li Yangben are deleted after recording the reference sample or are present in the dynamic logistics data.
According to a second aspect of the present invention, the present invention claims a simulation model-based crisper Li Lenglian logistics fresh-keeping control system, the system comprising:
the logistics data acquisition module acquires dynamic logistics data;
the candidate environment analysis module is used for importing the dynamic logistics data into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information;
the fresh and crisp Li Xinxian degree identification module is used for identifying fresh and crisp Li Yangben from a reference sample, so as to obtain environmental information and corresponding air-conditioning ratio of the fresh and crisp plum sample, wherein the reference sample is obtained by recording a logistics storage space provided with fresh and crisp Li Yangben, and the reference sample corresponds to the same recording area as the dynamic logistics data; determining green and crisp Li Wuliu information according to the environment information of the fresh green and crisp plum samples, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information, wherein the green and crisp plum logistics information comprises the air-conditioning ratio;
The dynamic candidate environment information comprises dynamic label environment information, and the step of determining the air-conditioning ratio according to the environment information of the fresh green and crisp plum sample, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information comprises the following steps:
determining the environmental information of each logistics storage space according to the dynamic tag environmental information;
extracting reference density from dynamic green and crisp plum environment information of each cold chain storage box in each logistics storage space in the dynamic logistics data under the condition that the logistics storage space is a candidate monitoring logistics storage space;
determining a reference fresh green crisp Li Yangben according to the reference density and the environmental information of the fresh green crisp plum sample;
and determining the air-conditioning ratio corresponding to the cold chain storage box according to the air-conditioning ratio corresponding to the reference fresh crisp Li Yangben.
Further, before the candidate environment analysis module imports the dynamic logistics data into the simulation model obtained by training in advance, the candidate environment analysis module further includes:
carrying out space correction on the dynamic logistics data so as to enable the logistics storage space in the dynamic logistics data to be in a horizontal environment; the candidate monitoring material flow storage space is obtained through the following steps:
Importing standard logistics data into the simulation model to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard tag environment information and standard green and crisp plum environment information;
determining the height of each logistics storage space according to the environmental information of the standard labels adjacent to each other;
determining the number of standard cold chain storage boxes in each logistics storage space according to the standard green and crisp plum environmental information and the standard label environmental information;
and aiming at each logistics storage space, determining the logistics storage space as a candidate monitoring logistics storage space under the condition that the height of the logistics storage space is larger than a cold chain storage box height threshold value or the number of standard cold chain storage boxes of the logistics storage space is larger than a cold chain storage box number threshold value.
Further, the system further comprises:
determining the number of dynamic cold chain storage boxes in the logistics storage space according to the dynamic green and crisp plum environment information under the condition that the logistics storage space is a candidate monitoring logistics storage space aiming at each logistics storage space in the dynamic logistics data;
and determining the number of the cold chain storage boxes without the fragile plums according to the number of the dynamic cold chain storage boxes and the number of the standard cold chain storage boxes in the logistics storage space.
Further, the standard cold chain storage box number is obtained through the following steps:
collecting standard logistics data, and importing the standard logistics data into a simulation model obtained by training in advance to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard green and crisp plum environment information;
and determining the number of standard cold chain storage boxes according to the standard green and crisp plum environment information.
Further, the simulation model is obtained through training the following steps:
deep learning is carried out through a green and crisp plum logistics data set collected in advance, so that a simulation model is obtained, and each green and crisp Li Wuliu data in the green and crisp plum logistics data set is marked with green and crisp plum environment information and/or label environment information;
the fresh crisps Li Yangben are deleted after recording the reference sample or are present in the dynamic logistics data.
The invention belongs to the field of transportation logistics freshness control, and particularly relates to a green and crisp Li Lenglian logistics freshness control method and system based on a simulation model, which are used for collecting dynamic logistics data; importing the dynamic logistics data into a simulation model obtained by training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information; identifying fresh green and crisp Li Yangben from a reference sample, obtaining environmental information and corresponding air-conditioning ratio of the fresh green and crisp plum sample, recording the reference sample in a logistics storage space provided with the fresh green and crisp Li Yangben, and recording the same recording area corresponding to the reference sample and dynamic logistics data; and determining the green and crisp plum logistics information according to the environment information of the fresh green and crisp plum sample, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information. The invention further ensures that the quality attributes of fruits and vegetables are preserved, and quantifies the influence of other driving factors in the decay process of fresh agricultural products in the supply chain.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a workflow diagram of a method for controlling fresh keeping of a crisp Li Lenglian logistics based on a simulation model under a system architecture provided by an embodiment of the present invention;
fig. 2 is a second flowchart of a method for controlling fresh keeping of a crisp Li Lenglian logistics based on a simulation model under a system architecture provided by an embodiment of the present invention;
fig. 3 is a structural block diagram of a crisper Li Lenglian logistics fresh-keeping control system based on a simulation model under a system architecture provided by an embodiment of the present invention;
fig. 4 is a second structural block diagram of a crisper Li Lenglian logistics fresh-keeping control system based on a simulation model under a system architecture provided by an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a step flow chart of a method for controlling fresh keeping of a crisp Li Lenglian logistics based on a simulation model is shown, which comprises the following steps:
and step 101, collecting dynamic logistics data.
The dynamic logistics data are recorded in a crisp Li Zhanshi area, and the camera continuously records a crisp plum display area in a certain time period. It can be appreciated that the time period can be set according to the actual application scenario, and can be related to the transportation speed of the green and crisp plums. When the transportation speed of the green and crisp plums is high, the time period is short; when the transportation speed of the green and crisp plums is low, the time period is long.
In addition, the camera uniformly transmits the dynamic logistics data to a server for storage after recording the dynamic logistics data.
In the embodiment, the pre-cooling treatment removes field heat before storage and transportation after plum picking, achieves relatively low temperature, delays after-ripening, inhibits growth of microorganisms, prevents spoilage, delays aging, and can reduce loads of cold chain equipment, and commonly comprises air pre-cooling, cold water pre-cooling and vacuum pre-cooling. The browning and cold damage degree of the plum fruits treated by cold water are reduced, and the quality of the plum fruits treated by cold water is improved.
The heat treatment technology is to place the picked fruits in a specific heat environment, control the temperature between 30 ℃ and 50 ℃ and treat the fruits for several minutes to days. The fruits after heat treatment have no chemical residues, can effectively avoid infection of microorganisms and plant diseases and insect pests and spoilage of the fruits, and researches show that the fruits after heat treatment can slow down expansion of damaged wounds, inhibit peel oxidation, prevent brown scalds, improve low temperature resistance, enable the fruits to maintain oxygen activity under low temperature conditions, promote metabolic balance and maintain normal physiological activities of cell walls. The plum can be treated by combining hot air with a chitosan coating, and researches show that the epidermis loss in the transportation process of the plum is obviously relieved in the low-temperature process, the total phenols, flavones and total antioxidant activity in the plum are obviously increased, and meanwhile, the cold damage phenomenon in the transportation process of the fruit is reduced.
Step 102, importing the dynamic logistics data into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information.
The simulation model is imported into dynamic logistics data, and the dynamic logistics data are output as environment information corresponding to each candidate in the data.
It will be appreciated that when the dynamic logistics data yields at least one crisp plum and/or at least one tag, the result is a collection of environmental information.
Wherein, in this embodiment, the environmental information includes ultraviolet irradiation information;
specifically, the short wave ultraviolet rays with the wavelength of 250-270 nm prove to be the most effective sterilization wavelength, and the method is environment-friendly, simple and convenient and has no chemical residues. Through ultraviolet radiation, microorganisms on the surfaces of fruits and vegetables can be effectively inhibited, substances and enzyme activities related to a fruit defense mechanism are stimulated, the synthesis of ethylene can be reduced, the antioxidation capability of fruits is improved, the after-ripening is delayed, and the fresh-keeping period of the fruits and vegetables is prolonged. However, uv irradiation conditions are limited and the application needs to be precisely controlled. The poor penetration of ultraviolet radiation does not completely kill microorganisms, and the fruits are burned by treatment at higher doses, and the nutrients in the fruits and vegetables are susceptible to oxidation and degradation after long-term exposure to ultraviolet radiation. Researches show that the fruit decay rate of the fruits subjected to ultraviolet irradiation is remarkably low in the cold chain process and the fruits are blank, and the fruit fresh-keeping effect of the fruits can be enhanced by the synergistic use of the low temperature and ultraviolet sterilization technology.
And step 103, determining the crisp Li Wuliu information according to the standard information and the dynamic crisp plum environment information, wherein the standard information corresponds to the dynamic logistics data.
The standard information is used as a reference in determining the physical distribution information of the crisp plums, for example, the profile of which standard deterioration degree reaches different degrees in the data, and the standard information includes the environment of the profile, the deterioration degree and the like.
In practical application, for each camera of the display area for recording the green and crisp plums, corresponding standard information needs to be determined, and the standard information and the camera are bound and stored. So that corresponding standard information can be acquired for each camera.
Specifically, for each cold chain storage box, the green and crisp plum environment information can be compared with each standard information to obtain fresh green and crisp Li Yangben positioned on two sides of the green and crisp plum environment; and then determining the green and crisp plum logistics information corresponding to the green and crisp plum environment.
It is understood that the crisp Li Wuliu information corresponds to standard information. For example, if the fresh green and crisp Li Yangben on both sides of the green and crisp plum environment correspond to a degree of deterioration of 20% and 30%, respectively, the green and crisp plum logistics information ranges from 20% to 30%.
In practical application, the replenishment can be prompted when the air blending ratio reaches a certain threshold. Specifically, a message prompting restocking may be sent to the central server.
In the embodiment, according to the optimal storage condition of fruits and vegetables, the metabolism of plant cells can be controlled by effectively regulating the environment or the gas components in the fruit and vegetable package, the respiratory activity of the fruits is reduced, softening and maturation are delayed, and various physiological disorders and pathogen infection are reduced, so that the fresh-keeping effect is achieved. The controlled atmosphere fresh-keeping technology is generally realized by controlling O 2 、CO 2 、N 2 Equal gas proportion, changing the storage environment of fruits and vegetables, CO 2 Can inhibit bacterial growth and reproduction, has good antibacterial effect on most of aerobic bacteria, and maintains fruit and vegetable quality.
N 2 Is an inert gas which can be used as a filling gas. At present, the common air-conditioning fresh-keeping technology is to use CO in the environment 2 Increase, O 2 Concentration is reduced, and at the same time a certain amount of N is added 2 As a fill and buffer. However, the current modified atmosphere fresh-keeping has some problems, such as the difficulty in balancing the air permeability of the packaging film with the metabolism speed of fruits and vegetables, resulting in O in the packaging environment 2 The concentration is too low, so that the fruits and vegetables do not breathe by oxygen, consume organic substances of the fruits and vegetables or cause physiological damage. High oxygen fresh-keeping (general O) 2 The concentration is more than 70 percent) can avoid the problems of disordered gas exchange rate and metabolic balance of fruits and vegetables, reduce the metabolic rate of the fruits and vegetables and reduce decay. Fructus Pruni Salicinae at O 2 ，CO 2 The proportion is optimized, the fresh-keeping can be performed for more than 3 months, the decay rate is obviously reduced, the pulp color and luster are good, and the flavor is good. The high oxygen-controlled fresh-keeping technology has great development potential, but the air-controlled preservation cost in cold chain transportation is high, and the proportion and the temperature of the air in the packaging environment can not be adjusted in time, so the subsequent research direction should be towards the development of energy conservation, environmental protection, convenience and intelligence.
In summary, the embodiment of the invention provides a method for controlling fresh keeping of a crisp Li Lenglian logistics based on a simulation model, which comprises the following steps: collecting dynamic logistics data and standard information corresponding to the dynamic logistics data; the dynamic logistics data are imported into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information; and determining the crisp Li Wuliu information according to the standard information and the dynamic crisp plum environment information, wherein the standard information corresponds to the dynamic logistics data. The degree of deterioration can be represented according to the information of the crispness Li Wuliu, and the accuracy of the replenishment prompt can be improved.
Example two
Embodiments of the present application describe a method of controlling freshness of a fragile Li Lenglian logistics based optionally on a simulation model from the level of the system architecture.
Referring to fig. 2, a flowchart of specific steps of another method for controlling fresh keeping of a crisp Li Lenglian logistics based on a simulation model is shown.
In step 201, deep learning is performed through a previously collected green and crisp plum logistics data set, so as to obtain a simulation model, wherein each green and crisp Li Wuliu data in the green and crisp plum logistics data set is marked with green and crisp plum environment information and/or label environment information.
The green and crisp plum logistics data set can be data recorded for a real green and crisp plum transportation area, each complete green and crisp plum and each tag in the data is marked by a rectangular frame, and the environment information of the rectangular frame is represented by the coordinates of the lower left corner and the upper right corner or the upper left corner and the lower right corner.
Embodiments of the present invention may employ fast-RCNN (Faster Region with Convolutional Neural Network, faster candidate recognition convolutional neural network) for training. It will be appreciated that other deep learning models that can identify marker candidates from data, such as RCNN (Region with Convolutional Neural Network, candidate recognition convolutional neural network), fast-RCNN (Fast candidate recognition convolutional neural network), etc., may also be employed.
Step 202, identifying fresh green and crisp Li Yangben from a reference sample, and obtaining environmental information and corresponding air-conditioning ratio of the fresh green and crisp plum sample, wherein the reference sample is obtained by recording a logistics storage space provided with fresh green and crisp Li Yangben, and the reference sample corresponds to the same recording area as the dynamic logistics data.
Wherein, the reference sample has a standard area, and the standard area has fresh crisp Li Yangben, and the fresh crisp Li Yangben can be a contour indicating the air blending ratio. It will be appreciated that the reference sample may contain only standard regions and fresh green crisp Li Yangben, not green crisp plums. The green and crisp plums can also be arranged outside the standard area. However, in order to ensure the identification of the standard area and the fresh green and crisp plum sample, the green and crisp plum cannot be placed on the standard area.
In practical application, the standard region can be marked by lines with special patterns and colors, and the fresh crisp Li Yangben can be represented by outlines with characteristic patterns and colors.
Specifically, a standard region may be first determined from a reference sample; the special style, color profile is then identified in this region to yield a fresh crisp Li Yangben.
The embodiment of the invention can identify the fresh green crisp Li Yangben in the standard area, and can avoid identifying lines similar to the fresh green crisp Li Yangben, thereby improving the identification accuracy of the fresh green crisp plum sample.
In this example, chemical reagent insurance can be performed, hydrogen peroxide, ozone and the like are used as antibacterial agents to be applied to food and are regarded as safe chemical reagents, and as low-toxicity and safe decomposition products, the antibacterial agents are used as fruit film preservative, are attached to the surfaces of fruits, can destroy cell membranes of bacterial proteins, DNA and microbial cells, cause protein release, lipid peroxidation and cell permeability change, have biocidal effects on various bacteria, yeasts, moulds, viruses and spores, and can inhibit ethylene generation and reduce fruit transpiration. Studies have shown that increased pectin methylesterase (pectin methylesterase, PME) and Polygalacturonase (PG) activity in prunes accelerates pectin breakdown and decreases fruit hardness. Analysis of plum fruit related indicators at 0 ℃ can be performed by using 5 uL/L1-methylcyclopropene (1-MCP) fumigation treatment, and the result shows that compared with a control group, the reduction of the green-crisp Li Zhu hardness can be obviously inhibited after the 1-MCP treatment, and the content of protopectin is obviously higher than that of the control group, so that the 1-MCP can delay the decomposition of cell wall substances by reducing the activities of PG and PME, thereby keeping the fruit hardness, and simultaneously, the color conversion and the quality reduction of the plum fruits can be delayed. The 1-MCP fresh-keeping card can be placed in the quick delivery process of the prune electronic commerce, the influence of the fresh-keeping card on the subsequent shelf life is researched, and the result shows that compared with a control group, the 1-MCP processing can effectively slow down the aging process of the prune and prolong the shelf life.
Alternatively, in another embodiment of the present invention, the fresh crisp Li Yangben is deleted after recording the reference sample, or is present in the dynamic logistics data.
It will be appreciated that the fresh crisp Li Yangben can be wiped off the cold chain storage bin after the reference sample has been recorded, thereby ensuring cleanliness of the logistics storage space. Of course, it is also possible not to smear.
Step 203, collecting dynamic logistics data.
This step may refer to the detailed description of step 101, and will not be described herein.
And 204, performing spatial correction on the dynamic logistics data to enable the logistics storage space in the dynamic logistics data to be in a horizontal environment.
In practical application, because the camera is inclined when recording, the recorded dynamic logistics data is not in the horizontal direction, and in order to accurately represent the environments of logistics storage space, labels and crisp plums, the dynamic logistics data needs to be spatially corrected.
The spatial correction is already a relatively mature technology, and the embodiment of the present invention will not be described in detail.
Step 205, importing the dynamic logistics data into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information.
This step may refer to the detailed description of step 102, and will not be described herein.
And 206, determining the air mixing ratio according to the environment information of the fresh green and crisp plum samples, the corresponding air mixing ratio and the dynamic green and crisp plum environment information.
Specifically, firstly, determining fresh green and crisp Li Yangben positioned on the upper side and the lower side of the green and crisp plums according to the green and crisp plums environmental information; then collecting the air-conditioning proportion corresponding to fresh crisp Li Yangben on the upper side and the lower side; finally, the gas blending ratio is determined according to the two gas blending ratios, for example, the gas blending ratio is between the two gas blending ratios.
The edible preservative film is a future environment-friendly food packaging substitute, and the edible packaging film which is covered on the surfaces of fruits and vegetables is formed by utilizing polysaccharide, protein or lipid and the like extracted from plants, animals and marine organisms, has the characteristics of biodegradability, nontoxicity, air blocking, moisture loss prevention, mechanical damage resistance of fruits and vegetables improvement, microorganism growth and propagation inhibition and the like, so that the storage and preservation performances of fruits and vegetables in the refrigerating and transporting processes are improved. The change of crispness Li Pinzhi in the low-temperature storage process is discussed by combining different packaging materials with 1-MCP fumigation treatment, and the result shows that the fruit rot can be delayed, the color is yellow, and the fruit substance and the organic acid content can be better kept by using the nano modified packaging. The plant has many antimicrobial components and active substances, such as essential oil and fruit seed extract, and has good antibacterial, antifungal activity and biopolymer compatibility as natural additives, and can be directly applied or added into edible coating by spraying, soaking and the like, thus having the characteristics of low production cost, no toxicity and good biodegradability. The influence of Arabinogalactan (AG) soaking treatment on storage after green and crisp Li Cai is found that AG can delay the process of changing the color of the green and crisp plum fruits from green to yellow, delay the reduction of hardness and weight in the storage process, delay spoilage, delay the aging process of the green and crisp plum fruits, reduce the respiratory rate after picking and inhibit ethylene generation, and prolong the shelf life of the green and crisp plum fruits by more than 3 d. BAL et al found that alginate-treated plums with or without salicylic acid and oxalic acid effectively retarded the evolution of parameters related to postharvest maturation, such as soluble solids content, softening and reduced respiration rate, compared to controls. At the end of the shelf-life, the edible coating showed a positive effect on maintaining higher concentrations of total phenolics, total anthocyanins content and antioxidant activity, whereas the total phenolics, total anthocyanins and antioxidant activity of the control plums were reduced by excessive maturation and aging processes.
Optionally, in another embodiment of the present invention, the step 206 includes sub-steps 2061 to 2064:
substep 2061, determining the environmental information of each logistics storage space according to the dynamic label environmental information.
It can be understood that the labels are located in the logistics storage space, and for one logistics storage space, the width of the logistics storage space can be stuck with a plurality of labels at most, so that the environment and the number of the logistics storage space can be determined according to the difference value between different label densities.
Specifically, firstly, sorting the density of all the labels from small to large; then, calculating the difference value of the densities of two adjacent labels one by one; if the difference value of the densities of two adjacent labels is smaller than or equal to a preset multiple of the average height of the labels, the two labels are in the same logistics storage space; if the difference of the densities of two adjacent labels is larger than the preset multiple of the average height of the labels, the two labels are not positioned in the same logistics storage space; and finally, determining the environmental information of the logistics storage space according to the environmental information of all the labels in each logistics storage space.
It can be understood that the preset multiple can be determined according to the width of the logistics storage space and the average height of the labels; for example, if the width of the logistics storage space is 2 times the average height of the labels, the preset multiple is 2.
In step 2062, for each cold chain storage box in each logistics storage space in the dynamic logistics data, if the logistics storage space is a candidate monitoring logistics storage space, the reference density is extracted from the dynamic green and crisp plum environment information of the cold chain storage box.
In the embodiment of the invention, each green and crisp plum corresponds to each cold chain storage box.
Specifically, for a rectangular frame, the reference density may be the density of the lower left corner or the density of the upper right corner, or may be the density of the upper left corner or the density of the lower right corner.
In practical application, because the data is recorded, a part of recorded logistics storage space or other irregular logistics storage space exists, in the embodiment of the invention, the logistics storage space is not supervised. Thereby ensuring the identification accuracy.
Optionally, in another embodiment of the present invention, the candidate monitoring logistics storage space is obtained by:
and A1, importing standard logistics data into the simulation model to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard tag environment information and standard green and crisp plum environment information.
This step may refer to the detailed description of step 102, and will not be described in detail herein.
It will be appreciated that standard logistics data requires spatial modification first.
It will be appreciated that this step differs from step 102 in that the imported data is different.
And step A2, determining the height of each logistics storage space according to the environmental information of the standard labels adjacent up and down.
Specifically, the environmental information of each logistics storage space is first determined according to the detailed description of the substep 2061; and then calculating the difference value of the environmental information of the two adjacent logistics storage spaces to obtain the height of the logistics storage spaces.
And step A3, determining the number of standard cold chain storage boxes in each logistics storage space according to the standard green and crisp plum environment information and the standard label environment information.
Specifically, the green and crisp plums are divided into logistics storage spaces according to the density of the green and crisp plum environment information. For example, the density of the green and crisp plum environmental information is greater than that of the logistics storage space A and less than that of the logistics storage space B, and then the green and crisp Li Weiyu logistics storage space A. And counting the number of the green and crisp plum environmental information in each logistics storage space to obtain the number of the cold chain storage boxes in the logistics storage space.
And A4, determining the logistics storage space as a candidate monitoring object logistics storage space under the condition that the height of the logistics storage space is larger than a cold chain storage box height threshold value or the number of standard cold chain storage boxes of the logistics storage space is larger than a cold chain storage box number threshold value aiming at each logistics storage space.
The cold chain storage box height threshold value can be determined according to the average height of the logistics storage space, and the cold chain storage box number threshold value can be determined according to the average cold chain storage box number of the logistics storage space.
The embodiment of the invention does not monitor the too low or too small logistics storage space.
Sub-step 2063, determining a reference fresh green crisp Li Yangben based on the reference density and the environmental information of the fresh green crisp plum sample.
In the embodiment of the invention, the environmental information of the fresh green and crisp plum sample mainly refers to the density of the fresh green and crisp Li Yangben.
Specifically, the upper and lower fresh crisps Li Yangben closest to the reference density are selected from a plurality of fresh crisps Li Yangben.
And step 2064, determining the air-conditioning ratio corresponding to the cold chain storage box according to the air-conditioning ratio corresponding to the reference fresh crisp Li Yangben.
In the embodiment of the invention, each fresh crisp Li Yangben corresponds to one air-conditioning ratio.
Step 207, determining the number of dynamic cold chain storage boxes in the logistics storage space according to the dynamic green and crisp plum environment information under the condition that the logistics storage space is a candidate monitoring logistics storage space aiming at each logistics storage space in the dynamic logistics data.
In the embodiment of the invention, the environment information of the green and crisp plums is the environment information of the forefront green and crisp plums in each cold chain storage box, so that each green and crisp plum environment information corresponds to one cold chain storage box, namely: the number of the green and crisp plum environment information is the same as the number of the cold chain storage boxes.
And step 208, determining the number of cold chain storage boxes without the fragile plums according to the number of dynamic cold chain storage boxes and the number of standard cold chain storage boxes in the logistics storage space.
Specifically, the number of cold chain storage boxes without the fragile plums is the difference between the standard cold chain storage box number and the dynamic cold chain storage box number.
It can be understood that the larger the number of cold chain storage boxes without the fragile plums, the more serious the deterioration degree; the smaller the number of cold chain storage boxes without the green and crisp plums, the less serious the deterioration degree.
In practical application, the number of the cold chain storage boxes without the green and crisp plums can be sent to a designated system for display so as to prompt staff to replenish.
Alternatively, in another embodiment of the present invention, the standard cold chain storage box number is obtained by:
and B1, collecting standard logistics data, and importing the standard logistics data into a simulation model obtained by training in advance to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard green and crisp plum environment information.
The standard logistics data can be data of at least one green and crisp plum in each cold chain storage box, and the data comprise data of full green and crisp plums in a logistics storage space.
It will be appreciated that standard logistics data requires spatial modification first.
And B2, determining the number of standard cold chain storage boxes according to the standard green and crisp plum environment information.
According to the embodiment of the invention, the number of standard cold chain storage boxes is determined through the data of the green and crisp plums in each column.
The relationship between the number of cold chain storage boxes and the information of the crisp plum environment can be referred to in the detailed description of step 207, and will not be described herein. The difference is that: the standard cold chain bin count is determined using data with prunes in each column, and the data of step 207 may include at least one column without prunes.
In summary, the embodiment of the invention provides a method for controlling fresh keeping of a crisp Li Lenglian logistics based on a simulation model, which comprises the following steps: collecting dynamic logistics data and standard information corresponding to the dynamic logistics data; the dynamic logistics data are imported into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information; and determining the crisp Li Wuliu information according to the standard information and the dynamic crisp plum environment information, wherein the standard information corresponds to the dynamic logistics data. The degree of deterioration can be represented according to the information of the crispness Li Wuliu, and the accuracy of the replenishment prompt can be improved.
Example III
Referring to fig. 3, a structural diagram of a crisper Li Lenglian logistics fresh-keeping control system based on a simulation model is shown, which is specifically as follows.
The data acquisition module 301 is configured to acquire dynamic logistics data.
The green and crisp plum environment collection module 302 is configured to import the dynamic logistics data into a simulation model obtained by training in advance, so as to obtain dynamic candidate environment information, where the dynamic candidate environment information includes dynamic green and crisp plum environment information.
And the crispness Li Wuliu determining module 303 is configured to determine crispness Li Wuliu information according to the standard information and the dynamic crispness plum environment information, where the standard information corresponds to the dynamic logistics data.
In summary, the embodiment of the invention provides a crisp Li Lenglian logistics fresh-keeping control system based on a simulation model, which comprises: the data acquisition module is used for acquiring dynamic logistics data and acquiring standard information corresponding to the dynamic logistics data; the green and crisp plum environment acquisition module is used for importing the dynamic logistics data into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information; and the crispness Li Wuliu determining module is used for determining crispness Li Wuliu information according to the standard information and the dynamic crispness plum environment information, wherein the standard information corresponds to the dynamic logistics data. The degree of deterioration can be represented according to the information of the crispness Li Wuliu, and the accuracy of the replenishment prompt can be improved.
Example IV
Referring to fig. 4, a block diagram of another crisper Li Lenglian logistics fresh-keeping control system based on a simulation model is shown, which is specifically as follows.
The simulation model training module 401 is configured to perform deep learning through a pre-collected green and crisp plum logistics data set, so as to obtain a simulation model, where each green and crisp Li Wuliu data in the green and crisp plum logistics data set is marked with green and crisp plum environment information and/or label environment information.
The fresh crisp Li Yangben identification module 402 is configured to identify fresh crisp Li Yangben from a reference sample, obtain environmental information and a corresponding air-conditioning ratio of the fresh crisp plum sample, record the reference sample in a logistics storage space provided with fresh crisp Li Yangben, and correspond the same recording area with the dynamic logistics data.
The data acquisition module 403 is configured to acquire dynamic logistics data.
And the space correction module 404 is configured to spatially correct the dynamic logistics data, so that the logistics storage space in the dynamic logistics data is in a horizontal environment.
The green and crisp plum environment collection module 405 is configured to import the dynamic logistics data into a simulation model obtained by training in advance, so as to obtain dynamic candidate environment information, where the dynamic candidate environment information includes dynamic green and crisp plum environment information.
And the crispness Li Wuliu determining module 406 is configured to determine crispness Li Wuliu information according to the standard information and the dynamic crispness plum environment information, where the standard information corresponds to the dynamic logistics data. Optionally, in another embodiment of the present invention, the determining module 406 of the crispness Li Wuliu includes:
The air blending ratio determining submodule 4061 is configured to determine an air blending ratio according to the environmental information of the fresh green and crisp plum sample, the corresponding air blending ratio, and the dynamic green and crisp plum environmental information.
The dynamic cold chain storage box determining module 407 is configured to determine, for each logistics storage space in the dynamic logistics data, the number of dynamic cold chain storage boxes in the logistics storage space according to the dynamic green and crisp plum environmental information under the condition that the logistics storage space is a candidate monitoring logistics storage space.
The air-cooling chain storage box number determining module 408 is configured to determine the number of cold chain storage boxes in which the fragile plums are not placed according to the number of dynamic cold chain storage boxes in the logistics storage space and the number of standard cold chain storage boxes.
Alternatively, in another embodiment of the present invention, the air mix ratio determining submodule 4051 includes:
and the logistics storage space determining unit is used for determining the environment information of each logistics storage space according to the dynamic tag environment information.
The reference density extraction unit is used for extracting reference density from the dynamic green and crisp plum environment information of each cold chain storage box of each logistics storage space in the dynamic logistics data under the condition that the logistics storage space is a candidate monitoring logistics storage space.
And a reference fresh green crisp Li Yangben extraction unit for determining a reference fresh green crisp Li Yangben according to the reference density and the environmental information of the fresh green crisp plum sample.
And the air blending ratio determining unit is used for determining the air blending ratio corresponding to the cold chain storage box according to the air blending ratio corresponding to the reference fresh crisp Li Yangben.
Optionally, in another embodiment of the present invention, the candidate monitoring stream warehouse space is obtained by the following module:
the candidate environment acquisition module is used for importing standard logistics data into the simulation model to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard tag environment information and standard green and crisp plum environment information.
And the logistics storage space height determining module is used for determining the height of each logistics storage space according to the standard label environment information adjacent to each other.
And the standard cold chain storage box determining module is used for determining the number of the standard cold chain storage boxes in each logistics storage space according to the standard green and crisp plum environment information and the standard label environment information.
The monitoring logistics storage space determining module is used for determining that the logistics storage space is a candidate monitoring logistics storage space under the condition that the height of the logistics storage space is larger than a cold chain storage box height threshold value or the number of standard cold chain storage boxes of the logistics storage space is larger than a cold chain storage box number threshold value aiming at each logistics storage space.
Alternatively, in another embodiment of the present invention, the standard cold chain storage box number is obtained by the following modules:
the standard green and crisp plum environment acquisition module is used for acquiring standard logistics data, and importing the standard logistics data into a simulation model obtained through training in advance to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard green and crisp plum environment information.
And the standard cold chain storage box number determining module is used for determining the standard cold chain storage box number according to the standard green and crisp plum environment information.
Alternatively, in another embodiment of the present invention, the fresh crisp Li Yangben is deleted after recording the reference sample, or is present in the dynamic logistics data.
In summary, the embodiment of the invention provides a crisp Li Lenglian logistics fresh-keeping control system based on a simulation model, which comprises: the data acquisition module is used for acquiring dynamic logistics data and acquiring standard information corresponding to the dynamic logistics data; the green and crisp plum environment acquisition module is used for importing the dynamic logistics data into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information; and the crispness Li Wuliu determining module is used for determining crispness Li Wuliu information according to the standard information and the dynamic crispness plum environment information, wherein the standard information corresponds to the dynamic logistics data. The degree of deterioration can be represented according to the information of the crispness Li Wuliu, and the accuracy of the replenishment prompt can be improved.
The embodiment of the invention also provides electronic equipment, which comprises: the simulation model-based fresh keeping control method for the fragile Li Lenglian logistics comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the simulation model-based fresh keeping control method for the fragile Li Lenglian logistics when executing the program.
The embodiment of the invention also provides a readable storage medium, when the instructions in the storage medium are executed by a processor of the electronic equipment, the electronic equipment can execute the simulation model-based crispness Li Lenglian logistics fresh-keeping control method of the previous embodiment.
For system embodiments, the description is relatively simple as it is substantially similar to method embodiments, and reference is made to the description of method embodiments for relevant points.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, the present invention is not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that various modifications and improvements can be made to the disclosure. For example, the various devices or components described above may be implemented in hardware, or may be implemented in software, firmware, or a combination of some or all of the three.
A flowchart is used in this disclosure to describe the steps of a method according to an embodiment of the present disclosure. It should be understood that the steps that follow or before do not have to be performed in exact order. Rather, the various steps may be processed in reverse order or simultaneously. Also, other operations may be added to these processes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the methods described above may be implemented by a computer program to instruct related hardware, and the program may be stored in a computer readable storage medium, such as a read only memory, a magnetic disk, or an optical disk. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits. Accordingly, each module/unit in the above embodiment may be implemented in the form of hardware, or may be implemented in the form of a software functional module. The present disclosure is not limited to any specific form of combination of hardware and software.
Unless defined otherwise, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The foregoing is illustrative of the present disclosure and is not to be construed as limiting thereof. Although a few exemplary embodiments of this disclosure have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this disclosure. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the claims. It is to be understood that the foregoing is illustrative of the present disclosure and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The disclosure is defined by the claims and their equivalents.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (8)
1. A green and crisp Li Lenglian logistics fresh-keeping control method based on a simulation model is characterized by comprising the following steps:
collecting dynamic logistics data;
the dynamic logistics data are imported into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information;
identifying fresh green and crisp Li Yangben from a reference sample, and obtaining environmental information and corresponding air-conditioning ratio of the fresh green and crisp plum sample, wherein the reference sample is obtained by recording a logistics storage space provided with the fresh green and crisp Li Yangben, and the reference sample corresponds to the same recording area as the dynamic logistics data;
determining green and crisp Li Wuliu information according to the environment information of the fresh green and crisp plum samples, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information, wherein the green and crisp plum logistics information comprises the air-conditioning ratio;
The dynamic candidate environment information comprises dynamic label environment information, and the step of determining the air-conditioning ratio according to the environment information of the fresh green and crisp plum sample, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information comprises the following steps:
determining the environmental information of each logistics storage space according to the dynamic tag environmental information;
extracting reference density from dynamic green and crisp plum environment information of each cold chain storage box in each logistics storage space in the dynamic logistics data under the condition that the logistics storage space is a candidate monitoring logistics storage space;
determining a reference fresh green crisp Li Yangben according to the reference density and the environmental information of the fresh green crisp plum sample;
determining the air-conditioning ratio corresponding to the cold chain storage box according to the air-conditioning ratio corresponding to the reference fresh crisp Li Yangben;
the dynamic logistics data are recorded dynamic data aiming at a crisp Li Zhanshi area, and a camera continuously records a crisp Li Zhanshi area in a certain time period;
the environmental information includes ultraviolet irradiation information;
the simulation model is obtained through training the following steps:
deep learning is carried out through a green and crisp plum logistics data set collected in advance, so that a simulation model is obtained, and each green and crisp Li Wuliu data in the green and crisp plum logistics data set is marked with green and crisp plum environment information and/or label environment information;
The fresh crisps Li Yangben are deleted after recording the reference sample or are present in the dynamic logistics data.
2. The method of claim 1, further comprising, prior to the step of importing the dynamic logistics data into a simulation model derived from pre-training:
carrying out space correction on the dynamic logistics data so as to enable the logistics storage space in the dynamic logistics data to be in a horizontal environment; the candidate monitoring material flow storage space is obtained through the following steps:
importing standard logistics data into the simulation model to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard tag environment information and standard green and crisp plum environment information;
determining the height of each logistics storage space according to the environmental information of the standard labels adjacent to each other;
determining the number of standard cold chain storage boxes in each logistics storage space according to the standard green and crisp plum environmental information and the standard label environmental information;
and aiming at each logistics storage space, determining the logistics storage space as a candidate monitoring logistics storage space under the condition that the height of the logistics storage space is larger than a cold chain storage box height threshold value or the number of standard cold chain storage boxes of the logistics storage space is larger than a cold chain storage box number threshold value.
3. The method according to claim 1, wherein the method further comprises:
determining the number of dynamic cold chain storage boxes in the logistics storage space according to the dynamic green and crisp plum environment information under the condition that the logistics storage space is a candidate monitoring logistics storage space aiming at each logistics storage space in the dynamic logistics data;
and determining the number of the cold chain storage boxes without the fragile plums according to the number of the dynamic cold chain storage boxes and the number of the standard cold chain storage boxes in the logistics storage space.
4. The method of claim 3, wherein the standard cold chain storage bin count is obtained by:
collecting standard logistics data, and importing the standard logistics data into a simulation model obtained by training in advance to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard green and crisp plum environment information;
and determining the number of standard cold chain storage boxes according to the standard green and crisp plum environment information.
5. The utility model provides a crisp Li Lenglian commodity circulation fresh-keeping control system based on simulation model which characterized in that, the system includes:
the logistics data acquisition module acquires dynamic logistics data;
The candidate environment analysis module is used for importing the dynamic logistics data into a simulation model obtained through training in advance to obtain dynamic candidate environment information, wherein the dynamic candidate environment information comprises dynamic green and crisp plum environment information;
the fresh and crisp Li Xinxian degree identification module is used for identifying fresh and crisp Li Yangben from a reference sample, so as to obtain environmental information and corresponding air-conditioning ratio of the fresh and crisp plum sample, wherein the reference sample is obtained by recording a logistics storage space provided with fresh and crisp Li Yangben, and the reference sample corresponds to the same recording area as the dynamic logistics data; determining green and crisp Li Wuliu information according to the environment information of the fresh green and crisp plum samples, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information, wherein the green and crisp plum logistics information comprises the air-conditioning ratio;
the dynamic candidate environment information comprises dynamic label environment information, and the step of determining the air-conditioning ratio according to the environment information of the fresh green and crisp plum sample, the corresponding air-conditioning ratio and the dynamic green and crisp plum environment information comprises the following steps:
determining the environmental information of each logistics storage space according to the dynamic tag environmental information;
extracting reference density from dynamic green and crisp plum environment information of each cold chain storage box in each logistics storage space in the dynamic logistics data under the condition that the logistics storage space is a candidate monitoring logistics storage space;
Determining a reference fresh green crisp Li Yangben according to the reference density and the environmental information of the fresh green crisp plum sample;
determining the air-conditioning ratio corresponding to the cold chain storage box according to the air-conditioning ratio corresponding to the reference fresh crisp Li Yangben;
the dynamic logistics data are recorded dynamic data aiming at a crisp Li Zhanshi area, and a camera continuously records a crisp Li Zhanshi area in a certain time period;
the environmental information includes ultraviolet irradiation information;
the simulation model is obtained through training the following steps:
deep learning is carried out through a green and crisp plum logistics data set collected in advance, so that a simulation model is obtained, and each green and crisp Li Wuliu data in the green and crisp plum logistics data set is marked with green and crisp plum environment information and/or label environment information;
the fresh crisps Li Yangben are deleted after recording the reference sample or are present in the dynamic logistics data.
6. The system of claim 5, wherein the candidate environmental analysis module further comprises, prior to importing the dynamic logistic data into a simulation model that was trained in advance:
carrying out space correction on the dynamic logistics data so as to enable the logistics storage space in the dynamic logistics data to be in a horizontal environment; the candidate monitoring material flow storage space is obtained through the following steps:
Importing standard logistics data into the simulation model to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard tag environment information and standard green and crisp plum environment information;
determining the height of each logistics storage space according to the environmental information of the standard labels adjacent to each other;
determining the number of standard cold chain storage boxes in each logistics storage space according to the standard green and crisp plum environmental information and the standard label environmental information;
and aiming at each logistics storage space, determining the logistics storage space as a candidate monitoring logistics storage space under the condition that the height of the logistics storage space is larger than a cold chain storage box height threshold value or the number of standard cold chain storage boxes of the logistics storage space is larger than a cold chain storage box number threshold value.
7. The system of claim 6, wherein the system further comprises:
determining the number of dynamic cold chain storage boxes in the logistics storage space according to the dynamic green and crisp plum environment information under the condition that the logistics storage space is a candidate monitoring logistics storage space aiming at each logistics storage space in the dynamic logistics data;
and determining the number of the cold chain storage boxes without the fragile plums according to the number of the dynamic cold chain storage boxes and the number of the standard cold chain storage boxes in the logistics storage space.
8. The system of claim 7, wherein the standard cold chain storage bin count is obtained by:
collecting standard logistics data, and importing the standard logistics data into a simulation model obtained by training in advance to obtain standard candidate environment information, wherein the standard candidate environment information comprises standard green and crisp plum environment information;
and determining the number of standard cold chain storage boxes according to the standard green and crisp plum environment information.
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