Agricultural product cold-chain logistics whole-process interactive simulation system

Abstract

The invention discloses an agricultural product cold-chain logistics full-process interactive simulation system, and relates to the technical field of agricultural product cold-chain logistics, and the system comprises a cold-chain process module which is used for simulating logistics paths and temperature and humidity state changes of agricultural products in a plurality of links; the parameter configuration module is used for setting environment parameters, product parameters and equipment parameters for each link in the cold chain process; the logic judgment module is used for judging operation reasonability according to user configuration and built-in standard rules; the operation verification module is used for feeding back a logic judgment result and giving an operation suggestion; the data recording module is used for recording input parameters, judgment results and system responses in the whole simulation process; the visual display module is used for displaying the key indexes, the process states and the alarm events in a graphic mode; and the user interaction terminal is used for providing a user interface and performing interaction. According to the invention, the problems of scattered simulation links, lack of dynamic interaction and incomplete data tracing in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of cold chain logistics for agricultural products, and more specifically, to a full-process interactive simulation system for cold chain logistics of agricultural products. Background Art

[0002] In recent years, with the continuous increase in the demand for the circulation of agricultural products and the strict requirements for food safety and freshness, cold chain logistics management has been increasingly emphasized. The cold chain technology not only requires maintaining a low-temperature environment throughout the entire process from picking, processing, transportation to terminal display, but also requires information interconnection and data traceability between different links. Currently, some of the existing cold chain logistics simulation systems on the market mainly focus on the environmental simulation of a single link or a certain stage, such as only simulating the temperature and humidity control during the transportation link, or only verifying the temperature control status in the storage cold storage. However, most of these technical solutions have problems such as the lack of effective coordination between modules, the inability to achieve full-process dynamic monitoring, and the lack of real-time interactive feedback functions. At the same time, the existing methods often adopt decentralized processing in parameter configuration, abnormal alarm, and data traceability, and cannot effectively integrate the entire cold chain process of agricultural products from picking to terminal display, resulting in certain limitations in decision-making support, process evaluation, and emergency handling. In view of the above deficiencies, there is an urgent need in the industry for a cold chain logistics simulation system that can run through the entire process, have real-time dynamic interaction, and achieve comprehensive data recording and visual display, so as to more accurately reflect the operation conditions and operation risks of each node in the cold chain. However, the current technical means have not fully met this requirement. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a full-process interactive simulation system for cold chain logistics of agricultural products to solve the problems mentioned in the background art.

[0004] To achieve the above object, the present invention adopts the following technical solutions: A full-process interactive simulation system for cold chain logistics of agricultural products, comprising: A cold chain process module, used for simulating the logistics path and temperature and humidity state changes of agricultural products in multiple links; A parameter configuration module, used for setting environmental parameters, product parameters, and equipment parameters for each link in the cold chain process module; A logical judgment module, used for judging whether the operation logic is reasonable according to the user-configured parameters and built-in standard rules; An operation verification module, used for giving feedback on the logical judgment result and providing operation suggestions; A data recording module, used for recording all input parameters, judgment results, and system responses during the simulation process and forming a traceable data chain; A visualization display module, which is used to display key indicators, process status, and alarm events during the simulation process in a graphical manner; A user interaction terminal, which is used to provide a user interface and allow users to modify parameters, select links, and perform abnormal intervention operations during the simulation process.

[0005] In a further embodiment, the cold chain process module further includes a dynamic attenuation model module, which is used to simulate the quality change process of agricultural products affected by temperature, humidity, and gas composition in each link of the cold chain logistics.

[0006] In a further embodiment, the dynamic attenuation model module simulates the quality of agricultural products based on the following formula : ; Where: is the initial quality, is the quality attenuation rate, and t is the time; And: ; Where: is the frequency factor of agricultural products; is the activation energy of agricultural products; is the gas constant; is the absolute temperature; is the humidity is the influence function of humidity on agricultural products, is the gas concentration is the influence function of gas concentration on agricultural products.

[0007] In a further embodiment, the logic judgment module further includes a fuzzy logic judgment unit, which is used to evaluate the cumulative impact of temperature fluctuations on the quality of agricultural products when the temperature fluctuation does not reach the spoilage threshold.

[0008] In a further embodiment, the multiple links in the cold chain process module include any one or more of the following: picking, commercialization processing, pre-cooling, storage, transportation, and end display.

[0009] In a further embodiment, the logic judgment module includes a conditional tree structure generation unit and a determination execution unit. The conditional tree structure generation unit generates multi-layer conditional judgment logic based on user-set parameters, and the determination execution unit calculates the conditional tree layer by layer according to the real-time status of each variable during the simulation process to obtain a judgment result.

[0010] In a further embodiment, the data recording module includes an event coding sub-module and a timestamp marking sub-module. The event coding sub-module generates a unique event code for each parameter change, judgment result, and response action. The timestamp marking sub-module marks the time points of all event codes based on the simulation clock and stores them in the data traceability database.

[0011] In a further embodiment, the operation verification module includes a rule engine sub-module. The rule engine sub-module embeds multiple industry cold chain standard items. The rule engine sub-module calls the standard items for real-time comparison during the simulation execution and determines whether there are any illegal operations.

[0012] In a further embodiment, the transportation link in the cold chain process module supports route selection simulation. The system selects the optimal cold chain route based on the following path quality judgment formula: ; Where is the cold chain cost valuation function, is the estimated total transportation time, is the cold chain integrity risk level, is the on-the-way environmental volatility, and and are system preset weight coefficients.

[0013] In a further embodiment, the user interaction terminal is connected to the data recording module and supports the user to export a complete simulation report including simulation input, output, and process logs after the simulation is completed. The simulation report displays the whole process index changes and risk event distributions in a combined way of tables and graphs.

[0014] The advantages of the present invention over the prior art are as follows: By constructing a simulation system that covers the entire process of agricultural products from picking, commercial processing, pre-cooling, storage, transportation, to end display, the present invention can real-time simulate the dynamic changes of the logistics path and temperature and humidity states, thereby solving the problems of scattered simulation in each link, information silos, and lack of full-process interactive verification in the prior art. The system adopts a unified parameter configuration, logical judgment, and operation verification mechanism, enabling users to flexibly set environmental parameters, product parameters, and equipment parameters in each link, and judging the rationality of operations based on built-in standard rules to ensure that the process simulation results are closer to the actual situation. In addition, by introducing a dynamic decay model, the system can accurately simulate the quality decay of agricultural products caused by changes in temperature, humidity, and gas composition during cold chain logistics, providing a scientific basis for optimizing transportation conditions. Especially in mixed transportation scenarios, such as apples and bananas, the model proposes separation suggestions by analyzing the impact of ethylene, effectively extending the shelf life and reducing economic losses. The addition of the fuzzy logic judgment unit significantly improves the system's ability to handle temperature fluctuations, and more flexibly evaluates the cumulative impact compared to traditional binary judgments. For example, a short-term temperature deviation will not trigger a false alarm, but instead provides intervention suggestions through scientific reasoning, thus ensuring the quality stability of perishable products such as strawberries. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the overall system architecture diagram of the present invention; Figure 2 is the data flow diagram of the visualization module of the present invention; Figure 3 is the function diagram of the user interaction terminal of the present invention; Figure 4 is the flowchart of the fuzzy logic judgment unit of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] The following describes the specific embodiments of the present invention with reference to the accompanying drawings.

[0017] The present invention provides a full-process interactive simulation system for cold chain logistics of agricultural products, aiming to help users understand the operation mechanism of cold chain logistics and optimize relevant processes by simulating the logistics path and temperature and humidity state changes of agricultural products in multiple links from the place of origin to the hands of consumers. The following embodiments detail each component of the system and its functional implementation methods.

[0018] As shown in Figure 1, the overall system framework of the present invention is as follows. The present invention generally includes the following modules: Cold chain process module, used to simulate the logistics path and temperature and humidity state changes of agricultural products in multiple links; Parameter configuration module, used to set environmental parameters, product parameters, and equipment parameters for each link in the cold chain process module; A logical judgment module, which is used to judge whether the operation logic is reasonable according to the user configuration parameters and built-in standard rules; An operation verification module, which is used to give feedback on the logical judgment result and provide operation suggestions; A data recording module, which is used to record the input parameters, judgment results and system responses during all simulation processes and form a traceable data chain; A visualization display module, which is used to display the key indicators, process status and alarm events during the simulation process in a graphical way; A user interaction terminal, which is used to provide a user interface and allow users to modify parameters, select links and perform abnormal intervention operations during the simulation process.

[0019] Among them, the cold chain process module is the core part of the entire simulation system. This module can simulate the logistics path and temperature and humidity state changes of agricultural products in multiple links such as picking, commercialization processing, precooling, storage, transportation, and end display. These links cover the entire life cycle of agricultural product cold chain logistics. For example, starting from picking apples in the field, through commercialization processes such as washing and grading, then precooling to reduce the fruit temperature, then entering the cold storage, and finally being transported to the supermarket shelf for display by a refrigerated truck. The temperature and humidity changes in each link will be simulated in real time. For example, the temperature may increase due to external environmental fluctuations during transportation, and the system will record and reflect these changes. This comprehensive simulation design ensures that users can master every key point of cold chain logistics, thereby discovering potential problems and taking improvement measures.

[0020] In order to allow users to flexibly adjust the simulation conditions, the system is equipped with a parameter configuration module. The parameter configuration module allows users to set environmental parameters, product parameters and equipment parameters for each link in the cold chain process module.

[0021] The specific input methods can be very diverse. Users can define parameters by writing script files, or manually input through an intuitive graphical interface, or even batch import data from a database. For example, users can input the target product name (such as "apple"), the initial state (such as the temperature at picking is 20°C), the target transportation temperature (such as 2°C), the allowed time delay (such as a maximum delay of 2 hours), the packaging method (such as a foam box with ice packs), and the number of cold chain segments (such as divided into three segments: precooling, transportation, and display). The diverse input methods not only improve the flexibility of the system, but also allow users to perform customized simulations according to the characteristics of different agricultural products and actual cold chain requirements. For example, for perishable apples, users may require stricter temperature control, while for storable potatoes, some parameter restrictions can be relaxed.

[0022] In specific implementation, the script file can use the JSON or YAML format to describe each parameter. For example: { "Product": "Apple", "Initial State": {"Temperature": 20, "Humidity": 60}, "Transportation Section": {"Name": "Pre-cooling", "Temperature": 2, "Duration": 2}, {"Name": "Long-distance Transportation", "Temperature": 4, "Duration": 30} , "Packaging": "Foam box + Ice pack", "Delay Tolerance": 2 } The graphical user interface can adopt a multi-tab form with clear field types. For example, "Target Transportation Temperature" is a numeric input box with a unit dropdown (℃ / ℉), and "Number of Cold Chain Sections" is a dropdown selection (1 - 5 sections). The database batch import format can be through a CSV file, and the field order is the same as the above JSON. The system will verify the required fields and give a missing prompt when receiving.

[0023] During the simulation process, in order to judge the rationality of user operations, the present invention designs a logical judgment module. The logical judgment module will evaluate the operation logic according to the parameters configured by the user and the built-in standard rules. The logical judgment module internally contains two important units: a conditional tree structure generation unit and a determination execution unit.

[0024] The conditional tree structure generation unit will generate a multi-layer conditional judgment logic based on the parameters set by the user. For example, if the user requires the transportation temperature to be maintained between 0 - 4℃ and the humidity to be controlled between 85% - 95%, this unit will generate a hierarchical logic tree containing temperature and humidity conditions. The determination execution unit will, during the simulation process, calculate the conditional tree layer by layer according to the real-time status of each variable (such as the temperature suddenly rising to 6℃ during transportation), and finally obtain a judgment result (such as "Temperature exceeds the standard"). This conditional tree-based judgment mechanism can not only ensure the rigor of the logic but also efficiently handle complex multi-variable scenarios. In practice, the system first maps the user parameters into a set of "node conditions", and then constructs a binary or multi-way tree structure according to the parameter priority. Each leaf node stores a single judgment (such as "Temperature ≤ 4℃"), and each intermediate node stores a logical operator (AND / OR). The generation of the tree adopts a recursive method: traverse the parameter list, first generate the root node, and then generate subtrees for the sub-parameter lists respectively until the leaves.

[0025] After the judgment result is generated, the system will feedback the result through the operation verification module and provide specific operation suggestions to the user. The operation verification module has a built-in rule engine submodule, which embeds multiple industry cold chain standard entries, such as the internationally accepted agricultural product cold chain temperature range or domestic cold chain logistics specifications. During the simulation execution process, the rule engine submodule will call these standard entries and compare them with the real-time simulation data to determine whether there are any illegal operations. For example, if the temperature exceeds the standard upper limit during transportation, the module will mark it as a violation and recommend "increasing the power of the refrigeration equipment" or "shortening the transportation time." This feedback mechanism not only helps users find problems, but also provides practical optimization suggestions, thereby improving the actual effect of the cold chain process.

[0026] In order to ensure the traceability of the simulation process, the system also includes a data recording module. The data recording module will record the input parameters, judgment results and system responses in detail during all simulation processes, and form a traceable data chain. In terms of specific implementation, the data recording module is divided into two parts: the event coding submodule and the timestamp marking submodule. The event coding submodule generates a unique event code for each parameter change, judgment result or response action. For example, when the user adjusts the target temperature from 2℃ to 3℃, the system will generate a record coded as "E001". The timestamp marking submodule marks the specific time point for all event codes based on the simulation clock, such as "parameter adjustment occurs in the 5th minute of simulation", and stores this data in the data traceability database. This meticulous data recording method allows users to trace back the entire process after the simulation is completed and analyze the impact of key decisions.

[0027] As shown in FIG. 2 , the present invention also designs a visual display module to display key indicators, process status and alarm events in the simulation process to the user in a graphical form.

[0028] The visualization display module includes three sub-units: process visualization unit, indicator chart unit and abnormal prompt unit.

[0029] The process visualization unit will draw the state migration process of agricultural products in each process node. For example, a dynamic flowchart can be used to show the state changes of apples from picking to display at each step.

[0030] The indicator chart unit generates specific analysis charts, such as a temperature curve chart showing the change of temperature over time during transportation, a humidity change chart reflecting the humidity fluctuations during the storage stage, or a transportation time comparison chart comparing the efficiency of different routes.

[0031] The abnormal prompt unit generates a graphical warning sign based on the feedback information of the operation verification module, such as a red alarm icon popping up when the temperature exceeds the limit.

[0032] As shown in Figure 3, the present invention also provides a user interaction terminal implementation. The user interaction terminal implementation provides a friendly user interface that allows users to modify parameters in real time during the simulation, select specific links for key analysis, or perform intervention operations on abnormal situations. For example, when a user discovers an abnormal temperature during the transportation process, they can immediately adjust the parameters of the refrigeration equipment and observe the effects after the adjustment. In addition, the user interaction terminal is also connected to the data recording module, supporting the export of a complete simulation report after the simulation is completed. The report is presented in a combination of tables and graphs, detailing the index changes (such as temperature and humidity curves) and risk event distributions (such as the occurrence time and impact of transportation delays) throughout the process, providing users with a comprehensive analysis tool.

[0033] In the cold chain process module, the present invention designs an alarm simulation sub-module to enhance the system's abnormal handling ability. The alarm simulation sub-module will simulate and prompt abnormal events triggered during the simulation based on the alarm conditions set by the user. Abnormal events may include temperature overrun (such as exceeding 5°C), transportation delay (such as exceeding the planned time by 2 hours), humidity out of control (such as humidity below 70%), or cold chain breakage (such as a refrigeration equipment failure during transportation). For example, a user can set an alarm when the temperature exceeds 4°C, and when this situation occurs during the simulation, the system will prompt the user through sound or graphics to help them discover and handle the problem in a timely manner.

[0034] In the transportation process, the system also supports route selection simulation to help users find the optimal cold chain path. The specific selection is based on a path quality judgment formula: ; Among them, is the cold chain cost valuation function, is the estimated total transportation time, is the cold chain integrity risk level (such as the risk of chain breakage that may occur in the path), is the environmental volatility during the journey (such as the amplitude of temperature change), , , are system preset weight coefficients. These coefficients can be adjusted according to actual needs. For example, if a user pays more attention to time efficiency, they can increase the value of . By calculating the of different pathsFor values, the system will recommend the route with the lowest cost. For example, among two routes from the production area to the city, one has a short time consumption but high risk, and the other has a long time consumption but is more stable. The system will give suggestions after comprehensive evaluation. By default, α = 0.4, β = 0.3, and γ = 0.3 can be selected, and users can adjust them at the user interaction terminal according to the business focus. If more attention is paid to the time cost, the α value can be increased to 0.6; if the risk of broken chain is emphasized, β can be increased to 0.5. All adjusted weights are automatically normalized to ensure that α + β + γ = 1.

[0035] In this system, the risk level D is obtained through statistical analysis of historical broken chain probabilities, with a value range of [0, 1]. 0 indicates no risk of broken chain, and 1 indicates high risk. The specific calculation can be as follows: ; where is the per-unit-time broken chain probability of the i th section of the cold chain (which can be statistically analyzed from historical data). The environmental volatility R is represented by the standard deviation of temperature during transportation: ; where is the j rd sampled temperature, is the average temperature, and N is the number of sampling points.

[0036] To truly implement the full-process interactive simulation system for agricultural product cold chain logistics, a set of software and hardware combination solutions with both flexibility and practicality is required.

[0037] The core of the system lies in simulating the complex process of cold chain logistics. Therefore, it requires powerful simulation capabilities, an intuitive user interface, and reliable data management. First, professional tools such as MATLAB Simulink or AnyLogic can be selected as the simulation engine. Such software is good at handling multi-variable systems and can accurately simulate the temperature and humidity changes of agricultural products during picking, precooling, storage, and transportation, and can even incorporate dynamic adjustments of the logistics path. The design of the user interface tends to use cross-platform solutions, such as using Qt or Electron to create a graphical operation interface, allowing users to easily set parameters, select processes, and even intervene in the simulation process in real time.

[0038] In terms of data management, the system requires a stable backend support. Relational databases such as MySQL or PostgreSQL can be selected, which can store various parameters, event records, and user-defined configurations in the simulation, facilitating future traceability or analysis. To enable users to more intuitively understand the simulation results, front-end visualization libraries such as ECharts or D3.js can be integrated to dynamically generate temperature curves, humidity change graphs, or process status diagrams. In addition, to meet the needs of advanced users, the system can also batch-configure parameters or write custom logic through Python or Lua script interfaces to further enhance flexibility.

[0039] At the hardware level, the operation of the system needs to balance performance and accessibility. The core computing part can be deployed on cloud servers such as AWS EC2 or Alibaba Cloud ECS, which not only supports multiple users to use simultaneously but also can scale computing power according to needs to ensure the stability and efficiency of the simulation process. The user side is more flexible. Whether it is a PC, tablet, or smartphone, as long as the device supports modern browsers or installs a dedicated application, it can access the system at any time.

[0040] If you want the simulation to be closer to reality, a virtual sensor module can also be added. By software-simulating the input of real temperature and humidity sensor data, the system can better reproduce the dynamic changes in the cold chain environment. This combination of software and hardware not only ensures the usability of the system but also endows it with high performance and expansion potential, sufficient to meet the needs of users of different scales.

[0041] The scope of application of the present invention is very wide and can almost cover all fields related to cold chain logistics. The following are several main application directions: For cold chain logistics enterprises, the system can be a powerful tool for employee training. New employees can quickly familiarize themselves with the cold chain process through simulation operations and understand the risks that temperature fluctuations or delays may bring. At the same time, managers can also use it to optimize actual operations, such as adjusting transportation routes, improving equipment parameters, and even predicting cost changes under different strategies, thereby reducing losses and energy consumption.

[0042] Producers can observe the performance of different agricultural products in the cold chain through simulation, such as the difference in temperature sensitivity between apples and spinach, and then adjust the picking time, pre-cooling method, or packaging materials accordingly, ultimately improving the market competitiveness of the products.

[0043] Researchers can use it to test the performance of new refrigeration equipment, compare the advantages and disadvantages of different transportation routes, or even explore new cold chain management strategies, providing data support for the development of the industry.

[0044] In colleges or vocational training, the system can be used as a teaching aid. By operating the simulation, students can deeply understand the complexity of cold chain logistics, combine theoretical knowledge with practice, and improve learning effects.

[0045] Consider the following specific case to illustrate the present invention: A certain user mainly grows apples, and the products are sensitive to temperature and humidity. If the temperature fluctuates slightly or the time is delayed during transportation, the apples are prone to rot. In the past, they relied on traditional refrigerated trucks for transportation, but often encountered problems such as unstable temperature control or overly long route time consumption, resulting in a loss rate as high as 15%, seriously affecting the revenue.

[0046] The user decides to use this simulation system to solve the problem. The user first inputs the initial state of the apples into the system, for example, the temperature at the time of picking is 20°C, the target transportation temperature is set at 0 - 2°C, the maximum allowable transportation time is 24 hours, and the packaging method is a foam box with ice packs. Then, the system starts to simulate the entire process from picking to delivery, and real-time displays the temperature and humidity changes.

[0047] During the simulation process, the system finds that the temperature once rises to 5°C during transportation, exceeding the safe range, and triggers an alarm. In response to this problem, the operation verification module proposes two suggestions: one is to increase the number of ice packs, and the other is to shorten the transportation route. The user then asks the system to compare several optional routes, and based on the comprehensive evaluation of time and risk, selects a route with shorter time consumption and higher stability.

[0048] For the convenience of analysis, the system also generates detailed temperature curves and humidity change diagrams. Through these charts, the user clearly sees the specific time points and reasons for the temperature increase, and finally decides to add an additional set of ice packs to the packaging and adopt the recommended route.

[0049] After several simulation adjustments, the user applies the new strategy to the actual transportation. The results are exciting: the loss rate of the apples drops from the original 15% to 5%, the quality is highly recognized by customers, and the economic benefits are significantly improved. This case not only demonstrates the practicality of the system, but also shows how simulation helps users discover problems, optimize solutions, and ultimately achieve the goal of reducing costs and increasing efficiency.

[0050] In a further embodiment, there are two more important innovative points of the present invention: The present invention also sets up a dynamic decay model. In cold chain logistics, the quality of agricultural products is not static, but is dynamically affected by various factors such as temperature, humidity, and gas composition. The physiological characteristics of different agricultural products vary significantly. For example, when apples and bananas are transported together, the ethylene released by bananas will accelerate the respiration of apples, resulting in a decline in quality.

[0051] Specifically, a mathematical equation can be used to describe the quality decay process. Suppose Represents the quality of agricultural products at time t, and its change can be expressed by an exponential decay model: ; Where: is the initial quality (such as the quality value at the time of just picked), is the quality decay rate (affected by environmental conditions and types of agricultural products), and t is time. For different agricultural products, usually takes the percentage of freshness determined in the laboratory (with 100 as the full score); if there is no experimental data, it can also be agreed that = 100 represents "100% ideal state".

[0052] Decay rate is closely related to environmental factors, especially temperature. The higher the temperature, the greater it is, and the faster the quality decreases. Therefore, the model often combines with the Arrhenius equation to describe the influence of temperature on : , where: is the frequency factor (related to the type of agricultural product), and can be understood as the limit value of the decay rate when the temperature tends to be infinitely high. It reflects the potential decay speed of agricultural products without temperature limitation. The quality decay rate of agricultural products (such as spoilage rate, nutrient loss rate) can be measured at different temperatures, and the measured data is substituted into the Arrhenius equation for mathematical fitting calculation, and the subsequent is also the same; is the activation energy (reflecting the sensitivity of agricultural products to temperature), is the gas constant (8.314 J / (mol·K)); is the absolute temperature (unit: Kelvin).

[0053] In addition, the humidity and gas concentration (such as ethylene concentration) in the cold chain will also affect the decay rate. Therefore, can be extended to a multivariable function: ; where: is the influence function of humidity (the specific form needs to be determined according to experimental data, such as linear or exponential form), is the influence function of gas concentration (such as the accelerating spoilage effect of ethylene on certain fruits, and the specific form can also be determined according to experimental data, such as linear or exponential form).

[0054] In some embodiments, a linear form can be adopted, such as: ; where and are the reference humidity (e.g., 90%) and the reference gas concentration (e.g., 1 ppm ethylene) respectively. The coefficients a and b are determined through simulation experiments or small-scale field tests, and typical values are a ≈ 0.01 / % and b ≈ 0.05 / ppm.

[0055] In the cold chain logistics simulation system, the dynamic decay model is integrated with the process module. After the user inputs the transportation parameters (such as time, temperature, humidity), the system can simulate the quality change of agricultural products. For example, when transporting apples and bananas, the model will analyze the impact of ethylene released by bananas on apples and suggest separate transportation or adjustment of the refrigerated truck parameters (such as reducing the temperature or increasing ventilation) to slow down the quality decay of apples. The system can also generate a visual report to intuitively display the prediction results and optimization suggestions. In actual operation, the model supports dynamic regulation. The sensors in the refrigerated truck monitor the environmental data (such as ethylene concentration) in real time. If it exceeds the safe range, the system will automatically adjust the equipment parameters to ensure the quality of agricultural products. For example, when transporting strawberries, the model may suggest using a refrigerated truck to control the temperature at 2°C and adopting modified atmosphere packaging to reduce respiration, thereby minimizing the spoilage rate.

[0056] In addition, the present invention also includes the introduction of a fuzzy logic judgment unit, which makes the system show higher intelligence and flexibility when dealing with temperature fluctuations. The design purpose of this unit is to handle the situation when the temperature fluctuation does not reach the spoilage threshold. It evaluates the cumulative impact of these fluctuations on the quality of agricultural products through scientific methods, rather than simply relying on the alarm mechanism to make judgments. For example, when the temperature briefly exceeds the ideal range during transportation but has not reached the critical point of causing spoilage, the fuzzy logic judgment unit will analyze the potential effect of this fluctuation instead of directly triggering an alarm or completely ignoring it.

[0057] To achieve this, the system divides the temperature fluctuations into different categories, such as "normal", "slight fluctuation", "moderate fluctuation", and "severe fluctuation". Each category corresponds to a mathematical function called a membership function, which is used to quantify the degree to which a certain temperature change belongs to this category. For example, assume that the amplitude of the temperature deviation from the reference value (such as the ideal transportation temperature) at a certain moment is represented by The membership function can be used to describe the possibility that this fluctuation belongs to "slight fluctuation". One possible definition is: ; In this formula, if the temperature deviation amplitude is less than or equal to 2°C, the system considers the fluctuation to be completely within the "slight fluctuation" range; if the deviation amplitude is between 2°C and 5°C, the membership degree gradually decreases; once it exceeds 5°C, it is no longer classified as "slight fluctuation". Similar functions can also be used to define "moderate fluctuation" or "severe fluctuation", enabling the system to more precisely understand the characteristics of temperature changes.

[0058] In actual operation, the fuzzy logic judgment unit relies on a set of preset rules to deduce the impact of temperature fluctuations on quality. These rules usually originate from expert experience or experimental data. For example, when there is a slight temperature fluctuation and the duration is very short, the system may judge that there is almost no impact on the quality of agricultural products. If the fluctuation amplitude reaches a moderate level and the duration is relatively long, the system may determine that the quality has been slightly damaged. In this way, the fuzzy logic judgment unit can calculate the membership degree of each fluctuation based on the real-time monitored temperature data, and then combine the rules for reasoning to finally obtain the degree of quality impact.

[0059] Furthermore, to capture the cumulative effect of temperature fluctuations over a long period, the fuzzy logic judgment unit also introduces the concept of time integration. Suppose at a certain time point t, the fuzzy value of the quality impact calculated by the system is represented by Then the cumulative impact during the entire transportation process can be expressed by the following formula: ; This cumulative value reflects the overall effect of temperature fluctuations on quality. If exceeds a certain preset critical value, the system may recommend taking intervention measures, such as reducing the temperature of the refrigerated truck or accelerating the transportation process. This method is more delicate than the traditional binary judgment (i.e., whether the temperature exceeds a certain fixed threshold), and can avoid misjudgments caused by ignoring short-term fluctuations or overly strict alarms.

[0060] In the simulation, discrete time steps can be approximated: if the total is divided into N steps, and the time step size is Δt then: ; where is the membership degree of the quality impact calculated at the j step. The system accumulates after each step calculation and triggers a reminder when I exceeds the threshold (for example, I >0.5h).

[0061] Taking a specific scenario as an example, assume that when transporting strawberries, the ideal temperature range is 0 - 2°C. During a certain transportation, the temperature rises from 2°C to 5°C within 5 minutes and then drops back to 2°C. The traditional system might choose not to alarm because this fluctuation does not reach the spoilage threshold (such as 10°C). However, the fuzzy logic judgment unit will analyze the amplitude (3°C) and duration (5 minutes) of this fluctuation, and combined with the membership function and rules, determine that it may have a slight impact on the strawberry quality. If such fluctuations occur repeatedly during transportation and the cumulative impact gradually increases, the system will remind the manager to take actions at an appropriate time, such as checking the cold chain equipment or optimizing the transportation route.

[0062] In summary, the full - process interactive simulation system for agricultural product cold chain logistics of the present invention realizes all - around functions from parameter configuration to logical judgment, operation verification, data recording, visualization display, and user interaction through modular design. Each part of the system collaborates closely to provide users with a flexible, comprehensive, and easy - to - use simulation platform, helping to improve the efficiency and quality of agricultural product cold chain logistics. Through rich parameter settings, real - time feedback, and intuitive display, users can not only simulate complex cold chain scenarios but also obtain optimization strategies and decision - making support from them.

[0063] The above - mentioned is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent replacements or changes, and all should be covered within the protection scope of the present invention.

Claims

1. An interactive simulation system for the entire process of cold chain logistics of agricultural products, characterized in that, Including: A cold chain process module for simulating the logistics path and temperature and humidity state changes of agricultural products in multiple links; A parameter configuration module for setting environmental parameters, product parameters, and equipment parameters for each link in the cold chain process module; A logical judgment module for judging whether the operation logic is reasonable according to user-configured parameters and built-in standard rules; An operation verification module for providing feedback on the logical judgment result and giving operation suggestions; A data recording module for recording input parameters, judgment results, and system responses during all simulation processes and forming a traceable data chain; A visualization display module for graphically displaying key indicators, process states, and alarm events during the simulation process; A user interaction terminal for providing a user interface and allowing users to perform parameter modification, link selection, and exception intervention operations during the simulation process.

2. The agricultural product cold chain logistics full-process interactive simulation system according to claim 1, characterized in that The cold chain process module further includes a dynamic attenuation model module, and the dynamic attenuation model module is used to simulate the quality change process of agricultural products affected by temperature, humidity, and gas components in each link of the cold chain logistics.

3. The agricultural product cold chain logistics full-process interactive simulation system according to claim 2, characterized in that, The dynamic decay model module simulates the quality of agricultural products based on the following formula : ； Wherein: is the initial quality, is the quality attenuation rate, and t is the time; And: ； Wherein: is the frequency factor of agricultural products; is the activation energy of agricultural products; is the gas constant; is the absolute temperature; is the humidity influence function on agricultural products, is the gas concentration influence function on agricultural products.

4. The agricultural product cold chain logistics full-process interactive simulation system according to claim 1, wherein, The logical judgment module further includes a fuzzy logic judgment unit, and the fuzzy logic judgment unit is used to evaluate the cumulative impact on the quality of agricultural products when the temperature fluctuation does not reach the spoilage threshold.

5. The agricultural product cold chain logistics full-process interaction simulation system according to claim 1, wherein The multiple links in the cold chain process module include any one or more of the following: picking, commercialization processing, precooling, storage, transportation, and end display.

6. The agricultural product cold chain logistics full-process interactive simulation system according to claim 1, wherein, The logical judgment module includes a conditional tree structure generation unit and a judgment execution unit. The conditional tree structure generation unit generates multi-layer conditional judgment logic based on user-set parameters, and the judgment execution unit calculates the conditional tree layer by layer according to the real-time states of various variables during the simulation process to obtain a judgment result.

7. The agricultural product cold chain logistics full-process interactive simulation system according to claim 1, wherein The data recording module includes an event coding sub-module and a timestamp marking sub-module. The event coding sub-module generates a unique event code for each parameter change, judgment result, and response action, and the timestamp marking sub-module marks the time points of all event codes based on the simulation clock and stores them in the data traceability database.

8. The agricultural product cold chain logistics full-process interactive simulation system according to claim 1, characterized in that The operation verification module includes a rule engine sub-module. The rule engine sub-module embeds multiple industry cold chain standard items, and the rule engine sub-module calls the standard items for real-time comparison during the simulation execution process and judges whether there are any illegal operations.

9. The whole-process interactive simulation system for agricultural product cold chain logistics according to claim 1, wherein, The transportation link in the cold chain process module supports route selection simulation, and the system selects the optimal cold chain path based on the following path superiority judgment formula: ； Among them, is the cold chain cost valuation function, is the estimated total transportation time, is the cold chain integrity risk level, is the volatility of the in-transit environment, , , are the system preset weight coefficients.

10. The agricultural product cold chain logistics full-process interactive simulation system according to claim 1, wherein The user interaction terminal is connected to the data recording module and supports users to export a complete simulation report including simulation input, output, and process logs after the simulation is completed. The simulation report displays the whole process index changes and risk event distributions in a combined way of tables and graphs.

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