A food delivery intelligent control method and system based on big data

Abstract

The present invention proposes a method and system for intelligent food distribution control based on big data, wherein the method includes: real-time collection of the temperature and humidity of the delivery compartment during the delivery process, collection of the location information and speed of the delivery vehicle, and acquisition of sensitive parameters of the food delivered in the delivery vehicle; acquisition of all accessible paths, and use of a prediction model to predict the remaining freshness time of the food delivered on all accessible paths based on the current internal environmental parameters of the compartment, the freshness status data of the food, and the subsequent path parameters, wherein the path parameters include weather and road conditions, and selection of accessible paths in which the difference between the remaining freshness time and the arrival time of the accessible paths does not exceed a preset time threshold; and acquisition of the accessible path with the lowest cost calculated by the environmentally sensitive supply chain cost model from the selected accessible paths. Through this method and the corresponding system, it is ensured that the food arrives at the lowest cost and maintains optimal freshness upon delivery completion, thereby reducing quality losses caused by transportation.

Description

Technical Field

[0001] The present invention proposes a food delivery intelligent control method and system based on big data, which relate to the field of artificial intelligence technology. Background Art

[0002] In the current development context, big data technology has gradually been introduced into intelligent control systems for food delivery to improve efficiency, reduce costs, and enhance customer satisfaction. However, existing intelligent control methods for food delivery still have some significant flaws and challenges: Current intelligent distribution control systems often lack real-time dynamic adjustment capabilities. Once traffic congestion, equipment failure, or demand changes occur, the system is difficult to respond and adjust in a timely manner. Most systems rely solely on pre-planned plans, lacking flexibility and resulting in inadequate resource utilization. Many methods fail to effectively predict and control food loss caused by dynamic changes in environmental factors (such as temperature and humidity). The lack of sophisticated environmental monitoring leads to insufficient visibility of cold chain management and delivery status, which can easily lead to food damage and waste. Most existing systems rely on rule-driven decision-making models rather than data-driven intelligent analysis, and are unable to fully utilize machine learning and artificial intelligence technologies to optimize route selection. Summary of the Invention

[0003] The present invention provides a method and system for intelligent food distribution control based on big data to solve the above-mentioned problems:

[0004] The present invention proposes a method for intelligent food distribution control based on big data, the method comprising:

[0005] Real-time collection of temperature and humidity in the delivery compartment during the delivery process, collection of the delivery vehicle's location information and speed, and acquisition of sensitive parameters of the food being delivered in the delivery vehicle;

[0006] Obtain all accessible routes and use a prediction model to predict the remaining shelf life of the food delivered along all accessible routes based on the current vehicle interior environmental parameters, the food's freshness status data, and the upcoming route parameters, including weather and road conditions. Filter out accessible routes where the difference between the remaining shelf life and the arrival time of the accessible route does not exceed a preset time threshold.

[0007] The reachable path with the minimum cost calculated by the environmentally sensitive supply chain cost model is obtained from the screened reachable paths.

[0008] Furthermore, the temperature and humidity in the delivery compartment during the delivery process are collected in real time, as well as the location information and speed of the delivery vehicle, to obtain sensitive parameters of the food being delivered in the delivery compartment, including:

[0009] The temperature and humidity of the food delivery compartment are collected through temperature sensors and humidity sensors respectively;

[0010] The location information and speed of delivery vehicles are collected in real time through GPS devices and on-board OBD devices;

[0011] Get the sensitive parameters of the food delivered by the delivery vehicle.

[0012] Furthermore, sensitive parameters of the food delivered by the delivery vehicle are obtained, including:

[0013] Determine the range of environmental variables that need to be tested, including: setting the temperature gradient, setting the humidity gradient, and setting the test time period to select the target food sample;

[0014] Storing target food samples under different temperature and humidity conditions and regularly recording their freshness status, including weight, nutritional content, moisture loss rate, color change, and texture change. The color change is detected by a colorimeter, and the texture change is detected by a hardness meter.

[0015] Record the temperature and humidity in the test environment in real time, take samples within a preset time, and save the collected data as a structured data table. The structured data table includes the following fields: time, temperature, humidity, and freshness status;

[0016] Performing preliminary data cleaning on the data stored in the structured data table, wherein the preliminary data cleaning includes: removing outliers and averaging the data of repeated experiments under the same conditions;

[0017] Substitute the experimental data into the sensitive parameter model. Specifically, the sensitive parameter model is:

[0018]

[0019] in, represents the rate of change of food freshness, k represents the process rate constant, T is the temperature in the actual operating environment, represents the temperature constant, H represents the relative humidity, the logarithm of the sensitive parameter model is taken, n represents the sensitivity coefficient of the food p loss to temperature, m represents the sensitivity coefficient of the food p loss to humidity, the sensitive parameter model is converted into a linear equation, and multiple linear regression is used to calculate k, n and m in the sensitive parameter model;

[0020] The least squares method is used to optimize the fitting error and verify the distribution of the fitting residuals. When the residual distribution does not exceed a preset threshold, the final sensitive parameter model is obtained.

[0021] Furthermore, all accessible routes are obtained, and a prediction model is used to predict the remaining freshness time of the delivered ingredients on all accessible routes based on the current interior environment parameters of the vehicle, the freshness status data of the ingredients, and the upcoming route parameters. The route parameters include weather and road conditions. The accessible routes are screened out, and the difference between the remaining freshness time and the arrival time of the accessible routes does not exceed a preset time threshold. These include:

[0022] Obtaining historical temperature and humidity data inside the vehicle compartment, and obtaining historical food preservation status data, the historical food preservation status data including weight, nutritional content, moisture loss rate, color change, texture change, sugar content, and food sensitive parameters, the food sensitive parameters including sensitivity coefficients to temperature and humidity;

[0023] Obtain forecast data for weather conditions along the upcoming transport route, process and clean the collected historical temperature, humidity, and freshness data, remove outliers and noise, and extract important features from the cleaned historical data. These features include the rate of change of temperature inside the vehicle compartment, the rate of change of freshness data, and the forecasted temperature and relative humidity.

[0024] Constructing an LSTM model, incorporating the extracted important features into the LSTM model, dividing the historical data into a training set, a validation set, and a test set, and training the LSTM model using the training set so that the model input features are: the temperature change rate inside the vehicle compartment, the change rate of the fresh-keeping status data, and the temperature and relative humidity of the forecast data, and the output is the remaining fresh-keeping time. The validation set verifies the accuracy of the LSTM model, and then adjusts the model parameters. After the adjustments, the performance of the LSTM model is evaluated using the test set.

[0025] Obtain important features of the current food transportation process, input the important features into the trained LSTM model, obtain the remaining shelf life of the food, and screen out reachable paths where the difference between the remaining shelf life and the arrival time of the reachable path does not exceed a preset time threshold.

[0026] Furthermore, the accessible path with the minimum cost calculated by the environmentally sensitive supply chain cost model is obtained from the screened accessible paths, including:

[0027] Define an environmentally sensitive supply chain cost model. Specifically, the environmentally sensitive supply chain cost model is:

[0028]

[0029] Where R(x,y) represents the transportation volume from the starting point to the point (x,y), C(x,y) represents the cost per unit of transportation volume, a represents the unit of loss cost, n represents the sensitivity coefficient of the loss of food p to temperature, and m represents the sensitivity coefficient of the loss of food p to humidity. represents the loss rate of food p, represents the end time of the transport operation at a specific location (x,y) and a specific product p,

[0030] The constraints of the environmentally sensitive supply chain cost model are:

[0031] , constraining each customer's location to be visited once and only once;

[0032] ≤ the preset loss threshold, constraining the total loss to not exceed the acceptable loss;

[0033] Each screened path is evaluated using the environmentally sensitive supply chain cost model, the total cost of each path is calculated, the costs of all paths are compared, and the path with the minimum cost is found.

[0034] The present invention proposes a food delivery intelligent control system based on big data, the system comprising:

[0035] The collection module is used to collect real-time temperature and humidity information in the delivery compartment during the delivery process, collect the location information and speed of the delivery vehicle, and obtain sensitive parameters of the delivered ingredients in the delivery vehicle;

[0036] A prediction and screening module is used to obtain all accessible routes and use a prediction model to predict the remaining freshness time of the ingredients delivered on all accessible routes based on the current internal vehicle environmental parameters, the freshness status data of the ingredients, and the upcoming route parameters. Route parameters include weather and road conditions. The module then screens out accessible routes where the difference between the remaining freshness time and the arrival time of the accessible routes does not exceed a preset time threshold.

[0037] The module for obtaining the minimum cost path is used to obtain the minimum cost accessible path calculated by the environmentally sensitive supply chain cost model from the screened accessible paths.

[0038] Furthermore, the acquisition module includes:

[0039] The temperature and humidity collection module is used to collect the temperature and humidity of the food delivery compartment through temperature sensors and humidity sensors respectively;

[0040] The vehicle information collection module is used to collect the location information and speed of the delivery vehicle in real time through the GPS device and the on-board OBD device;

[0041] The module for obtaining sensitive parameters of ingredients is used to obtain sensitive parameters of ingredients delivered by the delivery vehicle.

[0042] Furthermore, the module for obtaining sensitive parameters of ingredients includes:

[0043] The module for determining the range of environmental variables is used to determine the range of environmental variables that need to be tested, including: setting the temperature gradient, setting the humidity gradient, and setting the test time period to select the target food sample;

[0044] A recording module is used to store target food samples under different temperature and humidity conditions and regularly record their freshness status, including weight, nutritional content, moisture loss rate, color change, and texture change. The color change is detected by a colorimeter, and the texture change is detected by a hardness meter.

[0045] The structured data module is used to record the temperature and humidity in the test environment in real time, perform sampling within a preset time, and save the collected data as a structured data table. The structured data table includes the following fields: time, temperature, humidity, and freshness status;

[0046] A preliminary cleaning module is used to perform preliminary data cleaning on the data stored in the structured data table, wherein the preliminary data cleaning includes: removing outliers and averaging the data of repeated experiments under the same conditions;

[0047] The preliminary calculation module is used to substitute the experimental data into the sensitive parameter model. Specifically, the sensitive parameter model is:

[0048]

[0049] in, represents the rate of change of food freshness, k represents the process rate constant, T is the temperature in the actual operating environment, represents the temperature constant, H represents the relative humidity, the logarithm of the sensitive parameter model is taken, n represents the sensitivity coefficient of the food p loss to temperature, m represents the sensitivity coefficient of the food p loss to humidity, the sensitive parameter model is converted into a linear equation, and multiple linear regression is used to calculate k, n and m in the sensitive parameter model;

[0050] The fitting error module is used to optimize the fitting error using the least squares method, verify the fitting residual distribution, and obtain the final sensitive parameter model when the residual distribution does not exceed a preset threshold.

[0051] Furthermore, the prediction and screening module includes:

[0052] a data acquisition module for acquiring historical temperature and humidity data within the carriage, and acquiring historical food preservation status data, wherein the historical food preservation status data includes weight, nutritional content, moisture loss rate, color change, texture change, sugar content, and food sensitive parameters, wherein the food sensitive parameters include sensitivity coefficients to temperature and sensitivity coefficients to humidity;

[0053] The feature extraction module is used to obtain forecast data on weather conditions along the next transportation route, process and clean the collected historical temperature, humidity, and freshness data, remove outliers and noise, and extract important features from the cleaned historical data. These important features include the temperature change rate inside the carriage, the change rate of freshness data, and the temperature and relative humidity of the forecast data.

[0054] A training model module is used to construct an LSTM model, incorporate the extracted important features into the LSTM model, divide the historical data into a training set, a validation set, and a test set, and train the LSTM model using the training set. The model input features are: the temperature change rate inside the vehicle compartment, the change rate of the fresh-keeping status data, the temperature and relative humidity of the forecast data, and the output is the remaining fresh-keeping time. The validation set verifies the accuracy of the LSTM model, and then adjusts the model parameters. After the adjustments, the performance of the LSTM model is evaluated using the test set.

[0055] The prediction module is used to obtain important features of the current food transportation process, input the important features into the trained LSTM model, obtain the remaining shelf life of the food, and screen out reachable paths in which the difference between the remaining shelf life and the arrival time of the reachable path does not exceed a preset time threshold.

[0056] Furthermore, the module for obtaining the minimum cost path includes:

[0057] Define an environmentally sensitive supply chain cost model. Specifically, the environmentally sensitive supply chain cost model is:

[0058]

[0059] Where R(x,y) represents the transportation volume from the starting point to the point (x,y), C(x,y) represents the cost per unit of transportation volume, a represents the unit of loss cost, n represents the sensitivity coefficient of the loss of food p to temperature, and m represents the sensitivity coefficient of the loss of food p to humidity. represents the loss rate of food p, represents the end time of the transport operation at a specific location (x,y) and a specific product p,

[0060] The constraints of the environmentally sensitive supply chain cost model are:

[0061] , constraining each customer's location to be visited once and only once;

[0062] ≤ the preset loss threshold, constraining the total loss to not exceed the acceptable loss;

[0063] Each screened path is evaluated using the environmentally sensitive supply chain cost model, the total cost of each path is calculated, the costs of all paths are compared, and the path with the minimum cost is found.

[0064] The beneficial effects of the present invention are as follows: improving freshness, ensuring that food ingredients maintain optimal freshness upon delivery by accurately controlling and predicting the freshness time, and reducing quality losses caused by transportation; reducing costs, optimizing route selection by considering not only time but also cost-effectiveness, and ensuring that quality targets are achieved at the lowest cost during transportation; having dynamic adjustment capabilities, which can be flexibly adjusted according to real-time environmental changes and predictions, and improving the response speed of delivery route selection; enhancing decision-making efficiency, improving the scientific nature and operability of delivery decisions and improving logistics efficiency by integrating a variety of real-time and predictive data; improving customer satisfaction, ensuring that products are delivered to customers in the best condition and at the lowest cost, and improving customer satisfaction and loyalty; through the above technical solutions, the delivery process is not only optimized in terms of time and cost, but also effectively guarantees product quality, fully reflecting the advantages of intelligent logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a schematic diagram of a method for intelligent food delivery control based on big data as described in the present invention. DETAILED DESCRIPTION

[0066] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein may be combined with each other.

[0067] The following description sets forth numerous specific details to facilitate a thorough understanding of the present invention. The embodiments described are merely a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0069] One embodiment of the present invention provides a method for intelligently controlling food delivery based on big data, the method comprising:

[0070] Real-time collection of temperature and humidity in the delivery compartment during the delivery process, collection of the delivery vehicle's location information and speed, and acquisition of sensitive parameters of the food being delivered in the delivery vehicle;

[0071] Obtain all accessible routes and use a prediction model to predict the remaining shelf life of the food delivered along all accessible routes based on the current vehicle interior environmental parameters, the food's freshness status data, and the upcoming route parameters, including weather and road conditions. Filter out accessible routes where the difference between the remaining shelf life and the arrival time of the accessible route does not exceed a preset time threshold.

[0072] The reachable path with the minimum cost calculated by the environmentally sensitive supply chain cost model is obtained from the screened reachable paths.

[0073] The working principle and effect of the above technical solution are: real-time collection of temperature and humidity data in the delivery compartment to monitor and record environmental conditions that affect the preservation of food, collection of location information and speed of the delivery vehicle to calculate transportation time and evaluate the feasibility of different routes, acquisition and recording of sensitive parameters of the delivered food, such as temperature and humidity sensitivity, and the freshness status of each food; route information acquisition, obtain information on all accessible routes, including weather forecasts, road conditions and estimated driving time, which provide basic data for route selection; use deep learning or other prediction models to comprehensively analyze the current compartment environmental parameters (temperature, humidity), The freshness status of ingredients and the environmental conditions of future routes are used to predict the remaining freshness time of ingredients delivered on each route; based on the current situation and predictions, each optional route is analyzed to calculate the freshness status of ingredients at the end of delivery; the estimated arrival time of each achievable route and the remaining freshness time of ingredients are compared, and the route where the difference between the freshness time and the arrival time does not exceed the preset threshold is selected. This step ensures that the ingredients still meet the quality standards when they reach the destination; cost optimization, applying the environmentally sensitive supply chain cost model to the selected routes, calculating the comprehensive transportation cost, including logistics costs and possible loss costs; and selecting the achievable route with the lowest comprehensive cost as the final transportation plan. Improve freshness by accurately controlling and predicting the shelf life to ensure that ingredients maintain optimal freshness upon delivery, thereby reducing quality losses caused by transportation; reduce costs by optimizing route selection to consider not only time but also cost-effectiveness to ensure that quality targets are achieved at the lowest cost during transportation; have dynamic adjustment capabilities to flexibly adjust according to real-time environmental changes and predictions, thereby improving the responsiveness of delivery route selection; enhance decision-making efficiency by integrating a variety of real-time and predictive data to improve the scientific nature and operability of delivery decisions and enhance logistics efficiency; improve customer satisfaction by ensuring that products are delivered to customers in the best condition and at the lowest cost, thereby improving customer satisfaction and loyalty; through the above technical solutions, the delivery process is not only optimized in terms of time and cost, but also effectively guarantees product quality, fully reflecting the advantages of intelligent logistics management.

[0074] One embodiment of the present invention collects the temperature and humidity in the delivery compartment in real time during the delivery process, collects the location information and speed of the delivery vehicle, and obtains sensitive parameters of the food delivered by the delivery compartment, including:

[0075] The temperature and humidity of the food delivery compartment are collected through temperature sensors and humidity sensors respectively;

[0076] The location information and speed of delivery vehicles are collected in real time through GPS devices and on-board OBD devices;

[0077] Get the sensitive parameters of the food delivered by the delivery vehicle.

[0078] The working principle and effect of the above technical solution are as follows: through the coordinated work of multiple sensors and devices, the freshness of food and logistics efficiency during the delivery process are monitored and optimized in real time. The temperature sensor is installed inside the delivery vehicle to monitor the temperature changes in the vehicle in real time and ensure that the temperature is maintained within the range suitable for food freshness. The humidity sensor is also installed in the vehicle to monitor the humidity level in real time. Some food is very sensitive to humidity, and maintaining the appropriate humidity can effectively extend the shelf life of the food.

[0079] GPS devices provide highly accurate geographic location information, ensuring route accuracy by tracking the vehicle's route and location in real time. On-Board Diagnostics (OBD) devices connect to the vehicle's OBD port and collect real-time data on the vehicle's speed and other dynamic conditions, helping to understand the vehicle's operating status and detect any issues that may affect transportation efficiency. The database stores and reads sensitive parameters for each delivered ingredient, including optimal temperature and humidity values, as well as critical preservation conditions. These parameters provide specific guidance for environmental control and help predict the potential risk of food spoilage during delivery. Improved freshness: Continuous monitoring of temperature and humidity ensures a stable vehicle environment, maximizing the freshness and shelf life of food. Real-time monitoring and response: Combined with vehicle dynamics information provided by devices like GPS and OBD, logistics managers can adjust delivery strategies in real time, such as changing routes or strengthening cold chain measures mid-transit, allowing for quicker responses to unexpected situations like traffic jams or equipment failures. All collected data can be recorded and analyzed, providing detailed data support for subsequent optimization and making the logistics process more transparent and controllable. Improved operational efficiency: By proactively identifying and preparing adjustments and repairs for possible temperature and humidity fluctuations or logistics delays, unnecessary losses and costs can be reduced, thereby improving overall operational efficiency. Real-time data collection and monitoring can also help identify loopholes or issues in the logistics process (such as vehicle failures or sensor errors) so that corrective measures can be taken quickly. Through this solution, companies can not only optimize their supply chain and logistics management, but also significantly improve product quality and customer satisfaction. This high-precision, real-time monitoring technology is a crucial component of modern intelligent transportation systems.

[0080] In one embodiment of the present invention, obtaining sensitive parameters of food delivered by a delivery vehicle includes:

[0081] Determine the range of environmental variables that need to be tested, including: setting the temperature gradient, setting the humidity gradient, and setting the test time period to select the target food sample;

[0082] Storing target food samples under different temperature and humidity conditions and regularly recording their freshness status, including weight, nutritional content, moisture loss rate, color change, and texture change. The color change is detected by a colorimeter, and the texture change is detected by a hardness meter.

[0083] Record the temperature and humidity in the test environment in real time, take samples within a preset time, and save the collected data as a structured data table. The structured data table includes the following fields: time, temperature, humidity, and freshness status;

[0084] Performing preliminary data cleaning on the data stored in the structured data table, wherein the preliminary data cleaning includes: removing outliers and averaging the data of repeated experiments under the same conditions;

[0085] Substitute the experimental data into the sensitive parameter model. Specifically, the sensitive parameter model is:

[0086]

[0087] in, represents the rate of change of food freshness, k represents the process rate constant, T is the temperature in the actual operating environment, represents the temperature constant, H represents the relative humidity, the logarithm of the sensitive parameter model is taken, n represents the sensitivity coefficient of the food p loss to temperature, m represents the sensitivity coefficient of the food p loss to humidity, the sensitive parameter model is converted into a linear equation, and multiple linear regression is used to calculate k, n and m in the sensitive parameter model;

[0088] The least squares method is used to optimize the fitting error and verify the distribution of the fitting residuals. When the residual distribution does not exceed a preset threshold, the final sensitive parameter model is obtained.

[0089] The working principle and effect of the above technical solution are as follows: the above technical solution is used to quantify the freshness changes of food under different temperature and humidity conditions, prepare the test environment, set the variable range, and determine different environmental conditions by adjusting the temperature gradient, humidity gradient and test time period to test the preservation response of food samples; store the selected food samples under these conditions, and use tools such as colorimeters and hardness meters to monitor and record the color and texture changes of the food, and record the preservation status such as weight, nutritional content and moisture loss rate; monitor and record temperature and humidity data in real time, as well as the preservation status of the food at the above time points, and store these data in a structured data table format; clean the collected data, remove outliers, and average repeated data under the same experimental conditions to reduce noise; substitute specific sensitive parameter model equations, use multiple linear regression, convert the equations into linear equations by taking the logarithm of the equations, calculate the model parameters, use the least squares method to optimize the fitting error, verify the distribution of the fitting residuals, and ensure that the residuals are within an acceptable preset threshold range to ensure the accuracy of the model. Generating accurate sensitive parameter models can better predict the freshness changes of food ingredients under specific environmental conditions, thereby optimizing storage and transportation strategies. Through quantitative analysis of freshness changes, it can assist in formulating temperature and humidity control rules to extend the shelf life and improve product quality. It can help logistics and food supply chain managers optimize cold chain management and reduce food loss and economic losses caused by improper environment. It not only improves the understanding of the food preservation process, but also strengthens the possibility of scientific management and data-driven optimization measures.

[0090] One embodiment of the present invention obtains all accessible routes, uses a prediction model to predict the remaining freshness time of food delivered on all accessible routes based on current vehicle interior environmental parameters, food freshness status data, and upcoming route parameters, where the route parameters include weather and road conditions, and selects accessible routes where the difference between the remaining freshness time and the arrival time of the accessible routes does not exceed a preset time threshold, including:

[0091] Obtaining historical temperature and humidity data inside the vehicle compartment, and obtaining historical food preservation status data, the historical food preservation status data including weight, nutritional content, moisture loss rate, color change, texture change, sugar content, and food sensitive parameters, the food sensitive parameters including sensitivity coefficients to temperature and humidity;

[0092] Obtain forecast data for weather conditions along the upcoming transport route, process and clean the collected historical temperature, humidity, and freshness data, remove outliers and noise, and extract important features from the cleaned historical data. These features include the rate of change of temperature inside the vehicle compartment, the rate of change of freshness data, and the forecasted temperature and relative humidity.

[0093] Constructing an LSTM model, incorporating the extracted important features into the LSTM model, dividing the historical data into a training set, a validation set, and a test set, and training the LSTM model using the training set so that the model input features are: the temperature change rate inside the vehicle compartment, the change rate of the fresh-keeping status data, and the temperature and relative humidity of the forecast data, and the output is the remaining fresh-keeping time. The validation set verifies the accuracy of the LSTM model, and then adjusts the model parameters. After the adjustments, the performance of the LSTM model is evaluated using the test set.

[0094] Obtain important features of the current food transportation process, input the important features into the trained LSTM model, obtain the remaining shelf life of the food, and screen out reachable paths where the difference between the remaining shelf life and the arrival time of the reachable path does not exceed a preset time threshold.

[0095] The working principle and effect of the above technical solution are: data collection and organization, acquisition of historical data, collection of historical temperature and humidity data inside the carriage, and historical freshness status data of food ingredients. These data include multiple attributes, such as weight, nutrients, water loss, color change, texture change, sugar content, and sensitivity coefficients to temperature and humidity; obtaining weather forecast data for the upcoming transportation route, including temperature and relative humidity; removing outliers and noise in the data to ensure data quality, and extracting important features from the cleaned data, such as the rate of change of temperature inside the carriage, the rate of change of freshness status data, and the temperature and relative humidity in the forecast data. These features will provide input variables for the model; constructing an LSTM model, and using the extracted important features to construct an LSTM model to capture the dynamic changes and long-term dependencies of time series data; historical The historical data is divided into training set, validation set and test set for model training, parameter adjustment and performance evaluation; the LSTM model is trained with the input features of temperature change rate, freshness state change rate, predicted temperature and relative humidity; the output is the remaining freshness time; the accuracy of the model is tested through the validation set, and the model parameters are adjusted according to the results, and the final model performance is evaluated using the test set to ensure that it can work reliably in practical applications; real-time important features are obtained during the current transportation process, and these features are input into the trained LSTM model to predict the remaining freshness time of the food; and those reachable paths are screened out where the difference between the remaining freshness time and the path arrival time does not exceed the preset time threshold. Precise freshness prediction: Using the LSTM model to accurately predict the remaining freshness of ingredients under different environmental conditions, this improves control over the freshness of ingredients during delivery. Optimize delivery routes: Incorporating information about the remaining freshness, the most appropriate delivery route is selected to ensure that ingredients arrive in optimal freshness. Improve logistics efficiency and customer satisfaction: By optimizing route selection and transportation conditions management, food loss is reduced, delivery efficiency is improved, and customer satisfaction is enhanced. Dynamic adjustment and real-time decision support: During transportation, transportation strategies are adjusted based on real-time data to respond to sudden changes, enhancing the flexibility of logistics management. Provide a foundation for supply chain optimization: The insights and data analysis gained through this technical solution can provide support and basis for further optimization of the cold chain supply chain. By integrating advanced data analysis technologies and predictive models, not only can the freshness of ingredients during delivery be effectively managed, but the overall efficiency and responsiveness of transportation and logistics management can also be greatly improved.

[0096] In one embodiment of the present invention, obtaining a reachable path with the minimum cost calculated by an environmentally sensitive supply chain cost model from among the screened reachable paths includes:

[0097] Define an environmentally sensitive supply chain cost model. Specifically, the environmentally sensitive supply chain cost model is:

[0098]

[0099] Where R(x,y) represents the transportation volume from the starting point to the point (x,y), C(x,y) represents the cost per unit of transportation volume, a represents the unit of loss cost, n represents the sensitivity coefficient of the loss of food p to temperature, and m represents the sensitivity coefficient of the loss of food p to humidity. represents the loss rate of food p, represents the end time of the transport operation at a specific location (x,y) and a specific product p,

[0100] The constraints of the environmentally sensitive supply chain cost model are:

[0101] , constraining each customer's location to be visited once and only once;

[0102] ≤ the preset loss threshold, constraining the total loss to not exceed the acceptable loss;

[0103] Each screened path is evaluated using the environmentally sensitive supply chain cost model, the total cost of each path is calculated, the costs of all paths are compared, and the path with the minimum cost is found.

[0104] The working principle and effect of the above technical solution are as follows: when delivering food to customers, the delivery order will affect the quality of certain products (such as temperature-sensitive fresh products). In order to better deal with this situation, the environmentally sensitive supply chain cost model is designed to be more refined. The goal is to minimize the direct costs of transportation routes. The route selection and the order in which the ingredients are delivered to customers are not only related to the transportation distance, but also affect the fuel usage and driving time, and the continuous loss. The term considers the effects of temperature and humidity on food preservation, and evaluates the state changes of food through the derivative of the loss rate. The freshness of food directly affects the quality and safety of the final product. Optimizing this part can reduce the risk of food spoilage due to environmental conditions; reduce economic losses, decreased customer satisfaction, and possible return costs caused by food spoilage; by better controlling losses, food waste can be reduced and sustainable development can be promoted; the loss of fresh food does not occur instantaneously, but accumulates during transportation. The integral term can naturally express this cumulative effect by calculating the small losses at each moment and summing them to obtain the total loss; environmental parameters (temperature and humidity) change over time, and the loss rate also changes accordingly. The integral formula can clearly express this time dependence and accurately reflect the impact of environmental conditions at different time points on product loss; in actual transportation, such as cold chain transportation, temperature and humidity are not constant. The use of integrals can dynamically evaluate the impact of these environmental parameters on the product over time, so that the model can more accurately simulate the actual situation; through integration, It can accurately accumulate the loss at each moment and reflect the precise loss situation; it can flexibly adjust the model according to the temperature and humidity information at different time points, without assuming that the environmental parameters are constant; it is suitable for transportation simulation and optimization in different regions and under different weather conditions; assuming that a specific product p is particularly sensitive to temperature, the loss rate of the product will increase significantly with every 1 degree increase in temperature. Then, if a heat wave is encountered during transportation, the loss of this product will increase sharply for a certain period of time. In this case, the integral formula can well capture the dynamic changes in the loss rate and reflect the actual total loss. Using this integral term to calculate the loss can carefully and accurately evaluate the loss caused to the product due to changes in environmental factors such as time, temperature, and humidity during the entire logistics process. It can very effectively simulate and optimize the logistics distribution process in complex actual environments, and improve the accuracy and scientificity of the model. Overall cost minimization: By integrating all relevant costs, the system achieves comprehensive optimization of the total supply chain cost, enabling companies to specifically identify and reduce unnecessary expenses in the logistics process. Loss control: By assessing and minimizing quality loss costs caused by environmental factors such as temperature and humidity, the system improves product freshness and customer satisfaction. Supporting large-scale data analysis and integrating machine learning models (such as LSTM) enables more accurate and predictive decision-making. Supply chain efficiency is improved by comparing the total costs of different transportation options, facilitating the rational allocation of logistics resources and selecting the optimal route. By considering environmental sensitivity, the system enables rapid response to market changes, such as shifting climate conditions and customer demand. Through a clear mathematical structure and parametric design, the system provides a method for integrating complex data analysis and operational optimization into logistics and supply chain management. This not only enhances companies' responsiveness and decision-making efficiency in market competition, but also improves the cost-effectiveness of the entire supply chain, promoting sustainable development.

[0105] One embodiment of the present invention provides a food delivery intelligent control system based on big data, the system comprising:

[0106] The collection module is used to collect real-time temperature and humidity information in the delivery compartment during the delivery process, collect the location information and speed of the delivery vehicle, and obtain sensitive parameters of the delivered ingredients in the delivery vehicle;

[0107] A prediction and screening module is used to obtain all accessible routes and use a prediction model to predict the remaining freshness time of the ingredients delivered on all accessible routes based on the current internal vehicle environmental parameters, the freshness status data of the ingredients, and the upcoming route parameters. Route parameters include weather and road conditions. The module then screens out accessible routes where the difference between the remaining freshness time and the arrival time of the accessible routes does not exceed a preset time threshold.

[0108] The module for obtaining the minimum cost path is used to obtain the minimum cost accessible path calculated by the environmentally sensitive supply chain cost model from the screened accessible paths.

[0109] The working principle and effect of the above technical solution are: real-time collection of temperature and humidity data in the delivery compartment to monitor and record environmental conditions that affect the preservation of food, collection of location information and speed of the delivery vehicle to calculate transportation time and evaluate the feasibility of different routes, acquisition and recording of sensitive parameters of the delivered food, such as temperature and humidity sensitivity, and the freshness status of each food; route information acquisition, obtain information on all accessible routes, including weather forecasts, road conditions and estimated driving time, which provide basic data for route selection; use deep learning or other prediction models to comprehensively analyze the current compartment environmental parameters (temperature, humidity), The freshness status of ingredients and the environmental conditions of future routes are used to predict the remaining freshness time of ingredients delivered on each route; based on the current situation and predictions, each optional route is analyzed to calculate the freshness status of ingredients at the end of delivery; the estimated arrival time of each achievable route and the remaining freshness time of ingredients are compared, and the route where the difference between the freshness time and the arrival time does not exceed the preset threshold is selected. This step ensures that the ingredients still meet the quality standards when they reach the destination; cost optimization, applying the environmentally sensitive supply chain cost model to the selected routes, calculating the comprehensive transportation cost, including logistics costs and possible loss costs; and selecting the achievable route with the lowest comprehensive cost as the final transportation plan. Improve freshness by accurately controlling and predicting the shelf life to ensure that ingredients maintain optimal freshness upon delivery, thereby reducing quality losses caused by transportation; reduce costs by optimizing route selection to consider not only time but also cost-effectiveness to ensure that quality targets are achieved at the lowest cost during transportation; have dynamic adjustment capabilities to flexibly adjust according to real-time environmental changes and predictions, thereby improving the responsiveness of delivery route selection; enhance decision-making efficiency by integrating a variety of real-time and predictive data to improve the scientific nature and operability of delivery decisions and enhance logistics efficiency; improve customer satisfaction by ensuring that products are delivered to customers in the best condition and at the lowest cost, thereby improving customer satisfaction and loyalty; through the above technical solutions, the delivery process is not only optimized in terms of time and cost, but also effectively guarantees product quality, fully reflecting the advantages of intelligent logistics management.

[0110] In one embodiment of the present invention, the acquisition module includes:

[0111] The temperature and humidity collection module is used to collect the temperature and humidity of the food delivery compartment through temperature sensors and humidity sensors respectively;

[0112] The vehicle information collection module is used to collect the location information and speed of the delivery vehicle in real time through the GPS device and the on-board OBD device;

[0113] The module for obtaining sensitive parameters of ingredients is used to obtain sensitive parameters of ingredients delivered by the delivery vehicle.

[0114] The working principle and effect of the above technical solution are as follows: through the coordinated work of multiple sensors and devices, the freshness of food and logistics efficiency during the delivery process are monitored and optimized in real time. The temperature sensor is installed inside the delivery vehicle to monitor the temperature changes in the vehicle in real time and ensure that the temperature is maintained within the range suitable for food freshness. The humidity sensor is also installed in the vehicle to monitor the humidity level in real time. Some food is very sensitive to humidity, and maintaining the appropriate humidity can effectively extend the shelf life of the food.

[0115] GPS devices provide highly accurate geographic location information, ensuring route accuracy by tracking the vehicle's route and location in real time. On-Board Diagnostics (OBD) devices connect to the vehicle's OBD port and collect real-time data on the vehicle's speed and other dynamic conditions, helping to understand the vehicle's operating status and detect any issues that may affect transportation efficiency. The database stores and reads sensitive parameters for each delivered ingredient, including optimal temperature and humidity values, as well as critical preservation conditions. These parameters provide specific guidance for environmental control and help predict the potential risk of food spoilage during delivery. Improved freshness: Continuous monitoring of temperature and humidity ensures a stable vehicle environment, maximizing the freshness and shelf life of food. Real-time monitoring and response: Combined with vehicle dynamics information provided by devices like GPS and OBD, logistics managers can adjust delivery strategies in real time, such as changing routes or strengthening cold chain measures mid-transit, allowing for quicker responses to unexpected situations like traffic jams or equipment failures. All collected data can be recorded and analyzed, providing detailed data support for subsequent optimization and making the logistics process more transparent and controllable. Improved operational efficiency: By proactively identifying and preparing adjustments and repairs for possible temperature and humidity fluctuations or logistics delays, unnecessary losses and costs can be reduced, thereby improving overall operational efficiency. Real-time data collection and monitoring can also help identify loopholes or issues in the logistics process (such as vehicle failures or sensor errors) so that corrective measures can be taken quickly. Through this solution, companies can not only optimize their supply chain and logistics management, but also significantly improve product quality and customer satisfaction. This high-precision, real-time monitoring technology is a crucial component of modern intelligent transportation systems.

[0116] In one embodiment of the present invention, the module for obtaining sensitive parameters of food materials includes:

[0117] The module for determining the range of environmental variables is used to determine the range of environmental variables that need to be tested, including: setting the temperature gradient, setting the humidity gradient, and setting the test time period to select the target food sample;

[0118] A recording module is used to store target food samples under different temperature and humidity conditions and regularly record their freshness status, including weight, nutritional content, moisture loss rate, color change, and texture change. The color change is detected by a colorimeter, and the texture change is detected by a hardness meter.

[0119] The structured data module is used to record the temperature and humidity in the test environment in real time, perform sampling within a preset time, and save the collected data as a structured data table. The structured data table includes the following fields: time, temperature, humidity, and freshness status;

[0120] A preliminary cleaning module is used to perform preliminary data cleaning on the data stored in the structured data table, wherein the preliminary data cleaning includes: removing outliers and averaging the data of repeated experiments under the same conditions;

[0121] The preliminary calculation module is used to substitute the experimental data into the sensitive parameter model. Specifically, the sensitive parameter model is:

[0122]

[0123] in, represents the rate of change of food freshness, k represents the process rate constant, T is the temperature in the actual operating environment, represents the temperature constant, H represents the relative humidity, the logarithm of the sensitive parameter model is taken, n represents the sensitivity coefficient of the food p loss to temperature, m represents the sensitivity coefficient of the food p loss to humidity, the sensitive parameter model is converted into a linear equation, and multiple linear regression is used to calculate k, n and m in the sensitive parameter model;

[0124] The fitting error module is used to optimize the fitting error using the least squares method, verify the fitting residual distribution, and obtain the final sensitive parameter model when the residual distribution does not exceed a preset threshold.

[0125] The working principle and effect of the above technical solution are as follows: the above technical solution is used to quantify the freshness changes of food under different temperature and humidity conditions, prepare the test environment, set the variable range, and determine different environmental conditions by adjusting the temperature gradient, humidity gradient and test time period to test the preservation response of food samples; store the selected food samples under these conditions, and use tools such as colorimeters and hardness meters to monitor and record the color and texture changes of the food, and record the preservation status such as weight, nutritional content and moisture loss rate; monitor and record temperature and humidity data in real time, as well as the preservation status of the food at the above time points, and store these data in a structured data table format; clean the collected data, remove outliers, and average repeated data under the same experimental conditions to reduce noise; substitute specific sensitive parameter model equations, use multiple linear regression, convert the equations into linear equations by taking the logarithm of the equations, calculate the model parameters, use the least squares method to optimize the fitting error, verify the distribution of the fitting residuals, and ensure that the residuals are within an acceptable preset threshold range to ensure the accuracy of the model. Generating accurate sensitive parameter models can better predict the freshness changes of food ingredients under specific environmental conditions, thereby optimizing storage and transportation strategies. Through quantitative analysis of freshness changes, it can assist in formulating temperature and humidity control rules to extend the shelf life and improve product quality. It can help logistics and food supply chain managers optimize cold chain management and reduce food loss and economic losses caused by improper environment. It not only improves the understanding of the food preservation process, but also strengthens the possibility of scientific management and data-driven optimization measures.

[0126] In one embodiment of the present invention, the prediction and screening module includes:

[0127] a data acquisition module for acquiring historical temperature and humidity data within the carriage, and acquiring historical food preservation status data, wherein the historical food preservation status data includes weight, nutritional content, moisture loss rate, color change, texture change, sugar content, and food sensitive parameters, wherein the food sensitive parameters include sensitivity coefficients to temperature and sensitivity coefficients to humidity;

[0128] The feature extraction module is used to obtain forecast data on weather conditions along the next transportation route, process and clean the collected historical temperature, humidity, and freshness data, remove outliers and noise, and extract important features from the cleaned historical data. These important features include the temperature change rate inside the carriage, the change rate of freshness data, and the temperature and relative humidity of the forecast data.

[0129] A training model module is used to construct an LSTM model, incorporate the extracted important features into the LSTM model, divide the historical data into a training set, a validation set, and a test set, and train the LSTM model using the training set. The model input features are: the temperature change rate inside the vehicle compartment, the change rate of the fresh-keeping status data, the temperature and relative humidity of the forecast data, and the output is the remaining fresh-keeping time. The validation set verifies the accuracy of the LSTM model, and then adjusts the model parameters. After the adjustments, the performance of the LSTM model is evaluated using the test set.

[0130] The prediction module is used to obtain important features of the current food transportation process, input the important features into the trained LSTM model, obtain the remaining shelf life of the food, and screen out reachable paths in which the difference between the remaining shelf life and the arrival time of the reachable path does not exceed a preset time threshold.

[0131] The working principle and effect of the above technical solution are: data collection and organization, acquisition of historical data, collection of historical temperature and humidity data inside the carriage, and historical freshness status data of food ingredients. These data include multiple attributes, such as weight, nutrients, water loss, color change, texture change, sugar content, and sensitivity coefficients to temperature and humidity; obtaining weather forecast data for the upcoming transportation route, including temperature and relative humidity; removing outliers and noise in the data to ensure data quality, and extracting important features from the cleaned data, such as the rate of change of temperature inside the carriage, the rate of change of freshness status data, and the temperature and relative humidity in the forecast data. These features will provide input variables for the model; constructing an LSTM model, and using the extracted important features to construct an LSTM model to capture the dynamic changes and long-term dependencies of time series data; historical The historical data is divided into training set, validation set and test set for model training, parameter adjustment and performance evaluation; the LSTM model is trained with the input features of temperature change rate, freshness state change rate, predicted temperature and relative humidity; the output is the remaining freshness time; the accuracy of the model is tested through the validation set, and the model parameters are adjusted according to the results, and the final model performance is evaluated using the test set to ensure that it can work reliably in practical applications; real-time important features are obtained during the current transportation process, and these features are input into the trained LSTM model to predict the remaining freshness time of the food; and those reachable paths are screened out where the difference between the remaining freshness time and the path arrival time does not exceed the preset time threshold. Precise freshness prediction: Using the LSTM model to accurately predict the remaining freshness of ingredients under different environmental conditions, this improves control over the freshness of ingredients during delivery. Optimize delivery routes: Incorporating information about the remaining freshness, the most appropriate delivery route is selected to ensure that ingredients arrive in optimal freshness. Improve logistics efficiency and customer satisfaction: By optimizing route selection and transportation conditions management, food loss is reduced, delivery efficiency is improved, and customer satisfaction is enhanced. Dynamic adjustment and real-time decision support: During transportation, transportation strategies are adjusted based on real-time data to respond to sudden changes, enhancing the flexibility of logistics management. Provide a foundation for supply chain optimization: The insights and data analysis gained through this technical solution can provide support and basis for further optimization of the cold chain supply chain. By integrating advanced data analysis technologies and predictive models, not only can the freshness of ingredients during delivery be effectively managed, but the overall efficiency and responsiveness of transportation and logistics management can also be greatly improved.

[0132] In one embodiment of the present invention, the module for obtaining the minimum cost path includes:

[0133] Define an environmentally sensitive supply chain cost model. Specifically, the environmentally sensitive supply chain cost model is:

[0134]

[0135] Where R(x,y) represents the transportation volume from the starting point to the point (x,y), C(x,y) represents the cost per unit of transportation volume, a represents the unit of loss cost, n represents the sensitivity coefficient of the loss of food p to temperature, and m represents the sensitivity coefficient of the loss of food p to humidity. represents the loss rate of food p, represents the end time of the transport operation at a specific location (x,y) and a specific product p,

[0136] The constraints of the environmentally sensitive supply chain cost model are:

[0137] , constraining each customer's location to be visited once and only once;

[0138] ≤ the preset loss threshold, constraining the total loss to not exceed the acceptable loss;

[0139] Each screened path is evaluated using the environmentally sensitive supply chain cost model, the total cost of each path is calculated, the costs of all paths are compared, and the path with the minimum cost is found.

[0140] The working principle and effect of the above technical solution are as follows: when delivering food to customers, the delivery order will affect the quality of certain products (such as temperature-sensitive fresh products). In order to better deal with this situation, the environmentally sensitive supply chain cost model is designed to be more refined. The goal is to minimize the direct costs of transportation routes. The route selection and the order in which the ingredients are delivered to customers are not only related to the transportation distance, but also affect the fuel usage and driving time, and the continuous loss. The term considers the effects of temperature and humidity on food preservation, and evaluates the state changes of food through the derivative of the loss rate. The freshness of food directly affects the quality and safety of the final product. Optimizing this part can reduce the risk of food spoilage due to environmental conditions; reduce economic losses, decreased customer satisfaction, and possible return costs caused by food spoilage; by better controlling losses, food waste can be reduced and sustainable development can be promoted; the loss of food freshness does not occur instantaneously, but accumulates during transportation. The integral term can naturally express this cumulative effect by calculating the small losses at each moment and summing them to obtain the total loss; environmental parameters (temperature and humidity) change over time, and the loss rate also changes accordingly. The integral formula can clearly express this time dependence and accurately reflect the impact of environmental conditions at different time points on product loss; in actual transportation, such as cold chain transportation, temperature and humidity are not constant. The use of integrals can dynamically evaluate the impact of these environmental parameters on the product over time, so that the model can more accurately simulate the actual situation; through integration, it can be accurately It accurately accumulates the loss at each moment and reflects the precise loss situation; the model can be flexibly adjusted according to the temperature and humidity information at different time points, without assuming that the environmental parameters are constant; it is suitable for transportation simulation and optimization in different regions and under different weather conditions; assuming that a specific product p is particularly sensitive to temperature, the loss rate of the product will increase significantly with every 1 degree increase in temperature. Then, if a heat wave is encountered on the transportation route during transportation, the loss of this product will increase sharply for a certain period of time. In this case, the integral formula can well capture the dynamic changes in the loss rate and reflect the actual total loss. Using this integral term to calculate the loss can carefully and accurately evaluate the loss caused by changes in environmental factors such as time, temperature, and humidity throughout the entire logistics process. It can very effectively simulate and optimize the logistics and distribution process in complex actual environments, and improve the accuracy and scientificity of the model. Overall cost minimization: By integrating all relevant costs, the system achieves comprehensive optimization of the total supply chain cost, enabling companies to specifically identify and reduce unnecessary expenses in the logistics process. Loss control: By assessing and minimizing quality loss costs caused by environmental factors such as temperature and humidity, the system improves product freshness and customer satisfaction. Supporting large-scale data analysis and integrating machine learning models (such as LSTM) enables more accurate and predictive decision-making. Supply chain efficiency is improved by comparing the total costs of different transportation options, facilitating the rational allocation of logistics resources and selecting the optimal route. By considering environmental sensitivity, the system enables rapid response to market changes, such as shifting climate conditions and customer demand. Through a clear mathematical structure and parametric design, the system provides a method for integrating complex data analysis and operational optimization into logistics and supply chain management. This not only enhances companies' responsiveness and decision-making efficiency in market competition, but also improves the cost-effectiveness of the entire supply chain, promoting sustainable development.

[0141] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for intelligent food distribution control based on big data, characterized in that: The method comprises: Real-time collection of temperature and humidity in the delivery compartment during the delivery process, collection of the delivery vehicle's location information and speed, and acquisition of sensitive parameters of the food being delivered in the delivery vehicle; Obtain all accessible routes and use a prediction model to predict the remaining shelf life of the food delivered along all accessible routes based on the current vehicle interior environmental parameters, the food's freshness status data, and the upcoming route parameters, including weather and road conditions. Filter out accessible routes where the difference between the remaining shelf life and the arrival time of the accessible route does not exceed a preset time threshold. Obtain the reachable path with the minimum cost calculated by the environmentally sensitive supply chain cost model from the screened reachable paths, including: Define an environmentally sensitive supply chain cost model. Specifically, the environmentally sensitive supply chain cost model is: Among them, R (x, y) represents the transportation volume from the starting point to the point (x, y), C (x, y) represents the cost per unit of transportation volume, and a represents the cost unit of loss. represents the loss rate of food p, represents the end time of a transport operation at a specific location (x,y) and for a specific product p, The constraints of the environmentally sensitive supply chain cost model are: , constraining each customer's location to be visited once and only once; ≤ the preset loss threshold, constraining the total loss to not exceed the acceptable loss; Each screened path is evaluated using the environmentally sensitive supply chain cost model, the total cost of each path is calculated, the costs of all paths are compared, and the path with the minimum cost is found.

2. According to claim 1, a method for intelligent control of food distribution based on big data is characterized in that: Real-time data collection of temperature and humidity in the delivery compartment during the delivery process, as well as the location and speed of the delivery vehicle, is used to obtain sensitive parameters of the food being delivered in the delivery compartment, including: The temperature and humidity of the food delivery compartment are collected through temperature sensors and humidity sensors respectively; The location information and speed of delivery vehicles are collected in real time through GPS devices and on-board OBD devices; Get the sensitive parameters of the food delivered by the delivery vehicle.

3. The method for intelligent food distribution control based on big data according to claim 2, characterized in that: Get sensitive parameters of food delivered in the delivery vehicle, including: Determine the range of environmental variables that need to be tested, including: setting the temperature gradient, setting the humidity gradient, and setting the test time period to select the target food sample; Storing target food samples under different temperature and humidity conditions and regularly recording their freshness status, including weight, nutritional content, moisture loss rate, color change, and texture change. The color change is detected by a colorimeter, and the texture change is detected by a hardness meter. Record the temperature and humidity in the test environment in real time, take samples within a preset time, and save the collected data as a structured data table. The structured data table includes the following fields: time, temperature, humidity, and freshness status; Performing preliminary data cleaning on the data stored in the structured data table, wherein the preliminary data cleaning includes: removing outliers and averaging the data of repeated experiments under the same conditions; Substitute the experimental data into the sensitive parameter model. Specifically, the sensitive parameter model is: in, represents the rate of change of food freshness, k represents the process rate constant, T is the temperature in the actual operating environment, represents the temperature constant, H represents the relative humidity, the logarithm of the sensitive parameter model is taken, n represents the sensitivity coefficient of the food p loss to temperature, m represents the sensitivity coefficient of the food p loss to humidity, the sensitive parameter model is converted into a linear equation, and multiple linear regression is used to calculate k, n and m in the sensitive parameter model; The least squares method is used to optimize the fitting error and verify the distribution of the fitting residuals. When the residual distribution does not exceed a preset threshold, the final sensitive parameter model is obtained.

4. The method for intelligent food distribution control based on big data according to claim 1, characterized in that: Obtain all accessible routes and use the prediction model to predict the remaining freshness time of the delivered ingredients on all accessible routes based on the current interior environment parameters of the vehicle, the freshness status data of the ingredients, and the upcoming route parameters. Route parameters include weather and road conditions. Select accessible routes where the difference between the remaining freshness time and the arrival time of the accessible routes does not exceed the preset time threshold, including: Obtaining historical temperature and humidity data inside the vehicle compartment, and obtaining historical food preservation status data, the historical food preservation status data including weight, nutritional content, moisture loss rate, color change, texture change, sugar content, and food sensitive parameters, the food sensitive parameters including sensitivity coefficients to temperature and humidity; Obtain forecast data for weather conditions along the upcoming transport route, process and clean the collected historical temperature, humidity, and freshness data, remove outliers and noise, and extract important features from the cleaned historical data. These features include the rate of change of temperature inside the vehicle compartment, the rate of change of freshness data, and the forecasted temperature and relative humidity. Constructing an LSTM model, incorporating the extracted important features into the LSTM model, dividing the historical data into a training set, a validation set, and a test set, and training the LSTM model using the training set so that the model input features are: the temperature change rate inside the vehicle compartment, the change rate of the fresh-keeping status data, and the temperature and relative humidity of the forecast data, and the output is the remaining fresh-keeping time. The validation set verifies the accuracy of the LSTM model, and then adjusts the model parameters. After the adjustments, the performance of the LSTM model is evaluated using the test set. Obtain important features of the current food transportation process, input the important features into the trained LSTM model, obtain the remaining shelf life of the food, and screen out reachable paths where the difference between the remaining shelf life and the arrival time of the reachable path does not exceed a preset time threshold.

5. A food delivery intelligent control system based on big data, characterized by: The system comprises: The collection module is used to collect real-time temperature and humidity information in the delivery compartment during the delivery process, collect the location information and speed of the delivery vehicle, and obtain sensitive parameters of the delivered ingredients in the delivery vehicle; A prediction and screening module is used to obtain all accessible routes and use a prediction model to predict the remaining freshness time of the ingredients delivered on all accessible routes based on the current internal vehicle environmental parameters, the freshness status data of the ingredients, and the upcoming route parameters. Route parameters include weather and road conditions. The module then screens out accessible routes where the difference between the remaining freshness time and the arrival time of the accessible routes does not exceed a preset time threshold. The module for obtaining the minimum cost path is used to obtain the minimum cost path calculated by the environmentally sensitive supply chain cost model from the selected reachable paths, including: Define an environmentally sensitive supply chain cost model. Specifically, the environmentally sensitive supply chain cost model is: Among them, R (x, y) represents the transportation volume from the starting point to the point (x, y), C (x, y) represents the cost per unit of transportation volume, and a represents the cost unit of loss. represents the loss rate of food p, represents the end time of a transport operation at a specific location (x,y) and for a specific product p, The constraints of the environmentally sensitive supply chain cost model are: , constraining each customer's location to be visited once and only once; ≤ the preset loss threshold, constraining the total loss to not exceed the acceptable loss; Each screened path is evaluated using the environmentally sensitive supply chain cost model, the total cost of each path is calculated, the costs of all paths are compared, and the path with the minimum cost is found.

6. The food delivery intelligent control system based on big data according to claim 5 is characterized in that: The acquisition module includes: The temperature and humidity collection module is used to collect the temperature and humidity of the food delivery compartment through temperature sensors and humidity sensors respectively; The vehicle information collection module is used to collect the location information and speed of the delivery vehicle in real time through the GPS device and the on-board OBD device; The module for obtaining sensitive parameters of ingredients is used to obtain sensitive parameters of ingredients delivered by the delivery vehicle.

7. The food delivery intelligent control system based on big data according to claim 6 is characterized in that: The module for obtaining sensitive parameters of ingredients includes: The module for determining the range of environmental variables is used to determine the range of environmental variables that need to be tested, including: setting the temperature gradient, setting the humidity gradient, and setting the test time period to select the target food sample; A recording module is used to store target food samples under different temperature and humidity conditions and regularly record their freshness status, including weight, nutritional content, moisture loss rate, color change, and texture change. The color change is detected by a colorimeter, and the texture change is detected by a hardness meter. The structured data module is used to record the temperature and humidity in the test environment in real time, perform sampling within a preset time, and save the collected data as a structured data table. The structured data table includes the following fields: time, temperature, humidity, and freshness status; A preliminary cleaning module is used to perform preliminary data cleaning on the data stored in the structured data table, wherein the preliminary data cleaning includes: removing outliers and averaging the data of repeated experiments under the same conditions; The preliminary calculation module is used to substitute the experimental data into the sensitive parameter model. Specifically, the sensitive parameter model is: in, represents the rate of change of food freshness, k represents the process rate constant, T is the temperature in the actual operating environment, represents the temperature constant, H represents the relative humidity, the logarithm of the sensitive parameter model is taken, n represents the sensitivity coefficient of the food p loss to temperature, m represents the sensitivity coefficient of the food p loss to humidity, the sensitive parameter model is converted into a linear equation, and multiple linear regression is used to calculate k, n and m in the sensitive parameter model; The fitting error module is used to optimize the fitting error using the least squares method, verify the fitting residual distribution, and obtain the final sensitive parameter model when the residual distribution does not exceed a preset threshold.

8. The food delivery intelligent control system based on big data according to claim 5 is characterized in that: The prediction and screening module includes: a data acquisition module for acquiring historical temperature and humidity data within the carriage, and acquiring historical food preservation status data, wherein the historical food preservation status data includes weight, nutritional content, moisture loss rate, color change, texture change, sugar content, and food sensitive parameters, wherein the food sensitive parameters include sensitivity coefficients to temperature and sensitivity coefficients to humidity; The feature extraction module is used to obtain forecast data on weather conditions along the next transportation route, process and clean the collected historical temperature, humidity, and freshness data, remove outliers and noise, and extract important features from the cleaned historical data. These important features include the temperature change rate inside the carriage, the change rate of freshness data, and the temperature and relative humidity of the forecast data. A training model module is used to construct an LSTM model, incorporate the extracted important features into the LSTM model, divide the historical data into a training set, a validation set, and a test set, and train the LSTM model using the training set. The model input features are: the temperature change rate inside the vehicle compartment, the change rate of the fresh-keeping status data, the temperature and relative humidity of the forecast data, and the output is the remaining fresh-keeping time. The validation set verifies the accuracy of the LSTM model, and then adjusts the model parameters. After the adjustments, the performance of the LSTM model is evaluated using the test set. The prediction module is used to obtain important features of the current food transportation process, input the important features into the trained LSTM model, obtain the remaining shelf life of the food, and screen out reachable paths in which the difference between the remaining shelf life and the arrival time of the reachable path does not exceed a preset time threshold.

Images