Food end transportation scheduling management system based on big data

Abstract

The invention belongs to the technical field of food transportation management, and particularly relates to a food end transportation scheduling management system based on big data, which comprises a data fusion and convergence module, a dynamic traffic sensing module, an intelligent order analysis module, a vehicle state evaluation module, a scheduling strategy generation module and a transportation scheduling management end. According to the invention, traffic perception analysis information, order analysis information and vehicle state evaluation information obtained through analysis are sent to a scheduling strategy generation module through a dynamic traffic perception module, an intelligent order analysis module and a vehicle state evaluation module; and the scheduling strategy generation module performs comprehensive analysis based on various types of information to generate an optimal food transportation scheduling strategy, so that optimal configuration of transportation resources is realized, intelligent and refined management of food terminal transportation scheduling is facilitated, the transportation efficiency is comprehensively improved, the transportation cost is reduced, and the food transportation safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of food transportation management, and in particular to a food terminal transportation scheduling and management system based on big data. Background Art

[0002] In the entire food supply chain, from production to consumer dining, terminal transportation plays a relatively late role. It takes over the storage and distribution of food at transit nodes such as regional warehousing and distribution centers, and then delivers the food directly to the sales terminal or designated delivery location. This is the last logistics process before food comes into direct contact with consumers.

[0003] In the food delivery process, traditional dispatch management methods often rely on manual experience and fail to fully consider factors such as traffic conditions, order requirements, and vehicle status. This leads to low transportation efficiency and increased costs. These methods are unable to meet the complex needs of food delivery, accurately identify food delivery anomalies, and reasonably evaluate transportation dispatch management performance, hindering the safety and efficiency of food delivery.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a food terminal transportation scheduling and management system based on big data, which solves the problem that the existing technology is difficult to comprehensively consider traffic conditions, order requirements and vehicle status factors, resulting in low transportation efficiency and increased costs, and cannot accurately identify food transportation anomalies and reasonably evaluate transportation scheduling management performance, which is not conducive to ensuring the safety and efficiency of food terminal transportation.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The big data-based food terminal transportation scheduling and management system includes a data fusion and aggregation module, a dynamic traffic perception module, an intelligent order analysis module, a vehicle status assessment module, a scheduling strategy generation module, and a transportation scheduling management terminal. The data fusion and aggregation module connects with the food sales system, vehicle monitoring system, and traffic information platform to collect, integrate, and store various data from different data sources. The dynamic traffic perception module perceives and analyzes traffic conditions in real time and sends the traffic perception and analysis information to the scheduling strategy generation module.

[0008] The intelligent order parsing module conducts in-depth analysis of order data, explores the demand characteristics and patterns behind the orders, and sends the order parsing information to the scheduling strategy generation module; the vehicle status assessment module assesses the vehicle status in real time and sends the vehicle status assessment information to the scheduling strategy generation module; the scheduling strategy generation module is used to generate the optimal food transportation scheduling strategy through comprehensive analysis, including vehicle allocation, route planning and delivery time scheduling, and sends the generated food transportation scheduling strategy to the transportation scheduling management end.

[0009] Furthermore, the dynamic traffic perception module obtains real-time traffic data from the data fusion and aggregation module, combines historical traffic data and geographic information system information, and conducts a comprehensive analysis of the current traffic conditions. By analyzing traffic flow, road congestion and traffic accident information, it predicts traffic trends in the future. Based on the real-time location and destination of the vehicle, it plans multiple driving routes and evaluates and ranks the expected driving time and congestion probability of each route.

[0010] Furthermore, the intelligent order parsing module obtains order data from the data fusion and aggregation module, and uses data mining and machine learning algorithms to analyze the time distribution, spatial distribution and order volume changes of orders. By analyzing historical order data, it finds the peak and trough periods of orders in different time periods and regions, as well as the seasonal changes in order volume, and classifies and prioritizes orders based on the urgency of the order and the type of food.

[0011] Furthermore, the vehicle status assessment module obtains vehicle data from the data fusion and aggregation module, and evaluates various performance indicators of the vehicle in combination with the vehicle model, age and maintenance record information. By analyzing the vehicle's mileage, fuel consumption and engine speed data, it determines whether the vehicle's mechanical performance is normal; based on the vehicle's load sensor data, it monitors the vehicle's load in real time to ensure that it does not exceed the vehicle's rated load; based on the vehicle's battery power or fuel tank level, it calculates the vehicle's remaining cruising range, and based on the driving route and traffic conditions, it determines whether the vehicle needs to be refueled or charged midway.

[0012] Furthermore, the scheduling strategy generation module integrates the comprehensive data, traffic perception analysis information, order parsing information and vehicle status assessment information provided by the data fusion aggregation module, and uses the intelligent optimization algorithm to generate the optimal scheduling strategy; reasonably allocates vehicles according to the order priority and the vehicle's load capacity; plans the optimal driving route based on the traffic conditions and the vehicle's remaining range; and arranges a reasonable delivery time based on the order's delivery time requirements and the vehicle's driving time.

[0013] Furthermore, the transport scheduling management end is communicated with the transport abnormality monitoring module, which monitors the transport process of the vehicle, determines through analysis whether a transport abnormality signal is generated, and sends the transport abnormality signal to the transport scheduling management end when the transport abnormality signal is generated. The transport scheduling management end displays the transport abnormality signal and issues a corresponding warning.

[0014] Furthermore, the specific analysis process of the transportation anomaly monitoring module is as follows:

[0015] The actual driving path of the transport vehicle is obtained and compared with the set planned path. When the vehicle deviates from the planned route, the time is counted and the deviation distance is counted. When the duration or distance of the vehicle deviating from the planned route exceeds the corresponding preset threshold, a transport abnormality signal is generated; if the vehicle does not deviate from the planned route within a unit time, or the duration and distance of the vehicle deviating from the planned route do not exceed the corresponding preset threshold, the transport speed storage is used for progressive analysis to determine whether a transport abnormality signal is generated.

[0016] Furthermore, the specific analysis process of the progressive analysis of transportation speed and storage is as follows:

[0017] The speed curve of the transport vehicle in a unit time is collected, and several detection points are set on the speed curve. The difference between the values ​​of two adjacent groups of detection points is marked as the speed fluctuation value. The speed fluctuation value is compared with the preset speed fluctuation threshold. If the speed fluctuation value exceeds the preset speed fluctuation threshold, the corresponding speed fluctuation value is marked as the speed high wave value;

[0018] The number of high-speed fluctuation values ​​within a unit time is obtained and marked as a high-speed characteristic value, and the time period during which the speed of the transport vehicle is not within the preset appropriate speed range within a unit time is marked as a speed off-time value, and the speed fluctuation value with the largest value within a unit time is marked as a speed fluctuation risk value; a speed detection analysis value is obtained by weighted summing the high-speed characteristic value, the speed off-time value, and the speed fluctuation risk value, and the speed detection analysis value is numerically compared with a preset speed detection analysis threshold value. If the speed detection analysis value exceeds the preset speed detection analysis threshold value, a transport abnormality signal is generated;

[0019] If the transport speed detection analysis value does not exceed the preset transport speed detection analysis threshold, the environmental information in the storage compartment of the transport vehicle is collected in real time, and the temperature deviation value, humidity deviation value and vibration amplitude value in the storage compartment are weighted and summed to obtain the compartment control abnormality characteristic value, and the total time length of the compartment control abnormality characteristic value exceeding the preset compartment control abnormality characteristic threshold within a unit time is marked as the compartment control abnormality time measurement value, and the average of all compartment control abnormality characteristic values ​​within a unit time is calculated to obtain the compartment control abnormality performance value;

[0020] The carriage control anomaly measured value and the carriage control anomaly performance value are numerically compared with the preset carriage control anomaly measured threshold and the preset carriage control anomaly performance threshold respectively. If the carriage control anomaly measured value or the carriage control anomaly performance value exceeds the corresponding preset threshold, a transportation abnormality signal is generated.

[0021] Furthermore, the transportation anomaly monitoring module is communicatively connected to the transportation scheduling management analysis module. The transportation anomaly monitoring module sends the transportation anomaly signal to the transportation scheduling management analysis module. The transportation scheduling management analysis module is used to set a monitoring period and analyze the transportation scheduling management performance within the monitoring period. Through the analysis, a transportation scheduling management qualification signal or a transportation scheduling management alarm signal is generated, and the transportation scheduling management qualification signal or the transportation scheduling management alarm signal is sent to the transportation scheduling management end. When the transportation scheduling management end receives the transportation scheduling management alarm signal, it issues a corresponding warning.

[0022] Furthermore, the specific analysis process of the transportation scheduling management analysis module is as follows:

[0023] During the corresponding food transportation process, the number of times the transportation abnormality signal is generated is obtained and marked as a transportation abnormality value. The transportation abnormality value is numerically compared with the corresponding preset transportation abnormality threshold. If the transportation abnormality value exceeds the preset transportation abnormality threshold, the corresponding transportation process is marked as a control optimization process;

[0024] Obtain the number of control optimization processes during the monitoring period and calculate the ratio between it and the total number of food transportation times to obtain a control optimization detection value. Compare the control optimization detection value with a preset control optimization detection threshold. If the control optimization detection value exceeds the preset control optimization detection threshold, a transportation scheduling management alarm signal is generated.

[0025] If the control optimization detection value does not exceed the preset control optimization detection threshold, the number of times the food is not delivered on time during the monitoring period is obtained and the ratio thereof is calculated by comparing it with the total number of food transportation times to obtain a non-timely detection value, and the average value of the delayed delivery time during the monitoring period is marked as the delayed arrival performance value; and the number of times the ratio of the loss amount of food damaged during the food transportation process during the monitoring period exceeds the corresponding preset threshold and the total number of food transportation times is calculated to obtain a high loss detection value, and the average of the loss amount ratios of all food transportation processes during the monitoring period is calculated to obtain a loss amount performance value;

[0026] The transport decision evaluation value is obtained by weighted summing up the non-timely detection value, delayed arrival performance value, high-damage detection value and loss amount performance value, and the transport decision evaluation value is numerically compared with the preset transport decision evaluation threshold. If the transport decision evaluation value exceeds the preset transport decision evaluation threshold, a transport scheduling management alarm signal is generated; if the transport decision evaluation value does not exceed the preset transport decision evaluation threshold, a transport scheduling management qualified signal is generated.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. In the present invention, traffic perception analysis information, order parsing information, and vehicle status assessment information obtained through analysis are sent to a scheduling strategy generation module. The scheduling strategy generation module performs a comprehensive analysis based on multiple types of information to generate an optimal food transportation scheduling strategy, thereby achieving optimal allocation of transportation resources. This is conducive to the intelligent and refined management of food terminal transportation scheduling, comprehensively improving transportation efficiency, reducing transportation costs, and ensuring food transportation safety.

[0029] 2. In the present invention, the transportation process of the vehicle is monitored through the transportation anomaly monitoring module, which is conducive to real-time response to emergencies during the transportation process, ensuring the smooth completion of the food transportation task, and significantly reducing the difficulty of supervision of the food transportation process. The transportation scheduling management performance during the monitoring period is analyzed through the transportation scheduling management analysis module. When the transportation scheduling management alarm signal is generated, the subsequent food transportation control is strengthened and the system is optimized in a timely manner, further ensuring the safety and efficiency of food transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0031] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0032] Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] Example 1: Figure 1 As shown in the figure, the food terminal transportation scheduling and management system based on big data proposed in the present invention includes a data fusion and aggregation module, a dynamic traffic perception module, an intelligent order parsing module, a vehicle status assessment module, a scheduling strategy generation module and a transportation scheduling management terminal;

[0035] The data fusion and aggregation module connects with the food sales system, vehicle monitoring system and traffic information platform to obtain order data (including order quantity, delivery address, delivery time requirements, etc.), vehicle data (vehicle location, speed, load, fuel consumption, etc.) and traffic data (real-time road conditions, traffic control information, etc.) in real time, and cleans and preprocesses the acquired data to remove duplicate, erroneous and invalid data, converts data in different formats into a unified format and stores it for other modules to call and analyze. It can collect, integrate and store various types of data from different data sources, providing a comprehensive and accurate data foundation for the system, avoiding scheduling decision errors caused by incomplete or inaccurate data.

[0036] The dynamic traffic perception module perceives and analyzes traffic conditions in real time and sends the traffic perception and analysis information to the scheduling strategy generation module. It can grasp traffic dynamics in real time, which is conducive to selecting the optimal driving route for vehicles, reducing transportation time and improving transportation efficiency.

[0037] Specifically, the dynamic traffic perception module obtains real-time traffic data from the data fusion and aggregation module, combines historical traffic data and geographic information system (GIS) information to conduct a comprehensive analysis of the current traffic conditions, and predicts traffic trends in the future by analyzing traffic flow, road congestion, and traffic accident information. At the same time, based on the real-time location and destination of the vehicle, it plans multiple driving routes and evaluates and ranks the expected driving time and congestion probability of each route.

[0038] The intelligent order parsing module conducts in-depth analysis of order data, explores the demand characteristics and patterns behind the orders, and sends the order parsing information to the scheduling strategy generation module. Through intelligent order parsing, it can better understand order demand, provide support for formulating reasonable scheduling strategies, and improve the timeliness and accuracy of order delivery.

[0039] Specifically, the intelligent order parsing module obtains order data from the data fusion and aggregation module, and uses data mining and machine learning algorithms to analyze the time distribution, spatial distribution and order volume changes of orders. For example, by analyzing historical order data, it can find out the peak and trough periods of orders in different time periods and regions, as well as the seasonal changes in order volume. At the same time, orders are classified and prioritized according to factors such as the urgency of the order and the type of food.

[0040] The vehicle status assessment module assesses the vehicle status in real time and sends the vehicle status assessment information to the scheduling strategy generation module. This module can promptly identify potential vehicle problems, rationally arrange vehicle usage, avoid transportation delays caused by vehicle failures, and improve transportation safety and reliability.

[0041] Specifically, the vehicle status assessment module obtains vehicle data from the data fusion and aggregation module, and evaluates the various performance indicators of the vehicle based on information such as the vehicle model, age and maintenance record. For example, by analyzing data such as the vehicle's mileage, fuel consumption and engine speed, it determines whether the vehicle's mechanical performance is normal; based on the vehicle's load sensor data, it monitors the vehicle's load in real time to ensure that it does not exceed the vehicle's rated load; based on the vehicle's battery power or fuel tank level, it calculates the vehicle's remaining cruising range, and based on the driving route and traffic conditions, determines whether the vehicle needs to be refueled or charged midway.

[0042] The scheduling strategy generation module is used to generate the optimal food transportation scheduling strategy through comprehensive analysis, including vehicle allocation, route planning and delivery time arrangement, and send the generated food transportation scheduling strategy to the transportation scheduling management terminal. It can generate a scientific and reasonable scheduling strategy, fully consider various factors, achieve the optimal allocation of transportation resources, improve transportation efficiency and reduce transportation costs;

[0043] Specifically, the scheduling strategy generation module integrates the comprehensive data, traffic perception analysis information, order parsing information and vehicle status assessment information provided by the data fusion aggregation module, and uses intelligent optimization algorithms (such as genetic algorithms, ant colony algorithms, etc.) to generate the optimal scheduling strategy; for example, according to the priority of the order and the load capacity of the vehicle, the vehicle is reasonably allocated; the optimal driving route is planned based on the traffic conditions and the remaining range of the vehicle; and a reasonable delivery time is arranged according to the delivery time requirements of the order and the driving time of the vehicle.

[0044] Example 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the transportation scheduling management terminal is communicatively connected to the transportation abnormality monitoring module. The transportation abnormality monitoring module monitors the transportation process of the vehicle, determines whether a transportation abnormality signal is generated through analysis, and sends the transportation abnormality signal to the transportation scheduling management terminal when it is generated; the transportation scheduling management terminal displays the transportation abnormality signal and issues a corresponding warning to remind management personnel to investigate the cause and take corresponding treatment measures, which is conducive to real-time response to emergencies in the transportation process, ensuring the smooth completion of food transportation tasks, and significantly reducing the difficulty of supervision of the food transportation process; the specific analysis process of the transportation abnormality monitoring module is as follows:

[0045] The actual driving path of the transport vehicle is obtained and compared with the planned path. When the vehicle deviates from the planned route, the time is counted and the deviation distance is calculated. If the duration or distance of the vehicle deviating from the planned route exceeds the corresponding preset threshold, it indicates that the execution status of the transport route is poor and timely investigation and route correction are required, and a transport abnormality signal is generated;

[0046] If the vehicle does not deviate from the scheduled route within a unit time, or the time and distance that the vehicle deviates from the scheduled route do not exceed the corresponding preset thresholds, the speed curve of the transport vehicle within the unit time is collected, and several detection points are set on the speed curve. The numerical difference between two adjacent groups of detection points is marked as the speed fluctuation value, and the speed fluctuation value is compared with the preset speed fluctuation threshold. If the speed fluctuation value exceeds the preset speed fluctuation threshold, it indicates that the speed fluctuation within the corresponding interval is too large, and the corresponding speed fluctuation value is marked as the speed high wave value;

[0047] The number of high-speed fluctuation values ​​per unit time is obtained and marked as the high-speed characteristic value, the duration during which the speed of the transport vehicle is not within the preset appropriate speed range per unit time is marked as the speed off-time value, and the speed fluctuation value with the largest value per unit time is marked as the speed fluctuation risk value;

[0048] The speed detection analysis value is obtained by performing weighted sum calculation on the high-wave characteristic value, the speed departure time value, and the speed wave risk value. That is, the high-wave characteristic value, the speed departure time value, and the speed wave risk value are respectively assigned corresponding preset weight coefficients, and the high-wave characteristic value, the speed departure time value, and the speed wave risk value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the speed detection analysis value;

[0049] It should be noted that the larger the speed detection analysis value, the worse the overall speed control performance of the transport vehicle per unit time, which is more detrimental to the safe transportation of food. The speed detection analysis value is numerically compared with the preset speed detection analysis threshold. If the speed detection analysis value exceeds the preset speed detection analysis threshold, it indicates that the overall speed control performance of the transport vehicle per unit time is poor, which is detrimental to the safe transportation of food, and a transport abnormality signal is generated.

[0050] If the transportation speed detection analysis value does not exceed the preset transportation speed detection analysis threshold, the environmental information in the storage compartment of the transport vehicle is collected in real time, and the temperature deviation value (that is, the deviation value of the real-time temperature of the compartment compared to the set standard storage temperature), humidity deviation value (that is, the deviation value of the real-time humidity of the compartment compared to the set standard storage humidity) and vibration amplitude value in the storage compartment are weighted and summed to obtain the compartment control abnormality characteristic value, that is, the temperature deviation value, humidity deviation value and vibration amplitude value are respectively assigned corresponding preset weight coefficients, and the temperature deviation value, humidity deviation value and vibration amplitude value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the compartment control abnormality characteristic value;

[0051] It should be noted that the larger the value of the cabin control abnormality characteristic value, the worse the overall real-time condition of the cabin storage environment is; the total time that the cabin control abnormality characteristic value exceeds the preset cabin control abnormality characteristic threshold within a unit time is marked as the cabin control abnormality time measurement value, and the cabin control abnormality performance value is obtained by averaging all the cabin control abnormality characteristic values ​​within a unit time;

[0052] The carriage control anomaly measured value and carriage control anomaly performance value are numerically compared with the preset carriage control anomaly measured threshold and preset carriage control anomaly performance threshold respectively. If the carriage control anomaly measured value or carriage control anomaly performance value exceeds the corresponding preset threshold, it indicates that the control status of the carriage storage environment is poor, which is not conducive to the safe storage of food during transportation, and a transportation abnormality signal is generated.

[0053] Example 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the transport anomaly monitoring module is communicatively connected to the transport scheduling management and analysis module. The transport anomaly monitoring module sends a transport anomaly signal to the transport scheduling management and analysis module. The transport scheduling management and analysis module is used to set a monitoring period, analyze the transport scheduling management performance during the monitoring period, and generate a transport scheduling management qualified signal or a transport scheduling management alarm signal through the analysis;

[0054] The transport scheduling management qualified signal or transport scheduling management alarm signal is sent to the transport scheduling management terminal. When the transport scheduling management terminal receives the transport scheduling management alarm signal, it issues a corresponding warning to remind management personnel to strengthen subsequent food transportation control and timely optimize the system to further ensure the safety and efficiency of food transportation. The specific analysis process of the transport scheduling management analysis module is as follows:

[0055] During the corresponding food transportation process, the number of times the transportation abnormality signal is generated is obtained and marked as a transportation abnormality value, and the transportation abnormality value is numerically compared with the corresponding preset transportation abnormality threshold. If the transportation abnormality value exceeds the preset transportation abnormality threshold, it indicates that the control status of the corresponding food transportation process is poor, and the corresponding transportation process is marked as a control optimization process;

[0056] The number of control optimization processes during the monitoring period is obtained and the ratio is calculated with the total number of food transportation times to obtain the control optimization detection value, and the control optimization detection value is numerically compared with the preset control optimization detection threshold. If the control optimization detection value exceeds the preset control optimization detection threshold, it indicates that the food scheduling and transportation management performance during the monitoring period is poor, and a transportation scheduling management alarm signal is generated.

[0057] Furthermore, if the control optimization detection value does not exceed the preset control optimization detection threshold, the number of untimely deliveries during the monitoring period is obtained and the ratio thereof is calculated with the total number of food transportation times to obtain a non-timely detection value, and the average value of the delayed delivery duration during the monitoring period is marked as the delayed arrival performance value; and the number of times the ratio of the loss amount of food damaged during food transportation during the monitoring period exceeds the corresponding preset threshold and the total number of food transportation times is calculated with the ratio thereof to obtain a high loss detection value, and the average of the loss amount ratios of all food transportation processes during the monitoring period is calculated to obtain a loss amount performance value;

[0058] The transport decision evaluation value is obtained by performing a weighted sum calculation on the non-timely detection value, the delayed arrival performance value, the high-damage detection value, and the loss amount performance value; that is, the non-timely detection value, the delayed arrival performance value, the high-damage detection value, and the loss amount performance value are respectively assigned corresponding preset weight coefficients, and the non-timely detection value, the delayed arrival performance value, the high-damage detection value, and the loss amount performance value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the four sets of product results is marked as the transport decision evaluation value;

[0059] It should be noted that the larger the numerical value of the transport decision evaluation value, the worse the food transportation management situation during the monitoring period; the transport decision evaluation value is numerically compared with the preset transport decision evaluation threshold. If the transport decision evaluation value exceeds the preset transport decision evaluation threshold, it indicates that the food scheduling and transportation management performance during the monitoring period is poor, and a transport scheduling management alarm signal is generated; if the transport decision evaluation value does not exceed the preset transport decision evaluation threshold, it indicates that the food scheduling and transportation management performance during the monitoring period is good, and a transport scheduling management qualified signal is generated.

[0060] The working principle of the present invention is as follows: when in use, various types of data from different data sources are collected, integrated and stored through the data fusion and aggregation module; the dynamic traffic perception module, the intelligent order parsing module and the vehicle status assessment module perform analysis based on the collected data, and send the traffic perception analysis information, order parsing information and vehicle status assessment information to the scheduling strategy generation module; the scheduling strategy generation module generates the optimal food transportation scheduling strategy through comprehensive analysis and sends it to the transportation scheduling management end, thereby achieving the optimal configuration of transportation resources, being conducive to the intelligent and refined management of food terminal transportation scheduling, comprehensively improving transportation efficiency, reducing transportation costs and ensuring food transportation safety.

[0061] The thresholds, preset values, preset ranges, etc. in the technical solution of the present invention are set for result comparison and analysis in order to determine whether they are good or bad. As for their size, they are set for entry and storage based on a combination of large-scale model analysis of sample data and manual experience, and can also be appropriately adjusted based on seasonal or common-sense influencing conditions. As for the settings of preset weight coefficients, influencing factors, etc., specific numerical values ​​are assigned based on the influence of each parameter on the result, ultimately reflecting the influence on the result. They are also set for entry and storage based on a combination of large-scale model analysis of sample data and manual experience, and can also be appropriately adjusted based on seasonal or common-sense influencing conditions.

[0062] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. The preferred embodiments do not describe all details in detail, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention and enable those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The food terminal transportation scheduling and management system based on big data is characterized by: It includes a data fusion and aggregation module, a dynamic traffic perception module, an intelligent order analysis module, a vehicle status assessment module, a scheduling strategy generation module, and a transportation scheduling management terminal. The data fusion and aggregation module connects with the food sales system, vehicle monitoring system, and traffic information platform to collect, integrate, and store various data from different data sources. The dynamic traffic perception module perceives and analyzes traffic conditions in real time and sends the traffic perception and analysis information to the scheduling strategy generation module. The intelligent order parsing module conducts in-depth analysis of order data, explores the demand characteristics and patterns behind the orders, and sends the order parsing information to the scheduling strategy generation module; the vehicle status assessment module assesses the vehicle status in real time, and sends the vehicle status assessment information to the scheduling strategy generation module; the scheduling strategy generation module is used to generate the optimal food transportation scheduling strategy through comprehensive analysis, and sends the generated food transportation scheduling strategy to the transportation scheduling management end.

2. The food terminal transportation scheduling and management system based on big data according to claim 1 is characterized in that: The dynamic traffic perception module obtains real-time traffic data from the data fusion and aggregation module, combines historical traffic data and geographic information system information, and conducts a comprehensive analysis of the current traffic conditions. Based on the real-time location and destination of the vehicle, it plans multiple driving routes and evaluates and ranks the expected driving time and congestion probability of each route.

3. The food terminal transportation scheduling and management system based on big data according to claim 1 is characterized in that: The intelligent order parsing module obtains order data from the data fusion and aggregation module, and uses data mining and machine learning algorithms to analyze the time distribution, spatial distribution and order volume changes of orders, and classifies and prioritizes orders based on the urgency of the order and the type of food.

4. The food terminal transportation scheduling and management system based on big data according to claim 1 is characterized in that: The vehicle status assessment module obtains vehicle data from the data fusion and aggregation module, and evaluates various performance indicators of the vehicle based on the vehicle model, age and maintenance record information.

5. The food terminal transportation scheduling and management system based on big data according to claim 1 is characterized in that: The dispatching strategy generation module integrates the comprehensive data, traffic perception analysis information, order parsing information and vehicle status assessment information provided by the data fusion aggregation module, and uses intelligent optimization algorithms to generate the optimal dispatching strategy.

6. The food terminal transportation scheduling and management system based on big data according to claim 1 is characterized in that: The transport scheduling management terminal is communicatively connected to the transport anomaly monitoring module, which monitors the transport process of the vehicle, determines whether a transport anomaly signal is generated through analysis, and sends the transport anomaly signal to the transport scheduling management terminal when it is generated.

7. The food terminal transportation scheduling and management system based on big data according to claim 6 is characterized in that: The specific analysis process of the transportation abnormality monitoring module is as follows: When a vehicle deviates from the scheduled route, the time is counted and the deviation distance is calculated. When the duration or distance of the vehicle deviating from the scheduled route exceeds the corresponding preset threshold, a transport abnormality signal is generated; If the vehicle does not deviate from the scheduled route within a unit of time, or the duration and distance of the vehicle's deviation from the scheduled route do not exceed the corresponding preset thresholds, the speed storage is progressively analyzed to determine whether a transportation abnormality signal is generated.

8. The food terminal transportation scheduling and management system based on big data according to claim 7 is characterized in that: The specific analysis process of the speed storage progressive analysis is: the speed detection analysis value is obtained by weighted summing up the high wave characteristic value, the speed off-time value and the speed wave risk value. If the speed detection analysis value exceeds the preset speed detection analysis threshold, a transportation abnormality signal is generated; if the speed detection analysis value does not exceed the preset speed detection analysis threshold, the car control abnormality time measurement value and the car control abnormality performance value are numerically compared with the preset car control abnormality time measurement threshold and the preset car control abnormality performance threshold respectively. If the car control abnormality time measurement value or the car control abnormality performance value exceeds the corresponding preset threshold, a transportation abnormality signal is generated.

9. The food terminal transportation scheduling and management system based on big data according to claim 6 is characterized in that: The transportation anomaly monitoring module is communicatively connected to the transportation scheduling management and analysis module. The transportation anomaly monitoring module sends the transportation anomaly signal to the transportation scheduling management and analysis module. The transportation scheduling management and analysis module analyzes the transportation scheduling management performance during the monitoring period and sends the transportation scheduling management qualified signal or the transportation scheduling management alarm signal to the transportation scheduling management end.

10. The food terminal transportation scheduling and management system based on big data according to claim 9 is characterized in that: The specific analysis process of the transportation scheduling management analysis module is as follows: The number of control optimization processes during the monitoring period is obtained and the ratio thereof is calculated with the total number of food transportation times to obtain the control optimization detection value. If the control optimization detection value exceeds the preset control optimization detection threshold, a transportation scheduling management alarm signal is generated; if the control optimization detection value does not exceed the preset control optimization detection threshold, the transportation decision evaluation value is obtained by weighted summing up the non-timely detection value, delayed arrival performance value, high loss detection value and loss amount performance value. If the transportation decision evaluation value exceeds the preset transportation decision evaluation threshold, a transportation scheduling management alarm signal is generated; otherwise, a transportation scheduling management qualified signal is generated.

Images