Internet-of-things-driven logistics service platform innovative architecture construction method and system

Abstract

The invention relates to the technical field of logistics, in particular to an internet-of-things-driven logistics service platform innovative architecture construction method and system, and the method comprises the following steps: collecting the real-time position and fuel consumption data of a vehicle, carrying out the optimization of a vehicle driving route through employing constraint planning, obtaining a preliminary vehicle scheduling scheme, and carrying out the optimization of a vehicle driving route based on the preliminary vehicle scheduling scheme. And integrating the traffic condition data to optimize the vehicle loading capacity and the advancing route to obtain a final vehicle scheduling scheme. According to the invention, the vehicle position and the fuel consumption are monitored in real time, and the travel route is optimized by adopting constraint planning, so that the efficiency of logistics operation is improved. This data-based decision support ensures that route adjustments can immediately respond to traffic condition changes, thereby reducing travel time and fuel consumption. Through the integrated traffic and energy use data, the transportation process is optimized, and through intelligent energy management, the operation cost and environmental influence of the warehouse are reduced.

Description

Technical Field

[0001] The present invention relates to the field of logistics technologies, and particularly to a method and system for constructing an innovative architecture of an Internet of Things-driven logistics service platform. Background Art

[0002] The field of logistics technologies focuses on optimizing and managing the processes of procurement, storage, transportation, and distribution of materials, aiming to improve efficiency, reduce costs, and enhance the transparency and responsiveness of the supply chain.

[0003] Existing logistics technologies focus on managing the procurement, storage, and distribution of materials, lacking the ability of dynamic optimization, especially in terms of instant data analysis and real-time adjustment. In the traditional mode, route planning and energy management are often preset and lack flexibility, making it difficult to adapt to sudden traffic congestion or changing transportation demands, resulting in low efficiency and resource waste. In addition, the failure to integrate key data, such as the real-time location of vehicles and energy consumption information, limits the accuracy and timeliness of decision-making. Such deficiencies may cause enterprises to lose their advantages in a highly competitive market environment and fail to meet customers' expectations for speed and cost efficiency. Summary of the Invention

[0004] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose a method and system for constructing an innovative architecture of an Internet of Things-driven logistics service platform.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions. The method for constructing an innovative architecture of an Internet of Things-driven logistics service platform includes the following steps:

[0006] Collect real-time vehicle location and fuel consumption data, optimize the vehicle travel route using constraint programming to obtain a preliminary vehicle scheduling plan, and based on the preliminary vehicle scheduling plan, integrate traffic condition data to optimize the vehicle load and travel route to obtain a final vehicle scheduling plan;

[0007] According to the final vehicle scheduling plan, calculate the estimated fuel consumption and travel time, and use linear programming to optimize the vehicle scheduling again to obtain an adjusted vehicle scheduling plan;

[0008] Collect warehouse energy consumption data, conduct energy consumption analysis based on the usage of temperature control and lighting to obtain a preliminary energy usage report; based on the preliminary energy usage report, apply load balancing and demand response strategies to optimize energy consumption to obtain an optimized energy management plan;

[0009] According to the optimized energy management plan, dynamically adjust the warehouse energy usage settings, monitor the effects, obtain the adjusted energy usage data, analyze the adjustment effects and energy usage trends, and obtain the energy optimization results.

[0010] Preferably, the steps for obtaining the preliminary vehicle scheduling plan are as follows:

[0011] Collect the real-time location and fuel consumption data of the vehicles through in-vehicle GPS and fuel monitoring devices to obtain a real-time data set;

[0012] Based on the real-time data set, use a constraint programming algorithm to optimize the vehicle travel routes. The formula is:

[0013]

[0014] where L is the set of routes, c i is the driving cost of the i-th section of the route, t i is the driving time of the i-th section of the route, ∈ is to prevent zero or small values, f j is the fuel consumption, α and β are weight parameters, k represents the number of sections of the travel route, m is the number of vehicles, and L opt is the optimized route;

[0015] Based on the optimized route, considering the route efficiency and minimizing fuel consumption, obtain the preliminary vehicle scheduling plan.

[0016] Preferably, the steps for obtaining the final vehicle scheduling plan are as follows:

[0017] Based on the preliminary vehicle scheduling plan, collect the current position and speed of the vehicles in real time, and synchronously receive congestion, accident, and road maintenance events to generate a comprehensive traffic condition report;

[0018] According to the comprehensive traffic condition report, use GIS and real-time traffic data to recalculate the expected travel time of each vehicle, adjust the loading capacity and travel route of each vehicle, and obtain the adjusted vehicle scheduling plan;

[0019] Based on the adjusted vehicle scheduling plan, conduct a simulation operation test, analyze the application effect, and obtain and confirm the final vehicle scheduling plan.

[0020] Preferably, the steps for obtaining the adjusted vehicle scheduling plan are as follows:

[0021] Based on the final vehicle scheduling plan, calculate the expected fuel consumption and travel time of each vehicle on the route to obtain the expected fuel consumption data and travel time data;

[0022] Based on the expected fuel consumption data and travel time data, use a linear programming model to optimize the fuel efficiency of the vehicles again to obtain an optimized vehicle scheduling plan;

[0023] Based on the optimized vehicle scheduling plan, recalculate and analyze the fuel efficiency of each vehicle to obtain the adjusted vehicle scheduling plan.

[0024] Preferably, the steps for obtaining the preliminary energy usage report are as follows:

[0025] Install sensors on the temperature control equipment and lighting devices in the warehouse, record the energy consumption in real time, and generate an energy consumption record file;

[0026] Based on the energy consumption record file, identify high-energy consumption areas and usage time periods, and form an energy consumption analysis report;

[0027] Summarize and compare the data in the energy consumption analysis report, and compile a preliminary energy usage report including an overview of energy consumption and the main energy-consuming areas.

[0028] Preferably, the steps for obtaining the optimized energy management plan are as follows:

[0029] Based on the preliminary energy usage report, identify energy-consuming equipment and time periods to obtain an analysis of key energy consumption points;

[0030] Based on the analysis of key energy consumption points, calculate the total energy consumption. The calculation formula is:

[0031]

[0032] Where E opt represents the total energy consumption, SL i represents the energy consumption of the i-th equipment in the target time period, R i represents the response rate, p is the number of equipment, and ∈ is to prevent zero or small values;

[0033] Based on the total energy consumption, apply load balancing and demand response strategies to optimize energy consumption, and judge the optimization effect by recalculating the total energy consumption to obtain an optimized energy management plan.

[0034] Preferably, the steps for obtaining the adjusted energy usage data are as follows:

[0035] Based on the optimized energy management plan, adjust the temperature control system and lighting devices in the warehouse to obtain an operation record of the implementation of the adjustment;

[0036] Based on the operation record of the implementation of the adjustment, track the effect of the adjustment, monitor the energy usage of the temperature control system and lighting devices, and collect the adjusted energy consumption data;

[0037] Based on the adjusted energy consumption data, analyze the percentage reduction in energy usage to obtain the adjusted energy usage data.

[0038] Preferably, the steps for obtaining the energy optimization result are as follows:

[0039] Collect the adjusted energy usage data, compare the data before and after the adjustment, and obtain a data comparison report;

[0040] Based on the data comparison report, calculate the percentage improvement in energy usage efficiency. The calculation formula is:

[0041]

[0042] where P eff represents the percentage improvement in energy efficiency, and E before and E after represent the energy usage before and after the adjustment respectively;

[0043] Based on the percentage improvement in efficiency, considering the influence of seasonal changes and operating habits, determine and obtain the energy optimization result.

[0044] The present invention provides a system for constructing an innovative architecture of a logistics service platform, including:

[0045] A vehicle scheduling module that collects the real-time location and fuel consumption data of vehicles, determines the routes of vehicle travel, and performs preliminary scheduling on the vehicles to obtain a preliminary scheduling plan;

[0046] A vehicle optimization module that, based on the preliminary scheduling plan and combined with traffic condition data, optimizes the loading capacity and travel routes of the vehicles, determines the travel routes and loading capacity of the vehicles, and obtains an optimized scheduling plan;

[0047] A scheduling re-optimization module that, based on the optimized scheduling plan, calculates the estimated fuel consumption and travel time, and performs re-optimization on vehicle scheduling to obtain an adjusted scheduling plan;

[0048] An energy management analysis module that collects the energy consumption data of the warehouse, analyzes the temperature control and lighting usage conditions, and obtains an energy usage report;

[0049] An energy optimization module that, based on the energy usage report, applies load balancing and demand response strategies to optimize the energy consumption of the warehouse, dynamically adjusts the energy usage settings of the warehouse, monitors the adjustment effect, and obtains an energy optimization result.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] The present invention optimizes the travel route through constraint programming by monitoring the vehicle position and fuel consumption in real time, improving the efficiency of logistics operations. This data-based decision support ensures that route adjustments can respond immediately to changes in traffic conditions, thereby reducing travel time and fuel consumption. Through the integrated traffic and energy usage data, not only the transportation process is optimized, but also the operating costs and environmental impact of the warehouse are reduced through intelligent energy management. Load balancing and demand response strategies make energy usage more precise, avoiding unnecessary waste and improving overall economy and sustainability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart for obtaining the adjusted vehicle scheduling plan in the present invention;

[0053] Figure 2 It is a flowchart for obtaining the energy optimization result in the present invention;

[0054] Figure 3 It is a flowchart for obtaining the final vehicle scheduling plan in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0055] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] Please refer to Figure 1-2 , the present invention provides a technical solution, a method for constructing an innovative architecture of an Internet of Things-driven logistics service platform, including the following steps:

[0057] Collect vehicle real-time position and fuel consumption data, optimize the vehicle travel route using constraint programming to obtain a preliminary vehicle scheduling plan, and based on the preliminary vehicle scheduling plan, integrate traffic condition data to optimize the vehicle load and travel route to obtain a final vehicle scheduling plan;

[0058] According to the final vehicle scheduling plan, calculate the estimated fuel consumption and travel time, and use linear programming to optimize the vehicle scheduling again to obtain an adjusted vehicle scheduling plan;

[0059] Collect warehouse energy consumption data, conduct energy consumption analysis based on the usage of temperature control and lighting to obtain a preliminary energy usage report; based on the preliminary energy usage report, apply load balancing and demand response strategies to optimize energy consumption to obtain an optimized energy management plan;

[0060] According to the optimized energy management plan, dynamically adjust the warehouse energy usage settings and monitor the effects to obtain adjusted energy usage data, analyze the adjustment effects and energy usage trends to obtain energy optimization results.

[0061] The steps to obtain the preliminary vehicle scheduling plan are as follows:

[0062] Collect the real-time position and fuel consumption data of vehicles through on-vehicle GPS and fuel monitoring devices to obtain a real-time data set;

[0063] Based on the real-time data set, use the constraint programming algorithm to optimize the vehicle travel route. The formula is:

[0064]

[0065] where L is the set of routes, c i is the driving cost of the i-th section of the route, t i is the driving time of the i-th section of the route, ∈ is to prevent zero or small values, f j is the fuel consumption, α and β are weight parameters, k represents the number of segments of the travel route, m is the number of vehicles, and L opt is the optimized route;

[0066] Based on the optimized route, considering the route efficiency and minimizing fuel consumption, obtain the preliminary vehicle scheduling plan.

[0067] Specifically, through on-vehicle GPS and fuel monitoring devices, collect the real-time position and fuel consumption data of vehicles. This process ensures the real-time update and accurate recording of data, generates a real-time data set, which contains the accurate geographical location information and real-time fuel consumption rate of vehicles, providing basic data support for subsequent route optimization.

[0068] The benefit of the formula is to select the travel route by dynamically weighing the cost and fuel efficiency, improving the overall transportation efficiency and reducing the operating cost. The steps to obtain the parameters are: c i (the driving cost of the i-th section of the route) is directly obtained through the vehicle driving recorder, and t i (the corresponding driving time) is calculated through GPS tracking data, and f j (the fuel consumption) is obtained through fuel sensor monitoring. α and β are set as weights according to historical data and experience to achieve the balance of cost and efficiency;

[0069] Calculation process:

[0070] Consider two possible routes A and B. The specific data is as follows:

[0071] Route A: c A =[100, 150], t A =[2, 3], f A =[40, 60];

[0072] Route B: c B= [120, 130], t B = [1.5, 2.5], f B = [30, 45];

[0073] Let α = 0.5, β = 0.3, ∈ = 0.1;

[0074] Calculation of the total cost of Route A:

[0075]

[0076] Cost A = 0.5(47.62 + 48.39) + 0.3·10

[0077] Cost A = 0.5·96.01 + 3 = 51.005

[0078] Calculation of the total cost of Route B:

[0079]

[0080] Cost B = 0.5(75 + 50) + 0.3·8.66

[0081] Cost B = 0.5·125 + 2.598 = 65.098

[0082] Through calculation, Route A (Cost A = 51.005) is superior to Route B (Cost B = 65.098), so L opt is Route A.

[0083] This result indicates that through the optimization algorithm, the most cost-effective transportation mode can be determined as Route A, which means that by scheduling vehicles along this path, a balance between cost and fuel efficiency can be achieved.

[0084] According to the calculated set of routes, adjust the actual vehicle scheduling arrangement to ensure that each vehicle runs on the most economical route, thereby achieving a reduction in the overall transportation cost and an improvement in fuel efficiency.

[0085] Please refer to Figure 3 , the steps to obtain the final vehicle scheduling plan are as follows:

[0086] Based on the preliminary vehicle scheduling plan, collect the current position and speed of the vehicle in real time, and synchronously receive congestion, accident, and road maintenance events to generate a comprehensive traffic condition report;

[0087] According to the comprehensive traffic condition report, using GIS and real-time traffic data, recalculate the expected travel time of each vehicle, adjust the loading capacity and travel route of each vehicle, and obtain the adjusted vehicle scheduling plan;

[0088] Based on the adjusted vehicle scheduling plan, conduct a simulation operation test, analyze the application effect, and obtain and confirm the final vehicle scheduling plan.

[0089] Specifically, based on the preliminary vehicle scheduling plan, install in-vehicle devices with GPS and data transmission functions to receive the position and speed data of each vehicle in real time, synchronize the instant road condition information provided by the traffic management system, including traffic flow, the location and time of accident occurrence points, and road construction areas, analyze the route congestion situation, evaluate the possible delay time and the feasibility of alternative routes, and generate a comprehensive traffic condition report.

[0090] According to the comprehensive traffic condition report, use the Geographic Information System (GIS) to dynamically adjust the routes of all vehicles. Through the real-time traffic data update system, call GIS for route analysis, calculate the expected driving time and traffic delay of each vehicle, optimize the loading capacity and scheduled routes of the vehicles, and select the travel path and loading strategy by simulating various route plans, so as to obtain the adjusted vehicle scheduling plan.

[0091] Based on the adjusted vehicle scheduling plan, perform a simulation operation test, use the simulation in a virtual environment to reproduce the actual road and traffic conditions, monitor the performance of the vehicles under different routes and loading states, evaluate the actual effect and fuel consumption of the scheduled routes in the plan, and compare the efficiency and cost of different scheduling plans to determine the route and loading capacity configuration.

[0092] The steps to obtain the adjusted vehicle scheduling plan are as follows:

[0093] Based on the final vehicle scheduling plan, calculate the expected fuel consumption and travel time of each vehicle on the route to obtain the expected fuel consumption data and travel time data;

[0094] Based on the expected fuel consumption data and travel time data, use a linear programming model to optimize the fuel efficiency of the vehicles again to obtain an optimized vehicle scheduling plan;

[0095] Based on the optimized vehicle scheduling plan, recalculate and analyze the fuel efficiency of each vehicle to obtain the adjusted vehicle scheduling plan.

[0096] Specifically, when implementing the vehicle scheduling plan, first collect the driving data of each vehicle, including speed, load, and fuel consumption. This data is uploaded in real-time through on-vehicle sensors, and the start and end times and fuel consumption of each trip are recorded. Then, analyze the fuel consumption changes of each vehicle. For abnormal fuel consumption situations, ensure the accuracy and availability of the data by checking whether the data points are within the normal operating range, that is, the standard safety threshold of fuel consumption not exceeding 0.2 liters per kilometer. Next, calculate the average fuel consumption of each trip based on the real-time monitoring data, and summarize to obtain the overall estimated fuel consumption and trip time.

[0097] Based on the estimated fuel consumption and trip time data, optimize and adjust the vehicle scheduling plan. First, set the optimization objective function, that is, minimize the total fuel consumption while minimizing the total trip time as much as possible. During implementation, identify the high fuel consumption sections in each vehicle's trip, re-plan the travel routes of these sections, avoid sections with traffic congestion or large terrain undulations, and at the same time adjust the departure time and speed of the vehicle to make the trip smoother. In addition, adjust the loading volume of the vehicle to ensure that the loading volume of each vehicle is within the safe range, that is, not exceeding 90% of the vehicle's maximum load capacity, so as to reduce the vehicle's burden and improve fuel efficiency.

[0098] Based on the adjusted vehicle scheduling plan, recalculate the fuel efficiency of each vehicle. The specific process includes collecting the actual driving data of each vehicle after adjustment, monitoring the fuel consumption and trip time during the implementation of the new plan, and comparing with the data before optimization. By calculating the average fuel consumption of each vehicle under the new plan, detect whether the fuel consumption of each vehicle remains within the predetermined economic operating range, that is, the fuel consumption per kilometer decreases by at least 2%.

[0099] The steps to obtain the preliminary energy usage report are as follows:

[0100] Install sensors on the temperature control equipment and lighting devices in the warehouse to record the energy consumption in real-time and generate an energy consumption record file;

[0101] Based on the energy consumption record file, identify the high energy consumption areas and usage time periods to form an energy consumption analysis report;

[0102] Summarize and compare the data in the energy consumption analysis report to compile a preliminary energy usage report including an overview of energy consumption and the main energy-consuming areas.

[0103] Specifically, install energy monitoring sensors on the temperature regulation and lighting devices in the warehouse to capture the power usage and operation time of the devices in real-time. Use the data to evaluate the operation efficiency of the devices, mark abnormal energy consumption patterns, and set specific thresholds. For example, if the energy consumption per hour exceeds 10 kWh, it is considered abnormal. In this way, quickly identify the areas of energy waste that exceed the set safety range. All these steps are completed by an automated system without manual intervention to obtain an energy consumption record file.

[0104] Based on the generated energy consumption record file, identify the energy usage trends of specific time periods and devices, screen and compare data points, determine high-energy consumption periods such as 9:00 AM to 5:00 PM on weekdays, and draw an energy consumption curve to discover the peaks and valleys of energy usage. For the identified low-efficiency areas, such as the lights in the back of the warehouse being left on for a long time, propose energy-saving measures to form an energy consumption analysis report.

[0105] From the energy consumption analysis report, the team summarized and compared the energy consumption data of each area and device, and used charts and data tables to show the energy consumption distribution of different areas. Further analysis determined the main contributors to energy consumption, such as the excessive use of the temperature control system during the summer peak period, and thus derived energy-saving measures, including reconfiguring the temperature control strategy and optimizing the lighting plan, to reduce unnecessary energy waste. After integrating these data and suggestions, a preliminary energy usage report was compiled.

[0106] The steps to obtain the optimized energy management plan are as follows:

[0107] Based on the preliminary energy usage report, identify the energy-consuming devices and time periods to obtain an analysis of key energy consumption points;

[0108] Based on the analysis of key energy consumption points, calculate the total energy consumption. The calculation formula is:

[0109]

[0110] Where E opt represents the total energy consumption, SL i represents the energy consumption of the i-th device in the target time period, R i represents the response rate, p is the number of devices, and ∈ is to prevent zero and small values;

[0111] Based on the total energy consumption, apply load balancing and demand response strategies to optimize energy consumption, and judge the optimization effect by calculating the total energy consumption again to obtain the optimized energy management plan.

[0112] Specifically, based on the preliminary energy usage report, high-energy-consuming equipment and major energy-consuming periods are first identified. This involves observing and recording the energy usage in various areas within the warehouse, paying particular attention to equipment or systems operating at peak times and under high loads. Through the collation and analysis of this data, specific equipment and time periods with high energy consumption can be identified, and furthermore, the operating mode and energy consumption distribution of each piece of equipment can be determined. This provides crucial basic data and a basis for the next step of energy optimization, thus forming a critical energy consumption point analysis report.

[0113] In the formula, SL represents the actual energy consumption of the equipment i , and R represents the response rate i which indicates the rapid response ability of the equipment to energy usage adjustments (such as reducing energy consumption or responding to demand response instructions). A high response rate means the equipment can quickly adapt to changes in energy supply, such as reducing or increasing power consumption to match the energy supply status or price fluctuations. ∈ is a small value to avoid a zero denominator, usually taken as 0.01;

[0114] Calculation process: There are three pieces of equipment, with their energy consumptions SL i being 100, 200, and 150 kWh respectively, and their response rates R i being 0.9, 0.85, and 0.8 respectively, and ∈ = 0.01. Then the calculation process is as follows:

[0115]

[0116]

[0117] This result indicates that the total energy consumption is 317 kWh.

[0118] Based on the total energy consumption, load balancing and demand response strategies are implemented. On this basis, the operating time and load of each piece of equipment in the warehouse are adjusted. More stringent control measures are implemented for equipment or time periods with particularly high energy consumption, such as adjusting working hours, reducing energy consumption during off-peak periods, or replacing old equipment to improve energy efficiency. Through these operations, unnecessary energy consumption is reduced, and the total energy consumption is recalculated after the adjustment to determine whether optimization has been achieved.

[0119] The steps for obtaining the adjusted energy usage data are as follows:

[0120] Based on the optimized energy management plan, the temperature control system and lighting devices in the warehouse are adjusted to obtain the operation records of the implemented adjustments;

[0121] Based on the operation records of the implemented adjustments, the effects of the adjustments are traced, the energy usage of the temperature control system and lighting devices is monitored, and the adjusted energy consumption data is collected;

[0122] Based on the adjusted energy consumption data, analyze the percentage reduction in energy use to obtain the adjusted energy usage data.

[0123] Specifically, according to the adjustment strategy, modify the settings of the temperature control system and lighting devices to ensure that lighting and temperature are automatically reduced during low-demand periods to save energy. This includes setting low-energy consumption thresholds such as a temperature control range of 18°C to 22°C and 50% lighting intensity, further reducing the settings outside of working hours or when the warehouse is vacant, and simultaneously starting real-time monitoring to record the energy usage and activity status of each device. The recorded data is updated every 15 minutes to capture immediate changes in energy consumption.

[0124] Track the data in the operation records, especially in the adjusted temperature control and lighting systems, detect the energy consumption and operating efficiency, and calculate the energy consumption changes brought about by the adjustment measures. For example, by comparing the energy consumption records before and after the adjustment, pay special attention to the hourly energy consumption of each device before and after the implementation of the energy-saving measures.

[0125] Using detailed data analysis, comprehensively calculate the energy-saving effect from the adjusted energy consumption data collected, extract performance indicators from the data, such as the numerical value of the energy consumption reduction during a specific period, so as to confirm which energy-saving measures are the most effective. Through this data, analyze the overall percentage reduction in energy use and format the results in the final energy usage report.

[0126] The steps to obtain the energy optimization results are as follows:

[0127] Collect the adjusted energy usage data, compare the data before and after the adjustment, and obtain a data comparison report;

[0128] Based on the data comparison report, calculate the percentage improvement in energy use efficiency. The calculation formula is:

[0129]

[0130] where P eff represents the percentage improvement in energy efficiency, and E before and E after represent the energy usage before and after the adjustment respectively;

[0131] Based on the percentage improvement in efficiency, considering the influence of seasonal changes and operating habits, determine and obtain the energy optimization results.

[0132] Specifically, collect the adjusted energy usage data and compare it with the data before the adjustment to generate a data comparison report. The specific operations include recording the energy consumption readings of the temperature control system and lighting equipment at each time period before and after the adjustment.

[0133] The advantage of the formula lies in the introduction of square root and absolute value, which improves the sensitivity to abnormal changes and makes the result better reflect the actual improvement effect;

[0134] Calculation process: The energy consumption E before adjustment before is 500 kWh, and the energy consumption E after adjustment after is 450 kWh. The calculated percentage improvement P in energy efficiency eff will be:

[0135]

[0136] This result indicates that the energy efficiency has increased by 31.6% after adjustment, showing that the energy-saving measures have achieved good results.

[0137] Based on the percentage improvement in efficiency, considering the specific impacts of seasonal variations and operating habits on energy consumption, an energy optimization result report is generated; During operation, specifically focus on the impacts of different seasons on energy consumption, such as analyzing the energy consumption comparison between winter and summer, especially the differences in energy use of heating and cooling systems. In addition, evaluate the changes in operating habits, such as whether adjusting the temperature set point can effectively reduce the energy consumption of air conditioners. Use statistical methods to determine the safety thresholds for energy use, which are set based on historical data and industry standards, to ensure that all operations are carried out within the optimal energy efficiency range.

[0138] The present invention provides a system for constructing an innovative architecture of a logistics service platform, including:

[0139] A vehicle scheduling module, which collects the real-time location and fuel consumption data of vehicles, determines the routes of vehicle travel, and conducts preliminary scheduling of vehicles to obtain a preliminary scheduling plan;

[0140] A vehicle optimization module, which based on the preliminary scheduling plan and combined with traffic condition data, optimizes the loading capacity and travel routes of vehicles, determines the travel routes and loading capacity of vehicles, and obtains an optimized scheduling plan;

[0141] A scheduling re-optimization module, which based on the optimized scheduling plan, calculates the estimated fuel consumption and travel time, and re-optimizes the vehicle scheduling to obtain an adjusted scheduling plan;

[0142] An energy management analysis module, which collects the energy consumption data of the warehouse, analyzes the temperature control and lighting usage, and obtains an energy use report;

[0143] An energy optimization module, which based on the energy use report, applies load balancing and demand response strategies, optimizes the energy consumption of the warehouse, dynamically adjusts the energy use settings of the warehouse, monitors the adjustment effects, and obtains an energy optimization result.

[0144] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the relevant art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for constructing an innovative architecture of a logistics service platform driven by the Internet of Things, characterized in that: The following steps are involved: Collecting real-time vehicle location and fuel consumption data, optimizing vehicle routes using constraint programming to obtain a preliminary vehicle dispatching plan, and integrating traffic condition data based on the preliminary vehicle dispatching plan to optimize vehicle loading and routes to obtain a final vehicle dispatching plan; According to the final vehicle dispatching plan, the estimated fuel consumption and travel time are calculated, and the vehicle dispatching is optimized again using linear programming to obtain an adjusted vehicle dispatching plan; Collect warehouse energy consumption data, analyze energy consumption based on temperature control and lighting usage, and obtain a preliminary energy usage report; based on the preliminary energy usage report, apply load balancing and demand response strategies to optimize energy consumption and obtain an optimized energy management solution; According to the optimized energy management plan, the warehouse energy usage settings are dynamically adjusted, and the effects are monitored to obtain adjusted energy usage data, and the adjustment effects and energy usage trends are analyzed to obtain energy optimization results.

2. The method for constructing an innovative architecture of a logistics service platform driven by the Internet of Things according to claim 1, characterized in that: The steps for obtaining the preliminary vehicle dispatching plan are: The real-time location and fuel consumption data of the vehicle are collected through the vehicle-mounted GPS and fuel monitoring equipment to obtain a real-time data set; Based on the real-time data set, the constraint programming algorithm is used to optimize the vehicle route. The formula is: Among them, L is the route set, c i is the travel cost of the i-th route, t i is the travel time of the i-th route, ∈ is a small value to prevent zero, and f j is the fuel consumption, α and β are weight parameters, k represents the number of segments of the route, m is the number of vehicles, L opt To optimize the route; Based on the optimized route, a preliminary vehicle dispatching plan is obtained by taking into account route efficiency and minimization of fuel consumption.

3. The method for constructing an innovative architecture of a logistics service platform driven by the Internet of Things according to claim 1, characterized in that: The steps for obtaining the final vehicle dispatching plan are: Based on the preliminary vehicle dispatch plan, the current position and speed of the vehicle are collected in real time, and congestion, accidents and road maintenance events are received simultaneously to generate a comprehensive traffic status report; According to the comprehensive traffic status report, using GIS and real-time traffic data, recalculate the expected travel time of each vehicle, adjust the load and travel route of each vehicle, and obtain an adjusted vehicle dispatch plan; Based on the adjusted vehicle dispatching plan, a simulation operation test is carried out, and the application effect is analyzed to obtain and confirm the final vehicle dispatching plan.

4. The method for constructing an innovative architecture of a logistics service platform driven by the Internet of Things according to claim 1, characterized in that: The steps for obtaining the adjusted vehicle dispatching plan are: Based on the final vehicle dispatching plan, the estimated fuel consumption and travel time of each vehicle on the route are calculated to obtain estimated fuel consumption data and travel time data; Based on the predicted fuel consumption data and travel time data, a linear programming model is used to further optimize the fuel efficiency of the vehicle to obtain an optimized vehicle scheduling plan; Based on the optimized vehicle dispatching plan, the fuel efficiency of each vehicle is recalculated and analyzed to obtain an adjusted vehicle dispatching plan.

5. The method for constructing an innovative architecture of a logistics service platform driven by the Internet of Things according to claim 1, characterized in that: The steps for obtaining the preliminary energy usage report are: Install sensors on temperature control equipment and lighting devices in the warehouse to record energy consumption in real time and generate energy consumption record files; Based on the energy consumption record file, identify high energy consumption areas and usage time periods, and form an energy consumption analysis report; Summarize and compare the data in the energy consumption analysis report to prepare a preliminary energy use report including an overview of energy consumption and major energy consumption areas.

6. The method for constructing an innovative architecture of a logistics service platform driven by the Internet of Things according to claim 1, characterized in that: The steps for obtaining the optimized energy management plan are: Based on the preliminary energy usage report, identify energy-consuming equipment and time periods to obtain key energy consumption point analysis; Based on the analysis of the key energy consumption points, the total energy consumption is calculated using the following formula: Among them, E opt Represents total energy consumption, SL i represents the energy consumption of the i-th device in the target time period, R i represents the response rate, p is the number of devices, and ∈ is the small value to prevent zero; Based on the total energy consumption, load balancing and demand response strategies are applied to optimize energy consumption, and the optimization effect is judged by recalculating the total energy consumption to obtain an optimized energy management solution.

7. The method for constructing an innovative architecture of a logistics service platform driven by the Internet of Things according to claim 1, characterized in that: The steps for obtaining the adjusted energy usage data are as follows: Based on the optimized energy management solution, adjust the temperature control system and lighting devices in the warehouse, and obtain an operation record of implementing the adjustment; Based on the operational records of the implementation of the adjustments, tracking the effects of the adjustments, monitoring the energy usage of the temperature control system and lighting devices, and collecting energy consumption data after the adjustments; Based on the adjusted energy consumption data, the percentage of energy usage reduction is analyzed to obtain adjusted energy usage data.

8. The method for constructing an innovative architecture of a logistics service platform driven by the Internet of Things according to claim 1, characterized in that: The steps for obtaining the energy optimization result are: Collect the adjusted energy usage data, compare the data before and after the adjustment, and obtain a data comparison report; Based on the data comparison report, calculate the efficiency improvement percentage of energy use, the calculation formula is: Among them, P eff Indicates the percentage of energy efficiency improvement, E before and E after Respectively represent the energy usage before and after adjustment; Based on the efficiency improvement percentage, combined with the impact of seasonal changes and operating habits, energy optimization results are determined and obtained.

9. A logistics service platform innovation architecture construction system according to the method for constructing an innovation architecture of a logistics service platform driven by the Internet of Things according to any one of claims 1 to 8, characterized in that: include: The vehicle dispatch module collects the real-time location and fuel consumption data of the vehicle, determines the route of the vehicle, performs preliminary dispatch of the vehicle, and obtains a preliminary dispatch plan; The vehicle optimization module optimizes the vehicle's load and route based on the preliminary dispatch plan and combined with traffic condition data, determines the vehicle's route and load, and obtains an optimized dispatch plan; The scheduling re-optimization module calculates the expected fuel consumption and travel time based on the optimized scheduling plan, optimizes the vehicle scheduling again, and obtains the adjusted scheduling plan; Energy management analysis module, which collects energy consumption data of the warehouse, analyzes temperature control and lighting usage, and obtains energy usage reports; The energy optimization module applies load balancing and demand response strategies based on energy usage reports to optimize the warehouse's energy consumption, dynamically adjust the warehouse's energy usage settings, monitor the adjustment effects, and obtain energy optimization results.

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