A hydrogen consumption simulation system and method for vehicle transportation routes based on multiple models

Abstract

The present invention provides a hydrogen consumption simulation system and method for vehicle transportation routes based on multiple models. Belonging to the fields of new energy and simulation technologies, the method includes: based on a basic power demand model and a hydrogen consumption calculation model, combining the multi-source data collected in real time by a multi-source data acquisition network with historical data, optimizing the existing model through a machine learning algorithm, and updating the hydrogen consumption calculation model. By collecting multi-source data in real time, combining historical data and a machine learning algorithm to optimize the existing model, the hydrogen consumption situation can be predicted more accurately. At the same time, by collecting new data on external environmental factors in real time, optimizing the environmental impact model, and further correcting the hydrogen consumption calculation results, the accuracy of the simulation is improved.

Description

Technical Field

[0001] The present invention provides a hydrogen consumption simulation system and method for vehicle transportation routes based on multiple models, belonging to the fields of new energy and simulation technology. Background Art

[0002] As a clean and efficient form of energy, hydrogen energy has been increasingly valued in the field of transportation. Especially in vehicle transportation, hydrogen fuel cell vehicles are regarded as an important direction for future transportation development due to their zero emissions and long endurance. However, the hydrogen consumption of hydrogen fuel cell vehicles directly affects their operating costs and environmental benefits. Therefore, accurately predicting and optimizing the hydrogen consumption of vehicles on different transportation routes has become an urgent problem to be solved.

[0003] Traditional hydrogen consumption prediction methods mainly rely on theoretical calculations and experimental tests. Although these methods can reflect the hydrogen consumption of vehicles to a certain extent, they often suffer from problems such as insufficient accuracy and poor adaptability. Especially in complex actual operating environments, the power demand of vehicles, the efficiency of hydrogen fuel cells, and external environmental factors will all have a significant impact on hydrogen consumption. The interaction between these factors makes hydrogen consumption prediction more complex.

[0004] To overcome the limitations of traditional methods, in recent years, simulation methods based on multiple models have gradually been introduced into the prediction of hydrogen consumption for vehicle transportation routes. This method integrates multiple related models, comprehensively considers various factors such as the power demand of vehicles, the efficiency of hydrogen fuel cells, and external environmental factors, and realizes accurate prediction of hydrogen consumption. However, existing multi-model simulation methods still have some deficiencies, such as untimely model updates, poor adaptability to complex environments, and large deviations between simulation results and actual operating data. Summary of the Invention

[0005] The present invention provides a hydrogen consumption simulation system and method for vehicle transportation routes based on multiple models to solve the problems mentioned in the above background art:

[0006] A hydrogen consumption simulation method for vehicle transportation routes based on multiple models proposed by the present invention, the method includes:

[0007] S1. Based on the existing basic power demand model and hydrogen consumption calculation model, through the multi-source data collected in real time by the multi-source data acquisition network, combining the multi-source data with historical data, optimizing the existing models through machine learning algorithms, and updating the hydrogen consumption calculation model;

[0008] S2. Optimize the existing environmental impact model through the newly collected data of external environmental factors in real time, and further correct the hydrogen consumption calculation results according to the impact of environmental conditions on the efficiency of hydrogen fuel cells;

[0009] S3. Integrate the optimized basic power demand model, hydrogen consumption calculation model, and environmental impact model to form a multi-level comprehensive hydrogen consumption model;

[0010] S4. Input the relevant data of different transportation routes, and based on the comprehensive hydrogen consumption model, perform simulation calculations on different transportation routes to obtain the hydrogen consumption of each route under different working conditions; output the hydrogen consumption simulation results of each transportation route under different working conditions, and continuously optimize the model by comparing with the actual operation data;

[0011] S5. Based on the simulation results, determine the best transportation route through a multi-objective optimization algorithm; output the optimized best transportation route suggestion, and dynamically adjust the transportation route according to the actual operation situation;

[0012] S6. During the transportation process, collect the actual operation data in real time; based on the actual operation data, re-evaluate and optimize the comprehensive hydrogen consumption model.

[0013] A vehicle transportation route hydrogen consumption simulation system based on multiple models proposed by the present invention, the system includes:

[0014] Data acquisition module: Based on the existing basic power demand model and hydrogen consumption calculation model, collect multi-source data in real time through a multi-source data acquisition network; combine the multi-source data with historical data, optimize the existing model through machine learning algorithms, and update the hydrogen consumption calculation model;

[0015] Model construction module: Optimize the existing environmental impact model through the newly collected data of external environmental factors in real time, and further correct the hydrogen consumption calculation result according to the impact of environmental conditions on the efficiency of hydrogen fuel cells;

[0016] Model integration module: Integrate the optimized basic power demand model, hydrogen consumption calculation model, and environmental impact model to form a multi-level comprehensive hydrogen consumption model;

[0017] Result simulation module: Input the relevant data of different transportation routes, and based on the comprehensive hydrogen consumption model, perform simulation calculations on different transportation routes to obtain the hydrogen consumption of each route under different working conditions; output the hydrogen consumption simulation results of each transportation route under different working conditions, and continuously optimize the model by comparing with the actual operation data;

[0018] Route optimization module: Based on the latest simulation results, determine the best transportation route through a multi-objective optimization algorithm; output the optimized best transportation route suggestion, and dynamically adjust the transportation route according to the actual operation situation;

[0019] Dynamic correction module: During the transportation process, collect the actual operation data in real time; based on the actual operation data, re-evaluate and optimize the comprehensive hydrogen consumption model.

[0020] Advantages of the present invention: By collecting multi-source data in real time, optimizing the existing model by combining historical data and machine learning algorithms, the hydrogen consumption can be predicted more accurately. At the same time, by collecting new data on external environmental factors in real time, the environmental impact model is optimized, further correcting the hydrogen consumption calculation results and improving the accuracy of the simulation; According to the actual operation data and the comprehensive hydrogen consumption model, the simulation calculations are carried out for different transportation routes to obtain the hydrogen consumption of each route under different working conditions. By continuously optimizing the model by comparing the actual operation data, the transportation route can be dynamically adjusted according to the actual situation, and the best transportation route can be selected; The best transportation route is determined by the multi-objective optimization algorithm, the optimized best transportation route suggestion is output, and the transportation route is dynamically adjusted according to the actual operation situation. This helps to improve the transportation efficiency and reduce the transportation cost; During the transportation process, the actual operation data is collected in real time, and the comprehensive hydrogen consumption model is re-evaluated and optimized according to the actual operation data. This helps to timely discover problems and make adjustments to ensure the safety and efficiency of the transportation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of the method described in the present invention;

[0022] Figure 2 It is a block diagram of the system described in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0023] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0024] In the following description, many specific details are set forth in order to fully understand the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention.

[0026] An embodiment of the present invention, as Figure 1 shown, a method for simulating hydrogen consumption of a vehicle transportation route based on multiple models, the method comprising:

[0027] S1. Based on the existing basic power demand model and hydrogen consumption calculation model, collect multi-source data in real time through a multi-source data acquisition network; combine the multi-source data with historical data, optimize the existing models through machine learning algorithms, and update the hydrogen consumption calculation model;

[0028] S2. Optimize the existing environmental impact model through newly collected data on external environmental factors in real time, and further correct the hydrogen consumption calculation results according to the impact of environmental conditions on the efficiency of hydrogen fuel cells;

[0029] S3. Integrate the optimized basic power demand model, hydrogen consumption calculation model and environmental impact model to form a multi-level comprehensive hydrogen consumption model;

[0030] S4. Input the relevant data of different transportation routes, and based on the comprehensive hydrogen consumption model, conduct simulation calculations on different transportation routes to obtain the hydrogen consumption of each route under different working conditions; output the hydrogen consumption simulation results of each transportation route under different working conditions, and continuously optimize the model by comparing with the actual operation data;

[0031] S5. Based on the simulation results, determine the best transportation route through a multi-objective optimization algorithm; output the optimized best transportation route suggestions, and dynamically adjust the transportation route according to the actual operation situation;

[0032] S6. During the transportation process, collect actual operation data in real time; based on the actual operation data, re-evaluate and optimize the comprehensive hydrogen consumption model.

[0033] The working principle of the above technical solution is as follows: Through sensors installed on the vehicle and other data sources (such as GPS, weather stations, etc.), operating parameters of the vehicle such as speed, acceleration, and gradient are collected in real time, as well as data on external environmental factors such as temperature, humidity, and altitude. These new data are combined with historical data and trained through machine learning algorithms to optimize the existing basic power demand model and hydrogen consumption calculation model. Among them, the multi-source data acquisition network refers to a network system that obtains information from multiple different data sources. For example, sensors inside the vehicle can provide vehicle status data, while public weather services or dedicated environmental monitoring stations can provide external environmental conditions. The newly collected data on external environmental factors in real time are used to further optimize the environmental impact model. This model takes into account how environmental changes affect the efficiency of the hydrogen fuel cell and adjusts the hydrogen consumption calculation results according to the latest environmental conditions. This can ensure that the hydrogen consumption prediction is closer to the actual situation. Among them, the environmental impact model is a mathematical or physical model that describes the way external environmental factors (such as temperature, humidity) affect the efficiency of the hydrogen fuel cell. For example, low temperature may reduce the reaction rate of the fuel cell, thereby increasing hydrogen consumption. Combine the optimized basic power demand model and hydrogen consumption calculation model in S1 with the optimized environmental impact model in S2 to form a multi-level comprehensive hydrogen consumption model. This model can more accurately simulate the hydrogen consumption of the vehicle under different conditions. Input relevant data of various transportation routes, such as road type, traffic flow, weather forecast, etc., and use the optimized comprehensive hydrogen consumption model to simulate and calculate the hydrogen consumption of different routes. The simulation results not only help plan the most economical transportation route but also provide an opportunity to continuously optimize the model by comparing the differences between the actual operation data and the simulation results to adjust the model parameters. Based on the simulation results in S4, use a multi-objective optimization algorithm to determine the best transportation route. The multi-objective optimization algorithm takes into account multiple objectives (such as minimizing hydrogen consumption, shortening transportation time, etc.) and finds a balance point, that is, the Pareto optimal solution. The final output is a recommendation for the best transportation route that can both save fuel and meet other operation requirements. During the entire transportation process, continue to collect actual operation data in real time, including the vehicle's position, speed, hydrogen consumption, etc. Then, re-evaluate and optimize the comprehensive hydrogen consumption model based on these actual data to ensure that it always reflects the latest real situation. This process is cyclic, and as new data are continuously added, the model will become more and more accurate.

[0034] The effects of the above technical solutions are as follows: By combining the multi-source data collected in real time with historical data and using machine learning algorithms to optimize the existing model, it is possible to more accurately predict the hydrogen consumption under different working conditions. This helps logistics companies better plan and manage their operating costs; the continuous optimization of the environmental impact model takes into account the effects of external conditions such as temperature, humidity, and altitude on the efficiency of hydrogen fuel cells, ensuring reliable hydrogen consumption estimation under various climate and geographical conditions, and enhancing the flexibility and scope of application of the system; the multi-level comprehensive hydrogen consumption model integrates multiple aspects such as basic power demand, hydrogen consumption calculation, and environmental impact, providing a more comprehensive and detailed analysis tool to support the refined management and decision-making of logistics companies in their daily operations; based on the latest simulation results and multi-objective optimization algorithms, it is possible to provide the optimal transportation route recommendations for logistics companies. This method not only takes into account the minimization of hydrogen consumption but also can simultaneously consider other key factors (such as time, safety), thereby improving the overall operating efficiency; during the actual transportation process, the vehicle operation status information is continuously collected through the real-time data acquisition system, and based on this, the comprehensive hydrogen consumption model is re-evaluated and optimized. This feature enables the model to quickly respond to changing conditions and maintain long-term effectiveness and accuracy; more accurate hydrogen consumption estimation and the selection of the best transportation route directly result in lower fuel consumption and operating costs, reducing unnecessary expenditures and increasing the economic benefits of the enterprise; the hydrogen fuel cell vehicle itself is a clean energy transportation vehicle, and this simulation method further promotes the application and development of this technology, which is conducive to reducing greenhouse gas emissions and other pollutants and promoting the development of green logistics; through the scientific planning and dynamic adjustment of the transportation route, it is possible to reduce the transportation time and uncertainty and improve the on-time rate of goods delivery.

[0035] In one embodiment of the present invention, S1 includes:

[0036] S11. Define the types and locations of data collection points, and design the data collection frequency and accuracy requirements;

[0037] S12. Build a data transmission network, encrypt the data transmission process based on data encryption algorithms, integrate the data collection system, and uniformly receive, store, and manage multi-source data;

[0038] S13. Obtain historical data, and preprocess the collected real-time data and historical data;

[0039] S14. Based on the existing vehicle motion equation model and vehicle dynamics principle, combined with the newly collected real-time vehicle operation data, optimize and calculate the basic power demand under different working conditions;

[0040] S15. Based on the existing dynamic system response characteristic curve and efficiency curve model, use the new hydrogen fuel cell operating characteristic data to optimize the relationship between hydrogen consumption and electric energy output, and establish an updated hydrogen consumption calculation model;

[0041] S16. Combine the existing basic power demand model and the hydrogen consumption calculation model, and further optimize them through new data to more accurately calculate the hydrogen consumption under different working conditions. The hydrogen consumption situation, that is, the hydrogen consumption rate, is calculated by the following formula.

[0042]

[0043] where, represents the power demand of the vehicle under a certain working condition; represents the total efficiency of the hydrogen fuel cell system; represents the operating voltage of the hydrogen fuel cell; represents the Faraday constant; represents the start time of hydrogen consumption; represents the end time of hydrogen consumption.

[0044] The working principle of the above technical solution is as follows: Determine which types of sensors will be installed where, as well as the data collection frequency and accuracy of these sensors. For example, information such as speed, acceleration, and slope can be obtained through on-vehicle sensors, while external environmental factors such as temperature and humidity may be collected through weather stations or other environmental monitoring devices near the vehicle. The data collection point refers to the specific location where the sensor is installed. For instance, a speed sensor is installed on the wheel, temperature and humidity sensors are installed inside the vehicle compartment, and a slope sensor is installed on the vehicle chassis. The data collection frequency is the number of times data is collected per second or per minute. For example, for key parameters such as speed, a relatively high collection frequency (e.g., 10 times per second) may be required, while for factors that change more slowly such as temperature, a lower frequency (e.g., once per minute) can be used; the accuracy requirement refers to the demand for the accuracy of the measurement results. For example, the slope sensor may need to achieve an accuracy of ±0.5 degrees to ensure that it can accurately reflect the inclination of the road. Establish a secure and reliable data transmission network and use encryption algorithms to protect the data transmission between the vehicle and the data center. At the same time, integrate a data collection system for receiving data from different sources and centrally storing and managing it. The data transmission network can be a cellular network, Wi-Fi, or a dedicated wireless network, etc., to ensure that data can be quickly and stably transmitted from the vehicle to the server. Data encryption algorithms such as AES (Advanced Encryption Standard), RSA, etc., are used to ensure the security of data in the network and prevent unauthorized access. Collect past operation records as historical data, and then perform cleaning, formatting, and other necessary preprocessing operations on the newly obtained real-time data and these historical data so that they can be used for training machine learning models or directly participating in calculations. Use existing physical models to describe the motion characteristics of the vehicle and adjust these models according to the latest real-time data to more accurately predict the energy required under various driving conditions. For example, when climbing a slope, the vehicle requires additional power support, which can be more accurately estimated through the updated model. The vehicle motion equation model usually contains formulas including Newton's second law to express the power requirements when the vehicle accelerates, decelerates, turns, etc. For example, F = ma (force equals mass times acceleration). The operating condition refers to the operating state of the vehicle under specific conditions, such as driving at a constant speed on a flat road, accelerating to overtake, decelerating to stop, climbing a slope, etc. Use the established power system performance model and combine the working characteristics of the newly obtained hydrogen fuel cell (such as temperature, pressure, current density, etc.) to re-evaluate and optimize the relationship between hydrogen consumption and power output, thereby obtaining a more accurate hydrogen consumption calculation model. Among them, the response characteristic curve shows how the power system responds to different input conditions over time, such as the reaction speed of the system when the load increases. The efficiency curve model describes the proportion of the input energy converted into useful work by the system under different operating conditions. For example, as the temperature rises, the efficiency of the hydrogen fuel cell may decrease, resulting in more hydrogen consumption.Integrate and optimize the basic power demand model and hydrogen consumption calculation model to form a comprehensive framework that can dynamically adjust its parameters according to the latest vehicle operation data to achieve high-precision prediction of hydrogen consumption under different driving conditions.

[0045] The effects of the above technical solutions are as follows: By clarifying the type and installation location of the sensors, it is ensured that the collected data can comprehensively reflect the vehicle operating status and environmental conditions; reasonably setting the data collection frequency and accuracy can not only ensure the effectiveness of the data but also avoid unnecessary data redundancy and computational burden; high-precision data collection helps improve the accuracy of subsequent analysis, thereby enhancing the reliability and stability of the entire system; using encryption algorithms to protect the security of data transmission, preventing the leakage or tampering of sensitive information, and safeguarding the business secrets of logistics companies; integrating data from different sources onto one platform simplifies the data management and analysis processes and improves work efficiency; establishing a stable data transmission network supports real-time data uploading and remote monitoring, facilitating quick response to abnormal situations; combining historical data and real-time data provides a more complete analysis perspective, helping to discover long-term trends and short-term fluctuations; through preprocessing steps such as cleaning and formatting, noise and error values are eliminated, improving the quality and usability of the data; the standardized data can be directly used for modeling and simulation, reducing the data preparation time and accelerating the decision-making speed; through the updated model, the power demand of the vehicle under various working conditions (such as climbing slopes and accelerating) can be predicted more accurately, thus better planning fuel use; customizing the power demand model according to the specific characteristics of different vehicle models makes the simulation results closer to the actual situation; the optimized model helps identify the most economical driving mode, reducing energy consumption and improving operational efficiency; when the vehicle enters an uphill section, the model adjusts the power demand prediction according to the latest slope sensor data to ensure sufficient power to support climbing while minimizing hydrogen consumption; the model can automatically adjust according to the operating characteristics of the hydrogen fuel cell and changes in environmental factors, maintaining a high prediction accuracy; through the refined management of the relationship between hydrogen consumption and electrical energy output, optimal energy distribution is achieved, reducing unnecessary fuel waste; the continuously updated model can timely reflect the impact of factors such as equipment aging on efficiency, helping to maintain the best performance; by continuously introducing new data, the model can self-learn and optimize, always remaining up-to-date and adapting to the changing operating environment; the comprehensive model can more accurately simulate the hydrogen consumption under different working conditions, providing a reliable basis for the selection of transportation routes; the model has an adaptive ability and can be adjusted in real time according to the actual operating conditions to ensure that each prediction is based on the latest operating conditions. The above formula combines vehicle dynamics principles with real-time operating data, and the formula can accurately calculate the power demand under different working conditions, including factors such as vehicle weight, wind resistance, rolling resistance, and climbing resistance, ensuring the accuracy of the calculation results. Real-time data collection allows the formula to dynamically adjust the calculation to adapt to changes during vehicle operation, such as speed, acceleration, and slope. Combining the response characteristics and efficiency curve of the hydrogen fuel cell system to correct the power demand can optimize the hydrogen consumption rate and improve energy use efficiency. By accurately calculating hydrogen consumption, the operation of the vehicle power system can be optimized, reducing unnecessary energy consumption.Combining the basic power demand model with the hydrogen consumption calculation model provides a unified calculation framework, improving the reliability and stability of the overall system. By preprocessing historical data, the formula can learn and adapt to the vehicle's performance under different conditions, enhancing the reliability of the model. By reducing hydrogen fuel waste, the operating cost can be lowered, which means lower operating costs and higher economic benefits for vehicle operators. Precise hydrogen consumption calculation helps optimize the maintenance plan, reduce unnecessary maintenance operations, and lower the maintenance cost. By improving the efficiency of the hydrogen fuel cell and reducing hydrogen consumption, it indirectly reduces possible environmental pollution and greenhouse gas emissions. Precise calculation of hydrogen consumption helps optimize the use of hydrogen energy and promote the rational utilization of resources. By accurately calculating the power demand, overloading operation of the hydrogen fuel cell system can be avoided, prolonging the system life and improving vehicle safety.

[0046] In one embodiment of the present invention, S14 includes:

[0047] S141. Utilize the existing mass change measurement model, add new data for dynamic mass model optimization, and optimize the drag coefficient through CFD simulation combined with the latest wind speed and wind direction data;

[0048] S142. Based on the rolling resistance coefficient model, make dynamic adjustments using new data such as tire wear degree, tire type, tire pressure, and road surface type;

[0049] S143. Combine the new data of the high-precision slope sensor to optimize the relationship model between slope and climbing resistance, and combine Newton's second law and vehicle dynamics parameters to construct and optimize the dynamic equation of vehicle motion;

[0050] S144. Dynamically adjust the parameters in the dynamic equation according to the newly collected real-time data to optimize the power demand prediction;

[0051] S145. Analyze the new and old data using machine learning or pattern recognition algorithms to automatically identify the driving conditions;

[0052] S146. Based on the driving condition recognition result, optimize the power demand prediction model to ensure that the adaptive adjustment mechanism can reflect the latest operating conditions and external environment changes.

[0053] The working principle of the above technical solution is as follows: The model describes the mass change of the vehicle when loading different goods. By real-time monitoring the vehicle's load weight, the total mass of the vehicle in various states can be estimated more accurately; it is used to simulate the impact of air flow on the vehicle, especially when the vehicle is traveling at high speed. By inputting the latest wind speed and wind direction data, CFD simulation can help optimize the vehicle's drag coefficient (Cd value), thereby reducing unnecessary energy consumption; assume a logistics vehicle weighs 2 tons when empty and increases to 5 tons when fully loaded. By the weighing sensors installed on the vehicle, the current load weight can be obtained in real time and the mass change model can be updated; if the vehicle is traveling on the highway and encounters a crosswind with a wind speed of 20 km / h and the wind direction is at a 90-degree angle to the driving direction, the CFD simulation will consider these conditions to adjust the drag coefficient to ensure more accurate prediction; the rolling resistance coefficient model describes the magnitude of the frictional force between the tire and the ground, which affects the resistance that the vehicle needs to overcome. With tire wear, tire pressure changes, and different driving surfaces, the rolling resistance also changes; by regularly checking the tire condition (such as wear degree), monitoring the tire pressure, and according to different road surface types (such as asphalt road, dirt road), the rolling resistance coefficient can be dynamically adjusted to make the model closer to the actual situation; for example, the rolling resistance coefficient of new tires is lower, and as the tires gradually wear, the rolling resistance increases. The system can evaluate the wear degree based on the mileage of the tires or directly measure their thickness; modern vehicles are usually equipped with a tire pressure monitoring system (TPMS), which can report the air pressure of each tire in real time. When the tire pressure is lower than the recommended value, the rolling resistance increases, and the system will issue a warning and adjust the model parameters; the slope and climbing resistance relationship model reflects how the road slope affects the additional power required for the vehicle to climb the slope. By the high-precision slope sensors installed on the vehicle, the current road slope information can be obtained in real time; Newton's second law, i.e., F = ma (force equals mass times acceleration), is used to describe the relationship between the force acting on an object and its acceleration. In this case, it is used to analyze the force required for the vehicle to accelerate or decelerate at different slopes; vehicle dynamics parameters include vehicle mass, drag coefficient, rolling resistance coefficient, etc., which jointly determine the motion characteristics of the vehicle.By continuously updating the slope information and other relevant parameters provided by the sensors, the model can adjust the calculation of the required traction force in real time to ensure that the vehicle can achieve optimal performance under any road conditions; Based on real-time data (such as vehicle speed, acceleration, slope, etc.), automatically adjust the key parameters in the dynamic equation, such as mass, drag coefficient, etc., to reflect the latest operating conditions; By continuously updating the model parameters, improve the prediction accuracy of future power requirements to help the driver choose the most economical driving mode; For example, when the vehicle enters a steep uphill from a flat section, the system will immediately detect the change in slope and correspondingly increase the estimation of the required traction force; This helps to plan the working state of the engine in advance, avoid unnecessary power waste, and at the same time ensure that there is sufficient power to support the vehicle to climb the slope safely; Machine learning / pattern recognition algorithms can learn from a large amount of historical and real-time data, identify different types of driving conditions (such as constant speed driving, acceleration, deceleration, idling, etc.), and provide the optimal power demand prediction for each condition; Through continuous analysis of the sensor data, the system can automatically determine which condition the vehicle is currently in without manual intervention; For example, the system can identify whether the vehicle is turning, changing lanes or preparing to stop by analyzing data such as the vehicle's speed change rate, acceleration, and steering wheel angle; Once the driving condition is identified, the system can quickly adjust the power demand prediction model to adapt to the current driving behavior; According to the characteristics of different driving conditions, targetedly adjust the parameters of the power demand prediction model to make it more in line with the actual needs; The model has the ability of self-learning and can automatically adjust its own structure and parameters according to new operating data and external environment changes (such as weather, traffic flow) to maintain long-term high-precision prediction; For example, in urban congested sections where the vehicle starts and stops frequently, the system will adjust the model to better handle the situation of low-speed driving and frequent acceleration; While on the highway, more attention is paid to the power demand of high-speed stable driving; Over time, the vehicle may experience different seasons, climate conditions or regional changes, and the adaptive adjustment mechanism ensures that the model can always provide the most accurate power demand prediction, whether in the hot summer or the cold winter.

[0054] The effects of the above technical solutions are as follows: By means of a quality change model updated in real time, it is ensured that the estimation of the vehicle load is more accurate, thereby improving the accuracy of power demand prediction; Using the latest wind speed and wind direction data for CFD simulation can effectively reduce the influence of air resistance and further save energy consumption; Dynamically adjusting the rolling resistance coefficient according to different tire conditions and road surface conditions makes the model closer to the actual situation and improves the accuracy of prediction; By optimizing the tire pressure and other parameters, it helps to reduce tire wear, extend its service life, and indirectly reduce the maintenance cost; The real-time data provided by the high-precision slope sensor helps to optimize the power demand during climbing, ensuring that the vehicle has sufficient power to support safe climbing while minimizing energy waste; Integrating multiple dynamic parameters (such as mass, wind resistance, rolling resistance) provides a more comprehensive vehicle motion analysis tool and improves the efficiency of the overall system; The system can quickly respond to changes in the environment and vehicle status, automatically adjust the power demand prediction, ensure that each prediction is based on the latest conditions, and improve flexibility and adaptability; Through continuous optimization, unnecessary energy consumption is reduced, and the fuel economy and operating efficiency of the vehicle are improved; Automatically identifying different driving conditions (such as uniform driving, acceleration, deceleration, etc.) and providing the optimal power demand prediction for each condition without manual intervention improves the automation level; The intelligent identification and adaptive adjustment mechanism makes the driving experience smoother and more efficient, reducing the driver's operation burden; By continuously introducing new operation data and external environment information, the model can self-learn and optimize, always maintain the latest state, and adapt to the changing operating environment; The model has a strong adaptive ability and can be adjusted in real time according to the actual operation situation to ensure that each prediction reflects the latest operating conditions, improving the reliability and accuracy of the prediction.

[0055] In an embodiment of the present invention, S15 includes:

[0056] S151. Test through simulation means and optimize the response characteristic curve and efficiency curve of the power system based on measured data;

[0057] S152. Evaluate the adaptability of transient conditions, combine the chemical reaction principle of hydrogen fuel cells, and optimize the efficiency model;

[0058] S153. Analyze the temperature change law, optimize the temperature compensation mechanism and aging prediction model, and dynamically adjust the hydrogen consumption calculation parameters;

[0059] S154. Optimize the matching of the basic power demand and the output capacity of the power system according to the real-time operation data and the response characteristics of the power system;

[0060] S155. Establish a real-time feedback mechanism, dynamically adjust the correction strategy according to the actual operation situation and the hydrogen consumption calculation result, and continuously optimize the framework of the hydrogen consumption calculation model;

[0061] Integrate the response characteristic curve and efficiency curve of the optimized powertrain and the hydrogen consumption calculation model framework into the hydrogen consumption calculation model, verify the accuracy of the model, and continuously optimize it according to the feedback.

[0062] The working principle of the above technical solution is as follows: Use computer simulation software to simulate the behavior of the power system under different working conditions (such as acceleration, deceleration, constant-speed driving, etc.). These simulations can include the performance evaluation of multiple components such as engines, electric motors, hydrogen fuel cell stacks, etc.; Compare the simulation results with the data collected during actual operation, identify the differences and adjust the simulation parameters to make the simulation closer to the actual situation. The ultimate goal is to optimize the response characteristic curve (describing how the system reacts to changes in input) and the efficiency curve (showing the energy conversion efficiency under different operating conditions) of the power system; for example, the change in the output power of the power system under different load conditions. If the response is too fast or too slow at low loads, the control algorithm can be adjusted through simulation and measured data; such as the energy conversion efficiency of the hydrogen fuel cell under different temperature and pressure conditions. By combining simulation with measured data, the trend of efficiency change with conditions can be more accurately depicted; study the performance of the power system under rapidly changing operating conditions (such as sudden acceleration or sudden braking). Transient operating conditions usually pose challenges to the stability and efficiency of the system; consider the electrochemical reaction process occurring inside the hydrogen fuel cell, especially how it affects the voltage and efficiency of the cell when the current density changes rapidly; based on the above analysis, improve the existing efficiency model so that it can better predict the performance under transient operating conditions; for example, when the vehicle suddenly accelerates from an idle state to high speed, the power system needs to quickly provide additional power; the reaction rate of hydrogen and oxygen in the fuel cell affects the output voltage and efficiency of the cell.If the reaction is too fast, it may lead to local overheating or material degradation; conversely, it may reduce efficiency. Study the effect of temperature on the efficiency of hydrogen fuel cells and identify how efficiency changes as temperature increases or decreases. By automatically adjusting system settings to counteract the negative impact of temperature changes and maintain optimal working efficiency. Establish a model to predict the aging degree of hydrogen fuel cells over time, taking into account factors such as material wear and chemical consumption, and adjust the hydrogen consumption calculation parameters accordingly. For example, when starting in cold weather, the system will automatically preheat the fuel cell to ensure it can still work efficiently at lower temperatures. Assume the fuel cell loses 0.5% of its efficiency per year. The model can predict future efficiency decline based on the known aging rate and adjust the hydrogen consumption calculation parameters in advance to maintain performance. Continuously monitor information such as the vehicle's speed, acceleration, slope, etc., as well as various indicators of the power system (such as voltage, current, temperature, etc.). Use the existing power system response characteristic curve to ensure that the system can provide the required power under various conditions. Through real-time data analysis, dynamically adjust the power demand prediction model to ensure that the provided power meets the current demand without excessive energy consumption. For example, when the vehicle enters an uphill section, the system will immediately increase the power demand prediction based on the latest slope sensor data, while avoiding unnecessary energy waste caused by excessive acceleration. Create a closed-loop control system where the actual operation data is continuously fed back to the central processing unit for evaluating the accuracy of model predictions. According to the feedback information, adjust the parameters in the hydrogen consumption calculation model in real time to ensure that each prediction is as close to the actual situation as possible. The model has the ability to self-learn and can gradually improve the prediction accuracy over time. For example, if the predicted hydrogen consumption is higher than the actual value, the system will analyze the reason (such as overestimated wind resistance) and make corresponding adjustments in subsequent predictions. Assume that during a certain transportation process, abnormal traffic conditions occur, resulting in frequent starts and stops of the vehicle. The system will update the model parameters in a timely manner to adapt to this new working condition. Incorporate the optimized power system response characteristic curve, efficiency curve, and hydrogen consumption calculation model framework into the hydrogen consumption calculation model to form a comprehensive prediction tool. Through a series of tests (such as laboratory tests, field tests, etc.), confirm whether the model can accurately predict the hydrogen consumption situation. According to the test results and feedback in actual applications, further adjust and optimize the model to ensure it always remains up-to-date and accurate. For example, select several typical transportation routes for field tests and compare the difference between the predicted hydrogen consumption by the model and the actual consumption. If it is found that the model prediction is not accurate enough in certain specific situations (such as extreme weather), the model can be improved by collecting more data under such conditions.

[0063] The effects of the above technical solutions are as follows: By means of simulation tests and optimization based on measured data, the response characteristics and efficiency curve of the power system are ensured to be closer to the actual operating conditions, improving the accuracy of hydrogen consumption prediction; Dynamically adjusting the power demand prediction according to real-time operating data to ensure that the provided power meets the current demand without excessive energy consumption, reducing unnecessary energy waste; Optimizing the efficiency model in combination with the chemical reaction principle of hydrogen fuel cells enables the system to maintain high efficiency and stability under rapidly changing operating conditions (such as sudden acceleration or braking), enhancing the flexibility and reliability of the system; By analyzing the temperature change law, an automatically adjusted temperature compensation mechanism is developed, and an aging prediction model is established to dynamically adjust the hydrogen consumption calculation parameters. This not only improves the working efficiency in low or high temperature environments but also extends the service life of hydrogen fuel cells and reduces the maintenance cost; A closed-loop control system is created, which can dynamically adjust and correct the strategy according to the actual operating conditions and the hydrogen consumption calculation results to ensure that each prediction is as close to the actual situation as possible. This adaptive mechanism enables the model to continuously improve over time and always stay up-to-date; By continuously introducing new operating data and external environment information, the model has the ability of self-learning to ensure that it always reflects the latest operating conditions; Incorporating the optimized power system response characteristic curve, efficiency curve, and hydrogen consumption calculation model framework into the hydrogen consumption calculation model to form a comprehensive prediction tool. By verifying the model accuracy and continuously optimizing according to the feedback, the reliability and practicality of the model are ensured.

[0064] In one embodiment of the present invention, said S2 includes:

[0065] S21. Identify the environmental factors affecting the efficiency of hydrogen fuel cells and classify these factors based on existing models and new data;

[0066] S22. Evaluate the importance and relevance of each environmental factor, determine which factors should be considered in the model, and update the evaluation results using new data;

[0067] S23. Based on existing test methods, test hydrogen fuel cells in different environments and use new data to supplement or correct the performance parameter records;

[0068] S24. According to the new test data, use statistical analysis methods to update the mapping relationship between environmental factors and the efficiency of hydrogen fuel cells;

[0069] S25. Evaluate the goodness of fit and prediction ability of the preliminary model, introduce complexity evaluation indicators, judge whether the model needs to be simplified or complicated, and verify these decisions using new data;

[0070] S26. Identify the deficiencies in the preliminary model, design supplementary tests or collect more relevant data to enrich the training set, especially paying attention to the data points that can significantly improve the model accuracy.

[0071] S27. Optimize the input feature set by applying feature engineering techniques, and correct and retrain the model through machine learning algorithms to ensure that the model can adapt to the new training set;

[0072] S28. Introduce judgment logic to guide the model selection and optimization direction, set conditional branches to handle model applications under different environmental conditions, and at the same time maintain flexibility to adapt to possible future changes;

[0073] S29. Enable the model to automatically adjust parameters or select the best version through an adaptive mechanism, regularly evaluate the model performance, and when a performance decline is detected, trigger an update process and use the latest environmental and performance data for retraining and validation.

[0074] The working principle of the above technical solution is as follows: First, determine which external environmental conditions affect the efficiency of the hydrogen fuel cell. These factors may include temperature, humidity, air pressure, altitude, etc.; according to the existing theoretical models and newly collected data, classify these factors into direct effects (such as temperature directly changing the battery reaction rate) and indirect effects (such as humidity indirectly affecting efficiency by influencing heat dissipation); for example, an increase in temperature will reduce the electrochemical reaction efficiency of the hydrogen fuel cell; an increase in humidity may cause changes in the degree of hydration in the membrane, affecting proton conduction; indirect influencing factors include that a change in air pressure may affect oxygen supply, thereby indirectly affecting battery performance; use statistical analysis or machine learning methods to evaluate the degree of influence of each environmental factor on the efficiency of the hydrogen fuel cell; determine the mutual relationships between different environmental factors and identify those factors that have a significant impact on efficiency; as new data is added, continuously re-evaluate which factors are the most critical to ensure that the model always reflects the latest actual situation; assume that temperature is found to be the most important factor because its change has the most direct impact on battery efficiency; if a strong correlation is found between temperature and humidity, consider introducing an interaction effect into the model; use established experimental procedures to test the hydrogen fuel cell under various environmental conditions (such as different temperature and humidity levels); use the newly obtained test data to supplement or correct the existing performance parameters to make the model more accurately reflect the actual performance; for example, test the output power and efficiency of the hydrogen fuel cell at low temperature (-20°C) and high temperature (40°C) respectively, and update the temperature response curve in the model with this new data; apply statistical tools such as regression analysis and principal component analysis to establish the mathematical relationship between environmental factors and battery efficiency; adjust these mapping relationships according to the latest test data to ensure that they can better predict the battery performance under different environmental conditions; find the best-fit curve between temperature and battery efficiency through linear or nonlinear regression; identify which combinations of environmental factors can best explain the changes in battery efficiency; check the deviation between the model prediction value and the actual measurement value to evaluate the accuracy of the model; evaluate the prediction ability of the model for future unknown data through methods such as cross-validation; introduce complexity metrics (such as AIC / BIC) to balance the precision and generalization ability of the model and avoid overfitting or underfitting; if a complex model performs well on the training set but poorly on the test set, consider simplifying the model structure; by introducing new data sets, verify whether the simplified or complicated model has improved the prediction accuracy; analyze the regions where the model prediction error is large and find out the reasons for the error; design additional tests for specific conditions (such as extreme temperature or humidity), or obtain more relevant data from other sources to enrich the training set; if the model predicts inaccurately under high humidity conditions, more tests can be carried out under this condition to obtain more detailed data; utilize historical meteorological data and field test results, especially those working conditions that are not fully covered, such as high altitude areas;Select the features that best represent environmental factors and battery performance, remove redundant features, and create new feature combinations; use algorithms such as random forest and support vector machine to correct and retrain the model to improve its prediction ability and generalization performance; if it is found that certain features (such as temperature and humidity) contribute significantly to the model, retain these features while removing insignificant features; retrain the model using the enhanced feature set to ensure that it can better adapt to the new data distribution; introduce judgment logic to guide the model selection and optimization direction, set conditional branches to handle model applications under different environmental conditions, and maintain flexibility to adapt to possible future changes; provide clear rules for model selection to help decide when to use which model version or parameter configuration; automatically select the most suitable model or parameter settings according to real-time environmental conditions; ensure that the system can quickly respond to new conditions or technological advancements that may occur in the future; when the temperature is below a certain threshold, automatically switch to the model version optimized for low temperature; under high humidity conditions, the system will select a specially calibrated model to improve prediction accuracy; implement automatic adjustment of model parameters to ensure that they are always in the optimal state; set a periodic evaluation plan to monitor the change of model performance over time; once a performance decline is detected, immediately start the update process and retrain and validate using the latest environmental and performance data; if a sudden drop in battery efficiency is detected, the system will automatically adjust the model parameters to adapt to the new operating conditions; evaluate the performance of the model quarterly to ensure that it still meets the latest operating requirements; when the model prediction error increases, immediately retrain using the latest data and deploy the updated model after validation.

[0075] The effects of the above technical solutions are as follows: By classifying environmental factors in detail, it is ensured that the model can comprehensively consider all possible influencing factors. Continuously update the importance assessment of environmental factors using new data to ensure that the model always reflects the latest actual situation; Based on the new data obtained from existing test methods, the performance of the hydrogen fuel cell under different environmental conditions can be more accurately described, enhancing the scientific basis of the model; Establish an accurate mapping relationship between environmental factors and battery efficiency through statistical analysis methods, improving the reliability and prediction accuracy of the model; Introduce a complexity evaluation index to balance the accuracy and generalization ability of the model, avoid overfitting or underfitting, and ensure that the model is neither too simple nor too complex; By automatically adjusting parameters or selecting the best version, the model can be dynamically optimized according to the actual operating conditions to maintain long-term high-precision prediction; In response to the deficiencies in the preliminary model, design additional tests or collect more relevant data, especially those data points that can significantly improve the model accuracy. For example, conduct more tests under extreme temperature or humidity conditions to ensure that the model covers all key operating conditions; Remove redundant features through feature engineering, create new feature combinations, optimize the input feature set, and improve the data processing ability and prediction accuracy of the model; Introduce clear judgment logic and conditional branches to guide the model selection and optimization direction, ensure that the system can flexibly apply the most suitable model version under different environmental conditions, and at the same time have the ability to cope with future changes; By regularly evaluating the model performance and triggering the update process, ensure that the system can respond in a timely manner to changes in environmental and performance data and always maintain the optimal state.

[0076] In one embodiment of the present invention, step S3 includes:

[0077] S31. Stratify the hydrogen consumption influencing factors and deeply analyze the influencing factors of each layer based on the existing model;

[0078] S32. For the influencing factors of each layer, construct or optimize sub-models based on the existing sub-models and in combination with new data, and integrate these sub-models through a Bayesian network;

[0079] S33. Conduct a preliminary analysis and evaluation of the comprehensive hydrogen consumption model, use historical data to backtest the effectiveness of the model, and dynamically adjust the model according to new data;

[0080] S34. During the model prediction process, automatically switch to the most suitable sub-model for the current situation through conditional judgment logic;

[0081] S35. Dynamically adjust the model parameters or structure according to real-time data and prediction results, establish a feedback mechanism to compare the actual hydrogen consumption data with the predicted values, identify the error sources, and optimize the model accordingly;

[0082] S36. Further optimize the model using algorithms such as particle swarm optimization, and introduce new influencing factors and feature variables to improve the model structure;

[0083] S37. Verify the optimized model using an independent test set, and update and improve the model regularly to ensure that it continuously reflects the actual situation.

[0084] The working principle of the above technical solution is as follows: all factors that may affect the hydrogen consumption of a hydrogen fuel cell vehicle are divided into multiple levels, such as basic power demand, environmental conditions, driving behavior, etc.; using existing theoretical models and simulation tools, conduct a detailed study on the influencing factors at each level to find out how they act on hydrogen consumption individually and jointly.

[0085] Example of layering:

[0086] The first layer (basic power demand): includes vehicle mass, speed, acceleration, etc.

[0087] The second layer (environmental conditions): temperature, humidity, altitude, etc.

[0088] The third layer (driving behavior): hard acceleration, frequent braking, etc.

[0089] For example, in the first layer, analyze the energy required by the vehicle under different load conditions; in the second layer, study the impact of temperature changes on battery efficiency; in the third layer, evaluate the additional energy consumption under different driving habits.

[0090] According to the specific influencing factors at each level, use the existing model framework and combine the latest measured data to construct or optimize sub-models; adopt Bayesian networks as an integration tool to connect the various sub-models to form an overall multi-level model. Bayesian networks can handle uncertainty and provide probabilistic predictions; for sub-model construction, for example, for the basic power demand layer, a dynamics model based on Newton's second law can be constructed; for the environmental conditions layer, a thermodynamic model considering factors such as temperature and humidity can be constructed; assuming that both temperature and humidity affect battery efficiency, the Bayesian network can represent the relationship between the two and how they jointly affect the final hydrogen consumption.This network structure allows the model to make more reasonable predictions under uncertain conditions; conduct a preliminary test on the integrated multi-level model to check the logical correctness and the rationality of the calculation results; verify the performance of the model with past datasets to ensure that it can give accurate predictions in known situations; automatically update the model parameters or structure as new operating data continuously flows in to adapt to the changing actual situation; for example, select the actual operating data within a certain period of time, compare the difference between the predicted hydrogen consumption of the model and the actual consumption, and evaluate the accuracy of the model; if it is found that the model prediction is not accurate enough under certain specific working conditions (such as extreme weather), the model can be improved by collecting more data under such conditions; set a series of rules or conditions, and when specific conditions are met, the system will automatically select the most suitable sub-model for prediction; ensure that the model can flexibly select the best prediction scheme according to the real-time environment and operating conditions to improve the prediction accuracy; for example, when the temperature is lower than a certain threshold, the system will automatically switch to the sub-model optimized for low temperature; or when frequent starts and stops are detected, switch to the sub-model suitable for urban congestion road conditions; continuously adjust the model parameters or structure based on the real-time collected data and prediction results to keep the model in the latest state; create a closed-loop control system, compare the actual operating data with the predicted values, identify the sources of errors, and optimize the model accordingly; for example, if it is found that the predicted value is higher than the actual consumption, the system will analyze the reason (such as too high wind resistance estimation) and make corresponding adjustments in subsequent predictions; assume that during a certain transportation process, abnormal traffic conditions are encountered, resulting in frequent starts and stops of the vehicle, and the system will promptly update the model parameters to adapt to this new working condition; Particle Swarm Optimization (PSO) This is an optimization algorithm based on swarm intelligence, used to find the best combination of model parameters to minimize the error or maximize the performance index; according to new discoveries or requirements in practical applications, introduce more influencing factors and characteristic variables to make the model more comprehensive and accurate; for example, use the PSO algorithm to optimize the weights of each parameter in the model to make the prediction results as close to the actual values as possible; assume that it is found that the road gradient has a significant impact on hydrogen consumption, and the data of the gradient sensor can be added to the model to more accurately reflect the actual operating conditions; use an independent dataset not involved in training to verify the optimized model to ensure its generalization ability; set a periodic evaluation plan to monitor the change of the model performance over time, and update and improve it when necessary to maintain the long-term effectiveness of the model; for example, select several typical transportation routes for on-site testing, compare the difference between the predicted hydrogen consumption of the model and the actual consumption, and confirm the accuracy of the model; evaluate the performance of the model once every quarter to ensure that it still meets the latest operating requirements. If it is found that the model prediction error increases, immediately retrain with the latest data and deploy the updated model after verification.

[0091] The effects of the above technical solutions are as follows: By carefully classifying the factors affecting hydrogen consumption and deeply analyzing the influencing factors at each level, it ensures that the model can comprehensively consider all possible factors, improving the prediction fineness; Using a Bayesian network to integrate sub-models at each level not only enhances the scientificity and logic of the model but also can handle uncertainties and complex relationships, improving the prediction accuracy; By using historical data to backtest the effectiveness of the model, it verifies the performance of the model under known conditions, enhancing its reliability and credibility; Regularly using an independent test set to verify the optimized model ensures that it has good generalization ability and can make accurate predictions under unknown conditions; Automatically selecting the most suitable sub-model for the current situation through conditional judgment logic enables the system to flexibly apply the most appropriate prediction scheme under different working conditions, improving the adaptability and response speed of the system; Dynamically adjusting the model parameters or structure according to real-time data and prediction results, establishing a feedback mechanism to identify the error sources, and optimizing the model accordingly to ensure that the system is always in the optimal state; Introducing advanced algorithms such as particle swarm optimization to further optimize the model, and continuously introducing new influencing factors and feature variables to improve the model structure to ensure that the model can continuously improve with the progress of new data and technology; Setting a periodic evaluation plan to monitor the change of the model performance over time and updating and improving it when necessary to ensure that the model always remains the latest and most accurate state.

[0092] In one embodiment of the present invention, the S32 includes:

[0093] S321. For the vehicle state, using high-precision sensor data, construct a vehicle physical characteristic model, based on driving behavior data, adopt machine learning algorithms to identify different driving styles, and establish a quantitative model of the impact of driving behavior on hydrogen consumption;

[0094] S322. Combine the vehicle real-time operation data and the working characteristics of the hydrogen fuel cell to construct a dynamic efficiency model, and integrate the vehicle state model, driving behavior model, and hydrogen fuel cell efficiency model to form a hydrogen consumption prediction model at the micro level;

[0095] S323. Use GIS data and real-time traffic monitoring data to analyze the road conditions, traffic flow, congestion, etc. of different routes, construct a route selection model, and evaluate the impact of each route on hydrogen consumption;

[0096] S324. According to the geometric characteristics and traffic characteristics of the road type, establish an association model between the road type and vehicle energy consumption; Based on the route selection and road type analysis, combined with the vehicle real-time operation data, construct a hydrogen consumption optimization model at the meso level;

[0097] S325. Collect and analyze the climate data and policy environment between regions, and evaluate the indirect impact of these factors on hydrogen consumption; based on historical data and macroeconomic indicators, use time series analysis to predict the possible impact of macro factors on hydrogen consumption in a future period;

[0098] S326. Convert the regional climate and policy analysis results into quantifiable parameters, construct a quantitative model for the impact of hydrogen consumption at the macro level, and provide macro-level inputs for the comprehensive hydrogen consumption model;

[0099] S327. Design the Bayesian network structure according to the logical relationship between the sub-models at the micro, meso, and macro levels, and clarify the dependence and causal relationships between each node;

[0100] S328. Based on historical data and the expert knowledge base, set the prior probability and conditional probability for each node in the Bayesian network, and use the Bayesian network inference algorithm to integrate the prediction results of the sub-models at each level into the comprehensive hydrogen consumption model to achieve the fusion and comprehensive analysis of multi-source data.

[0101] The working principle of the above technical solution is as follows: Physical parameters during vehicle operation (such as speed, acceleration, slope, etc.) are collected through high-precision sensors (such as accelerometers, gyroscopes, etc.) to construct a mathematical model describing the vehicle's dynamic characteristics; Machine learning algorithms (such as decision trees, random forests, deep learning, etc.) are used to analyze driving behavior data (such as accelerator pedal position, braking frequency, etc.) to identify different driving styles (such as aggressive, economical, etc.); A quantitative relationship between driving behavior and hydrogen consumption is established to evaluate the specific impact of different driving styles on hydrogen consumption; For example, according to the changes in vehicle speed and acceleration, the changes in its kinetic energy and potential energy are predicted, and then the energy consumption is calculated; Suppose that by analyzing driving data, it is found that a certain driver frequently accelerates and brakes suddenly, the system can identify this as an aggressive driving style; Research shows that an aggressive driving style consumes about 20% more hydrogen fuel on average than a mild driving style; Considering the vehicle's real-time operation data (such as speed, load, etc.) and the working characteristics of the hydrogen fuel cell (such as power output, efficiency curve, etc.), a dynamic model that can reflect the efficiency change of the vehicle under different working conditions is constructed; The vehicle state model, driving behavior model, and hydrogen fuel cell efficiency model are integrated together to form a complete hydrogen consumption prediction model at the microscopic level; For example, the hydrogen fuel cell may be more efficient at high speeds; while at low speeds or during frequent starts and stops, the efficiency will decrease; Suppose the vehicle is driving at a constant speed on a flat road and the driver adopts an economical driving mode, the hydrogen consumption of this section of the road can be accurately predicted through the integrated model at this time; Using geographic information system (GIS) data and real-time traffic monitoring data, factors such as road conditions (such as slope, curvature), traffic flow, and congestion of different routes are analyzed; The specific impact of these factors on hydrogen consumption is evaluated to provide a basis for selecting the optimal transportation route; For example, although some routes are shorter but often congested, while some longer routes are smoother.The system can recommend the most energy-efficient route based on this information; for example, if a route passes through multiple traffic lights, resulting in frequent starts and stops, this will significantly increase hydrogen consumption; while another route, although longer, has a stable traffic flow and lower overall hydrogen consumption; establish a correlation model between road types and vehicle energy consumption based on the geometric characteristics of the road (such as width, bend radius, etc.) and traffic characteristics (such as speed limits, number of lanes, etc.); combine the results of route selection and road type analysis, as well as the real-time operation data of the vehicle, to construct a hydrogen consumption optimization model at the mesoscopic level to further optimize the selection of transportation routes; for example, highways usually have higher speed limits and encounter fewer traffic lights, so the hydrogen consumption of vehicles is lower when driving on highways compared to urban roads; assume that a transportation task needs to cross mountains, the system can adjust the vehicle speed according to the characteristics of mountain roads and optimize driving behavior to reduce hydrogen consumption; collect climate data (such as temperature, humidity, etc.) and policy environment (such as emission standards, subsidy policies, etc.) between regions and evaluate their indirect impact on hydrogen consumption; based on historical data and macroeconomic indicators, use time series analysis methods (such as ARIMA, LSTM, etc.) to predict the possible impact of macro factors on hydrogen consumption in a future period; for example, the efficiency of hydrogen fuel cells will decrease under low-temperature conditions, and the environmental protection policies in some regions may prompt companies to choose more efficient vehicles or routes; assume that based on data from the past few years, it is predicted that the temperature will be abnormally low in a certain quarter in the future, and the system can adjust the vehicle configuration or route planning in advance to cope with the potential increase in hydrogen consumption; convert the impact of climate conditions and policy environment into specific numerical parameters, such as temperature correction coefficients, policy incentive factors, etc.; construct a quantitative model for the impact of hydrogen consumption at the macroscopic level as one of the inputs to the comprehensive hydrogen consumption model to help comprehensively evaluate the impact of macro factors on hydrogen consumption; for example, for every 1°C decrease, the efficiency of hydrogen fuel cells decreases by 0.5%, which can be converted into a temperature correction coefficient; assume that a new subsidy policy is introduced to encourage the use of clean energy vehicles, and the system can quantify the impact of this policy on hydrogen consumption and incorporate it into the macroscopic level model.Design a Bayesian network structure for integrating sub-models at the micro, meso, and macro levels; clarify the dependencies and causal relationships between nodes to ensure that the model can correctly reflect the interactions between different factors; for example, construct a Bayesian network with multiple nodes such as vehicle state, driving behavior, road type, and climate conditions; assume that driving behavior affects vehicle state, and vehicle state in turn affects hydrogen consumption, and these causal relationships can be clearly expressed in the Bayesian network; based on historical data and expert knowledge bases, set reasonable prior probabilities and conditional probabilities for each node in the Bayesian network; use Bayesian network inference algorithms to integrate the prediction results of sub-models at each level into a comprehensive hydrogen consumption model to achieve the fusion and comprehensive analysis of multi-source data; for example, according to past experience, set the probability of a certain specific driving behavior occurring on a busy urban road to be 0.7; assume that the current weather is cold, and the system can infer the likelihood of increased hydrogen consumption in this situation through Bayesian network reasoning and adjust the transportation plan accordingly; construct a vehicle physical characteristics model through high-precision sensor data, identify the impact of driving behavior on hydrogen consumption, and combine with the working characteristics of hydrogen fuel cells to form a hydrogen consumption prediction model at the micro level; use GIS data and real-time traffic monitoring data to analyze road conditions, traffic flow, etc. of different routes, construct a route selection model, and optimize the transportation route selection in combination with the relationship between road type and vehicle energy consumption; analyze the indirect impact of climate data and policy environment on hydrogen consumption, predict the future trends of macro factors, and quantify these factors as parameters to be used as inputs to the comprehensive hydrogen consumption model; design a Bayesian network structure, clarify the dependencies and causal relationships between sub-models at each level, and use Bayesian network inference algorithms to integrate the prediction results at each level to achieve the fusion and comprehensive analysis of multi-source data.

[0102] The effects of the above technical solutions are as follows: By constructing a vehicle physical characteristics model with high-precision sensor data, identifying the impact of driving behavior on hydrogen consumption, and combining the working characteristics of hydrogen fuel cells, a hydrogen consumption prediction model at the microscopic level is formed; the prediction accuracy of hydrogen consumption for a single vehicle under specific driving conditions is improved, providing a scientific basis for personalized management and optimization; by using GIS data and real-time traffic monitoring data to analyze road conditions, traffic flow, etc. of different routes, constructing a route selection model, and combining the relationship between road type and vehicle energy consumption, the selection of transportation routes is optimized; the selection of transportation routes is optimized, hydrogen consumption is reduced, operation efficiency is improved, unnecessary fuel waste is reduced, and operation costs are directly reduced; by analyzing the indirect impact of climate data and policy environment on hydrogen consumption, predicting the change trends of future macro factors, and quantifying these factors into parameters as the input of the comprehensive hydrogen consumption model; the self-adaptability and long-term reliability of the system are enhanced, enabling it to respond promptly to changing actual situations, always maintain the optimal state, and avoid prediction deviations caused by external factors; more accurate hydrogen consumption estimation and optimized management help reduce energy consumption, indirectly reduce operation costs, and improve the company's profitability; by optimizing transportation routes and hydrogen consumption estimation, fuel consumption is reduced, operation costs are further reduced, and economic benefits are improved; more accurate hydrogen consumption estimation and optimized management help reduce energy consumption and greenhouse gas emissions, meet the goals of sustainable development, and promote the development of green logistics; the carbon footprint is reduced, meeting the requirements of environmental protection regulations, enhancing the company's social responsibility and social image; providing a scientific decision-making support tool, simplifying the operation process, improving work efficiency and service quality, and enhancing user satisfaction; the intelligent decision-making support system improves driving safety and comfort, and at the same time quickly responds to changes in customer needs, improving service quality and customer satisfaction; by optimizing transportation routes and hydrogen consumption estimation, the resource utilization efficiency is improved, and the company's competitiveness in the market is enhanced; efficient operation and low-cost advantages enable the company to stand out in the highly competitive market, win more business opportunities, and enhance market competitiveness; design a Bayesian network structure according to the logical relationship between sub-models at each level, clarify the dependency and causal relationships, set prior probabilities and conditional probabilities based on historical data and expert knowledge bases, and use the Bayesian inference algorithm to integrate the prediction results at each level; realize the fusion and comprehensive analysis of multi-source data, ensure that the model can comprehensively consider the influence of various factors, and improve the accuracy and reliability of prediction.

[0103] In one embodiment of the present invention, the S4 includes:

[0104] S41. Plan multiple transportation routes, consider the impact of traffic conditions in different time periods, and optimize by using existing traffic prediction models and adding new data.

[0105] S42. Based on historical meteorological data and real-time weather forecasts, simulate the transportation environment through simulation software to ensure that the simulation conditions are as close as possible to the actual situation;

[0106] S43. Set vehicle parameters for specific vehicle models, input the corresponding parameters of the hydrogen fuel cell, and optimize using the existing vehicle dynamics model combined with new data;

[0107] S44. According to real-time traffic data and historical statistical data, simulate different time and route conditions, and use cloud computing resources to execute the simulation calculations of multiple routes in parallel;

[0108] S45. Record the key data during the simulation process in real time, output the simulation results, and use these results for subsequent model optimization and verification.

[0109] The working principle of the above technical solution is as follows: Based on the starting point and the ending point, multiple possible transportation routes are generated. These routes can include different types of road sections such as highways and urban roads; considering the traffic flow changes at different times of the day (such as peak hours and off-peak hours), the existing traffic prediction models are used to evaluate the traffic efficiency at each time period; combining the latest real-time traffic data (such as from GPS, traffic cameras, intelligent transportation systems, etc.), the traffic prediction models are updated and optimized to ensure that they can reflect the latest traffic conditions; for example, assume there are multiple route options from City A to City B, one mainly through the highway and the other more through urban roads; between 8 am and 10 am, there may be serious traffic congestion on some road sections, so the routes that are smoother during this time period will be preferred during planning; if construction is underway or an accident occurs on a certain road section, the real-time traffic data can help adjust the route planning to avoid unnecessary delays; analyzing the meteorological data in the past few years to understand the weather patterns in different seasons, months, and even specific dates; incorporating the latest weather forecast information (such as temperature, humidity, precipitation probability, etc.) into the simulation environment to improve the authenticity of the simulation; using professional simulation software to create a virtual transportation environment, simulating various meteorological conditions during the vehicle driving process to ensure that the simulation results are as close to the real situation as possible; for example, there may be a risk of snowfall or icing in winter, and high temperature or heavy rain may occur in summer, all of which need to be considered in the simulation; if the forecast shows that there will be heavy rain on a certain day, the corresponding road surface slipperiness coefficient can be set in the simulation to affect the rolling resistance and braking performance of the vehicle; using the simulation software to simulate the vehicle driving scenario in rainy days, including factors such as reduced visibility and decreased road surface friction, to make the simulation more realistic; according to the actual vehicle types used (such as heavy trucks, light trucks, etc.), the basic parameters of the vehicle are set (such as mass, size, air resistance coefficient, etc.); inputting the parameters related to the hydrogen fuel cell (such as power output, efficiency curve, working temperature range, etc.) to ensure that the simulation can accurately reflect the behavior of this type of battery; using the existing vehicle dynamics model and combining the latest measured data (such as slope sensors, speed sensors, etc.) to optimize the model parameters and improve the simulation accuracy.For example, for a specific model of hydrogen fuel cell logistics vehicle, its unladen mass is set at 5 tons, its full load mass is 10 tons, and its frontal area is 7 square meters. Assume that the maximum output power of the hydrogen fuel cell of this vehicle model at room temperature is 150 kW, and its efficiency is about 60%, and the efficiency decreases as the temperature rises. By combining the latest collected data such as speed, acceleration, and slope, the parameters in the model are dynamically adjusted to ensure that it always reflects the latest operating conditions. By integrating real-time traffic data (such as current road conditions, accident information, etc.) and historical statistical data (such as average vehicle speed, congestion frequency, etc.), a detailed simulation scenario is created for each route. For multiple alternative routes, the traffic conditions during different time periods (such as morning and evening rush hours, off-peak hours) are respectively simulated, and the traffic efficiency and hydrogen consumption of each route are evaluated. With the powerful computing power of the cloud computing platform, multiple simulation tasks are executed in parallel at the same time, greatly shortening the simulation calculation time and improving work efficiency. For example, during the morning rush hour, the historical average vehicle speed of a certain route is 30 km / h, and currently, due to a traffic accident, the vehicle speed has dropped to 15 km / h. These factors will be considered during the simulation. Assume that three routes are available for selection, and the simulation is carried out at 7 am, 12 noon, and 7 pm respectively, and their traffic efficiency and hydrogen consumption during different time periods are compared. By running these three simulation tasks simultaneously on the cloud platform, the simulation results of all routes can be obtained in a short time, and the optimal choice can be made quickly. During the simulation process, important operating parameters (such as speed, acceleration, hydrogen consumption, etc.) and various working conditions encountered (such as climbing slopes, sudden braking, etc.) are recorded in real time. The data obtained from the simulation is organized into a report form and provided to decision-makers for reference to help them select the most suitable transportation route. Using the feedback information in the simulation results, the deficiencies in the model are identified and improved accordingly. At the same time, the new simulation results are used to verify the accuracy of the model to ensure its continuous optimization. For example, record the instantaneous power output of the hydrogen fuel cell, the average vehicle speed, and the total hydrogen consumption during each simulation process. Generate a detailed simulation report, including information such as the estimated driving time, total hydrogen consumption, and potential risk points of each route. If it is found that the predicted hydrogen consumption of the model is high under a certain specific condition (such as extremely low temperature), the model can be improved by collecting more data under such conditions, and its accuracy can be verified with the new simulation results.

[0110] The effects of the above technical solution are as follows: By planning multiple transportation routes and considering traffic conditions at different times, logistics companies can select the most efficient routes, reducing transportation time and fuel consumption; With the powerful computing power of cloud computing, the simulation calculations of multiple routes are executed in parallel, greatly shortening the simulation time and improving the decision-making speed; Combining new data to update the traffic prediction model ensures that it can reflect the latest traffic conditions, improving the accuracy and reliability of route selection; Constructing a simulation environment close to the actual situation through historical meteorological data and real-time weather forecasts makes the simulation results more credible and reduces the probability of unexpected situations; Using simulation software to simulate the transportation environment under various weather conditions to identify potential risk points in advance, such as slippery roads and bad weather, enhancing driving safety; Setting detailed vehicle parameters for specific vehicle models and inputting relevant parameters of hydrogen fuel cells to ensure that the simulation can accurately reflect the actual vehicle performance and improve the reliability of operation; Utilizing the existing vehicle dynamics model and combining new data to optimize the parameters of hydrogen fuel cells improves the accuracy of hydrogen consumption estimation and reduces unnecessary fuel waste; Selecting the optimal route based on the simulation results to avoid congested sections or high-energy-consuming operating conditions directly reduces the operating cost; More accurate hydrogen consumption estimation and optimized management help reduce energy consumption and greenhouse gas emissions, meeting the goals of sustainable development and promoting the development of green logistics; Providing scientific decision-making support through simulation calculations simplifies the operation process, improves driving safety and comfort, and enhances user satisfaction; Recording key data during the simulation process in real time, outputting simulation results, and using these results for subsequent model optimization and verification to ensure the long-term reliability and continuous improvement ability of the system.

[0111] In one embodiment of the present invention, the S44 includes:

[0112] S441. Integrate real-time traffic data from multiple channels; and preprocess the obtained real-time traffic data;

[0113] S442. Analyze historical traffic data, and use data mining algorithms to extract typical patterns and rules of traffic flow and congestion from historical data; Combining historical statistical data and current real-time data, adopt time series analysis algorithms to predict traffic conditions in the future for a period of time;

[0114] S443. According to the reliability of real-time data and the stability of historical data, assign different weights to the two to construct a dynamic fusion model; Based on real-time traffic data and historical prediction results, construct multiple traffic scenarios and evaluate the probability of each scenario;

[0115] S444. Set different simulation scenarios for each transportation route according to the constructed scenarios, and utilize the cloud computing platform to dynamically allocate computing resources based on the complexity and computing requirements of the simulation tasks; through parallel algorithms, enable the simulation calculations of multiple transportation routes to be carried out simultaneously;

[0116] S445. During the simulation process, monitor the computing progress and result quality in real time, adopt heuristic search to fine-tune the simulation parameters, and record the key data during the simulation process in real time;

[0117] S446. Summarize the simulation results of each transportation route to form a comprehensive evaluation report; conduct in-depth analysis of the simulation results, identify potential problems and improvement points, and put forward targeted suggestions and optimization plans.

[0118] The working principle of the above technical solution is as follows: collect real-time traffic data from different sources (such as traffic cameras, GPS devices, mobile applications, intelligent transportation systems, etc.); integrate these scattered data sources to ensure that all data can be processed and analyzed on a unified platform; for example, obtain real-time road conditions of urban roads through APIs provided by traffic management departments, and at the same time collect the speed and location information of each vehicle using the GPS devices carried by the vehicles; centralize these data into a data center for subsequent processing and analysis; remove noise in the data (such as duplicate records, error values or outliers) to improve data quality; standardize data from different sources and formats into a unified format and unit for subsequent processing; for example, remove abnormal records with speeds exceeding the speed limit by many times, or merge duplicate location records at the same time point; review traffic data over a past period of time to identify common traffic patterns and regularities; use data mining techniques such as clustering analysis and association rule learning to find features such as traffic flow peaks and frequently congested areas; for example, analyze the traffic flow changes between 5 pm and 7 pm every Friday in the past year and find that this period is a typical rush hour and traffic congestion is likely to occur; use the K-means clustering algorithm to find the sections where congestion most frequently occurs, or use association rule learning to find out which factors (such as weather conditions, holidays) will lead to an increase in traffic flow; combine the trends and periodicities of historical data and current real-time data to predict future short- and medium-term traffic conditions; use time series prediction models such as ARIMA and LSTM to generate estimates of future traffic conditions; for example, predict the traffic flow changes in the next hour based on the average vehicle speed from 8 am to 9 am every day in the past week and the current real-time vehicle speed; assume that an LSTM neural network model is used, which can accurately predict future traffic conditions based on the learning of historical and real-time data; evaluate the reliability and stability of real-time data and historical data and assign appropriate weights to both; construct a fusion model that comprehensively considers the impacts of real-time and historical data to provide more accurate traffic condition predictions; for example, during peak traffic hours, real-time data may be more important, so a higher weight is given; while during off-peak hours, historical data may be more valuable for reference, and its weight can be appropriately increased; assume that during a specific period, real-time traffic data indicates that an accident has occurred on a certain road, and at this time, more reliance should be placed on real-time data for prediction; while in other periods, more reliance is placed on historical data; simulate different traffic scenarios (such as normal traffic, slight congestion, severe congestion, etc.) based on real-time traffic data and historical prediction results; use Bayesian inference or other statistical methods to evaluate the probability of each scenario occurring; for example, assume that current real-time data shows that the traffic flow on a certain road is gradually increasing, and two scenarios of "slight congestion" and "severe congestion" can be constructed; use Bayesian inference to calculate the probabilities of these two scenarios occurring in the next half hour to help decision-makers make more informed choices;Set specific simulation parameters (such as estimated travel time, hydrogen consumption, etc.) for each transportation route according to different traffic scenarios; Dynamically allocate the computing resources of the cloud computing platform according to the complexity and computing requirements of the simulation task to ensure efficient execution; Through parallel computing technology, the simulation calculations of multiple transportation routes are carried out simultaneously, greatly shortening the simulation time; For example, for the "slight congestion" scenario, set the estimated travel time of a transportation route to 1.5 hours and the hydrogen consumption to 10 kgH2. Suppose a certain simulation task involves a large amount of data processing, the system will automatically allocate more computing nodes to accelerate the calculation; Through a distributed computing framework (such as Apache Spark), the simulation tasks of multiple routes are run simultaneously, improving the work efficiency; Continuously monitor the calculation progress and result quality during the simulation process to ensure the smooth progress of the simulation; Use heuristic search algorithms to optimize the simulation parameters to improve the accuracy and efficiency of the simulation; Record the key data during the simulation process, such as simulation parameter adjustments, calculation progress, etc., to provide support for subsequent analysis; For example, view the simulation progress bar of each route in real time to ensure that no simulation task gets stuck or fails; Suppose the simulation results show that the hydrogen consumption of a certain route is too high, the vehicle speed or acceleration parameters can be adjusted through heuristic search algorithms to optimize the hydrogen consumption; Record the time points and specific values of each parameter adjustment, as well as other key data during the simulation process, for subsequent analysis and report generation; Summarize the simulation results of all transportation routes to generate a detailed comprehensive evaluation report; Analyze the simulation results in detail to identify potential problems and improvement points; According to the analysis results, put forward targeted improvement suggestions and optimization plans to help logistics companies optimize their operation strategies; For example, generate a report containing the estimated travel time, hydrogen consumption, potential risk points, etc. of each route; Suppose a certain route often encounters traffic jams during a specific time period, the reason may be that there is a school dismissal or a shopping mall promotion activity nearby; It is recommended to avoid these time periods or choose alternative routes, and at the same time propose a plan to optimize the fleet scheduling and driving behavior to reduce hydrogen consumption and improve efficiency.;

[0119] The effects of the above technical solution are as follows: The above technical solution improves the quality and consistency of data, ensures the accuracy of subsequent analysis and prediction, and reduces misjudgments caused by data problems; through the comprehensive analysis of historical data and real-time data, the accuracy and reliability of traffic condition prediction are improved, helping logistics companies make preparations in advance and avoid potential risks; the self-adaptability and long-term reliability of the model are maintained, enabling it to respond promptly to changing actual situations and always remain in the optimal state, avoiding prediction deviations caused by model aging; by optimizing transportation routes and hydrogen consumption estimation, fuel consumption is reduced, further lowering operating costs and increasing economic benefits; energy consumption and greenhouse gas emissions are reduced, meeting the goals of sustainable development, promoting the development of green logistics, and enhancing the company's social responsibility and social image; the intelligent decision support system improves driving safety and comfort, and at the same time quickly responds to changes in customer needs, improving service quality and customer satisfaction; the advantages of efficient operation and low cost enable the company to stand out in the highly competitive market, win more business opportunities, and enhance market competitiveness; it ensures the efficiency of the simulation process and the accuracy of the results, provides more refined adjustment means, and improves the scientificity and rationality of the final decision; it provides comprehensive evaluation and improvement suggestions, helps logistics companies continuously optimize their operation strategies, and ensures the long-term effectiveness and continuous improvement ability of the system.

[0120] In one embodiment of the present invention, S5 includes:

[0121] S51. Define the optimization objectives, set weights based on the enterprise operation strategy and market demand, and construct a multi-objective optimization function;

[0122] S52. Evaluate the applicability of different multi-objective optimization algorithms, select the most suitable algorithm, and adjust the algorithm parameters according to new data;

[0123] S53. Use the simulation results as input to run the multi-objective optimization algorithm, generate a Pareto front solution set, analyze the performance of different solutions, select the best transportation route from the solution set, and continuously optimize the selection process using new data.

[0124] The working principle of the above technical solution is as follows: Determine multiple objectives that need to be optimized simultaneously, such as minimizing hydrogen consumption, reducing transportation time, and lowering operating costs; According to the enterprise's operation strategy (such as giving priority to cost control or delivery speed) and market demand (such as the time window required by customers), assign appropriate weights to each objective; Integrate multiple objectives into a mathematical function, and construct a multi-objective optimization model through weighted summation or other methods; Compare various multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization, NSGA-II, etc.), and evaluate their performance in dealing with specific problems; Select the algorithm most suitable for the current problem according to the evaluation results; As new data is introduced, dynamically adjust the algorithm parameters to maintain optimal performance; For example, for problems with complex constraints, genetic algorithms may perform better; For continuous optimization problems, particle swarm optimization may be a better choice; Assume that after evaluation, it is found that the NSGA-II algorithm can handle multi-objective optimization problems well and generates a Pareto front solution set with high quality, so this algorithm is selected; As more measured data is collected, it may be found that some parameters (such as population size, crossover probability, etc.) need to be adjusted to obtain better results; Use the simulation results obtained in S4 (such as hydrogen consumption of different routes, estimated travel time, etc.) as input data and provide them to the multi-objective optimization algorithm; Calculate a series of non-dominated solutions (i.e., Pareto optimal solutions) through the multi-objective optimization algorithm, and these solutions represent the trade-off relationships between different objectives; Evaluate the performance of each Pareto solution, understand their advantages and disadvantages in different objectives, and help decision-makers make choices; Select one or more optimal transportation routes from the Pareto front solution set according to the specific needs and preferences of the enterprise; As new simulation results and actual operation data accumulate, continuously update and optimize the selection process to ensure that the most suitable route is always selected; For example, input information such as hydrogen consumption, estimated travel time, and potential risk points of each route; Assume a set of different transportation routes is obtained, some of which have low hydrogen consumption but long time, and some have short time but high hydrogen consumption; Analyze the performance of these solutions in different objectives, for example, some routes may have slightly higher hydrogen consumption but can significantly shorten the transportation time, which is suitable for urgent orders; While other routes are more suitable for regular transportation tasks; Select the most suitable route from the Pareto front solution set according to the company's current needs (such as whether to give priority to cost or time); For example, if it is found that a certain route often encounters traffic congestion during a specific period, the selection process can be improved by collecting more real-time traffic data to avoid similar situations from happening again.

[0125] The effects of the above technical solutions are as follows: By determining multiple objectives that need to be optimized simultaneously and setting weights according to the enterprise operation strategy and market demand, a more scientific and reasonable multi-objective optimization function is constructed. For example, if a logistics company attaches more importance to cost control, higher weights can be assigned to "minimizing hydrogen consumption" and "reducing operating costs"; if customers have strict requirements for delivery time, the weight of "reducing transportation time" will be higher. By evaluating the applicability of different multi-objective optimization algorithms, selecting the algorithm most suitable for the current problem, and dynamically adjusting the algorithm parameters according to new data, it is ensured that the algorithm is always in the optimal state, improving the optimization accuracy and adaptability. By using the simulation results as input to run the multi-objective optimization algorithm, a series of non-dominated solutions (i.e., Pareto optimal solutions) are generated, which represent the trade-off relationships between different objectives and provide more flexibility in choices. By evaluating the performance of each Pareto solution, understanding their advantages and disadvantages in different objectives, it helps decision-makers make the most suitable route selection, enhancing the reliability and rationality of the selection. Through the objective of minimizing hydrogen consumption in the multi-objective optimization function, accurate prediction and optimized management of hydrogen consumption are achieved, reducing unnecessary fuel waste and directly lowering operating costs. Selecting the best transportation route from the Pareto front solution set to avoid congested sections or high-energy-consuming working conditions further reduces operating costs. More accurate prediction and optimized management of hydrogen consumption help reduce energy consumption and greenhouse gas emissions, meeting the goals of sustainable development and promoting the development of green logistics. Providing a scientific decision support tool simplifies the operation process, improves driving safety and comfort, and enhances user satisfaction. By continuously optimizing the selection process, it can quickly respond to changes in customer needs, improving service quality and customer satisfaction. Using simulation results and actual operation data to continuously optimize the selection process ensures the long-term reliability and continuous improvement ability of the system, enabling the model to always reflect the latest actual situation. With the introduction of new data, dynamically adjust the algorithm parameters to maintain the efficiency and accuracy of the optimization algorithm.

[0126] In one embodiment of the present invention, step S6 includes:

[0127] S61. Establish a real-time data acquisition system to continuously monitor the key data during vehicle operation, ensure real-time transmission and monitoring between the vehicle and the data center, and use encryption technology and security protocols to ensure data security;

[0128] S62. Compare the actual operation data with the simulation results, evaluate the model accuracy, identify the error sources and analyze the reasons, and use online learning algorithms to dynamically correct the model;

[0129] S63. Dynamically correct the comprehensive hydrogen consumption model according to the real-time data, and regularly evaluate the performance of the corrected model to ensure that the model is always in the optimal state;

[0130] S64. Re - conduct simulation calculations and optimization analyses based on the corrected model to ensure that the selection of transportation routes and the prediction of hydrogen consumption always remain at the optimal level.

[0131] The working principle of the above technical solution is as follows: Install sensors and other monitoring devices (such as GPS, speed sensors, acceleration sensors, temperature sensors, etc.) to continuously collect key data during vehicle operation; Implement real-time data transmission between the vehicle and the data center through Internet of Things (IoT) technology to ensure that data can reach and be processed immediately; Adopt encryption technologies and security protocols (such as SSL / TLS, AES encryption, etc.) to protect the security of data during transmission and storage, preventing data leakage or tampering; For example, sensors installed on the vehicle can record information such as speed, acceleration, position, battery temperature, etc. once per second; Use 4G / 5G networks or dedicated communication links to send this data to the data center in real time, and the data center can immediately analyze and respond to any anomalies; All transmitted data is encrypted with AES-256 to ensure the security of sensitive information even in the event of a cyber attack; Compare the actual operation data collected in real time with the results of previous simulation calculations to find the differences between the two; Through comparative analysis, evaluate the accuracy of the existing model, identify the sources of errors, and analyze the reasons for the errors; Apply online learning algorithms (such as incremental learning, adaptive filters, etc.) to dynamically adjust the model parameters according to new data to improve the prediction accuracy; For example, if the actual driving time of a certain transportation route is 10 minutes longer than the simulation prediction, this may be due to traffic congestion or other unforeseen factors; Analyze the reasons for these errors, such as whether they are caused by weather conditions, traffic flow changes, etc.; Suppose it is found that traffic jams often occur on a specific section of the road, the speed prediction model for this section can be automatically adjusted through an online learning algorithm to more accurately reflect the actual situation; Continuously update and correct the comprehensive hydrogen consumption model according to real-time data so that it can more accurately reflect the current operating conditions; Set a periodic evaluation plan to regularly check the performance of the corrected model to ensure that it is always in the optimal state; Create a closed-loop control system to compare the actual operation data with the predicted values, identify the sources of errors, and optimize the model accordingly; For example, if it is found that the hydrogen consumption is higher than expected during a certain period, relevant parameters in the model, such as the rolling resistance coefficient or the wind resistance coefficient, can be adjusted through real-time data; Conduct a comprehensive evaluation of the model's performance once every quarter to ensure that it still meets the latest operating requirements.If it is found that the model prediction error increases, retrain immediately using the latest data; assume that during a certain transportation process, abnormal traffic conditions are encountered, resulting in frequent vehicle starts and stops. The system will update the model parameters in a timely manner to adapt to this new working condition; based on the corrected comprehensive hydrogen consumption model, re - conduct simulation calculations to generate new simulation results; conduct in - depth analysis of the new simulation results, evaluate the selection of different transportation routes and hydrogen consumption estimation to ensure that they always remain at the optimal level; with the introduction of new data, continuously optimize the model and simulation calculations to ensure the long - term reliability and continuous improvement ability of the system; for example, if it is found that the traffic conditions on a certain route have changed significantly during a specific time period, re - conduct simulation calculations considering the impact of these changes; by analyzing the new simulation results, evaluate the traffic efficiency and hydrogen consumption of each route and select the most suitable transportation route; assume that over time, some influencing factors (such as road construction, policy changes, etc.) change, and the system can continuously optimize the model and simulation calculations according to the latest data and conditions.

[0132] The effects of the above - mentioned technical solution are as follows: Establish a real - time data acquisition system to ensure the accuracy and integrity of key operation data, and safeguard the security of data during transmission and storage through encryption technology and security protocols; enhance data security, prevent the leakage or tampering of sensitive information, and improve the reliability of the system and user trust; compare the actual operation data with the simulation results, evaluate the model accuracy, identify the error sources and analyze the reasons, and use online learning algorithms to dynamically correct the model; through continuous comparison and adjustment, improve the prediction accuracy of the model, reduce the prediction error, and ensure the accuracy of the decision - making basis; dynamically correct the comprehensive hydrogen consumption model according to real - time data and regularly evaluate the performance of the corrected model to ensure that the model is always in the optimal state; maintain the self - adaptability and long - term reliability of the model, enabling it to respond promptly to changing actual situations, always remain in the optimal state, and avoid prediction deviations caused by model aging; re - conduct simulation calculations and optimization analysis based on the corrected model to ensure that the selection of transportation routes and hydrogen consumption estimation always remain at the optimal level; optimize the selection of transportation routes, reduce hydrogen consumption, improve operation efficiency, reduce unnecessary fuel waste, and directly reduce operating costs; more accurate hydrogen consumption estimation and optimized management help reduce energy consumption, indirectly reduce operating costs, and improve the company's profitability; by optimizing transportation routes and hydrogen consumption estimation, reduce fuel consumption, further reduce operating costs, and improve economic benefits.

[0133] An embodiment of the present invention, as Figure 2 shown, a hydrogen consumption simulation system for vehicle transportation routes based on multiple models, the system includes:

[0134] Data acquisition module: Based on the existing basic power demand model and hydrogen consumption calculation model, multi-source data collected in real time through a multi-source data acquisition network; combining the multi-source data with historical data, optimizing the existing model through machine learning algorithms, and updating the hydrogen consumption calculation model;

[0135] Model construction module: Optimize the existing environmental impact model through newly collected data on external environmental factors in real time, and further correct the hydrogen consumption calculation results according to the impact of environmental conditions on the efficiency of hydrogen fuel cells;

[0136] Model integration module: Integrate the optimized basic power demand model, hydrogen consumption calculation model, and environmental impact model to form a multi-level comprehensive hydrogen consumption model;

[0137] Result simulation module: Input relevant data for different transportation routes, perform simulation calculations on different transportation routes based on the comprehensive hydrogen consumption model, and obtain the hydrogen consumption of each route under different working conditions; output the hydrogen consumption simulation results of each transportation route under different working conditions, and continuously optimize the model by comparing with actual operation data;

[0138] Route optimization module: Based on the latest simulation results, determine the optimal transportation route through a multi-objective optimization algorithm; output suggestions for the optimized best transportation route, and dynamically adjust the transportation route according to the actual operation situation;

[0139] Dynamic correction module: During the transportation process, collect actual operation data in real time; re-evaluate and optimize the comprehensive hydrogen consumption model according to the actual operation data.

[0140] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A hydrogen consumption simulation method for vehicle transportation routes based on multiple models, characterized in that, The method includes: S1. Based on the basic power demand model and the hydrogen consumption calculation model, combine the multi-source data collected in real time by the multi-source data acquisition network with historical data, optimize the existing basic power demand model and hydrogen consumption calculation model through machine learning algorithms, and update the hydrogen consumption calculation model; S2. Optimize the environmental impact model with the new data of external environmental factors collected in real time, and further correct the hydrogen consumption calculation results according to the impact of environmental conditions on the efficiency of hydrogen fuel cells; S3. Integrate the optimized basic power demand model, hydrogen consumption calculation model and environmental impact model to form a multi-level comprehensive hydrogen consumption model; S4. Input the relevant data of different transportation routes, and based on the comprehensive hydrogen consumption model, perform simulation calculations on different transportation routes to obtain the hydrogen consumption of each route under different working conditions; output the hydrogen consumption simulation results of each transportation route under different working conditions, and continuously optimize the model by comparing with the actual operation data; S5. Based on the simulation results, determine the optimal transportation route through a multi-objective optimization algorithm; output the optimized optimal transportation route suggestion, and dynamically adjust the transportation route according to the actual operation situation; S6. During the transportation process, collect the actual operation data in real time; re-evaluate and optimize the comprehensive hydrogen consumption model according to the actual operation data; The S1 includes: S11. Define the types and locations of data collection points, and design the data collection frequency and accuracy requirements; S12. Build a data transmission network, encrypt the data transmission process based on data encryption algorithms, integrate the data collection system, and uniformly receive, store and manage multi-source data; S13. Obtain historical data, and preprocess the collected real-time data and historical data; S14. Based on the vehicle motion equation model and vehicle dynamics principle, combine the newly collected real-time operation data of the vehicle to optimize the calculation of the basic power demand under different working conditions; S15. Based on the power system response characteristic curve and efficiency curve model, use the new working characteristic data of hydrogen fuel cells to optimize the relationship between hydrogen consumption and electric energy output, and establish a new hydrogen consumption calculation model; S16. Combine the existing basic power demand model and hydrogen consumption calculation model, further optimize with new data, and calculate the hydrogen consumption under different working conditions; The S14 includes: S141. Use the existing mass change measurement model, add new data for dynamic mass model optimization, and optimize the wind resistance coefficient through CFD simulation combined with the latest wind speed and wind direction data; S142. Based on the rolling resistance coefficient model, use new data for dynamic adjustment; the new data includes tire wear degree, tire type, tire pressure and road surface type; S143. Combine the new data of the high-precision slope sensor to optimize the relationship model between slope and climbing resistance, and combine Newton's second law and vehicle dynamics parameters to construct and optimize the dynamic equation of vehicle motion; S144. Dynamically adjust the parameters in the dynamic equation according to the newly collected real-time data to optimize the power demand prediction; S145. Analyze the new and old data using machine learning or pattern recognition algorithms to automatically identify the driving conditions; S146. Optimize the basic power demand model based on the working condition recognition result.

2. The method for simulating hydrogen consumption of a vehicle transportation route based on multiple models according to claim 1, wherein The said S15 includes: S151. Through simulation means, test and optimize the response characteristic curve and efficiency curve of the power system based on measured data; S152. Evaluate the adaptability to transient working conditions, and optimize the efficiency model in combination with the chemical reaction principle of the hydrogen fuel cell; S153. Analyze the temperature change law, optimize the temperature compensation mechanism and aging prediction model, and dynamically adjust the hydrogen consumption calculation parameters; S154. According to the real-time operation data and the response characteristics of the power system, optimize the matching of the basic power demand and the output capacity of the power system; S155. Establish a real-time feedback mechanism, dynamically adjust and correct the strategy according to the actual operation situation and the hydrogen consumption calculation result, and continuously optimize the framework of the hydrogen consumption calculation model; S156. Integrate the optimized response characteristic curve and efficiency curve of the power system and the framework of the hydrogen consumption calculation model into the hydrogen consumption calculation model, verify the accuracy of the model, and continuously optimize according to the feedback.

3. The hydrogen consumption simulation method for vehicle transportation routes based on multiple models according to claim 1, characterized in that The said S2 includes: S21. Identify the environmental factors affecting the efficiency of the hydrogen fuel cell, and classify these factors based on the model and new data; S22. Evaluate the importance and relevance of each environmental factor, determine the factors to be considered in the model, and update the evaluation results using new data; S23. Based on the existing test methods, test the hydrogen fuel cell under different environments, and use new data to supplement or correct the performance parameter records; S24. According to the new test data, use statistical analysis methods to update the mapping relationship between environmental factors and the efficiency of the hydrogen fuel cell; S25. Evaluate the goodness of fit and prediction ability of the preliminary model, introduce complexity evaluation indicators, judge whether the model needs to be simplified or complicated, and verify it with new data; S26. Identify the deficiencies in the model, design supplementary tests or collect more relevant data to enrich the training set; S27. Apply feature engineering techniques to optimize the input feature set, and correct and retrain the model through machine learning algorithms; S28. Introduce judgment logic to guide the model selection and optimization direction, and set conditional branches to handle the model application under different environmental conditions; S29. Through an adaptive mechanism, the model automatically adjusts parameters or selects the best version, regularly evaluates the model performance, and when a performance decline is found, triggers an update process, and uses the latest environment and performance data for retraining and verification.

4. The hydrogen consumption simulation method for vehicle transportation routes based on multiple models according to claim 1, wherein, The said S3 includes: S31. Stratify the hydrogen consumption influencing factors, and deeply analyze the influencing factors of each layer based on the existing model; S32. For the influencing factors of each layer, construct or optimize the sub-model based on the existing sub-model and combined with new data, and integrate the sub-models through a Bayesian network; S33. Conduct a preliminary analysis and evaluation of the comprehensive hydrogen consumption model, use historical data to backtest the effectiveness of the model, and dynamically adjust the model according to new data; S34. During the model prediction process, automatically switch to the most suitable sub-model for the current situation through conditional judgment logic; S35. Dynamically adjust the model parameters or structure according to the real-time data and prediction results, establish a feedback mechanism to compare the actual hydrogen consumption data with the predicted value, identify the error source, and optimize the model accordingly. S36. Further optimize the model using algorithms such as particle swarm optimization, and introduce new influencing factors and characteristic variables to improve the model structure; S37. Verify the optimized model using an independent test set, and regularly update and improve the model.

5. The hydrogen consumption simulation method for vehicle transportation routes based on multiple models according to claim 1, wherein, The above-mentioned S4 includes: S41. Plan multiple transportation routes, consider the impact of traffic conditions at different time periods, and optimize using the existing traffic prediction model and adding new data; S42. Based on historical meteorological data and real-time weather forecasts, simulate the transportation environment through simulation software; S43. Set vehicle parameters for specific vehicle models, input the corresponding parameters of the hydrogen fuel cell, and optimize using the existing vehicle dynamics model and combining new data; S44. According to real-time traffic data and historical statistical data, simulate different time and route conditions, and use cloud computing resources to execute the simulation calculations of multiple routes in parallel; S45. Record the key data during the simulation process in real time, output the simulation results, and use the simulation results for subsequent model optimization and verification.

6. The method for simulating hydrogen consumption of a vehicle transportation route based on multiple models according to claim 1, wherein The above-mentioned S5 includes: S51. Define the optimization objectives, set weights based on enterprise operation strategies and market demands, and construct a multi-objective optimization function; S52. Evaluate the applicability of different multi-objective optimization algorithms, and adjust the algorithm parameters according to new data; S53. Use the simulation results as input to run the multi-objective optimization algorithm, generate a Pareto front solution set, analyze the performance of different solutions, select the best transportation route from the solution set, and continuously optimize the selection process using new data.

7. A hydrogen consumption simulation method for vehicle transportation routes based on multiple models according to claim 1, characterized in that The above-mentioned S6 includes: S61. Establish a real-time data acquisition system to continuously monitor the key data during vehicle operation, and use encryption technology and security protocols to ensure data security; S62. Compare the actual operation data with the simulation results, evaluate the model accuracy, identify the error sources and analyze the reasons, and use online learning algorithms to dynamically correct the model; S63. Dynamically correct the comprehensive hydrogen consumption model according to real-time data, and regularly evaluate the performance of the corrected model; S64. Re-perform simulation calculations and optimization analysis based on the corrected model.

8. A system for implementing the multi-model-based vehicle transportation route hydrogen consumption simulation method as described in claim 1, characterized in that, The system includes: Data acquisition module: Based on the basic power demand model and hydrogen consumption calculation model, collect multi-source data in real time through a multi-source data acquisition network; combine the multi-source data with historical data, optimize the existing basic power demand model and hydrogen consumption calculation model through machine learning algorithms, and update the hydrogen consumption calculation model; Model construction module: Optimize the environmental impact model through new data of externally collected environmental factors in real time, and further correct the hydrogen consumption calculation results according to the impact of environmental conditions on the efficiency of hydrogen fuel cells; Model integration module: Integrate the optimized basic power demand model, hydrogen consumption calculation model, and environmental impact model to form a multi-level comprehensive hydrogen consumption model; Result simulation module: Input the relevant data of different transportation routes, and based on the comprehensive hydrogen consumption model, perform simulation calculations on different transportation routes to obtain the hydrogen consumption of each route under different working conditions; output the hydrogen consumption simulation results of each transportation route under different working conditions, and continuously optimize the model by comparing with the actual operation data; Route Optimization Module: Based on the latest simulation results, determine the optimal transportation route through a multi-objective optimization algorithm; output the recommended optimal transportation route after optimization, and dynamically adjust the transportation route according to the actual operation situation; Dynamic Correction Module: During the transportation process, collect actual operation data in real time; re-evaluate and optimize the comprehensive hydrogen consumption model according to the actual operation data.

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