A hydrogen energy full-link scheduling system and method based on big data

Abstract

The present application relates to hydrogen energy scheduling technical field, especially a kind of hydrogen energy full-link scheduling system and method based on big data.The system includes hydrogen energy link data acquisition module, hydrogen energy supply and demand prediction module, hydrogen production storage and transportation matching module, hydrogen energy link scheduling optimization module and safety feedback module.Through distributed sensor network, multi-dimensional operation data covering hydrogen production to terminal utilization are collected, random differential equation and neural random process are used to predict supply and demand trend, combined with link constraint, reinforcement learning optimization model is constructed to generate initial scheduling scheme, then artificial potential field algorithm is used to construct graph structure path planning model to optimize full-link scheduling, finally safety evaluation and closed-loop feedback control are realized through fuzzy logic control.The present application helps to promote the digitization, intelligentization and high efficiency development of each link operation of hydrogen energy industry chain, and comprehensively improves the hydrogen energy full-link operation efficiency and intelligent level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydrogen energy scheduling, in particular to a hydrogen energy full-link scheduling system and method based on big data. BACKGROUND

[0002] As a clean and efficient secondary energy, hydrogen energy plays an increasingly important role in the global energy structure transformation. Currently, hydrogen energy has been applied in the fields of transportation, industrial energy, distributed power generation, etc., and the industrial chain covers multiple links such as hydrogen production, hydrogen storage, hydrogen transportation, hydrogen refueling, and terminal utilization. With the continuous expansion of the scale of hydrogen energy application, the data volume and operation complexity of each link of the hydrogen energy full-link have increased significantly, and higher requirements are put forward for the efficient and collaborative scheduling of the hydrogen energy full-link. However, there are still the following problems: the existing system has a data disconnection problem between hydrogen production, storage, transportation, refueling, and terminal utilization, resulting in information islands between hydrogen production, storage, transportation, and terminal hydrogen use; traditional hydrogen supply and demand prediction mostly uses traditional statistical methods or simple time series analysis, which is difficult to fully capture the nonlinear coupling relationship between multi-source heterogeneous data affecting hydrogen circulation, resulting in insufficient accuracy and robustness of the prediction results; the scheduling strategy in the existing system usually adopts the fixed path or shortest path principle, without introducing graph structure modeling and dynamic avoidance strategy, which is difficult to effectively respond to the influence of bottleneck nodes, risk sections, and unexpected events in the link, and is prone to cause local resource waste and low overall operation efficiency. SUMMARY

[0003] To solve the above problems, the present application provides a hydrogen energy full-link scheduling system and method based on big data, which solves the problem of how to realize efficient data fusion and collaborative scheduling between each link in the hydrogen energy full-link, improve the accuracy and robustness of supply and demand prediction, and introduce intelligent path planning to dynamically avoid bottleneck and risk nodes, thereby building an intelligent, efficient, and safe hydrogen energy scheduling system, in the face of multi-link collaboration, data heterogeneous coupling, and link dynamic risk challenges under the background of continuous expansion of the scale of hydrogen energy application.

[0004] To achieve the above purpose, the technical scheme adopted by the present application is:

[0005] On the one hand, a hydrogen energy full-link scheduling system based on big data, comprising a hydrogen energy link data acquisition module, a hydrogen energy supply and demand prediction module, a hydrogen production and storage matching module, a hydrogen energy link scheduling optimization module, and a safety feedback module connected in sequence in communication;

[0006] The hydrogen energy link data acquisition module is used to acquire multi-dimensional operation data of the hydrogen energy full-link in real time through a distributed sensing network; the full-link includes the links of hydrogen production, compression, storage, transportation, refueling, and terminal utilization;

[0007] The hydrogen energy supply and demand prediction module is used to dynamically evolve and predict the spatiotemporal dynamic changes of hydrogen supply and demand based on the multidimensional operating data and a hydrogen energy supply and demand prediction model constructed using stochastic differential equations and neural stochastic processes, and output the supply and demand prediction results.

[0008] The hydrogen production, storage and transportation matching module is used to extract link operation constraints based on the supply and demand forecast results and the multi-dimensional operation data, and generate an initial scheduling scheme by adopting a reinforcement learning-driven multi-objective joint optimization strategy.

[0009] The hydrogen energy link scheduling optimization module is used to construct a hydrogen energy link scheduling model based on the initial scheduling scheme and a graph structure path planning method based on artificial potential field algorithm. It performs dynamic avoidance calculations on bottleneck nodes, congested sections and high-risk areas in the transportation path, and generates the optimal scheduling instruction set for the entire link by combining scheduling timing and resource cost constraints.

[0010] The security feedback module is used to collect real-time monitoring data based on the full-link optimal scheduling instruction set, use fuzzy logic control rules to perform security assessment and intervention decisions on link operation, identify abnormal states and trigger closed-loop control feedback, and output security intervention instructions, including scheduling correction suggestions, risk warning information and security control parameter adjustment instructions.

[0011] Furthermore, the multidimensional operational data includes hydrogen production, purity, pressure, temperature, remaining tank capacity, transportation route status, filling flow rate, terminal load demand, environmental parameters, and equipment operating status.

[0012] Furthermore, the operation of the hydrogen energy supply and demand forecasting module includes the following steps:

[0013] The multidimensional operational data is preprocessed, including aggregation, anomaly removal, missing data completion, and spatiotemporal feature normalization, and reconstructed into a structured, temporally continuous input feature matrix.

[0014] Based on the input feature matrix, a hydrogen energy supply and demand prediction model is constructed by jointly modeling stochastic differential equations and neural stochastic processes to characterize the dynamic evolution of hydrogen supply and demand.

[0015] The input feature matrix is ​​input into the hydrogen energy supply and demand prediction model. Numerical integration and neural network-driven stochastic process deduction are used to dynamically simulate and predict the spatiotemporal dynamic changes of hydrogen supply and demand in various regions. The supply and demand prediction results are output, including hydrogen supply and demand curves, corresponding confidence intervals, future supply and demand trends in various regions at different times, supply and demand gaps, prediction confidence intervals, and potential risk areas.

[0016] Furthermore, the construction process of the hydrogen energy supply and demand forecasting model includes the following steps:

[0017] Based on the multidimensional operational data, a stochastic differential equation containing state variable coupling terms and noise driving terms is constructed to simulate the stochastic disturbance characteristics and dynamic coupling behavior in the hydrogen supply and demand process.

[0018] A neural stochastic process framework is introduced, with the solution space of the stochastic differential equation as the prior structure. Through joint training on historical supply and demand data and related environmental characteristics, the nonlinear time-series distribution law of supply and demand curves in each region is learned.

[0019] The kernel function parameters and prior distribution of the neural stochastic process are dynamically adjusted using the maximum a posteriori estimation method, and the stochastic differential equations are structurally coupled with the neural stochastic process model to form a hydrogen energy supply and demand prediction model.

[0020] Based on the hydrogen energy supply and demand prediction model, the multidimensional operating data is input, and a numerical solver is used to extrapolate the future trajectory of supply and demand distribution, outputting the supply and demand prediction results.

[0021] Furthermore, the formula for the hydrogen energy supply and demand forecasting model is as follows:

[0022]

[0023] in, The hydrogen supply and demand state function represents region a. The first derivative with respect to time T, i.e. the direction and intensity of the change in the hydrogen energy supply and demand balance trend in the region; This represents the hydrogen supply and demand state function for region a at time T; T represents the time step. This indicates that region b at a historical moment Multidimensional operational data; This represents a nonlinear feature mapping function constructed based on a neural network; The time-sensitive kernel function represents the weighted integral; This represents the nonlinear coupling response function of region b to region a; Static weighting coefficients representing the strength of coupling between regions; This represents the system disturbance term in region a at time t; The term represents the second derivative of the supply and demand status of region a over time; N represents the total number of regions participating in the joint modeling or the number of hydrogen energy link nodes. The hydrogen supply and demand state function represents region a. The second derivative with respect to time T is the acceleration of the change in supply and demand. This represents the dynamic response inertia coefficient of region a.

[0024] Furthermore, the operation of the hydrogen production, storage, and transportation matching module includes the following steps:

[0025] Based on the multidimensional operational data and supply and demand forecast results, the operational constraints of the link are extracted, including the capacity boundary of the hydrogen production unit, the capacity limit of the hydrogen storage container, the accessibility map of the transportation route, the service time window and priority level of the refueling node;

[0026] By combining the aforementioned link operation constraints, a joint optimization objective is constructed, which includes minimizing hydrogen production energy consumption, minimizing storage and transportation delay, maximizing task completion rate, and avoiding path congestion.

[0027] Based on the joint optimization objective, a reinforcement learning framework integrating a hierarchical state encoder and a multi-policy behavior network is constructed. The dynamic prediction results of supply and demand are used as the state input, and the link resource scheduling cost and supply and demand matching deviation are used as the joint reward signal. The policy network is trained using the policy gradient optimization method, and the optimal solution domain scheduling scheme candidate set is output.

[0028] The Pareto optimal solution under the current constraints is selected from the candidate scheduling schemes to generate a structured initial scheduling scheme. The initial scheduling scheme includes the start-up and shutdown sequence of hydrogen production nodes, allocation of hydrogen storage containers, configuration of transportation routes, and priority ranking of refueling tasks.

[0029] Furthermore, the operation of the hydrogen energy link scheduling optimization module includes the following steps:

[0030] Based on the initial scheduling scheme and combined with the multi-dimensional operation data, a graph structure path planning method based on artificial potential field algorithm is used to construct a hydrogen energy link scheduling model. The hydrogen energy link scheduling model takes hydrogen energy link nodes as vertices and transportation and transfer paths as edges. Node attributes include resource capacity, task queue and safety status, and edge attributes include path reachability, time delay, transportation risk level and historical congestion records.

[0031] Based on the hydrogen energy link scheduling model, an attraction potential and a repulsion potential are applied to each transportation path to identify and mark potential bottleneck sections and high-risk nodes in the path in real time. The attraction potential is used to guide the scheduling object to move closer to the target node, and the repulsion potential is used to dynamically avoid bottleneck nodes, congested sections and high-risk areas.

[0032] The graph-structured path search method is adopted to dynamically and iteratively adjust the transportation path of each scheduling task under the influence of an artificial potential field, optimize the scheduling sequence, realize the active avoidance of bottleneck sections and high-risk nodes, and generate the optimal scheduling scheme for the entire link based on the actual link resource status and scheduling task requirements.

[0033] The optimal scheduling scheme is encoded into an end-to-end optimal scheduling instruction set, which includes a task execution schedule, a mapping diagram of transportation and storage resource allocation, path adjustment and switching strategies, and priority ranking of key node monitoring.

[0034] Furthermore, the formula for the hydrogen energy link scheduling model is as follows:

[0035]

[0036]

[0037]

[0038] in, This represents the value of the system's scheduling guidance function for the route from node i to node j at time t; This represents the spatial gradient operator for the artificial potential field in the path network; Represents the attraction potential function; This represents the repulsive potential function; Indicates the disturbance term on the path; This represents the current task load intensity of node j at time t; This represents the maximum resource service capacity of node j; This represents the path scheduling coupling strength from source node i through relay node k to target node j; This represents the current remaining available resources ratio of relay node k; M represents the total number of relay nodes in the current path that can participate in coordinated scheduling. This indicates the path risk level from node i to node j; This represents the historical congestion level from node i to node j; and The weighting coefficients of the attraction potential term are represented. This represents the weighting coefficient of the repulsive potential term.

[0039] On the other hand, a hydrogen energy end-to-end scheduling method based on big data includes the following steps:

[0040] Real-time collection of multi-dimensional operational data across the entire hydrogen energy chain through a distributed sensor network;

[0041] Based on the multidimensional operational data, a hydrogen energy supply and demand prediction model constructed using stochastic differential equations and neural stochastic processes is used to dynamically evolve and predict the spatiotemporal dynamic changes of hydrogen supply and demand, and output the supply and demand prediction results.

[0042] Based on the supply and demand forecast results and the multi-dimensional operation data, the link operation constraints are extracted, and an initial scheduling scheme is generated by adopting a reinforcement learning-driven multi-objective joint optimization strategy.

[0043] Based on the initial scheduling scheme, a graph structure path planning method based on artificial potential field algorithm is used to construct a hydrogen energy link scheduling model. Dynamic avoidance calculations are performed on bottleneck nodes, congested sections and high-risk areas in the transportation path. Combined with scheduling timing and resource cost constraints, the optimal scheduling instruction set for the entire link is generated.

[0044] Based on the aforementioned end-to-end optimal scheduling instruction set, real-time monitoring data is collected, fuzzy logic control rules are used to conduct security assessments and intervention decisions on link operation, abnormal states are identified and closed-loop control feedback is triggered, and security intervention instructions are output.

[0045] The beneficial effects of this invention are as follows:

[0046] This invention utilizes a hydrogen energy link data acquisition module. Based on a distributed sensor network, the system achieves real-time acquisition of multi-dimensional operational data across the entire hydrogen energy chain, including production, compression, storage, transportation, refueling, and end-use. This significantly improves the system's spatiotemporal awareness of the link status and its response speed, providing a reliable data foundation for subsequent modules. The hydrogen energy supply and demand forecasting module introduces a joint modeling mechanism using stochastic differential equations and neural stochastic processes. This mechanism can characterize the complex nonlinear evolution characteristics of both hydrogen supply and demand, effectively improving the accuracy of predicting hydrogen energy supply and demand trends in different regions and time scales, and supporting forward-looking resource allocation decisions. The hydrogen production, storage, and transportation matching module integrates supply and demand forecasting results with multi-dimensional operational data, extracts link operation constraints, and employs a reinforcement learning-driven multi-objective joint optimization strategy. This strategy can dynamically balance multiple objectives (such as energy efficiency, economy, and safety), effectively improving the efficiency of hydrogen energy resource allocation and the adaptability of scheduling strategies. The hydrogen energy link scheduling optimization module utilizes an artificial potential field algorithm and a graph-structured path planning method to dynamically identify and avoid bottleneck nodes, congested sections, and high-risk areas in the transportation path. It generates optimal scheduling instructions by combining scheduling timing and resource cost constraints, significantly improving the intelligence and robustness of the scheduling strategy. The safety feedback module collects operational data in real time and uses fuzzy logic control rules for safety assessment and decision intervention. It can identify abnormal states in real time and generate safety intervention instructions, including scheduling corrections, risk warnings, and parameter adjustments. This constructs a closed-loop control system covering prediction, response, and adjustment, significantly enhancing the safety and stability of system operation. Attached Figure Description

[0047] Fig. 1 This is a schematic diagram of a hydrogen energy end-to-end scheduling system based on big data according to the present invention.

[0048] Fig. 2 This is a flowchart illustrating the operation of the hydrogen energy link scheduling optimization module according to an embodiment of the present invention.

[0049] Fig. 3 This is a flowchart illustrating a hydrogen energy end-to-end scheduling method based on big data according to the present invention. Detailed Implementation

[0050] Please see Figs. 1-3 As shown, this invention relates to a hydrogen energy end-to-end scheduling system and method based on big data.

[0051] Example 1

[0052] A hydrogen energy full-link scheduling system based on big data includes a hydrogen energy link data acquisition module, a hydrogen energy supply and demand prediction module, a hydrogen production, storage and transportation matching module, a hydrogen energy link scheduling optimization module and a safety feedback module that are connected in sequence.

[0053] The hydrogen energy link data acquisition module is used to collect multi-dimensional operational data of the entire hydrogen energy link in real time through a distributed sensor network. The entire link includes hydrogen production, compression, storage, transportation, refueling and end-use. The multi-dimensional operational data includes hydrogen production, purity, pressure, temperature, remaining tank capacity, transportation route status, refueling flow rate, end-use load demand, environmental parameters and equipment operating status.

[0054] Specifically, in the processes of hydrogen production, compression, storage, transportation, refueling, and end-use, multi-source sensing terminals will be deployed as follows, based on process requirements and operational characteristics:

[0055] Hydrogen production / purity analyzer: Located in the hydrogen production and compression unit, it enables high-precision online monitoring of gas flow rate and purity (through mass spectrometry or chromatography).

[0056] Pressure / temperature transmitters: deployed along key points such as pipelines, storage tanks, transport vehicles, and filling machines, supporting high-frequency data acquisition.

[0057] Tank level / capacity sensor: Employs float, ultrasonic, or capacitive principles to monitor changes in the storage capacity of liquid or high-pressure gaseous hydrogen in real time.

[0058] Flow meter: Used at key flow nodes to monitor instantaneous and cumulative flow at refueling stations, transport pipelines / vehicles, and terminal hydrogen supply lines.

[0059] Transportation route and positioning module: All transport vehicles / mobile devices are equipped with Beidou / GPS dual-mode positioning and 4G / 5G communication modules to upload location and route status in real time.

[0060] Environmental monitoring instruments, such as temperature and humidity sensors, wind speed sensors, and air quality sensors (AQI), are deployed in key environmental areas for hydrogen storage, transportation, refueling, and use.

[0061] Intelligent cameras / machine vision terminals: used for video acquisition and anomaly identification in key areas, such as leakage in liquid hydrogen storage areas, abnormal open flames, and accidental personnel entry.

[0062] The data types and acquisition parameters are as follows:

[0063] Hydrogen production: Real-time flow rate of electrolyzer (Nm³) 3 / h), 1Hz refresh rate;

[0064] Hydrogen purity: Online gas chromatography (>99.999%), 5 minutes / cycle;

[0065] Pressure / Temperature: High pressure gauge (0-100MPa), thermocouple (-40~120℃), 1Hz refresh rate;

[0066] Remaining tank capacity: based on level gauge / mass flow meter, 10 seconds / time;

[0067] Transportation status: location coordinates (every 5 seconds), remaining hydrogen, driving speed, route deviation alarm;

[0068] Injection flow rate: Injection gun flow meter (L / min), cumulative volume per injection;

[0069] Terminal load: Use hydrogen terminal smart meters to collect actual hydrogen consumption and short-term load forecasts every 10 seconds;

[0070] Environmental parameters: temperature, humidity, wind speed, gas concentration, etc., updated every 30 seconds;

[0071] Equipment operating status: The equipment controller collects start / stop, fault codes, current, voltage, vibration acceleration, etc., once per second.

[0072] All data is aggregated through a local edge gateway, where it undergoes cleaning, noise reduction, and preliminary anomaly detection before being reported to the main data center in real time via 5G / wired network. This provides a highly reliable, low-latency data foundation for subsequent model training and scheduling decisions.

[0073] The hydrogen energy supply and demand prediction module is used to dynamically evolve and predict the spatiotemporal dynamic changes of hydrogen supply and demand based on the multidimensional operating data and a hydrogen energy supply and demand prediction model constructed using stochastic differential equations and neural stochastic processes, and output the supply and demand prediction results.

[0074] The operation of the hydrogen energy supply and demand forecasting module includes the following steps:

[0075] The multidimensional operational data is preprocessed, including aggregation, anomaly removal, missing data completion, and spatiotemporal feature normalization, and reconstructed into a structured, temporally continuous input feature matrix.

[0076] Based on the input feature matrix, a hydrogen energy supply and demand prediction model is constructed by jointly modeling stochastic differential equations and neural stochastic processes to characterize the dynamic evolution of hydrogen supply and demand.

[0077] The input feature matrix is ​​input into the hydrogen energy supply and demand prediction model. Numerical integration and neural network-driven stochastic process deduction are used to dynamically simulate and predict the spatiotemporal dynamic changes of hydrogen supply and demand in various regions. The supply and demand prediction results are output, including hydrogen supply and demand curves, corresponding confidence intervals, future supply and demand trends in various regions at different times, supply and demand gaps, prediction confidence intervals, and potential risk areas.

[0078] The process of constructing the hydrogen energy supply and demand forecasting model includes the following steps:

[0079] Based on the multidimensional operational data, a stochastic differential equation containing state variable coupling terms and noise driving terms is constructed to simulate the stochastic disturbance characteristics and dynamic coupling behavior in the hydrogen supply and demand process.

[0080] Specifically, key state variables on the hydrogen supply side (such as hydrogen production rate, storage tank capacity, and hydrogen refueling station capacity) and state variables on the demand side (such as terminal load, electric vehicle refueling frequency, and backup hydrogen storage requests) are defined. Coupled factors reflecting "supply and demand interaction feedback" are introduced into the differential equation structure, such as the hydrogen production response delay caused by changes in terminal demand and peak shaving by hydrogen storage facilities. Random disturbance sources, such as external temperature changes, electricity price fluctuations, and traffic congestion, are defined to model the disturbance response of the supply and demand system as Brownian motion driving terms. Different differential models are constructed for different regions, and the parameters are initialized according to regional wind speed, load density, and infrastructure capacity to support personalized dynamic simulation.

[0081] A neural stochastic process framework is introduced, with the solution space of the stochastic differential equation as the prior structure. Through joint training on historical supply and demand data and related environmental characteristics, the nonlinear time-series distribution law of supply and demand curves in each region is learned.

[0082] Specifically, the Latent Neural Process (LNP) is used as the modeling framework, which has the ability to handle uncertain inputs and output distribution predictions. The inputs include the current environmental temperature and humidity, load requests, road traffic flow, and historical supply and demand fluctuations. The output is a family of hydrogen supply and demand function curves in the current region, including confidence bands, rates of change, and periodic features. By sampling and training the normalized historical data multiple times, the model can generalize to input perturbations in different scenarios. Dual-channel convolution (time channel + regional channel) is used to jointly encode the input data, improving the modeling ability for long-term dependencies and cross-regional relationships.

[0083] The kernel function parameters and prior distribution of the neural stochastic process are dynamically adjusted using the maximum a posteriori estimation method, and the stochastic differential equations are structurally coupled with the neural stochastic process model to form a hydrogen energy supply and demand prediction model.

[0084] Specifically, the system periodically analyzes the latest operational and supply-demand data, dynamically and adaptively adjusting key parameters such as the kernel function parameters and noise intensity of the stochastic differential equations in the neural stochastic process model. Each region can independently optimize parameters based on historical errors and the latest trends to ensure prediction accuracy. When there are significant changes in the external environment or link status (such as a surge in demand or equipment fluctuations), the model automatically triggers a rapid tuning process, adjusting the neural network weights and prior distribution to maintain model adaptability. Combining historical data and the latest observations, the system automatically uses methods such as maximum a posteriori estimation to optimize model parameters in real time, recording all adjustment traces for traceability.

[0085] An efficient interface is established between the stochastic differential equation sub-model and the neural stochastic process model, allowing state variables (such as production volume, demand changes, and storage pressure) and their noise characteristics to be synchronized in real time between the sub-models. The neural stochastic process framework uses the dynamic state of the solution space of the stochastic differential equation as prior input to achieve model co-evolution.

[0086] Each time the model is periodically updated, the system first performs preliminary corrections on the parameters of the stochastic differential equation and the kernel function parameters of the neural process using historical data, and then enters the joint training phase. During the joint training phase, the model automatically calculates the overall prediction error and component contribution, and optimizes the weight allocation of the two parts of the model according to the gradient of the global loss function, achieving a deep integration of the advantages of "physical constraints + data-driven".

[0087] If extreme abnormal events occur during actual operation (such as equipment failures or extreme market fluctuations), the system can automatically identify and temporarily adjust the model structure (such as adding higher-order disturbance terms or activating the emergency response network). The structural coupling module has a self-diagnostic function, which can automatically analyze the synergistic effect of sub-models. If it finds that the predictive ability of a certain sub-model has declined, it will temporarily enhance the dominant role of another model to ensure the overall prediction stability and robustness.

[0088] The entire process of dynamic parameter adjustment and structural coupling is automatically executed by the backend engine without manual intervention. The system automatically generates visual reports for all parameter adjustment processes, model structure evolution processes, and collaborative prediction results, allowing scheduling and operations personnel to track and make decisions at any time. The operating system automatically records key parameter adjustments and significant changes to the model structure, meeting compliance and security management requirements.

[0089] Based on the hydrogen energy supply and demand prediction model, the multidimensional operating data is input, and a numerical solver is used to extrapolate the future trajectory of supply and demand distribution, outputting the supply and demand prediction results.

[0090] Specifically, the latest full-link multidimensional operational data within a single day is used as the prediction benchmark; and the differential equation is iteratively derived in multiple steps using a numerical integrator (such as the improved Euler-Maruyama method).

[0091] Confidence interval output: Outputs upper and lower confidence boundaries for each prediction time point; if the confidence interval is too wide or shows significant fluctuations, the system identifies it as a potential unstable point;

[0092] Supply and demand gap calculation: Identify gap sections where hydrogen supply is less than demand in real time and mark the size of the gap; transmit the gap sections as input to the subsequent "hydrogen production, storage and transportation matching module" for peak shaving;

[0093] High-risk area identification: If the forecast shows that there is a continuous shortage in the future period and it cannot be quickly compensated by storage and transportation, the system will automatically mark it as a high-risk area; the high-risk area will be linked to the "safety feedback module" to indicate possible supply and demand imbalance or abnormal scheduling pressure.

[0094] Furthermore, the formula for the hydrogen energy supply and demand forecasting model is as follows:

[0095]

[0096] in, The hydrogen supply and demand state function represents region a. The first derivative with respect to time T represents the direction and intensity of the change in the hydrogen supply and demand balance trend within the region. When this value is positive, it indicates an increase in supply or a decrease in demand, i.e., an expansion of the hydrogen surplus. When this value is negative, it indicates a decrease in supply or an increase in demand, i.e., an intensification of hydrogen shortage. When this value is close to zero, the system is in a relatively balanced state, suitable for scheduling lockout or energy-saving operation.

[0097] The hydrogen supply and demand state function for region a at time T is used to measure the difference between the total supply and demand of hydrogen per unit time in the region, with the unit being kilograms per hour (kg / h); T represents the time step. This indicates that region b at a historical moment Multidimensional operational data; This represents a nonlinear feature mapping function constructed based on a neural network; The time-sensitive kernel function represents the weighted integral; This represents the nonlinear coupling response function of region b to region a, reflecting the mutual influence between supply and demand states among multiple regions. It is particularly suitable for handling scenarios involving distributed hydrogen refueling stations, cross-regional transportation, and asynchronous scheduling. Static weighting coefficients representing the strength of coupling between regions; This represents the system disturbance term for region a at time t, used to model unpredictable fluctuation factors, such as sudden equipment failure, external environmental interference, or sampling anomalies. The term represents the second derivative of the supply and demand status of region a over time. This term is used to simulate the inertial characteristics of the physical processes within the link, such as the gas release response delay of the hydrogen storage tank, the dynamic lag caused by long-distance transportation, or the system adjustment inertia after sudden changes in terminal load; N represents the total number of regions participating in the joint modeling or the number of hydrogen energy link nodes. The hydrogen supply and demand state function represents region a. The second derivative with respect to time T is the acceleration of the change in supply and demand. The inertia coefficient represents the dynamic response of region a, and is used to adjust the sensitivity of the system to changes in the acceleration of supply and demand.

[0098] The calculation formula is as follows:

[0099]

[0100] in, This represents the time-sensitive kernel function of the weighted integral, i.e., the relationship between region b and region a at historical moments. Time decay weight; This represents the time sensitivity coefficient, used to control the window width for information retention, and can be estimated based on regional transportation distance and hydrogen storage response delay.

[0101] The calculation formula is as follows:

[0102]

[0103] in, The static weighting coefficient represents the strength of coupling between regions, i.e., the static influence of region b on the supply and demand changes in region a. This represents the average hydrogen flow rate transported from region b to region a per unit of historical time. This represents the average hydrogen flow rate transported from region c to region a per unit time in history. This represents the hydrogen quality consistency factor, calculated by combining hydrogen purity and temperature.

[0104] The hydrogen production, storage and transportation matching module is used to extract link operation constraints based on the supply and demand forecast results and the multi-dimensional operation data, and generate an initial scheduling scheme by adopting a reinforcement learning-driven multi-objective joint optimization strategy.

[0105] The operation of the hydrogen production, storage, and transportation matching module includes the following steps:

[0106] Based on the multidimensional operational data and supply and demand forecast results, the operational constraints of the link are extracted, including the capacity boundary of the hydrogen production unit, the capacity limit of the hydrogen storage container, the accessibility map of the transportation route, the service time window and priority level of the refueling node;

[0107] Specifically, the system automatically reads the latest multi-dimensional operational data (such as output, inventory, equipment status, and transportation resources) and supply and demand forecast results (regional demand and supply for each time period).

[0108] For each link in the chain, key operational constraints are extracted, including:

[0109] Hydrogen production capacity boundaries: the upper and lower limits of the capacity of each hydrogen production unit, the planned maintenance time, and the real-time availability.

[0110] Hydrogen storage capacity limits: the maximum storage capacity, minimum safe capacity, current inventory, and safety threshold for each hydrogen storage container.

[0111] Transportation route accessibility: Collect data on road conditions, pipeline accessibility, real-time traffic, and transportation resource (vehicles / tankers / pipeline segments) status to form an accessibility map.

[0112] Refueling node constraints: service time window of hydrogen refueling station (such as daily operating hours), single service capacity, priority of each node (such as emergency, key users, etc.).

[0113] By combining the aforementioned link operation constraints, a joint optimization objective is constructed, which includes minimizing hydrogen production energy consumption, minimizing storage and transportation delay, maximizing task completion rate, and avoiding path congestion.

[0114] Specifically, the system organizes the above constraints and, in conjunction with actual business needs, clarifies multiple optimization objectives:

[0115] Minimize hydrogen production energy consumption: Based on energy consumption models and peak-valley electricity prices, priority is given to scheduling low-energy-consumption production periods and equipment.

[0116] Minimize storage and transportation delays: Prioritize storage and transportation routes and nodes that are close to each other, high-speed, and low-congestion, and optimize the timeliness of the overall scheduling link.

[0117] Maximize task completion rate: Ensure that all tasks covered by supply and demand forecasts are promptly allocated to the corresponding link resources to improve matching and fulfillment rates.

[0118] Route congestion avoidance: Dynamically monitor real-time congestion and risk levels of routes, and prioritize avoiding high-risk, congestion-prone, and maintenance-under-maintenance routes.

[0119] The above optimization goals form a quantifiable indicator system to support subsequent intelligent algorithm optimization.

[0120] Based on the joint optimization objective, a reinforcement learning framework integrating a hierarchical state encoder and a multi-policy behavior network is constructed. The dynamic prediction results of supply and demand are used as the state input, and the link resource scheduling cost and supply and demand matching deviation are used as the joint reward signal. The policy network is trained using the policy gradient optimization method, and the optimal solution domain scheduling scheme candidate set is output.

[0121] Specifically, the system automatically summarizes the detailed status of each link in the current chain, including the start-up and shutdown status of hydrogen production equipment, the remaining capacity of storage tanks, the current location and availability of transport vehicles / pipelines, the queue length of refueling stations, the task waiting list, and the predicted supply and demand data for each time period.

[0122] The hierarchical state coding is as follows:

[0123] The first layer focuses on the real-time operating status of a single node (such as a hydrogen production device, a storage tank, or a transport vehicle), encoding its capacity, availability, load, maintenance status, etc.

[0124] The second layer focuses on the link level, including the overall characteristics of coding resource distribution, task queues, congestion and risk distribution, and service time windows.

[0125] After the state information is fused, it is input into the policy network through feature vectors to ensure that scheduling decisions take into account both local and global perspectives.

[0126] For different decision-making dimensions (hydrogen production, storage, transportation, and refueling), independent strategy branches are designed to output specific operational suggestions (such as adjusting production, switching storage tanks, allocating transportation routes, and prioritizing refueling tasks). The network structure supports parallel simulation of multiple feasible paths, enhancing adaptability to complex scenarios. The input is an encoded high-dimensional state vector, and the output is a set of executable resource scheduling actions and their probability distributions, which are sampled and selected by the system.

[0127] A multi-objective reward system is established: reduced energy consumption / costs bring positive rewards, while overproduction / overtime / high energy consumption incur penalties; unfinished tasks, unmet demands, and delays in resource allocation also trigger negative rewards; successfully avoiding congestion and rationally allocating resources provide additional positive incentives. Reward signals are calculated based on the actual effect of each scheduling action and subsequent feedback to guide strategy adjustments.

[0128] The system continuously simulates various scheduling scenarios based on the current real-world environment, collecting rewards and feedback under different scheduling actions, and optimizing policy network parameters using methods such as policy gradient. During actual operation, the system can combine offline training with online fine-tuning to continuously improve the adaptability and global optimality of the policy network. In special scenarios (such as equipment failure or urgent scheduling needs), it supports real-time online policy adjustments to quickly respond to changes.

[0129] The reinforcement learning policy network can output multiple scheduling schemes per round, covering different trade-offs (such as prioritizing energy consumption, timeliness, and task completion rate), and all schemes satisfy hard constraints. Each scheme is given a multi-objective performance score and labeled to facilitate subsequent Pareto selection and practical application.

[0130] The Pareto optimal solution under the current constraints is selected from the candidate scheduling schemes to generate a structured initial scheduling scheme. The initial scheduling scheme includes the start-up and shutdown sequence of hydrogen production nodes, allocation of hydrogen storage containers, configuration of transportation routes, and priority ranking of refueling tasks.

[0131] Specifically, the system performs multi-objective comparisons on candidate solutions and automatically selects non-dominated solutions (i.e., Pareto optimal solutions) that are not "completely dominated" by other solutions under all optimization objectives, thus forming the optimal solution domain.

[0132] Based on current operational constraints and future demand changes, an automatic structured initial scheduling scheme is generated, including:

[0133] Hydrogen production node start-up and shutdown sequence: Determine the start-up and shutdown times of each hydrogen production unit to ensure a balance between production and demand and optimal energy consumption.

[0134] Hydrogen storage container allocation: Rationally allocate the hydrogen loading and unloading tasks for each storage tank to avoid the risks of over-storage or under-storage.

[0135] Transportation route configuration: Assign specific vehicles, routes, start and end times to each transportation batch or task to avoid congestion and high-risk road sections.

[0136] Refueling task priority ranking: The refueling tasks of each hydrogen refueling station or end user are prioritized, and resources are allocated to key users or urgent tasks first.

[0137] The hydrogen energy link scheduling optimization module is used to construct a hydrogen energy link scheduling model based on the initial scheduling scheme and a graph structure path planning method based on artificial potential field algorithm. It performs dynamic avoidance calculations on bottleneck nodes, congested sections and high-risk areas in the transportation path, and generates the optimal scheduling instruction set for the entire link by combining scheduling timing and resource cost constraints.

[0138] The operation of the hydrogen energy link scheduling optimization module includes the following steps:

[0139] Based on the initial scheduling scheme and combined with the multi-dimensional operation data, a graph structure path planning method based on artificial potential field algorithm is used to construct a hydrogen energy link scheduling model. The hydrogen energy link scheduling model takes hydrogen energy link nodes as vertices and transportation and transfer paths as edges. Node attributes include resource capacity, task queue and safety status, and edge attributes include path reachability, time delay, transportation risk level and historical congestion records.

[0140] Specifically, the system reads the initial scheduling scheme and the latest multi-dimensional operational data, and automatically generates a global directed graph model of the hydrogen energy link:

[0141] Nodes represent hydrogen production plants, hydrogen storage stations, transportation hubs, refueling stations, end users, etc. Each node's attributes include real-time resource capacity (such as maximum / minimum available capacity), current task queue, and historical and current safety status (such as equipment health and incident frequency).

[0142] Edges: Represent hydrogen transportation and transshipment routes, such as pipelines, roads, and tracks. Each edge's attributes include path reachability (whether it is unobstructed), estimated delay, transportation risk level (such as natural disasters or traffic congestion), and historical congestion records.

[0143] Real-time synchronization of status information for each node and path, integration of historical operational data and current monitoring data, ensures that the model reflects the true dynamic status of the entire link.

[0144] Based on the hydrogen energy link scheduling model, an attraction potential and a repulsion potential are applied to each transportation path to identify and mark potential bottleneck sections and high-risk nodes in the path in real time. The attraction potential is used to guide the scheduling object to move closer to the target node, and the repulsion potential is used to dynamically avoid bottleneck nodes, congested sections and high-risk areas.

[0145] Specifically, the attraction potential allocation system automatically sets the attraction potential for all target nodes (such as terminal demand points and key refueling stations). The attraction potential can be dynamically adjusted based on the urgency, demand, and priority of the task at each node. For example, emergency medical hydrogen points can be assigned a stronger attraction potential to ensure priority supply. The attraction potential parameters of target nodes can be updated in real time as scheduling targets change, and the attraction potential of a node can be temporarily increased in the event of a sudden event.

[0146] Repulsion potential allocation: For detected bottleneck nodes (such as a storage tank nearing full load, a transportation channel congested, or equipment frequently alarming), the system dynamically assigns a high repulsion potential, prompting the route planning process to avoid these risky areas. The repulsion potential parameter is calculated based on factors such as the node / route's real-time capacity, historical faults, current failure rate, and accident probability, and is automatically adjusted according to monitoring results.

[0147] The system continuously collects real-time operational data for each node / path, such as remaining capacity, queue length, task backlog, and equipment status. When a node reaches or approaches a set threshold (e.g., capacity utilization exceeds 90%, equipment health is below the safety line, or failure frequency is higher than the historical average), it is automatically marked as a bottleneck node and its repulsion potential is updated. Historical data mining supports the early prediction of potential bottlenecks, such as using time series analysis to predict future potential backlogs in transportation routes.

[0148] By combining multi-dimensional data (such as meteorology, traffic, security, and equipment early warning), certain areas are automatically identified as high-risk zones. If affected by extreme weather, road construction, or environmental anomalies, the repulsion potential of relevant paths and nodes is adjusted upwards in real time. For events involving "known high risk + temporary emergencies," the system automatically pushes risk updates, and the relevant nodes / paths immediately take effect in the model.

[0149] Potential field parameters are refreshed in real time at fixed intervals (e.g., 5 minutes) or through event-driven methods to ensure that the model reflects the latest changes in the link status and achieve dynamic security control.

[0150] The graph-structured path search method is adopted to dynamically and iteratively adjust the transportation path of each scheduling task under the influence of an artificial potential field, optimize the scheduling sequence, realize the active avoidance of bottleneck sections and high-risk nodes, and generate the optimal scheduling scheme for the entire link based on the actual link resource status and scheduling task requirements.

[0151] Specifically, in the graph structure model, the system utilizes an artificial potential field algorithm to perform path search for each scheduling task. The task's starting point actively seeks the target node based on the attractive potential, automatically avoiding paths and nodes with high repulsive potentials during the process. Dynamic multi-path parallel search is supported, generating multiple alternative paths for the same task and evaluating their timeliness, resource consumption, and risk level in real time.

[0152] Upon receiving new real-time operational data (such as sudden congestion, equipment malfunctions, or changes in task status), the system can immediately trigger dynamic adjustments to local routes. If the primary route is deemed high-risk or unreachable, the system automatically selects a suboptimal or backup route, adjusting the departure time, route, or even changing transportation methods for the scheduled tasks. High-priority tasks can occupy the best resources and routes first, while low-priority tasks are automatically queued or their dispatch plans are adjusted.

[0153] The path search results are integrated with the end-to-end resource pool in real time to ensure that every scheduling decision does not result in over-allocation or local resource depletion. An optimal time window and resource allocation table are automatically generated for each task, reasonably avoiding peak periods, equipment maintenance periods, or other already occupied resources. By comprehensively evaluating global timeliness, transportation and storage resource utilization, and risk distribution, the system automatically generates an end-to-end scheduling scheme that satisfies all constraints and achieves optimal performance. The system possesses continuous self-learning capabilities, continuously adjusting path search preferences and resource allocation logic based on historical execution feedback to improve scheduling robustness and response speed.

[0154] The optimal scheduling scheme is encoded into an end-to-end optimal scheduling instruction set, which includes a task execution schedule, a mapping diagram of transportation and storage resource allocation, path adjustment and switching strategies, and priority ranking of key node monitoring.

[0155] Specifically, the optimized scheduling scheme is automatically transcoded into a structured set of scheduling instructions, which facilitates direct execution by downstream modules and field equipment.

[0156] The instruction set includes:

[0157] Task execution schedule: Clearly define the planned start / end times for each scheduled task.

[0158] Transportation and storage resource allocation map: details of the allocation of resources such as vehicles, pipelines, and storage tanks used for each task.

[0159] Route adjustment and switching strategies: Alternative routes and emergency switching procedures in the event of sudden congestion or changes in risk.

[0160] Prioritize monitoring of critical nodes: Dynamically generate a list of nodes and paths that require key monitoring to improve security and operational response efficiency.

[0161] The instruction set is automatically pushed to the execution system and monitoring center, supporting real-time monitoring of scheduling execution effects, anomaly feedback, and secondary optimization.

[0162] Furthermore, the formula for the hydrogen energy link scheduling model is as follows:

[0163]

[0164]

[0165]

[0166] in, This represents the scheduling guidance function value of the system from node i to node j at time t. It is used to measure whether to prioritize transmitting tasks through this path. The larger the value, the more suitable the path is as a scheduling channel for hydrogen energy tasks. This function will be directly used for path selection and task routing priority ranking in the graph structure, and is the input basis for generating the optimal scheduling instruction set for the entire link. This represents the spatial gradient operator for the artificial potential field in the path network; This represents the attraction potential function, used to measure the sufficiency of node resources and the degree of relay cooperation; This represents the repulsive potential function, used to measure path risk (such as equipment failure, environmental disturbance) and historical congestion; Indicates the disturbance term on the path; This represents the current task load intensity of node j at time t; This represents the maximum resource service capacity of node j; This represents the path scheduling coupling strength from source node i through relay node k to target node j; This represents the current remaining available resources ratio of relay node k; M represents the total number of relay nodes in the current path that can participate in coordinated scheduling. This indicates the path risk level from node i to node j; This represents the historical congestion level from node i to node j; and The weighting coefficients of the attraction potential term are represented. This represents the weighting coefficient of the repulsive potential term.

[0167] The calculation formula is as follows:

[0168]

[0169] in,; This represents the path scheduling coupling strength from source node i through relay node k to target node j; and This represents the path distance from i to k and from k to j; This indicates the current length of the task waiting queue for relay node k.

[0170] The security feedback module is used to collect real-time monitoring data based on the full-link optimal scheduling instruction set, use fuzzy logic control rules to perform security assessment and intervention decisions on link operation, identify abnormal states and trigger closed-loop control feedback, and output security intervention instructions, including scheduling correction suggestions, risk warning information and security control parameter adjustment instructions.

[0171] It should be noted that the fuzzy logic control rules are specifically combined with typical risk scenarios at each node of the entire hydrogen energy chain (such as overpressure, leakage, overload, hysteresis, prolonged high temperature, valve abnormality, etc.) to establish a multi-level fuzzy logic rule base:

[0172] Input variables: pressure, temperature, flow rate, hydrogen concentration, transportation delay, equipment failure rate, alarm signals, etc.

[0173] Membership functions are adaptively adjusted and automatically optimized based on historical accident data;

[0174] Example of a rule:

[0175] If the tank pressure is high, the temperature rises rapidly, and the hydrogen leak signal is true, then the safety risk is extremely high.

[0176] If the "injection flow rate is too high" and the "terminal load fluctuates", then "speed limiting adjustment is required".

[0177] Real-time fuzzy reasoning is performed on the status of each node and the entire link, and the output is: security level classification (safe, attention, warning, emergency); risk cause identification (such as equipment aging, environmental degradation, scheduling conflict, etc.); and decision threshold for triggering intervention mechanisms.

[0178] By combining fuzzy inference output and time-series data, the following anomaly types can be automatically identified: storage and transportation overpressure, leakage, overtemperature, and explosion criticality; transportation delays or route deviations; equipment health deterioration (based on vibration spectrum, abnormal energy consumption, etc.); and data islands / dead zones (signal loss).

[0179] Once the risk level reaches the preset threshold, the module will automatically output the following safety intervention instructions:

[0180] Recommended adjustments to the scheduling process: adjust transportation routes (e.g., switch to alternative routes), optimize scheduling timing (e.g., delay / advance refueling), and change the order of hydrogen storage tanks.

[0181] Risk warning information: Detailed warning information will be pushed via SMS / APP / platform dashboard, indicating the risk points, causes and recommended measures;

[0182] Safety control parameter adjustment: Automatically issue pressure / flow limits, start / stop commands, or activate emergency plans (such as emergency shutdown, evacuation, isolation, ventilation, etc.).

[0183] Example 2

[0184] A big data-based hydrogen energy end-to-end scheduling method includes the following steps:

[0185] Real-time collection of multi-dimensional operational data across the entire hydrogen energy chain through a distributed sensor network;

[0186] Based on the multidimensional operational data, a hydrogen energy supply and demand prediction model constructed using stochastic differential equations and neural stochastic processes is used to dynamically evolve and predict the spatiotemporal dynamic changes of hydrogen supply and demand, and output the supply and demand prediction results.

[0187] Based on the supply and demand forecast results and the multi-dimensional operation data, the link operation constraints are extracted, and an initial scheduling scheme is generated by adopting a reinforcement learning-driven multi-objective joint optimization strategy.

[0188] Based on the initial scheduling scheme, a graph structure path planning method based on artificial potential field algorithm is used to construct a hydrogen energy link scheduling model. Dynamic avoidance calculations are performed on bottleneck nodes, congested sections and high-risk areas in the transportation path. Combined with scheduling timing and resource cost constraints, the optimal scheduling instruction set for the entire link is generated.

[0189] Based on the aforementioned end-to-end optimal scheduling instruction set, real-time monitoring data is collected, fuzzy logic control rules are used to conduct security assessments and intervention decisions on link operation, abnormal states are identified and closed-loop control feedback is triggered, and security intervention instructions are output.

[0190] In this embodiment, a big data-based hydrogen energy end-to-end scheduling method is applied to the big data-based hydrogen energy end-to-end scheduling system described in Embodiment 1, which will not be repeated here.

[0191] In summary, this invention constructs a multi-dimensional operational data acquisition system with a distributed sensor network at its core, covering key indicators such as hydrogen production, purity, pressure, temperature, transportation path, terminal load, environmental parameters, and equipment status. By combining edge computing and 5G communication, it achieves end-to-end, low-latency data aggregation, providing high-quality support for prediction and decision-making.

[0192] This invention introduces a supply and demand forecasting module that employs joint modeling of stochastic differential equations and neural stochastic processes to accurately capture the dynamic coupling behavior and uncertain disturbances in the supply and demand system. It outputs supply and demand trends, gap predictions, and confidence intervals, achieving highly forward-looking control over the operational status of the hydrogen energy chain. The model possesses adaptive structural adjustment and parameter calibration capabilities, maintaining predictive stability even under extreme events. The hydrogen production, storage, and transportation matching module utilizes a reinforcement learning optimization strategy. Based on chain constraints and prediction results, it constructs a multi-objective joint optimization model, outputting an initial scheduling scheme covering hydrogen production start-up and shutdown, tank allocation, transportation routes, and refueling sequencing. The reinforcement learning network achieves a multi-dimensional trade-off between timeliness, energy consumption, completion rate, and risk, enhancing the system's intelligent decision-making capabilities and global optimality.

[0193] In this invention, the scheduling optimization module constructs a graph-structured path planning model using an artificial potential field algorithm. It integrates attractive and repulsive potential mechanisms to dynamically avoid bottleneck nodes and high-risk areas. Combining resource capacity and historical status, it generates an optimal scheduling instruction set for the entire link, improving the timeliness, flexibility, and security of path planning. The security feedback module, based on fuzzy logic control rules, integrates real-time data to classify and identify risks in the link's security status. It can automatically output scheduling correction suggestions, risk warnings, and parameter intervention instructions, constructing a closed-loop security response mechanism that effectively ensures the system's stable operation and risk prevention capabilities.

[0194] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A hydrogen energy end-to-end scheduling system based on big data, characterized in that, It includes a hydrogen energy link data acquisition module, a hydrogen energy supply and demand prediction module, a hydrogen production, storage and transportation matching module, a hydrogen energy link scheduling optimization module and a safety feedback module, which are connected in sequence via communication. The hydrogen energy link data acquisition module is used to collect multi-dimensional operational data of the entire hydrogen energy link in real time through a distributed sensor network; the entire link includes hydrogen production, compression, storage, transportation, refueling and end-use. The hydrogen energy supply and demand prediction module is used to dynamically evolve and predict the spatiotemporal dynamic changes of hydrogen supply and demand based on the multidimensional operating data and a hydrogen energy supply and demand prediction model constructed using stochastic differential equations and neural stochastic processes, and output the supply and demand prediction results. The hydrogen production, storage and transportation matching module is used to extract link operation constraints based on the supply and demand forecast results and the multi-dimensional operation data, and generate an initial scheduling scheme by adopting a reinforcement learning-driven multi-objective joint optimization strategy. The hydrogen energy link scheduling optimization module is used to construct a hydrogen energy link scheduling model based on the initial scheduling scheme and a graph structure path planning method based on artificial potential field algorithm. It performs dynamic avoidance calculations on bottleneck nodes, congested sections and high-risk areas in the transportation path, and generates the optimal scheduling instruction set for the entire link by combining scheduling timing and resource cost constraints. The security feedback module is used to collect real-time monitoring data based on the full-link optimal scheduling instruction set, use fuzzy logic control rules to conduct security assessment and intervention decisions on link operation, identify abnormal states and trigger closed-loop control feedback, and output security intervention instructions, including scheduling correction suggestions, risk warning information and security control parameter adjustment instructions. The operation of the hydrogen energy supply and demand forecasting module includes the following steps: The multidimensional operational data is preprocessed, including aggregation, anomaly removal, missing data completion, and spatiotemporal feature normalization, and reconstructed into a structured, temporally continuous input feature matrix. Based on the input feature matrix, a hydrogen energy supply and demand prediction model is constructed by jointly modeling stochastic differential equations and neural stochastic processes to characterize the dynamic evolution of hydrogen supply and demand. The input feature matrix is ​​input into the hydrogen energy supply and demand prediction model. Numerical integration and neural network-driven stochastic process deduction are used to dynamically simulate and predict the spatiotemporal dynamic changes of hydrogen supply and demand in various regions. The supply and demand prediction results are output, including hydrogen supply and demand curves, corresponding confidence intervals, future supply and demand trends in various regions at different times, supply and demand gaps, prediction confidence intervals, and potential risk areas.

2. The hydrogen energy end-to-end scheduling system based on big data according to claim 1, characterized in that, The multidimensional operational data includes hydrogen production, purity, pressure, temperature, remaining tank capacity, transportation route status, filling flow rate, terminal load demand, environmental parameters, and equipment operating status.

3. The hydrogen energy end-to-end scheduling system based on big data according to claim 1, characterized in that, The process of constructing the hydrogen energy supply and demand forecasting model includes the following steps: Based on the multidimensional operational data, a stochastic differential equation containing state variable coupling terms and noise driving terms is constructed to simulate the stochastic disturbance characteristics and dynamic coupling behavior in the hydrogen supply and demand process. A neural stochastic process framework is introduced, with the solution space of the stochastic differential equation as the prior structure. Through joint training on historical supply and demand data and related environmental characteristics, the nonlinear time-series distribution law of supply and demand curves in each region is learned. The kernel function parameters and prior distribution of the neural stochastic process are dynamically adjusted using the maximum a posteriori estimation method, and the stochastic differential equations are structurally coupled with the neural stochastic process model to form a hydrogen energy supply and demand prediction model. Based on the hydrogen energy supply and demand prediction model, the multidimensional operating data is input, and a numerical solver is used to extrapolate the future trajectory of supply and demand distribution, outputting the supply and demand prediction results.

4. The hydrogen energy end-to-end scheduling system based on big data according to claim 1, characterized in that, The formula for the hydrogen energy supply and demand forecasting model is as follows: in, The hydrogen supply and demand state function represents region a. The first derivative with respect to time T, i.e. the direction and intensity of the change in the hydrogen energy supply and demand balance trend in the region; This represents the hydrogen supply and demand state function for region a at time T; T represents the time step. This indicates that region b at a historical moment Multidimensional operational data; This represents a nonlinear feature mapping function constructed based on a neural network; The time-sensitive kernel function represents the weighted integral; This represents the nonlinear coupling response function of region b to region a; Static weighting coefficients representing the strength of coupling between regions; This represents the system disturbance term in region a at time t; The term represents the second derivative of the supply and demand status of region a over time; N represents the total number of regions participating in the joint modeling or the number of hydrogen energy link nodes. The hydrogen supply and demand state function represents region a. The second derivative with respect to time T is the acceleration of the change in supply and demand. This represents the dynamic response inertia coefficient of region a.

5. A hydrogen energy end-to-end scheduling system based on big data according to claim 1, characterized in that, The operation of the hydrogen production, storage, and transportation matching module includes the following steps: Based on the multidimensional operational data and supply and demand forecast results, the operational constraints of the link are extracted, including the capacity boundary of the hydrogen production unit, the capacity limit of the hydrogen storage container, the accessibility map of the transportation route, the service time window and priority level of the refueling node; By combining the aforementioned link operation constraints, a joint optimization objective is constructed, which includes minimizing hydrogen production energy consumption, minimizing storage and transportation delay, maximizing task completion rate, and avoiding path congestion. Based on the joint optimization objective, a reinforcement learning framework integrating a hierarchical state encoder and a multi-policy behavior network is constructed. The dynamic prediction results of supply and demand are used as the state input, and the link resource scheduling cost and supply and demand matching deviation are used as the joint reward signal. The policy network is trained using the policy gradient optimization method, and the optimal solution domain scheduling scheme candidate set is output. The Pareto optimal solution under the current constraints is selected from the candidate scheduling schemes to generate a structured initial scheduling scheme. The initial scheduling scheme includes the start-up and shutdown sequence of hydrogen production nodes, allocation of hydrogen storage containers, configuration of transportation routes, and priority ranking of refueling tasks.

6. A hydrogen energy end-to-end scheduling system based on big data according to claim 1, characterized in that, The operation of the hydrogen energy link scheduling optimization module includes the following steps: Based on the initial scheduling scheme and combined with the multi-dimensional operation data, a graph structure path planning method based on artificial potential field algorithm is used to construct a hydrogen energy link scheduling model. The hydrogen energy link scheduling model takes hydrogen energy link nodes as vertices and transportation and transfer paths as edges. Node attributes include resource capacity, task queue and safety status, and edge attributes include path reachability, time delay, transportation risk level and historical congestion records. Based on the hydrogen energy link scheduling model, an attraction potential and a repulsion potential are applied to each transportation path to identify and mark potential bottleneck sections and high-risk nodes in the path in real time. The attraction potential is used to guide the scheduling object to move closer to the target node, and the repulsion potential is used to dynamically avoid bottleneck nodes, congested sections and high-risk areas. The graph-structured path search method is adopted to dynamically and iteratively adjust the transportation path of each scheduling task under the influence of an artificial potential field, optimize the scheduling sequence, realize the active avoidance of bottleneck sections and high-risk nodes, and generate the optimal scheduling scheme for the entire link based on the actual link resource status and scheduling task requirements. The optimal scheduling scheme is encoded into an end-to-end optimal scheduling instruction set, which includes a task execution schedule, a mapping diagram of transportation and storage resource allocation, path adjustment and switching strategies, and priority ranking of key node monitoring.

7. A hydrogen energy end-to-end scheduling system based on big data according to claim 6, characterized in that, The formula for the hydrogen energy link scheduling model is as follows: in, This represents the value of the system's scheduling guidance function for the route from node i to node j at time t; This represents the spatial gradient operator for the artificial potential field in the path network; Represents the attraction potential function; This represents the repulsive potential function; Indicates the disturbance term on the path; This represents the current task load intensity of node j at time t; This represents the maximum resource service capacity of node j; This represents the path scheduling coupling strength from source node i through relay node k to target node j; This represents the current remaining available resources ratio of relay node k; M represents the total number of relay nodes in the current path that can participate in coordinated scheduling. This indicates the path risk level from node i to node j; This represents the historical congestion level from node i to node j; and The weighting coefficients of the attraction potential term are represented. This represents the weighting coefficient of the repulsive potential term.

8. A hydrogen energy end-to-end scheduling method based on big data, characterized in that, The method, applied to a big data-based hydrogen energy end-to-end scheduling system as described in any one of claims 1-7, includes the following steps: Real-time collection of multi-dimensional operational data across the entire hydrogen energy chain through a distributed sensor network; Based on the multidimensional operational data, a hydrogen energy supply and demand prediction model constructed using stochastic differential equations and neural stochastic processes is used to dynamically evolve and predict the spatiotemporal dynamic changes of hydrogen supply and demand, and output the supply and demand prediction results. Based on the supply and demand forecast results and the multi-dimensional operation data, the link operation constraints are extracted, and an initial scheduling scheme is generated by adopting a reinforcement learning-driven multi-objective joint optimization strategy. Based on the initial scheduling scheme, a graph structure path planning method based on artificial potential field algorithm is used to construct a hydrogen energy link scheduling model. Dynamic avoidance calculations are performed on bottleneck nodes, congested sections and high-risk areas in the transportation path. Combined with scheduling timing and resource cost constraints, the optimal scheduling instruction set for the entire link is generated. Based on the full-link optimal scheduling instruction set, real-time monitoring data is collected, fuzzy logic control rules are used to conduct security assessment and intervention decisions on link operation, abnormal states are identified and closed-loop control feedback is triggered, and security intervention instructions are output. The hydrogen energy supply and demand forecasting model, constructed using stochastic differential equations and neural stochastic processes based on the multidimensional operational data, dynamically evolves and predicts the spatiotemporal dynamic changes in hydrogen supply and demand, outputting supply and demand forecasting results, including: The multidimensional operational data is preprocessed, including aggregation, anomaly removal, missing data completion, and spatiotemporal feature normalization, and reconstructed into a structured, temporally continuous input feature matrix. Based on the input feature matrix, a hydrogen energy supply and demand prediction model is constructed by jointly modeling stochastic differential equations and neural stochastic processes to characterize the dynamic evolution of hydrogen supply and demand. The input feature matrix is ​​input into the hydrogen energy supply and demand prediction model. Numerical integration and neural network-driven stochastic process deduction are used to dynamically simulate and predict the spatiotemporal dynamic changes of hydrogen supply and demand in various regions. The supply and demand prediction results are output, including hydrogen supply and demand curves, corresponding confidence intervals, future supply and demand trends in various regions at different times, supply and demand gaps, prediction confidence intervals, and potential risk areas.

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