Gravity energy storage multi-rail transportation scheduling system

Abstract

The invention provides a gravity energy storage multi-rail transportation scheduling system, which relates to the technical field of intelligent control, and comprises an instruction receiving module used for receiving a control signal sent by a master controller in a power valley period and setting a loading vehicle to be in a gravity energy storage mode according to the control signal; the loading vehicle driving module is used for starting a loading vehicle from a starting point of a material receiving rail of the low-position energy storage platform according to the control signal, so that the loading vehicle drives along a preset gravity energy storage transportation line and goes to a material loading position; and the intelligent turnout switching module is used for automatically switching the rails according to the real-time running state and position of the loading vehicle when the loading vehicle runs to the outlet end of the low-position material receiving rail, so that the loading vehicle enters the upper rail. According to the invention, efficient gravity energy storage can be realized in a power valley period.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a gravity energy storage multi-track transportation scheduling system. Background Technology

[0002] Gravity energy storage technology, as a novel energy storage method, stores and releases energy by utilizing the height difference of objects. It has advantages such as high energy density and being environmentally friendly and pollution-free, thus showing broad application prospects in the energy field. However, existing gravity energy storage systems suffer from problems such as low transportation efficiency, high energy consumption, and low automation during material transportation and energy storage.

[0003] For example, material transportation may rely mainly on manual operation and simple mechanized equipment, resulting in slow transportation speed. Due to the lack of an intelligent scheduling system, loading vehicles often experience waiting and empty runs during transportation, further reducing transportation efficiency. Summary of the Invention

[0004] The technical problem this invention aims to solve is to provide a gravity energy storage multi-track transportation scheduling system capable of efficient gravity energy storage during periods of low electricity demand. The loading vehicle can automatically travel along a pre-set transportation route according to the instructions of the central controller, completing the loading, transportation, and unloading of materials, greatly improving transportation efficiency.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Gravity energy storage multi-rail transportation scheduling system, including:

[0007] The receiving instruction module is used to receive control signals sent by the main controller during periods of low power, and to set the loading vehicle to gravity energy storage mode according to the control signals.

[0008] The loading vehicle driving module is used to start the loading vehicle from the receiving rail starting point of the low-level energy storage platform according to the control signal, so that the loading vehicle can travel along the pre-set gravity energy storage transportation line to the location where the material is loaded.

[0009] The intelligent turnout switching module is used to automatically switch tracks based on the real-time driving status and position of the loader when it travels to the exit end of the low-level receiving rail, so that the loader can enter the upward rail.

[0010] The loading vehicle uphill transport module is used to enable the loading vehicle to start climbing the slope under the action of the drive station after entering the uphill rail; the climbing speed and timing are dynamically adjusted according to the priority of the loading vehicle and the track occupancy; if multiple uphill rails are available, the corresponding uphill rail is allocated to the loading vehicle according to real-time data to balance the load of each rail.

[0011] The unloading module is used to allocate a corresponding return path to the loading vehicle based on the current usage of each return track after the loading vehicle reaches the designated position of the high-level energy storage bin and completes unloading; during the return process of the loading vehicle, real-time path planning and scheduling are performed to prevent congestion.

[0012] The standby state module is used to add a loading vehicle to the pool of available loading vehicles after it returns to the starting point and enters the standby state, so as to realize the rescheduling of tasks. During rescheduling, resources are allocated according to the status and location of each loading vehicle, as well as the urgency and priority of the current task.

[0013] Furthermore, it receives control signals from the main controller during periods of low power demand and sets the loader to gravity energy storage mode based on these signals, including:

[0014] Initialize the particle position and velocity. The position of each particle represents a set of control signal parameters, and the velocity determines the particle's direction of movement and step size in the search space.

[0015] An evaluation function is determined to assess the quality of the control signal parameters represented by each particle. The evaluation function represents a comprehensive index of the operating efficiency and stability of the loading vehicle in gravity energy storage mode, and the evaluation function value is calculated.

[0016] Update the particle position and velocity until a preset number of iterations is reached to obtain the final position, and use the control signal parameters corresponding to the final position as the final control signal of the loading vehicle;

[0017] Based on the final control signal, the loading vehicle is set to gravity energy storage mode.

[0018] Furthermore, an evaluation function is determined to assess the quality of the control signal parameters represented by each particle, and the evaluation function value is calculated, including:

[0019] The first sub-item is generated based on the reciprocal of the total power consumption of the loading vehicle during the task time period, where the total power consumption is the integral of the power consumption from the start time to the end time of the task.

[0020] The second sub-item is generated based on the reciprocal of the total task duration, where the total task duration is the difference between the end time and the start time.

[0021] The third sub-item is generated based on the reciprocal of the number of times the loader deviates from the predetermined path, the reciprocal of the standard deviation of the loader speed, and the negative value of the ratio of the number of rapid acceleration or deceleration events to the total number of operations.

[0022] The first sub-item, the second sub-item, and the third sub-item are merged to obtain the evaluation function value.

[0023] Furthermore, when the loader reaches the exit end of the low-position receiving rail, the rail is automatically switched based on the loader's real-time driving status and position, so that the loader can enter the upward rail, including:

[0024] The current position of the loader is obtained in real time through the position sensor, the real-time speed of the loader is obtained through the speed sensor, and the driving direction of the loader is obtained through the direction sensor.

[0025] When the loader travels to the set distance threshold, the track switching is initiated;

[0026] When all conditions are met, a track switching command is sent.

[0027] When the track switching system receives the track switching command, it guides the loading vehicle from the low-position receiving track to the upward track.

[0028] Furthermore, after the loader enters the uphill rail, it begins to climb the slope under the action of the drive station; the climbing speed and timing are dynamically adjusted according to the loader's priority and track occupancy; if multiple uphill rails are available, the corresponding uphill rail is allocated to the loader based on real-time data to balance the load on each rail, including:

[0029] Once the loader successfully enters the uphill rail via the intelligent switch, it sends a confirmation signal to the central control system, indicating that it is ready to climb the slope.

[0030] After receiving the confirmation signal, the central control system queries and evaluates the priority of the loading vehicle to obtain the following information: check the current occupancy status of all upward rails, and the position and speed data of the remaining loading vehicles.

[0031] Based on the priority evaluation results, the central control system sets an initial climbing speed for the loader. If it detects that the track ahead is occupied or there is a potential conflict, it calculates a timing interval and notifies the loader to delay the start or adjust the speed. After receiving the speed and timing instructions, the loader adjusts the corresponding power output through its drive system to achieve climbing.

[0032] If multiple uplink tracks are available, the central control system outputs a corresponding track allocation scheme through a load balancing algorithm.

[0033] According to the track allocation scheme, the central control system sends specific uphill track allocation instructions to the loader. After receiving the instructions, the loader automatically adjusts to the designated uphill track through the intelligent switch switching module. During the loader's climbing process, the central control system continuously monitors its position, speed, and the condition of the track ahead. If any abnormality is detected, it sends adjustment instructions to the loader in real time.

[0034] Furthermore, if multiple uplink tracks are available, the central control system outputs a corresponding track allocation scheme through a load balancing algorithm, including:

[0035] The central control system collects real-time status information of all available uplink rails, including the current occupancy status of each rail, the time of the last use, and the maintenance status; it also updates information on all loading vehicles waiting to be assigned rails, including their corresponding positions, priorities, and target high-level stacks.

[0036] The collected track status information and loading vehicle information are used as input data for the load balancing algorithm. The load balancing algorithm evaluates the suitability of each available uplink track, determines a corresponding track selection for each loading vehicle waiting to be assigned, and outputs a specific track allocation scheme after execution.

[0037] Furthermore, the load balancing algorithm evaluates the suitability of each available uplink rail, including:

[0038] Key metrics for assessing track suitability include the proportion of time the track is idle, the time since the last use, the expected likelihood of conflict, the physical condition of the track, and the usage status of adjacent tracks.

[0039] The real-time data collected for each track is standardized, and a weight is assigned to each evaluation metric.

[0040] For each available uprail, a weighted fitness score is calculated using an algorithm based on the evaluation metrics and corresponding weights.

[0041] The above-described solutions of the present invention include at least the following beneficial effects.

[0042] Through intelligent transportation scheduling and efficient track switching mechanisms, this system can achieve efficient gravity energy storage during off-peak electricity periods. The loader can automatically travel along a pre-set transportation route according to the instructions of the central controller, completing the loading, transportation, and unloading of materials, greatly improving transportation efficiency.

[0043] This system utilizes electricity during off-peak hours to assist the loader in climbing inclines, transporting materials from a lower energy storage bin to a higher one, thus achieving efficient energy utilization. Simultaneously, through gravity energy storage technology, the system can rapidly release stored energy when needed, further reducing energy consumption.

[0044] This system, through the design of intelligent modules such as the instruction receiving module and the intelligent turnout switching module, realizes automatic control of the loading vehicle and automatic track switching, which greatly improves the automation level of the system and reduces the complexity and cost of manual operation. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of a gravity energy storage multi-track transportation scheduling system provided in an embodiment of the present invention.

[0046] Figure 2 This is a schematic flowchart of the gravity energy storage multi-track transportation scheduling method provided in the embodiments of the present invention. Detailed Implementation

[0047] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0048] like Figure 1 As shown, an embodiment of the present invention proposes a gravity energy storage multi-track transportation scheduling system, comprising:

[0049] The receiving instruction module is used to receive control signals sent by the main controller during periods of low power, and to set the loading vehicle to gravity energy storage mode according to the control signals.

[0050] The loading vehicle driving module is used to start the loading vehicle from the receiving rail starting point of the low-level energy storage platform according to the control signal, so that the loading vehicle can travel along the pre-set gravity energy storage transportation line to the location where the material is loaded.

[0051] The intelligent turnout switching module is used to automatically switch tracks based on the real-time driving status and position of the loader when it travels to the exit end of the low-level receiving rail, so that the loader can enter the upward rail.

[0052] The loading vehicle uphill transport module is used to enable the loading vehicle to start climbing the slope under the action of the drive station after entering the uphill rail; the climbing speed and timing are dynamically adjusted according to the priority of the loading vehicle and the track occupancy; if multiple uphill rails are available, the corresponding uphill rail is allocated to the loading vehicle according to real-time data to balance the load of each rail.

[0053] The unloading module is used to allocate a corresponding return path to the loading vehicle based on the current usage of each return track after the loading vehicle reaches the designated position of the high-level energy storage bin and completes unloading; during the return process of the loading vehicle, real-time path planning and scheduling are performed to prevent congestion.

[0054] The standby state module is used to add a loading vehicle to the pool of available loading vehicles after it returns to the starting point and enters the standby state, so as to realize the rescheduling of tasks. During rescheduling, resources are allocated according to the status and location of each loading vehicle, as well as the urgency and priority of the current task.

[0055] In a specific embodiment of the present invention, by receiving control signals from the main controller during periods of low power, the system can intelligently determine when to perform gravity energy storage operation, reducing operating costs; setting the loader to gravity energy storage mode ensures that the loader operates in the most efficient manner during transportation, reducing energy waste; the loader starts from the receiving rail of the low-level energy storage platform, ensuring the accuracy of the start and end points of the transportation process, improving the precision and reliability of transportation; the pre-set gravity energy storage transportation route ensures that the loader reaches the location of the loaded material in the shortest path and in the most efficient way, improving transportation efficiency; the intelligent switch switching module can adjust the loader's position according to the real-time driving status of the loader. The system automatically switches between states and positions, improving automation and accuracy. Automatic track switching ensures the loader smoothly enters the upward track, reducing waiting time and energy waste. The loader utilizes off-peak electricity to assist in climbing, taking full advantage of off-peak electricity prices and reducing operating costs. It transports solid waste from the low-level energy storage bin to the high-level energy storage bin, achieving gravity energy storage. When the loader reaches the designated position in the high-level energy storage bin, the unloading mechanism automatically unloads, reducing manual operation time and costs and improving work efficiency. The loader returns along the track to the entrance of the receiving track, ensuring it returns along the predetermined path, improving system reliability and safety.

[0056] Embodiments of the present invention propose a gravity energy storage multi-track transportation scheduling system, which receives control signals from the central controller during off-peak electricity periods and sets the loading vehicle to gravity energy storage mode according to the control signals, including:

[0057] Initialize the particle position and velocity. The position of each particle represents a set of control signal parameters, and the velocity determines the particle's direction of movement and step size in the search space.

[0058] An evaluation function is determined to assess the quality of the control signal parameters represented by each particle. The evaluation function represents a comprehensive index of the operating efficiency and stability of the loading vehicle in gravity energy storage mode, and the evaluation function value is calculated.

[0059] Update the particle position and velocity until a preset number of iterations is reached to obtain the final position, and use the control signal parameters corresponding to the final position as the final control signal of the loading vehicle;

[0060] Based on the final control signal, the loading vehicle is set to gravity energy storage mode.

[0061] In a specific embodiment of the present invention, before the loading vehicle enters the gravity energy storage mode, the on-board sensors and system monitoring module are activated to collect the following key data in real time:

[0062] Power consumption data is collected by an on-board power meter. The instantaneous power values ​​of the drive motor, steering system and other equipment are continuously recorded from the start time of the task (such as when the off-peak control signal is received) to the end time (when unloading is completed and the vehicle returns to the starting point). This data is used for subsequent integration calculation of total energy consumption.

[0063] Time data is used to mark the start and end timestamps of the task using the system clock, and the total task duration is calculated (accurate to the second).

[0064] Path deviation data is collected by using track sensors or onboard GPS to monitor in real time whether the loading vehicle deviates from the preset track (if it deviates from the track centerline by ±0.5 meters, it is considered a deviation), and the number of deviations is counted.

[0065] Speed ​​stability data is obtained by collecting speed signals from the vehicle encoder (sampling frequency ≥ 10Hz) and calculating the standard deviation of the speed sequence to reflect the degree of speed fluctuation during operation.

[0066] Operational smoothness data is used to identify rapid acceleration / deceleration events (such as absolute acceleration > 0.3 m / s²) via an accelerometer. 2 The system triggers a counter and simultaneously counts the total number of operations (such as turnout switching, acceleration / deceleration commands, etc.) and calculates the percentage of emergency operation events.

[0067] Sub-item calculation logic

[0068] First sub-item: Energy efficiency indicators

[0069] Calculation method: Integrate the instantaneous power values ​​collected during the task period to obtain the total power consumption (unit: kilowatt-hour, kWh).

[0070] Second sub-item: Time efficiency indicators

[0071] Calculation method: Subtract the start timestamp from the task end timestamp to get the total task duration (unit: hours, h).

[0072] Third sub-item: Stability and smoothness indicators

[0073] Path deviation penalty: Count the total number of times the loading vehicle deviates from the predetermined path, and take the reciprocal to penalize the deviation behavior (for example, when there is one deviation, the value of this part is 1 / 1 = 1; if there is no deviation, in actual processing, it can be set to a large fixed value, such as 100, to highlight the advantage of no deviation).

[0074] Speed ​​stability bonus: Calculate the standard deviation of the speed sequence (unit: m / s), take the reciprocal to reward smooth operation (for example: when the standard deviation is 0.5 m / s, this part is 1 / 0.5 = 2; if it drops to 0.2 m / s, the sub-item value increases to 5).

[0075] Sudden action penalty: Calculate the ratio of the number of sudden acceleration / deceleration events to the total number of operations (e.g., if 10 sudden actions occur out of 100 total operations, the ratio is 0.1), and take a negative value to suppress the behavior (this part is -0.1, and the higher the ratio, the stronger the penalty).

[0076] The weight preset allows you to set preset weight coefficients for the three sub-items based on the current optimization goal of the system (such as energy consumption priority, efficiency priority, or stability priority). For example, energy consumption weight is 50%, efficiency weight is 30%, and stability weight is 20%.

[0077] The final evaluation function value is obtained by linearly weighting and summing the three sub-terms by their respective weighting coefficients. The higher this value, the better the corresponding control signal parameters.

[0078] Coordination process with Particle Swarm Optimization:

[0079] Particle initialization involves randomly generating multiple particles (e.g., 50) within the search space. The position of each particle corresponds to a set of control signal parameters (e.g., drive station power, climbing speed limit, turnout switching threshold, etc.), and the speed determines the direction and magnitude of parameter adjustment.

[0080] Iterative optimization:

[0081] For each particle, the loading vehicle is controlled based on the parameters of its current position. The above five types of data are collected and the evaluation function value is calculated. The individual optimal position and global optimal position of the particle are updated according to the evaluation value. The particle speed and position are adjusted (such as moving towards the parameter combination with a higher evaluation value). The above process is repeated until the preset number of iterations (such as 100 times) is reached. Finally, the parameters corresponding to the global optimal position are selected as the final control signal. The optimized control signal parameters are sent to the loading vehicle control system so that it operates according to the optimal strategy in gravity energy storage mode.

[0082] A set of initial positions and velocities of particles are randomly generated in the search space. An evaluation function is designed to assess the quality of the control signal parameters represented by each particle. Based on the evaluation function and the current position of the particle, the evaluation function value of each particle is calculated. According to the rules of the particle swarm optimization algorithm, the position and velocity of each particle are updated. Through multiple iterations, the algorithm gradually converges to a set of optimized control signal parameters. The final optimized control signal parameters are used as the control commands for the loading vehicle. In this invention, by optimizing the control signal parameters, the loading vehicle can drive more efficiently, reducing unnecessary waiting and empty driving time, thereby improving overall transportation efficiency. The evaluation function comprehensively considers factors such as the smoothness of the loading vehicle's driving, which helps to improve the stability of the system and reduce the occurrence of failures and unexpected situations. The optimized control strategy enables the loading vehicle to be more energy-efficient during transportation, especially when using electric power to assist in climbing during off-peak hours, it can utilize energy more effectively and reduce operating costs.

[0083] Embodiments of the present invention propose a gravity energy storage multi-track transportation scheduling system, which determines an evaluation function for assessing the quality of control signal parameters represented by each particle, and calculates the evaluation function value, including:

[0084] The first sub-item is generated based on the reciprocal of the total power consumption of the loading vehicle during the task time period, where the total power consumption is the integral of the power consumption from the start time to the end time of the task.

[0085] The second sub-item is generated based on the reciprocal of the total task duration, where the total task duration is the difference between the end time and the start time.

[0086] The third sub-item is generated based on the reciprocal of the number of times the loader deviates from the predetermined path, the reciprocal of the standard deviation of the loader speed, and the negative value of the ratio of the number of rapid acceleration or deceleration events to the total number of operations.

[0087] The first sub-item, the second sub-item, and the third sub-item are merged to obtain the evaluation function value.

[0088] In practical applications, the above evaluation function can be implemented using the following specific formula. For example, the calculation formula for the evaluation function is:

[0089]

[0090] Where P(t) is the power consumption of the loading vehicle at time t; t0 and t f These are the start and end times of the task, respectively; N d It is the number of times the loader deviates from the predetermined path; σ v N is the standard deviation of the loading vehicle speed; j N represents the number of events involving rapid acceleration or deceleration. tThis represents the total number of operations performed by the loading vehicle; w1, w2, and w3 are weighting coefficients.

[0091] In a specific embodiment of the present invention, during the execution of a task by the loader, real-time data is collected on the loader's power consumption over time, the start and end times of the task, the number of times the loader deviates from the predetermined path, the standard deviation of the loader's speed, the number of rapid acceleration or deceleration events, the total number of operations of the loader, and the weighting coefficient. Based on the collected data, a given evaluation function formula is used for calculation. Based on the evaluation function value, the control signal parameters of the loader are adjusted and optimized. The optimized control signal parameters are applied to the loader's control system, enabling the loader to operate according to the optimized parameters in gravity energy storage mode. By optimizing power consumption, the energy consumption of the loader during operation is reduced. This invention reduces energy consumption and improves energy efficiency. It also reduces the number of times the loader deviates from its predetermined path, improving transportation accuracy and efficiency. By optimizing the speed standard deviation and reducing rapid acceleration / deceleration events, the loader's operating speed becomes more stable, reducing mechanical wear and failure risks. Furthermore, by reducing the total number of operations, the loader's operation becomes simpler and more efficient, reducing operational complexity. The evaluation function comprehensively considers multiple performance indicators; by optimizing these indicators, the overall performance of the loader in gravity energy storage mode can be comprehensively improved. The entire optimization process can be integrated into an automated control system, enabling automatic adjustment and optimization of the loader's operating parameters and improving the system's intelligence level.

[0092] Embodiments of this invention propose a gravity-based energy storage multi-track transportation scheduling system. In updating particle position and velocity, the velocity update formula is:

[0093] v i (t+1)=w(t)×v i (t)+c1×r1×(p i (t)-x i (t))+c2×r2×(g(t)-x i (t))+c3×r3×(p avg -x i (t))+c4×r4×(x r -x i (t));

[0094] Among them, v i (t) and x i (t) represents the velocity and position of particle i at time t, respectively, p i p(t) is the individual optimal position of particle i at time t, g(t) is the global optimal position at time t, w(t) is the inertia weight at time t, c1, c2, c3 and c4 are learning factors, and r1, r2, r3 and r4 are random numbers; avgIt is the average of the optimal positions of all individual particles; x r It is a randomly selected position of a particle.

[0095] In a specific embodiment of the present invention, by initializing parameters and evaluating the function, the evaluation function value of the current position of each particle is calculated. For each particle, the global optimal position is updated, and the velocity of each particle is updated according to the given velocity update formula. In the present invention, by reasonably selecting the inertia weight and learning factor, the global search capability and local search capability of the particles can be balanced, thereby achieving rapid convergence to the global optimal solution. By introducing random numbers and information from multiple optimal positions, the particle swarm optimization algorithm is a swarm-based optimization method that is independent of the specific domain of the problem and can be flexibly applied to various optimization problems. The algorithm has a simple structure, is easy to program and implement, and has high computational efficiency, making it suitable for handling complex optimization problems.

[0096] Embodiments of this invention propose a gravity-based energy storage multi-track transportation scheduling system. In updating particle position and velocity, the position update formula is as follows:

[0097] x i (t+1)=x i (t)+v i (t+1);

[0098] Among them, v i (t+1) and x i (t+1) represents the velocity and position of particle i at time t+1, respectively.

[0099] An embodiment of the present invention proposes a gravity-storage multi-track transportation scheduling system, wherein the inertial weight w(t) at time t is calculated using the following formula:

[0100]

[0101] Among them, w start It is the initial inertia weight; w end It is the final inertia weight; t is the current iteration number; T max It represents the maximum number of iterations.

[0102] In a specific embodiment of the present invention, the inertia weight is used to control the degree to which the particle velocity is maintained. For each particle, the value of the inertia weight is updated in each iteration according to the given inertia weight calculation formula. By dynamically adjusting the inertia weight, the algorithm can perform extensive global search in the early stage of iteration to avoid getting trapped in local optima too early, and perform fine local search in the later stage of iteration to improve the accuracy of the solution. Gradually reducing the inertia weight helps the algorithm to converge to the global optimum faster in the later stage of iteration, thereby improving the efficiency of the algorithm. The dynamic adjustment strategy makes the algorithm more adaptable to different problems and initial conditions, thereby improving the robustness of the algorithm. Through the preset inertia weight adjustment strategy, the number of parameters that users need to manually adjust is reduced, simplifying the use of the algorithm.

[0103] When the loader reaches the exit end of the low-level receiving rail, the track is automatically switched based on the loader's real-time driving status and position, so that the loader can enter the upward rail, including:

[0104] The current position of the loader is obtained in real time through the position sensor, the real-time speed of the loader is obtained through the speed sensor, and the driving direction of the loader is obtained through the direction sensor.

[0105] When the loader travels to the set distance threshold, the track switching is initiated;

[0106] When all conditions are met, a track switching command is sent.

[0107] When the track switching system receives the track switching command, it guides the loading vehicle from the low-position receiving track to the upward track.

[0108] In a specific embodiment of the present invention, the current position of the loading vehicle is acquired in real time by a position sensor to ensure accurate positioning of the loading vehicle. The system continuously monitors the position of the loading vehicle. When it travels to a distance threshold set at the exit end of the low-position receiving rail, it means that the loading vehicle is about to reach the track switching point. The system comprehensively analyzes the real-time position, speed, and direction data of the loading vehicle to determine whether the conditions for track switching are met. Once all switching conditions are met, the system automatically sends a track switching command. After receiving the switching command, the track switching system immediately activates the relevant mechanical devices and electronic control systems. After the switching is completed, the system verifies again through sensors whether the loading vehicle has successfully entered the upward rail and ensures that it travels stably on the new rail. In this invention, automatic track switching reduces the reliance on manual operation, improves the automation level of the entire transportation scheduling system, and enhances operational safety. Automated track switching greatly shortens the conversion time of the loading vehicle between tracks, improves overall transportation efficiency, and reduces labor costs. The intelligent track switching system can flexibly adjust the driving path of the loading vehicle according to the actual situation to better adapt to changing transportation needs.

[0109] Embodiments of this invention propose a gravity energy storage multi-track transportation scheduling system. After the loading vehicle enters the uphill track, it begins to climb the slope under the action of the drive station. The climbing speed and timing are dynamically adjusted according to the priority of the loading vehicle and the track occupancy. If multiple uphill tracks are available, the corresponding uphill track is allocated to the loading vehicle based on real-time data to balance the load on each track, including:

[0110] Once the loader successfully enters the uphill rail via the intelligent switch, it sends a confirmation signal to the central control system, indicating that it is ready to climb the slope.

[0111] After receiving the confirmation signal, the central control system queries and evaluates the priority of the loading vehicle to obtain the following information: check the current occupancy status of all upward rails, and the position and speed data of the remaining loading vehicles.

[0112] Based on the priority evaluation results, the central control system sets an initial climbing speed for the loader. If it detects that the track ahead is occupied or there is a potential conflict, it calculates a timing interval and notifies the loader to delay the start or adjust the speed. After receiving the speed and timing instructions, the loader adjusts the corresponding power output through its drive system to achieve climbing.

[0113] If multiple uplink tracks are available, the central control system outputs a corresponding track allocation scheme through a load balancing algorithm.

[0114] According to the track allocation scheme, the central control system sends specific uphill track allocation instructions to the loader. After receiving the instructions, the loader automatically adjusts to the designated uphill track through the intelligent switch switching module. During the loader's climbing process, the central control system continuously monitors its position, speed, and the condition of the track ahead. If any abnormality is detected, it sends adjustment instructions to the loader in real time.

[0115] In this embodiment of the invention, by dynamically adjusting the climbing speed and timing of the loading vehicles, track occupancy and conflicts are effectively avoided, ensuring that the loading vehicles can climb smoothly and efficiently. Simultaneously, the application of a load balancing algorithm allows for the rational utilization of multiple available uphill tracks, further improving overall transportation efficiency. The central control system can flexibly set the optimal climbing speed and timing scheme for each loading vehicle based on its priority and real-time track data. This flexibility enables the system to better cope with complex and changing transportation demands, improving its adaptability and responsiveness. During the loading vehicle's climbing process, the central control system continuously monitors its position, speed, and the condition of the track ahead. If any abnormality is detected, the system immediately sends adjustment commands to the loading vehicle, ensuring it can adjust its state in a timely manner and avoid potential safety risks. By rationally allocating multiple available uphill tracks through a load balancing algorithm, the system achieves efficient utilization of track resources. This not only reduces track resource waste but also lowers system operating costs and improves economic efficiency. The gravity energy storage multi-track transportation scheduling system in this embodiment of the invention fully utilizes advanced intelligent technologies, such as intelligent turnout switching modules and a central control system. The application of these technologies has improved the intelligence level of the system, making the entire transportation process more intelligent, convenient, and efficient.

[0116] Embodiments of this invention propose a gravity-storage multi-track transportation scheduling system. If multiple available up-track lines exist, the central control system outputs a corresponding track allocation scheme through a load balancing algorithm, including:

[0117] The central control system collects real-time status information of all available uplink rails, including the current occupancy status of each rail, the time of the last use, and the maintenance status; it also updates information on all loading vehicles waiting to be assigned rails, including their corresponding positions, priorities, and target high-level stacks.

[0118] The collected track status information and loading vehicle information are used as input data for the load balancing algorithm. The load balancing algorithm evaluates the suitability of each available uplink track, determines a corresponding track selection for each loading vehicle waiting to be assigned, and outputs a specific track allocation scheme after execution.

[0119] Embodiments of this invention propose a gravity-storage multi-rail transportation scheduling system. A load balancing algorithm evaluates the suitability of each available uprail, including:

[0120] Key metrics for assessing track suitability include the proportion of time the track is idle, the time since the last use, the expected likelihood of conflict, the physical condition of the track, and the usage status of adjacent tracks.

[0121] The real-time data collected for each track is standardized, and a weight is assigned to each evaluation metric.

[0122] For each available uprail, a weighted fitness score is calculated using an algorithm based on the evaluation metrics and corresponding weights.

[0123] In this embodiment of the invention, by collecting real-time status information of all available up-track rails and information on loading vehicles waiting to be assigned tracks, the load balancing algorithm can comprehensively and accurately evaluate the suitability of each track, thereby achieving efficient resource allocation. This allocation method ensures maximum utilization of track resources and reduces idle and congestion phenomena. The load balancing algorithm determines a corresponding track selection for each loading vehicle waiting to be assigned and outputs a specific track allocation scheme. This helps optimize the transportation process, reduce the time consumed by loading vehicles in track switching and waiting, and improve overall transportation efficiency. By considering factors such as the proportion of idle time on the track, the time of the most recent use, and the expected probability of conflict, the load balancing algorithm can reduce the risk of conflicts between loading vehicles during transportation. This enhances the safety and stability of the system. The algorithm also considers the physical conditions of the track and the usage status of adjacent tracks, which enables the system to better adapt to complex and changing transportation environments. Regardless of changes in gradient, differences in track length, or interference from adjacent tracks, the system can make reasonable track allocation decisions. The embodiments of the present invention, by introducing a load balancing algorithm and a comprehensive suitability evaluation method, significantly improve the intelligence level of the gravity energy storage multi-track transportation scheduling system. This intelligent decision-making approach not only improves the system's operational efficiency but also reduces the need for human intervention, further enhancing the system's automation level.

[0124] As shown in Figure 2, the gravity energy storage multi-rail transportation scheduling method includes:

[0125] Step 11: The receiving instruction module receives the control signal sent by the main controller during the power off-peak period and sets the loading vehicle to gravity energy storage mode according to the control signal.

[0126] Step 12: The loading vehicle driving module starts the loading vehicle from the receiving rail starting point of the low-level energy storage platform according to the control signal, so that the loading vehicle travels along the pre-set gravity energy storage transportation line to the location where the material is loaded.

[0127] Step 13: When the loader travels to the exit end of the low-position receiving rail, the intelligent turnout switching module automatically switches the rail according to the real-time driving status and position of the loader so that the loader can enter the upward rail.

[0128] Step 13: After the loader enters the uphill rail, it begins to climb the slope under the action of the drive station; the climbing speed and timing are dynamically adjusted according to the priority of the loader and the occupancy of the rail; if multiple uphill rails are available, the corresponding uphill rail is allocated to the loader according to real-time data to balance the load of each rail.

[0129] Step 14: When the loader arrives at the designated position of the high-level energy storage bin, its unloading mechanism automatically dumps the material into the designated area. After dumping is completed, the loader returns to the inlet end of the receiving rail along the track.

[0130] Step 15: After the loading vehicle returns to the starting point and enters standby mode, it is included in the pool of available loading vehicles to realize the rescheduling of tasks. During rescheduling, resources are allocated according to the status and location of each loading vehicle, as well as the urgency and priority of the current task.

[0131] In a specific embodiment of the present invention, the receiving instruction module receives a control signal from the main controller during a power outage. Based on the received control signal, the loader is set to gravity energy storage mode, ready for a gravity energy storage transportation task. The loader driving module, according to the control signal, starts the loader from the starting point of the receiving rail on the low-level energy storage platform. The loader travels along a pre-set gravity energy storage transportation route to the location where the material is loaded. When the loader reaches the exit end of the low-level receiving rail, the intelligent switch switching module starts working. After entering the upward rail, the loader begins to climb the slope under the action of the drive station. During a power outage, the loader uses electric power to assist in climbing the slope, transporting solid waste from the low-level energy storage bin to the high-level energy storage bin. When the loader reaches the designated location in the high-level energy storage bin... Its unloading mechanism is automatically activated, dumping the material into the designated area. After dumping, the loader returns along the track to the entrance of the receiving track. When the loader returns to the starting point, the system automatically puts it into standby mode. In this invention, gravity energy storage transportation is carried out during off-peak electricity periods, effectively utilizing off-peak electricity and optimizing energy use. Through automated loading, transportation, unloading, and return processes, work efficiency is greatly improved. The intelligent switch module enables the loader to automatically switch tracks according to real-time driving status and position, enhancing the system's flexibility and adaptability. Through gravity energy storage mode, unnecessary energy consumption and emissions are reduced, which is beneficial to environmental protection and sustainable development. The automated operating system reduces the possibility of human error and improves the safety of the entire transportation process.

[0132] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A gravity-storage multi-rail train transportation dispatching system, characterized in that, include: The receiving instruction module is used to receive control signals sent by the main controller during periods of low power, and to set the loading vehicle to gravity energy storage mode according to the control signals. Based on the current usage of each track and stack, assign corresponding travel paths to the loading vehicles; The loading vehicle driving module is used to start the loading vehicle from the receiving rail of the low-level energy storage platform according to the control signal, so that the loading vehicle can travel along the pre-set gravity energy storage transportation line to the location of loading materials; it monitors the occupancy of each rail in real time, and if potential congestion is detected, it dynamically adjusts the driving path of the loading vehicle or delays the start time. The intelligent turnout switching module is used to automatically switch tracks when the loader reaches the exit end of the low-level receiving rail, based on the real-time driving status and position of the loader, so that the loader can enter the upward rail; through communication with the central control system, it coordinates the switching sequence between different loaders to prevent conflicts between multiple loaders. The loading vehicle uphill transport module is used to enable the loading vehicle to start climbing the slope under the action of the drive station after entering the uphill rail. The climbing speed and timing are dynamically adjusted according to the priority of the loading vehicle and the occupancy of the rail. If multiple uphill rails are available, the corresponding uphill rail is allocated to the loading vehicle according to real-time data to balance the load of each rail.

2. The gravity energy storage multi-track transportation scheduling system according to claim 1, characterized in that, Also includes: The unloading module is used to assign a corresponding return path to the loading vehicle based on the current usage of each return track after the loading vehicle reaches the designated position of the high-level energy storage bin and completes unloading. During the return journey of the loading vehicle, real-time route planning and scheduling are performed to prevent congestion. The standby state module is used to add a loading vehicle to the pool of available loading vehicles after it returns to the starting point and enters the standby state, so as to realize the rescheduling of tasks. During rescheduling, resources are allocated according to the status and location of each loading vehicle, as well as the urgency and priority of the current task.

3. The gravity energy storage multi-track transportation scheduling system according to claim 2, characterized in that, Receive control signals from the main controller during off-peak electricity periods and set the loader to gravity energy storage mode according to the control signals, including: Initialize the particle position and velocity. The position of each particle represents a set of control signal parameters, and the velocity determines the particle's direction of movement and step size in the search space. Determine the evaluation function used to evaluate the quality of the control signal parameters represented by each particle, and calculate the evaluation function value; Update the particle position and velocity until a preset number of iterations is reached to obtain the final position, and use the control signal parameters corresponding to the final position as the final control signal of the loading vehicle; Based on the final control signal, the loading vehicle is set to gravity energy storage mode.

4. The gravity energy storage multi-track transportation scheduling system according to claim 3, characterized in that, Determine the evaluation function used to assess the quality of the control signal parameters represented by each particle, and calculate the evaluation function value, including: The first sub-item is generated based on the reciprocal of the total power consumption of the loading vehicle during the task time period, where the total power consumption is the integral of the power consumption from the start time to the end time of the task. The second sub-item is generated based on the reciprocal of the total task duration, where the total task duration is the difference between the end time and the start time. The third sub-item is generated based on the reciprocal of the number of times the loader deviates from the predetermined path, the reciprocal of the standard deviation of the loader speed, and the negative value of the ratio of the number of rapid acceleration or deceleration events to the total number of operations. The first sub-item, the second sub-item, and the third sub-item are merged to obtain the evaluation function value.

5. The gravity energy storage multi-track transportation scheduling system according to claim 4, characterized in that, When the loader reaches the exit end of the low-level receiving rail, the track is automatically switched based on the loader's real-time driving status and position, so that the loader can enter the upward rail, including: The current position of the loader is obtained in real time through the position sensor, the real-time speed of the loader is obtained through the speed sensor, and the driving direction of the loader is obtained through the direction sensor. When the loader travels to the set distance threshold, the track switching is initiated; When all conditions are met, a track switching command is sent. When the track switching system receives the track switching command, it guides the loading vehicle from the low-position receiving track to the upward track.

6. The gravity energy storage multi-track transportation scheduling system according to claim 5, characterized in that, After the loader enters the uphill rail, it begins to climb the slope under the action of the drive station; the climbing speed and timing are dynamically adjusted according to the priority of the loader and the track occupancy. If multiple uphill rails are available, the corresponding uphill rail is allocated to the loading vehicle based on real-time data to balance the load on each rail, including: Once the loader successfully enters the uphill rail via the intelligent switch, it sends a confirmation signal to the central control system, indicating that it is ready to climb the slope. After receiving the confirmation signal, the central control system queries and evaluates the priority of the loading vehicle to obtain the following information: check the current occupancy status of all upward rails, and the position and speed data of the remaining loading vehicles. Based on the priority evaluation results, the central control system sets an initial climbing speed for the loader. If it detects that the track ahead is occupied or there is a potential conflict, it calculates a timing interval and notifies the loader to delay the start or adjust the speed. After receiving the speed and timing instructions, the loader adjusts the corresponding power output through its drive system to achieve climbing. If multiple uplink tracks are available, the central control system outputs a corresponding track allocation scheme through a load balancing algorithm. According to the track allocation scheme, the central control system sends specific uphill track allocation instructions to the loading vehicle. After receiving the instructions, the loading vehicle automatically adjusts to the designated uphill track through the intelligent turnout switching module. During the loading vehicle's climbing process, the central control system continuously monitors its position, speed, and the condition of the track ahead. If any abnormality is detected, an adjustment instruction will be sent to the loading vehicle in real time.

7. The gravity energy storage multi-track transportation scheduling system according to claim 6, characterized in that, If multiple uplink tracks are available, the central control system outputs a corresponding track allocation scheme through a load balancing algorithm, including: The central control system collects real-time status information of all available uprails, including the current occupancy status of each rail. The collected track status information and loading vehicle information are used as input data for the load balancing algorithm. After the load balancing is completed, a specific track allocation scheme is output.

8. The gravity energy storage multi-track transportation scheduling system according to claim 7, characterized in that, The load balancing algorithm evaluates the suitability of each available uplink rail, including: Key metrics for assessing track suitability include the proportion of time the track is idle, the time since the last use, the expected likelihood of conflict, the physical condition of the track, and the usage status of adjacent tracks. The real-time data collected for each track is standardized, and a weight is assigned to each evaluation metric. For each available uprail, a weighted fitness score is calculated using an algorithm based on the evaluation metrics and corresponding weights.

9. A gravity-storage multi-rail transportation scheduling method, characterized in that, This method is used to implement the system as described in any one of claims 1 to 8, the method comprising: The receiving instruction module receives control signals from the main controller during periods of low power, and sets the loading vehicle to gravity energy storage mode according to the control signals. The loading vehicle driving module starts the loading vehicle from the receiving rail of the low-level energy storage platform according to the control signal, so that the loading vehicle travels along the pre-set gravity energy storage transportation line to the location where the material is loaded; When the loader reaches the exit end of the low-position receiving rail, the intelligent turnout switching module automatically switches the rails according to the real-time driving status and position of the loader so that the loader can enter the upward rail. After the loader enters the uphill rail, it begins to climb the slope under the action of the drive station; the climbing speed and timing are dynamically adjusted according to the priority of the loader and the occupancy of the rail; if multiple uphill rails are available, the corresponding uphill rail is allocated to the loader according to real-time data to balance the load of each rail. When the loader arrives at the designated position of the high-level energy storage silo, its unloading mechanism automatically dumps the material into the designated area. After dumping, the loader returns to the inlet end of the receiving rail along the track. Once the loading vehicle returns to the starting point and enters standby mode, it is added to the pool of available loading vehicles to enable task rescheduling. During rescheduling, resources are allocated based on the status and location of each loading vehicle, as well as the urgency and priority of the current task.

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