Elevator system and control method, control system therefor

Abstract

This application discloses an elevator system and its control method and control system, relating to the technical field of elevator systems. The control method includes: acquiring operational characteristic data of the elevator system, the state of charge of the energy storage unit, and the electricity price period of the external power grid; calculating the net energy flow trend value of the elevator system in a future target period using a preset prediction model; dynamically adjusting the energy target threshold of the energy storage unit; and controlling the energy storage unit to perform corresponding charging and discharging actions. The elevator system control method proposed in this application, by predicting the future power generation conditions of the elevator, dynamically adjusts the current charging threshold, proactively reserving storage space for the regenerative energy to be generated; and by combining future electricity demand with the time-of-use electricity price signal of the power grid, dynamically adjusts the energy threshold, guiding the system to store energy during off-peak electricity price periods, thereby achieving comprehensive optimization of energy-saving and economic benefits.

Description

Technical Field

[0001] This application relates to the technical field of elevator systems, and in particular to an elevator system and its control method and control system. Background Technology

[0002] When an elevator is driving a load, it acts as a consumer of electrical energy; when the load's potential or kinetic energy is released, its drive motor operates in regenerative braking mode, becoming a producer of electrical energy. To recover and utilize this valuable regenerative energy, existing technologies equip elevators with local energy storage systems.

[0003] However, existing solutions generally employ a static, passive control strategy, but such systems often have a fixed charging upper limit threshold. When the SOC of the energy storage device reaches this threshold, charging from any power source stops, and excess energy can only be consumed through a braking resistor. Although this existing technology achieves basic energy recovery, its lack of intelligence and predictability exposes the following problems in actual operation, hindering the maximization of energy conservation and economy: 1. No place to store generated electricity; 2. No reserve of electricity for consumption. If the elevator is expected to operate with high energy consumption, the ideal strategy is to store enough electricity in advance during low-price periods to discharge during peak periods, replacing high-price grid electricity. However, the fixed threshold strategy cannot perform this kind of intentional storage across time periods, resulting in insufficient battery capacity available for dispatch during peak electricity consumption periods when discharge is truly needed. The system has to purchase electricity from the grid at a high price, missing the opportunity to save on electricity costs. Summary of the Invention

[0004] The main purpose of this application is to provide an elevator system and its control method and control system, which aims to solve the technical problems of energy waste and poor economic efficiency caused by the use of fixed charging and discharging thresholds in existing elevator energy storage systems.

[0005] To achieve the above objectives, this application proposes a control method for an elevator system, the elevator system including an energy storage unit, comprising:

[0006] The system acquires operational characteristic data of the elevator system, the state of charge of the energy storage unit, and the electricity price and time period of the external power grid. Based on the aforementioned operational characteristic data, a preset prediction model is used to calculate the net energy flow trend value of the elevator system during a future target period; wherein, the net energy flow trend value is used to characterize the balance between the regenerative electrical energy generated and the consumed electrical energy of the elevator system during the future target period; The target threshold for the energy storage unit is dynamically adjusted based on the net energy flow trend value. Based on the comparison between the current state of charge and the target energy threshold, and the electricity price period, the energy storage unit is controlled to perform corresponding charging and discharging actions.

[0007] In one embodiment, the operational characteristic data includes: The power generation and power consumption of the elevator system are recorded according to a preset period, and a power generation history sequence and a power generation history sequence are constructed respectively. Alternatively, the charge of the energy storage unit can be recorded at a preset period to obtain a sequence of historical state of charge changes.

[0008] In one embodiment, the specific steps of calculating the net energy flow trend value of the elevator system in a future target time period based on the operational characteristic data and using a preset prediction model include: Using the preset prediction model, the historical power generation sequence and the historical power generation sequence are weighted and calculated to obtain the predicted power generation and predicted power consumption for the future target period, respectively. The difference between the predicted power generation and the predicted power consumption is calculated to obtain the net energy flow trend value. Alternatively, the preset prediction model can be used to perform weighted calculations on the historical state of charge change sequence to obtain the predicted state of charge change, and the predicted state of charge change can be used as the net energy flow trend value.

[0009] In one embodiment, the operational characteristic data includes the elevator car load, direction of travel, and speed; the specific steps of calculating the net energy flow trend value of the elevator system in a future target time period based on the operational characteristic data using a preset prediction model include: Based on the car load, running direction and running speed, identify and statistically analyze the power generation-related operating conditions and power consumption-related operating conditions within a preset period; Based on the statistical results of the power generation-related operating conditions and the power consumption-related operating conditions, the value of the power generation operating condition and the value of the power consumption operating condition are calculated respectively, and a historical value sequence is constructed. Using the preset prediction model, the historical data in the historical value sequence that have the same time period characteristics as the future target time period are weighted and calculated to obtain the predicted power generation value and the predicted power consumption value, respectively. The net energy flow trend value is obtained based on the difference between the predicted power generation value and the predicted power consumption value.

[0010] In one embodiment, the preset prediction model is a period-weighted prediction model with time decay weights, and the formula of the period-weighted prediction model includes: ; in, For predicted values, The attenuation factor is N, where N is the reference historical period number. For the first Observational data corresponding to each historical cycle period.

[0011] In one embodiment, the electricity price period includes peak period, flat period, and valley period; the energy storage unit has a static full-charge threshold and a static discharge threshold; the specific steps of dynamically adjusting the energy target threshold of the energy storage unit according to the net energy flow trend value include: If the net energy flow trend value indicates a net power generation trend, the target power threshold is lowered to below the static full charge threshold of the energy storage unit in order to reserve energy storage capacity. If the net energy flow trend value indicates a net electricity consumption trend, the target electricity threshold is adjusted to be higher than the static discharge threshold of the energy storage unit in order to reserve electricity in advance and prevent insufficient electricity during discharge.

[0012] In one embodiment, the specific steps of controlling the energy storage unit to perform charging and discharging operations based on the comparison result between the current state of charge and the target energy threshold, and the electricity price period, include: If the current electricity price is at its lowest point and the current state of charge is less than the target electricity threshold, then the energy storage unit is controlled to charge from the external power grid. If the current state of charge is greater than or equal to the target power threshold, the energy storage unit is stopped from charging from the external power grid, and when the elevator system is in a power-consuming operation state, the energy storage unit is controlled to discharge to the elevator system first.

[0013] Furthermore, this application proposes a control method for an elevator system, comprising: The system acquires operational characteristic data of the elevator system, the state of charge of the energy storage unit, and the electricity price and time period of the external power grid. The system state vector, which includes the operational characteristic data, the state of charge of the energy storage unit, the electricity price period and historical context information, is input into a preset strategy network model so that the strategy network model outputs the electricity target threshold corresponding to the current system state. Based on the comparison between the current state of charge and the target energy threshold, and the electricity price period, the energy storage unit is controlled to perform corresponding charging and discharging actions.

[0014] In addition, to achieve the above objectives, this application also proposes a control system, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the elevator system as described above.

[0015] In addition, to achieve the above objectives, this application also proposes an elevator system, including an energy storage unit, a battery monitoring unit, a charging and discharging unit, and a control system as described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: The elevator system control method proposed in this application dynamically adjusts the current charging threshold by predicting the elevator's future power generation conditions, proactively reserving storage space for the regenerative energy to be generated. By combining future electricity demand with the grid's time-of-use pricing signal, it dynamically adjusts the power threshold, guiding the system to store energy during off-peak electricity periods. This invention aims to transform the energy storage system from a passive, fixed energy buffer device into an active, intelligent energy dispatching unit, achieving comprehensive optimization of energy-saving and economic benefits. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the overall process of an elevator system control method according to an embodiment of the present invention; Figure 2 This is a flowchart of the elevator system control method according to the first embodiment of the present invention; Figure 3 This is a flowchart of the elevator system control method according to the second embodiment of the present invention; Figure 4 This is a flowchart of the elevator system control method according to the third embodiment of the present invention; Figure 5 This is a schematic diagram of the structural framework of an elevator system provided in an embodiment of the present invention.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] As a key link in energy consumption, the urgency of energy-saving and consumption-reducing renovations in the building sector is becoming increasingly prominent. Among various high-energy-consuming equipment in buildings, elevators are typical energy-consuming units due to their wide distribution and frequent operation. The energy flow of elevator operation has a significant bidirectional characteristic: when driving the load, such as when moving upwards under heavy load or accelerating, it acts as an energy-consuming device; while during the release of load potential energy or kinetic energy, such as when moving downwards under heavy load or braking and decelerating, its drive motor can operate in a regenerative braking state, generating electrical energy in reverse, thus transforming it into an energy producer in a short period of time.

[0024] To recover and utilize this considerable amount of regenerative energy, existing technologies generally employ the solution of configuring local energy storage systems for elevators. These systems are typically based on a static, passive control strategy. Their core logic involves setting a fixed upper limit threshold for the state of charge (SOC) of the energy storage device, such as 95%. When the SOC of the energy storage device reaches this preset threshold, the system stops charging from any power source, causing any subsequent excess regenerative energy to be dissipated as heat through a braking resistor. Although this solution achieves basic energy recovery, its control logic lacks intelligence and foresight, and in actual operation, the following prominent problems have gradually emerged, severely restricting the maximization of the system's energy-saving effect and economic efficiency: First, there is the dilemma of "nowhere to store generated electricity," leading to energy waste. In actual operation, elevator energy consumption and power generation patterns often exhibit regular or predictable fluctuations. For example, during the morning rush hour in office buildings, there may be concentrated heavy-load descents, generating a large amount of regenerative energy. An ideal energy efficiency management strategy should proactively reserve sufficient capacity for energy storage devices before peak power generation arrives. However, fixed threshold strategies cannot predict such peak power generation, and energy storage devices may have already been charged to their upper threshold limit in advance. When a large amount of regenerative energy is generated, due to insufficient storage space, all excess energy can only be consumed by braking resistors, resulting in a serious waste of clean energy that could have been recovered.

[0025] Secondly, there is the economic dilemma of "no electricity reserves." In a time-of-use pricing electricity market environment, there is a significant peak-valley difference in grid electricity prices. If an elevator needs to operate with high energy consumption during peak electricity price periods, the ideal strategy is to store enough energy in advance during off-peak hours and discharge it during peak hours to replace the high-priced grid electricity, thereby reducing electricity costs. However, a fixed threshold strategy cannot support this kind of intentional cross-time storage based on electricity price signals. As a result, during peak electricity consumption periods when discharge is truly needed, the energy storage device may lack sufficient power, forcing the system to purchase electricity from the grid at a higher price, missing an important opportunity to save on electricity costs through energy time-shifting.

[0026] To address the aforementioned problems, this application proposes a control method for an elevator system. The hardware architecture of the elevator system includes: an energy storage unit installed on the DC bus side of the elevator, a BMS (Battery Management System) responsible for monitoring battery parameters, a charging and discharging unit for bidirectional energy conversion, and a control system. Its framework is as follows: Figure 5 As shown, the DC bus of the elevator inverter is connected to the charging and discharging unit, which in turn is connected to the energy storage unit. A bidirectional energy meter or current and voltage sensor is installed at the DC bus to capture the generated power and consumed power in real time. The control method flow of the elevator system is as follows: Figure 1 As shown, steps S100 to S400 are included: S100: Acquire operational characteristic data of the elevator system, state of charge of the energy storage unit, and electricity price and time period of the external power grid; S200: Based on operational characteristic data, calculate the net energy flow trend value of the elevator system in the future target period through a preset prediction model; wherein, the net energy flow trend value is used to characterize the balance between the regenerative electrical energy generated and the consumed electrical energy of the elevator system in the future target period. S300: Dynamically adjusts the target threshold of the energy storage unit based on the net energy flow trend value; S400: Based on the comparison between the current state of charge and the target energy threshold, as well as the electricity price period, control the energy storage unit to perform the corresponding charging and discharging actions.

[0027] The elevator system control method proposed in this application dynamically adjusts the current charging threshold by predicting the elevator's future power generation conditions, proactively reserving storage space for the regenerative energy to be generated. By combining future electricity demand with the grid's time-of-use pricing signal, it dynamically adjusts the power threshold, guiding the system to store energy during off-peak electricity periods. This invention aims to transform the energy storage system from a passive, fixed energy buffer device into an active, intelligent energy dispatching unit, achieving comprehensive optimization of energy-saving and economic benefits. Compared to traditional solutions, this application not only reduces elevator operating energy consumption and electricity costs but also avoids the stress of long-term full charge and discharge of batteries, effectively extending their service life and simultaneously reducing hardware wear and long-term maintenance costs. This solution is widely applicable to various multi-elevator application scenarios, fully demonstrating the important role of technological innovation in promoting green, low-carbon, and sustainable development.

[0028] Example 1: A dynamic threshold control method based on weighted historical electricity prediction, such as... Figure 2 As shown, it includes steps SA1 to SA4.

[0029] SA1: Record the power generation and consumption of the elevator system according to a preset period, and construct historical power generation and consumption sequences respectively; or, record the charge of the energy storage unit according to a preset period to obtain a historical state of charge change sequence. At the same time, obtain the electricity price periods of the external power grid, including peak periods, flat periods, and valley periods.

[0030] SA2: A periodic weighted prediction model with time decay weights is used to calculate the predicted power generation and predicted power consumption for the future target period by weighting the historical power generation series and the historical power consumption series. The difference between the predicted power generation and the predicted power consumption is calculated to obtain the net energy flow trend value. Alternatively, a periodically weighted prediction model with time decay weights can be used to calculate the predicted change in state of charge by weighting the historical sequence of changes in state of charge, and the predicted change in state of charge can be used as the net energy flow trend value.

[0031] SA3: If the net energy flow trend value indicates a net power generation trend, the power target threshold will be lowered to below the static full charge threshold of the energy storage unit to reserve energy storage capacity; if the net energy flow trend value indicates a net power consumption trend, the power target threshold will be adjusted to above the static discharge threshold of the energy storage unit to reserve energy in advance and prevent insufficient power during discharge.

[0032] SA4: If the current electricity price is at its lowest point and the current state of charge is less than the target power threshold, control the energy storage unit to charge from the external power grid; if the current state of charge is greater than or equal to the target power threshold, stop the energy storage unit from charging from the external power grid, and when the elevator system is in a power-consuming operation state, control the energy storage unit to discharge to the elevator system first.

[0033] This embodiment can be understood as detailing a control method for adjusting energy storage thresholds based on historical energy trend prediction. The specific hardware framework includes a battery pack, a battery monitoring unit, a charging unit, a control system, and a bidirectional energy meter between the DC bus and the battery. - (Optional, this could be a bidirectional power acquisition point, as shown below), and a switch for each inverter's DC bus. - The specific execution flow of this method is as follows: In step SA1, a bidirectional energy meter between the elevator and the DC bus of the energy storage system is used to measure energy at a fixed period. (For example, every 15 minutes) two key data points are collected and recorded synchronously: the total regenerative power generated by the elevator within the cycle ( ) and the total electricity consumption of the elevator ( Divide each day into several... For each time period, historical power generation and consumption sequences are constructed using date and time period indexes, respectively. If bidirectional energy meters are unavailable, and accurate real-time SOC values ​​are obtainable, then within the cycle... Obtain the change in battery SOC within a time period Construct a sequence of SOC changes.

[0034] In step SA2, when it is necessary to target a certain time period in the future... When making predictions (e.g., within the next 1-2 hours), the system retrieves relevant data from the database. Historical data from the same period. Based on the high correlation between the data and recent data, as well as the high correlation between periodic data, the following weighted average model is used for prediction:

[0035]

[0036] in ， and Target time period Forecasted power generation and forecasted electricity consumption; express At the same time two days ago, express The same time period as a week ago; for The number of historical cycles to consider; Attenuation factor Net energy at this time The change in SOC can be expressed as:

[0037] in, The mapping coefficient (unit: % / kWh) is related to the total battery capacity and charge / discharge efficiency and needs to be calibrated in advance.

[0038] For scenarios using the "SOC change series", the predicted SOC change can be directly calculated using the following formula.

[0039]

[0040] In the calculation of SOC change, the attenuation factor It is a core parameter that needs to be calibrated through cross-validation using historical data, and its value is usually between 0.5 and 0.9. The closer the value is to 1, the more sensitive the model is to long-term historical trends; the closer it is to 0, the more sensitive it is to recent fluctuations. Calculation period. It needs to be compatible with the elevator's operating patterns and the electricity price switching cycle.

[0041] In step SA3, if (Net electricity generation), then At this point, the maximum SOC (State of Charge) value is predicted to reserve battery storage space for the upcoming regenerative energy, avoiding waste. If (Net power consumption), then At this point, the minimum remaining state of charge (SOC) is predicted to guide the system to store more energy before peak power consumption to replace expensive grid electricity. 、 The upper and lower limits of battery safety (e.g., 95%, 10%) For safety margin.

[0042] In step SA4, during the current time period Internally, the system executes the following rules: Rule 1: If the time period Within the period when the real-time electricity price is at its lowest, when the battery... When necessary, the control system controls the charging module to charge the battery, ensuring that the battery has sufficient capacity to store power in elevator power generation scenarios and sufficient remaining power in power consumption scenarios. This replenishes energy during low-cost periods and ensures the charging process is protected. constraint.

[0043] Rule 2: If the time period Inside, when the battery At this time, the charging module does not need to be charged, and when an elevator needs to consume power, the switch is guaranteed to operate. - When the elevator is in a closed state, it prioritizes the use of battery power to make full use of battery energy.

[0044] Example 2: A dynamic threshold control method based on energy value assessment, such as... Figure 3 As shown, steps SB1 to SB4 are included.

[0045] SB1: Obtain the elevator car load, direction of travel, and speed of the elevator system. Based on these parameters, identify and statistically analyze power generation and power consumption related operating conditions within a preset period. Calculate the value of power generation and power consumption operating conditions based on the statistical results, and construct a historical value sequence. Simultaneously, obtain the external power grid's electricity price periods, including peak, off-peak, and valley periods.

[0046] SB2: A periodically weighted prediction model with time decay weights is used to calculate the predicted power generation value and the predicted power consumption value by weighting historical data in the historical value series that have the same time characteristics as the future target time period. The difference between the predicted power generation value and the predicted power consumption value is used to obtain the net energy flow trend value.

[0047] SB3: If the net energy flow trend value indicates a net power generation trend, the target power threshold is lowered to below the static full charge threshold of the energy storage unit to reserve energy storage capacity; if the net energy flow trend value indicates a net power consumption trend, the target power threshold is adjusted to above the static discharge threshold of the energy storage unit to reserve energy in advance and prevent insufficient power during discharge.

[0048] SB4: If the current electricity price is at its lowest point and the current state of charge is less than the target power threshold, then control the energy storage unit to charge from the external power grid; if the current state of charge is greater than or equal to the target power threshold, stop the energy storage unit from charging from the external power grid, and when the elevator system is in a power-consuming operation state, control the energy storage unit to discharge to the elevator system first.

[0049] This embodiment can be understood as detailing a dynamic threshold control method based on energy value assessment. Its specific application scenarios are those where electricity consumption or State of Charge (SOC) cannot be directly and accurately measured, or scenarios where sensor data is used for auxiliary judgment. In addition to the structure described in Embodiment 1, the hardware components of this method should also include elevator load, direction, and speed sensors. Using the signals from the elevator load, direction, and speed sensors, the value of the power generation condition and the value of the power consumption condition are defined and calculated.

[0050] Furthermore, it employs the same weighted prediction model as in Example 1: ; in, For predicted values, The attenuation factor is N, where N is the reference historical period number. For the first Observational data corresponding to each historical cycle period.

[0051] The specific execution flow of this method is as follows: In step SB1, instead of relying on meters or SOC, the elevator control system's inherent sensor signals are used. This is done at a fixed period. (e.g., 15 minutes) as a window to analyze elevator status in real time: Identify "Generation-Associated Operating Conditions": Heavy Load Downward (Load > And the direction is downhill) or lightly loaded uphill (load < (And the direction is upward) Record its cumulative duration and number of times ,in and It is related to the elevator's counterweight and structure.

[0052] Identify "Power Consumption Related Operating Conditions": Heavy Load Upstream (Load > And the direction is upward) or lightly loaded downward (load < And the direction is downward) record its cumulative duration. and number of times ,in and It is related to the elevator's counterweight and structure.

[0053] Calculate the value of energy:

[0054] in, 、 、 、 Weighting coefficients are used to quantify the contribution of different operating states to the overall energy trend. The second method transforms the energy trend, which is difficult to measure directly, into a relevant and valuable indicator that can be directly measured by standard sensors. Among these, cumulative duration... and number of times The weights reflect the average electricity change in the dimensions of time and frequency, respectively.

[0055] Step SB2 in this embodiment is mathematically and logically identical to step SA2 in embodiment one, except that the input data is changed from... Sequence replacement Sequence. Using the same decay factor. The predicted energy value is obtained using a weighted prediction formula. and Then calculate the energy value difference. Calculations yielded :

[0056] in, The mapping coefficient (unit: % / kWh) is used, and the dynamic threshold is generated using the exact same formula as in step SA3 of Example 1. In the absence of power data, a prediction and decision-making process identical to that in the previous embodiment was implemented. Furthermore, step SB4 in this embodiment is the same as step SA4 in embodiment one, and will not be described in detail here. This ensures that regardless of the data source from which the threshold is generated, the final control execution is consistent, reliable, and economical.

[0057] Example 3: A power threshold control method based on deep reinforcement learning, such as Figure 4 As shown, it includes steps SC1 to SC3.

[0058] SC1: Establish a strategy network model to obtain the operating characteristic data of the elevator system, the state of charge of the energy storage unit, and the electricity price period of the external power grid; wherein, the electricity price period of the external power grid includes peak period, flat period and valley period.

[0059] SC2: Input the system state vector, which includes operational feature data, the state of charge of energy storage units, electricity price periods and historical context information, into the policy network model trained based on deep reinforcement learning, so that the policy network model outputs the target electricity threshold corresponding to the current system state.

[0060] SC3: If the current electricity price is at its lowest point and the current state of charge is less than the target power threshold, the energy storage unit is controlled to charge from the external power grid; if the current state of charge is greater than or equal to the target power threshold, the energy storage unit is stopped from charging from the external power grid, and when the elevator system is in a power-consuming operation state, the energy storage unit is controlled to discharge to the elevator system first.

[0061] This embodiment can be understood as detailing a power threshold control method based on deep reinforcement learning. Building upon Embodiment 2, this embodiment further constructs and trains a DRL agent in the cloud. The agent's environment is defined as the elevator and energy storage system, and the reward function is defined as the power saving benefit. The agent continuously interacts with the environment and optimizes network parameters to autonomously learn the optimal threshold strategy that maximizes long-term rewards. The trained and converged agent (Actor network) is deployed in the cloud as a real-time inference service, calculating the optimal charging threshold based on the received system state stream. The cloud periodically sends control commands to the elevator-side control system via a secure, low-latency communication link. The control system uses the received commands as setpoints and strictly executes local deterministic charging and discharging rules to complete the final charging and discharging control.

[0062] Step SC1 includes steps SC11 to SC13. The control method deeply integrates the collaborative logic of cloud training and edge execution, and its specific execution steps are as follows: Step SC11: DRL Environment Modeling The edge control unit acquires multi-dimensional raw data of the elevator system and the external environment in real time and encapsulates it into a system state space. (at the moment) This vector, serving as the sole input for the agent to perceive its environment, contains the following feature dimensions: Time characteristics: time of day (sin / cos encoding), day of the week, and whether it is a holiday.

[0063] Elevator physical status: current car load (%), direction of travel (one-hot encoding), instantaneous speed, and current floor zone (e.g., high zone / middle zone / low zone).

[0064] Battery health status: current SOC, average battery temperature, and estimated SOH.

[0065] Power grid characteristics: Current real-time electricity price and its corresponding peak, valley, and normal periods.

[0066] Historical context information: Elevator average power trajectory and battery SOC change trend over the past 4 time periods (1 hour). It is n 3D continuous vectors provide a comprehensive environmental context for cloud-based inference.

[0067] The edge device uses a low-latency communication link (such as MQTT or CoAP protocol) to... Uploaded to the cloud. A pre-trained, converged policy network model (Actor network) is deployed in the cloud: Action space : It is a continuous scalar, namely the target threshold of the electrical energy output by the agent. .action The range is ,For example , For safety margin.

[0068] reward function Reward function Defined as energy saving benefit, the agent explores the optimal energy target threshold by maximizing the reward. .

[0069] Step SC12: Establishing the Cloud-Edge Collaborative System Architecture (1) Cloud system: Deployed on a high-performance server or cloud computing platform. Includes: 1) DRL training cluster: used to run agent training algorithms; 2) Post-training model service: encapsulates the trained agent (Actor network) into an online inference API.

[0070] (2) Edge system: Located locally in the elevator machine room. Includes: 1) Data acquisition and reporting unit: periodically collects elevator status, battery SOC, electricity price signals, etc., and packages and uploads them to the cloud; 2) Command receiving and execution unit: receives commands sent from the cloud. Dynamic SOC threshold, and drive the execution of local rules.

[0071] (3) Cloud-edge communication link: The system adopts protocols such as MQTT, CoAP or dedicated VPN to realize bidirectional communication of state data uplink and control command downlink. This architecture places the computationally intensive model training and policy optimization in the cloud with unlimited resources, ensuring the performance and evolvability of the algorithm; at the same time, it keeps the simple and reliable control execution at the edge, ensuring the real-time performance and reliability of the system.

[0072] Step SC13: Training and Optimization Iteration of Cloud-based DRL Agents This step is completed entirely in the cloud and is a process of "learning" and "evolving" for the agent. Advanced DRL algorithms suitable for continuous action spaces are employed, such as TD3 (Twin Delayed Deep Deterministic Policy Gradient) or SAC (Soft Actor-Critic). The Actor network (policy network) and two Critic networks (value networks) are constructed and initialized. The Actor network is configured with states... The Actor network takes this as input and adds exploration noise to obtain the final action. The Critic network input is ( , Output state-action value Q. The quadruple obtained from each interaction ( , , , The experience data is stored in a cloud-based experience replay buffer. During training, a small batch of experience data is periodically sampled randomly from the buffer, and the two Critic networks are updated by minimizing the temporal difference error. Using a gradient ascent strategy, the Actor network is updated along the gradient direction of the Q-value evaluated by the Critic network, enabling the Actor network to output actions with higher Q-values. This process is repeated millions to tens of millions of times. As the long-term accumulated reward... When the agent no longer shows significant growth over multiple consecutive training cycles, or when the policy performance tends to stabilize, it is considered that the agent has converged and training is complete.

[0073] In step SC2, after training is complete, the online application phase begins. The edge data acquisition unit records the real-time status at fixed intervals. The model inference service is encapsulated as a data packet and uploaded to the cloud via the cloud-edge communication link.

[0074] Cloud model service reception Then, it is fed into the deployed, trained Actor network. The network performs one forward propagation calculation and outputs the optimal charging threshold. This is the optimal decision made by the agent based on the current global state. The cloud will calculate the result. The value is encapsulated into a control command frame and sent to the command receiving unit at the edge in real time through the communication link.

[0075] In step SC3, the edge controller (control management unit) receives the data sent from the cloud. Then, this is used as the current charging target threshold. The internal engine of the control management unit does not contain any intelligent decision-making logic; it only strictly compares the current real-time battery SOC with the received threshold. In conjunction with the real-time electricity price signal obtained from the local clock, the same rules as step SA4 in Example 1 are executed.

[0076] Furthermore, this application also proposes a control system, the device including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the elevator system as described above. This can be implemented using a main controller, such as a DSP (Digital Signal Processor), FPGA (Field Programmable Gate Array), MCU (Microcontroller Unit), or SOC (System-on-Chip).

[0077] It is worth noting that since the control system of the present invention is applied to the control method of the elevator system described above, the embodiments of the control system of the present invention include all the technical solutions of all embodiments of the control method of the elevator system described above, and the achieved technical effects are exactly the same. This control system, by running a computer program, can achieve the same beneficial technical effects as the control method described above, namely, realizing intelligent prediction and dynamic scheduling of the energy storage unit, thereby improving energy recovery efficiency and reducing operating costs.

[0078] Furthermore, this application also proposes an elevator system, including an energy storage unit, a battery monitoring unit, a charging and discharging unit, and a control system as described above. The energy storage unit stores and releases electrical energy; the battery monitoring unit, connected to the energy storage unit, monitors its state of charge, voltage, temperature, and other parameters in real time; the charging and discharging unit, i.e., a bidirectional converter (PCS), has its input connected to the DC bus of the elevator inverter and its output connected to the energy storage unit, enabling controllable bidirectional energy flow between the DC bus and the energy storage unit; the control system, as described above, is communicatively connected to the battery monitoring unit, the charging and discharging unit, and the elevator main controller, receiving data from various sensors, such as signals from a bidirectional energy meter or current / voltage sensor installed on the DC bus to capture power generation / consumption, and generating control commands according to the control method described in this invention, sending them to the charging and discharging unit to execute specific charging and discharging actions.

[0079] In addition, it includes basic elevator drive and load-bearing components such as the traction machine, car, and control cabinet. This elevator system, through its integrated control system, constitutes a complete energy-saving elevator product. Its operation fully follows the description of the aforementioned method embodiments, and therefore possesses all the technical advantages of transforming the energy storage unit from passive buffering to active scheduling, comprehensively optimizing energy saving and economic benefits.

[0080] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A control method for an elevator system, the elevator system comprising an energy storage unit, characterized in that, include: The system acquires operational characteristic data of the elevator system, the state of charge of the energy storage unit, and the electricity price and time period of the external power grid. Based on the aforementioned operational characteristic data, a preset prediction model is used to calculate the net energy flow trend value of the elevator system during a future target period; wherein, the net energy flow trend value is used to characterize the balance between the regenerative electrical energy generated and the consumed electrical energy of the elevator system during the future target period; The target threshold for the energy storage unit is dynamically adjusted based on the net energy flow trend value. Based on the comparison between the current state of charge and the target energy threshold, and the electricity price period, the energy storage unit is controlled to perform corresponding charging and discharging actions.

2. The control method for the elevator system according to claim 1, characterized in that, The operational characteristic data includes: The power generation and power consumption of the elevator system are recorded according to a preset period, and a power generation history sequence and a power generation history sequence are constructed respectively. Alternatively, the charge of the energy storage unit can be recorded at a preset period to obtain a sequence of historical state of charge changes.

3. The control method for the elevator system according to claim 2, characterized in that, The specific steps for calculating the net energy flow trend value of the elevator system in a future target time period based on the operational characteristic data and using a preset prediction model include: Using the preset prediction model, the historical power generation sequence and the historical power generation sequence are weighted and calculated to obtain the predicted power generation and predicted power consumption for the future target period, respectively. The difference between the predicted power generation and the predicted power consumption is calculated to obtain the net energy flow trend value. Alternatively, the preset prediction model can be used to perform weighted calculations on the historical state of charge change sequence to obtain the predicted state of charge change, and the predicted state of charge change can be used as the net energy flow trend value.

4. The control method for the elevator system according to claim 1, characterized in that, The operational characteristic data includes the elevator car load, direction of travel, and speed; the specific steps for calculating the net energy flow trend value of the elevator system within a future target time period based on the operational characteristic data and using a preset prediction model include: Based on the car load, running direction and running speed, identify and statistically analyze the power generation-related operating conditions and power consumption-related operating conditions within a preset period; Based on the statistical results of the power generation-related operating conditions and the power consumption-related operating conditions, the value of the power generation operating condition and the value of the power consumption operating condition are calculated respectively, and a historical value sequence is constructed. Using the preset prediction model, the historical data in the historical value sequence that have the same time period characteristics as the future target time period are weighted and calculated to obtain the predicted power generation value and the predicted power consumption value, respectively. The net energy flow trend value is obtained based on the difference between the predicted power generation value and the predicted power consumption value.

5. The control method for the elevator system according to claim 3 or 4, characterized in that, The preset prediction model is a period-weighted prediction model with time decay weights, and the formula of the period-weighted prediction model includes: ; in, For predicted values, The attenuation factor is N, where N is the reference historical period number. For the first Observational data corresponding to each historical cycle period.

6. The control method for the elevator system according to claim 5, characterized in that, The electricity price periods include peak periods, average periods, and off-peak periods; the energy storage unit has a static full-charge threshold and a static discharge threshold; the specific steps for dynamically adjusting the energy target threshold of the energy storage unit based on the net energy flow trend value include: If the net energy flow trend value indicates a net power generation trend, the target power threshold is lowered to below the static full charge threshold of the energy storage unit in order to reserve energy storage capacity. If the net energy flow trend value indicates a net electricity consumption trend, the target electricity threshold is adjusted to be higher than the static discharge threshold of the energy storage unit in order to reserve electricity in advance and prevent insufficient electricity during discharge.

7. The control method for the elevator system according to claim 6, characterized in that, The specific steps for controlling the energy storage unit to perform charging and discharging actions based on the comparison result between the current state of charge and the target energy threshold, and the electricity price period, include: If the current electricity price is at its lowest point and the current state of charge is less than the target electricity threshold, then the energy storage unit is controlled to charge from the external power grid. If the current state of charge is greater than or equal to the target power threshold, the energy storage unit is stopped from charging from the external power grid, and when the elevator system is in a power-consuming operation state, the energy storage unit is controlled to discharge to the elevator system first.

8. A control method for an elevator system, characterized in that, include: The system acquires operational characteristic data of the elevator system, the state of charge of the energy storage unit, and the electricity price and time period of the external power grid. The system state vector, which includes the operational characteristic data, the state of charge of the energy storage unit, the electricity price period and historical context information, is input into a preset strategy network model so that the strategy network model outputs the electricity target threshold corresponding to the current system state. Based on the comparison between the current state of charge and the target energy threshold, and the electricity price period, the energy storage unit is controlled to perform corresponding charging and discharging actions.

9. A control system, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the elevator system as described in any one of claims 1 to 8.

10. An elevator system, characterized in that, It includes an energy storage unit, a battery monitoring unit, a charging and discharging unit, and a control system as described in claim 9.

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