Intelligent elevator passenger flow analysis and optimization system

Abstract

The invention relates to the technical field of intelligent buildings and elevators, and discloses an intelligent elevator passenger flow analysis and optimization system which comprises a flow monitoring module used for monitoring the passenger flow of an elevator waiting area in real time and obtaining passenger flow data; the data processing module is used for analyzing the passenger flow data and predicting the passenger flow in a future time period; the elevator dispatching module optimizes the dispatching strategy of the elevator according to the prediction result of the data processing module, and improves the efficiency of passengers to get on and off the elevator; and the energy efficiency optimization module adjusts the acceleration and deceleration process of the elevator according to the running state of the elevator so as to reduce the energy consumption of the elevator. By monitoring the passenger flow in the elevator waiting area in real time and conducting accurate analysis, the passenger flow in the future time period can be predicted, the elevator dispatching strategy is optimized, the waiting time of passengers is shortened, the waiting efficiency is improved, and intelligent dispatching is conducted on the elevator based on the shortest waiting time and the minimum energy consumption target.

Description

Technical Field

[0001] The present invention relates to the field of intelligent building and elevator technology, and in particular to an intelligent elevator passenger flow analysis and optimization system. Background Art

[0002] With the acceleration of urbanization, the number of high-rise buildings and commercial complexes continues to increase. As the core equipment of vertical transportation, the efficiency of elevator management and dispatching directly affects the operating efficiency of buildings and the travel experience of passengers. In buildings with high traffic, the waiting time and energy efficiency of elevators become key issues. How to reduce elevator energy consumption and improve resource utilization while ensuring the travel experience of passengers has become a technical challenge that needs to be solved in modern buildings. Most traditional elevator dispatching systems are based on simple control logic and cannot fully adapt to the dynamically changing passenger flow and various influencing factors inside and outside the building.

[0003] In the prior art, elevator dispatching systems generally rely on preset dispatching strategies or timing strategies to perform basic control over the operation of elevators. However, with the increase in building utilization and the fluctuation of passenger demand, traditional dispatching methods often cannot meet the needs of efficient dispatching, resulting in long waiting times for passengers, uneven waiting times, and low elevator energy efficiency. In addition, existing energy efficiency optimization solutions are mostly limited to static acceleration and deceleration control, lacking energy efficiency management for the entire process of elevator operation, and unable to effectively respond to changes in different usage scenarios, resulting in a certain amount of energy waste.

[0004] The main problem with the existing technology is the lack of dynamic adaptability and intelligent scheduling mechanism, which leads to low elevator operation efficiency, long passenger waiting time and high energy consumption. Especially in multi-elevator systems, the lack of collaborative scheduling function cannot effectively optimize the overall operation status of the elevator group. These problems urgently need to be solved through a more intelligent, dynamically adaptive elevator scheduling and energy efficiency optimization system. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent elevator passenger flow analysis and optimization system, which solves the problem that in the existing multi-elevator system, there is a lack of collaborative scheduling function and the overall operating status of the elevator group cannot be effectively optimized.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent elevator passenger flow analysis and optimization system, the system comprising:

[0007] The flow monitoring module is used to monitor the passenger flow in the elevator waiting area in real time and obtain passenger flow data;

[0008] A data processing module, used to analyze the passenger flow data and predict the passenger flow in future time periods;

[0009] The elevator dispatching module optimizes the dispatching strategy of the elevator according to the prediction results of the data processing module to improve the efficiency of passengers getting on and off the elevator;

[0010] The energy efficiency optimization module adjusts the acceleration and deceleration process of the elevator according to the operating status of the elevator to reduce the energy consumption of the elevator.

[0011] Preferably, the data processing module predicts future passenger flow based on historical flow data, time period, and weather factors through a machine learning algorithm.

[0012] Preferably, the elevator dispatching module adopts a multi-objective optimization algorithm to dispatch elevators based on the objectives of shortest waiting time and minimum energy consumption to optimize passengers' waiting experience.

[0013] Preferably, the energy efficiency optimization module reduces power consumption by adjusting the acceleration and deceleration process of the elevator and applying kinetic energy recovery technology.

[0014] Preferably, the system also includes an adaptive learning module, which automatically adjusts the scheduling and energy efficiency optimization strategies through real-time traffic data and elevator operation status, and continuously optimizes system performance based on a reinforcement learning algorithm.

[0015] Preferably, the adaptive learning module is based on a deep learning algorithm and combines elevator group scheduling data to predict the impact of different time periods, floors and passenger behaviors on elevator operation, and automatically adjusts scheduling and energy efficiency optimization strategies.

[0016] Preferably, the system also includes an elevator group scheduling module, which performs collaborative scheduling of multiple elevator groups based on cross-building data sharing to improve overall operating efficiency and reduce idle loads.

[0017] Preferably, the elevator group dispatching module dynamically adjusts the elevator operation strategy according to the real-time passenger flow data and historical data, and optimizes the elevator dispatching between multiple buildings.

[0018] Preferably, the system performs real-time data collection, transmission and processing through a control system, automatically optimizes elevator scheduling and energy efficiency strategies, and adjusts elevator operating parameters according to predicted traffic.

[0019] Preferably, the control system provides real-time feedback on the operating status of the elevators through integrated multiple data sources and sensors, and automatically adjusts the elevator scheduling algorithm through a computing platform to ensure the overall efficiency of the elevator group operation.

[0020] The present invention provides an intelligent elevator passenger flow analysis and optimization system. It has the following beneficial effects:

[0021] 1. The present invention can predict the passenger flow in the future time period by real-time monitoring of the passenger flow in the elevator waiting area and conducting accurate analysis, and optimize the elevator scheduling strategy, thereby reducing the waiting time of passengers and improving the waiting efficiency. The present invention adopts a multi-objective optimization algorithm and intelligently schedules the elevator based on the shortest waiting time and minimum energy consumption goals, which can effectively improve the overall operating efficiency of the elevator, ensure the waiting experience of passengers and reduce the energy consumption of the elevator operation.

[0022] 2. The present invention adjusts the acceleration and deceleration process of the elevator through the energy efficiency optimization module, and combines the kinetic energy recovery technology. The present invention can significantly reduce the energy consumption of the elevator and improve the energy efficiency of the elevator. Especially when the load is high or the operating conditions are unstable, it can effectively avoid energy waste and extend the service life of the elevator.

[0023] 3. The present invention introduces an adaptive learning module, adopts reinforcement learning and deep learning algorithms, and continuously optimizes the scheduling strategy and energy efficiency optimization scheme based on real-time data and historical data. The module can automatically adapt to different passenger flow patterns and operating conditions, and dynamically adjust the elevator operation strategy to maintain the optimal performance of the system.

[0024] 4. The present invention uses an elevator group dispatching module to achieve coordinated dispatching between multiple elevators across buildings and perform dynamic adjustments based on shared real-time data. This function not only improves the dispatching efficiency of elevators within a single building, but also optimizes the use efficiency of elevators between different buildings, reduces elevator idle time, and improves overall resource utilization.

[0025] 5. The adaptive learning module of the present invention is combined with a deep learning algorithm, which can adjust the elevator dispatching strategy according to multi-dimensional factors such as different time periods, weather changes, and passenger behavior, thereby enhancing the adaptability and flexibility of the system and maintaining excellent operating performance in a changing environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Please see attached Figure 1 The embodiment of the present invention provides an intelligent elevator passenger flow analysis and optimization system, the system comprising:

[0029] The flow monitoring module is used to monitor the passenger flow in the elevator waiting area in real time and obtain passenger flow data;

[0030] A data processing module is used to analyze passenger flow data and predict passenger flow in future periods;

[0031] The elevator dispatching module optimizes the elevator dispatching strategy based on the prediction results of the data processing module to improve the efficiency of passengers getting on and off the elevator;

[0032] The energy efficiency optimization module adjusts the acceleration and deceleration process of the elevator according to the operating status of the elevator to reduce the energy consumption of the elevator.

[0033] The data processing module uses machine learning algorithms to predict future passenger flow based on historical traffic data, time periods, and weather factors.

[0034] The elevator dispatching module adopts a multi-objective optimization algorithm to dispatch elevators based on the goals of shortest waiting time and minimum energy consumption to optimize passengers' waiting experience.

[0035] The energy efficiency optimization module reduces power consumption by adjusting the acceleration and deceleration process of the elevator and applying kinetic energy recovery technology.

[0036] The system also includes an adaptive learning module that automatically adjusts scheduling and energy efficiency optimization strategies through real-time traffic data and elevator operating status, and continuously optimizes system performance based on reinforcement learning algorithms.

[0037] The adaptive learning module is based on a deep learning algorithm and combines elevator group dispatch data to predict the impact of different time periods, floors and passenger behaviors on elevator operation, and automatically adjusts the dispatch and energy efficiency optimization strategies.

[0038] The system also includes an elevator group dispatching module, which coordinates the dispatching of multiple elevator groups based on cross-building data sharing to improve overall operating efficiency and reduce idle loads.

[0039] The elevator group dispatch module dynamically adjusts the elevator operation strategy based on real-time passenger flow data and historical data, and optimizes elevator dispatching between multiple buildings.

[0040] The system collects, transmits and processes real-time data through the control system, automatically optimizes the elevator's scheduling and energy efficiency strategy, and adjusts the elevator's operating parameters based on predicted traffic.

[0041] The control system integrates multiple data sources and sensors to provide real-time feedback on the operating status of the elevator, and automatically adjusts the elevator scheduling algorithm through the computing platform to ensure the overall efficiency of the elevator group operation.

[0042] In one embodiment, the flow monitoring module:

[0043] Each elevator waiting area is equipped with a variety of sensors, including infrared sensors, video surveillance cameras and pressure sensors, which monitor the passenger flow and distribution in the waiting area in real time. The flow monitoring module can not only calculate the number of passengers at each elevator entrance in real time, but also identify the waiting demand of different floors. The acquired flow data is uploaded to the data processing module in real time via the wireless network.

[0044] During the morning rush hour, a large number of passengers gather on the ground floor and in the elevator waiting area near the subway exit, while there are fewer passengers on the upper floors. The system can collect and analyze this flow information in real time, providing accurate data support for the elevator dispatch module.

[0045] Data processing module:

[0046] The data processing module uses machine learning algorithms to analyze data and predict future traffic based on historical passenger traffic, time periods, weather conditions, holidays, and other factors. Through deep learning models, the system can predict future passenger demand based on multiple variables such as time periods, days of the week, and weather, and accurately grasp the traffic change trend for each period.

[0047] The system can predict high demand on a specific floor or time period, and plan elevator dispatch in advance to avoid insufficient elevators or excessive waiting during peak hours.

[0048] Elevator dispatch module:

[0049] The elevator dispatch module uses a multi-objective optimization algorithm to adjust the elevator operation strategy based on the traffic prediction results provided by the data processing module. This algorithm not only considers the shortest waiting time, but also optimizes the energy efficiency target, improving the elevator operation efficiency by reasonably allocating elevator resources and scheduling strategies.

[0050] If it is predicted that the passenger flow on the ground floor will be large during a certain period of time, the system will automatically dispatch more elevators to stop at that floor and continuously adjust the elevator's running path based on real-time data to avoid empty elevators or excessive concentration of passengers in a certain elevator.

[0051] During off-peak hours, the system automatically reduces the number of unloaded elevators to avoid wasting energy.

[0052] Energy efficiency optimization module:

[0053] This module uses dynamic adjustment technology to reduce the energy consumption of the elevator by monitoring the acceleration and deceleration process and the operating status of the elevator. When the elevator accelerates and decelerates, the kinetic energy of the elevator during the downward process is converted into electrical energy storage by applying the elevator kinetic energy recovery technology, reducing dependence on the power grid and reducing power consumption.

[0054] When the elevator is going down, the kinetic energy recovery system converts the kinetic energy generated by the elevator into electrical energy and stores it in the battery for subsequent use when the elevator goes up. This technology effectively improves the overall energy efficiency of the elevator system, especially in buildings with high floors, where the elevator's downward process has great recovery potential.

[0055] Adaptive learning modules:

[0056] The adaptive learning module uses a reinforcement learning algorithm to dynamically adjust the dispatching strategy in real time based on data such as passenger flow and weather changes. The module can automatically optimize the elevator's operating strategy and energy efficiency strategy based on feedback during system operation.

[0057] The system automatically adjusts the elevator dispatching strategy by learning the impact of different time periods, holidays and special events (such as large-scale events, exhibitions, etc.) on passenger flow to ensure efficient use of elevator resources.

[0058] If the system finds that the traffic on a certain floor increases abnormally within a certain period of time, the system will automatically adjust the scheduling strategy to improve the availability of the elevator on that floor.

[0059] Elevator group dispatch module:

[0060] The elevator group dispatch module shares data across buildings in real time, enabling coordinated dispatch between multiple elevator groups. By optimizing the dispatch of multiple elevator groups, it reduces the idle rate and conflicts between elevators, and improves the overall elevator utilization efficiency.

[0061] In a commercial complex, multiple elevator groups (such as elevators between commercial buildings and office buildings) will share real-time traffic data to optimize the scheduling between multiple elevators. For example, when the elevator group in one building is too crowded, the system can dispatch some elevator resources to another building to reduce congestion.

[0062] Control system:

[0063] The control system is the brain of the entire system, coordinating the work of all modules. The control system automatically adjusts the elevator dispatch strategy and energy efficiency optimization strategy based on real-time traffic data, predicted traffic and dispatch requirements. Through an efficient computing platform, the system can process large amounts of data in real time and adjust the elevator's operating parameters to achieve the best dispatch effect.

[0064] When the predicted traffic data indicates that the demand on a certain floor increases sharply, the control system will immediately make adjustments based on the data feedback, dispatching more elevators to that floor while avoiding excessive concentration or empty-load operation of elevators on other floors.

[0065] Implementation steps:

[0066] Data collection and processing:

[0067] After the system is installed, the flow monitoring module will first collect passenger flow data in real time. The data processing module will conduct a preliminary analysis of the data and establish a prediction model. In the early stage, the system can predict passenger flow by setting default parameters.

[0068] During peak hours, based on real-time traffic data and forecast data, the system will dispatch more elevators to specific floors in advance to avoid congestion.

[0069] Elevator dispatching and optimization:

[0070] After the system receives real-time traffic data and combines it with the prediction results, the elevator dispatch module automatically optimizes the elevator dispatch path. The system uses an optimization algorithm to determine the elevator's stop floors, up and down time and other parameters to ensure that passengers have as little waiting time as possible and avoid empty-load operation.

[0071] Energy efficiency optimization:

[0072] The system intelligently adjusts the acceleration and deceleration process of the elevator through the energy efficiency optimization module. For example, the system can determine whether kinetic energy recovery technology can be used based on the current operating status of the elevator to convert the kinetic energy during the elevator's descent into electrical energy for storage.

[0073] When the elevator accelerates and decelerates rapidly, the system adjusts the elevator speed according to real-time operating parameters to ensure efficient and smooth operation of the elevator.

[0074] System adaptation and adjustment:

[0075] As time goes by, the system continuously adjusts through the adaptive learning module. For example, if the traffic on a certain floor increases significantly during holidays, the system will automatically adjust the elevator dispatch strategy based on this information to ensure efficient use of elevator resources during holidays.

[0076] Cross-building scheduling and collaboration:

[0077] With the collaborative work of multiple elevator groups between buildings, the system dynamically adjusts the elevator operation strategy based on real-time traffic data, historical data and passenger behavior. Through data sharing, the system can optimize elevator scheduling between building groups, reduce no-load operation, and improve the overall operation efficiency of elevators.

[0078] Experimental Example 1:

[0079] Elevator dispatch optimization effect experiment

[0080] Purpose:

[0081] Verify the improvement effect of the elevator dispatching module of the present invention on passenger waiting time and elevator utilization rate during peak hours compared with the traditional dispatching system.

[0082] Experimental equipment:

[0083] Experimental site: a commercial complex, number of floors: 20, number of elevators: 2.

[0084] Experimental system: An intelligent elevator passenger flow analysis and optimization system based on the present invention, including a flow monitoring module, a data processing module, an elevator dispatching module, an energy efficiency optimization module, etc.

[0085] Experimental steps:

[0086] Experimental environment setup: Set up experimental scenarios for different time periods (such as morning rush hour, lunch hour, and evening rush hour), and record the operating parameters of all elevators (including elevator stop time, passenger capacity, waiting time, etc.).

[0087] Comparison group setting: The experiment is divided into two groups:

[0088] Experimental group: the elevator dispatch optimization system according to the present invention is applied.

[0089] Control group: Use the traditional elevator dispatching system (i.e. dispatching at fixed time intervals and in a fixed order).

[0090] Data collection: The traffic monitoring module collects the waiting flow data of passengers on each floor in real time, and records the actual operation status of each round of scheduling (including the travel time, stop time, passenger boarding and alighting time of each elevator, etc.).

[0091] Comparative analysis: Calculate and compare indicators such as passenger waiting time, elevator utilization rate, and empty load rate between the experimental group and the control group.

[0092] index Embodiment 1 Experiment 1 Percent Change Average waiting time for passengers 45 64 -30％ Elevator utilization rate 88％ 70％ +25％ No-load rate 12％ 25％ -48％ Elevator dispatch response time 6 10 -40％ Energy consumption 15.2 18.3 -17％

[0093] Table 1

[0094] Average waiting time for passengers: The average waiting time of the experimental group during peak hours was significantly lower than that of the control group.

[0095] Elevator utilization rate: The experimental group had a higher elevator utilization rate, which reduced idle operation and improved the overall efficiency of the elevator.

[0096] Empty rate: The empty rate of elevators in the experimental group was reduced, which means that elevator scheduling is more accurate.

[0097] Elevator dispatch response time: The experimental group had a shorter dispatch response time, which improved the overall passenger experience.

[0098] Energy consumption: Under the condition of large passenger flow, the experimental group showed significant energy saving effect compared with the traditional dispatching system.

[0099] Experimental Example 2:

[0100] Energy efficiency optimization module experiment

[0101] Purpose:

[0102] Verify the energy-saving effect of the energy efficiency optimization module in elevator operation, especially the energy recovery performance during acceleration and deceleration.

[0103] Experimental equipment:

[0104] Experimental site: commercial building, 20 floors, 2 elevators on each floor, 2 elevators were used during the experiment.

[0105] Experimental system: includes energy efficiency optimization module and elevator control system.

[0106] Experimental steps:

[0107] Experimental environment setting: Set different operation scenarios, including up and down, acceleration, deceleration and other stages. Use the control system to simulate high-load and low-load operation states.

[0108] Energy efficiency test: In the experiment, the energy consumption of the elevator is monitored, including power consumption, battery power, kinetic energy recovery efficiency, etc. The power consumption with and without the energy efficiency optimization module is compared.

[0109] Data collection: Real-time recording of energy recovery during elevator acceleration and deceleration, as well as energy consumption during the elevator's upward and downward movements.

[0110] index With energy efficiency optimization module No energy efficiency optimization module Percent Change Kinetic energy recovery rate 20％ 5％ +15％ Power consumption 12.6 14.4 -12.5％ Elevator acceleration time 3 4 -25％ Elevator deceleration time 2 3 -33％ Total operating energy efficiency 85％ 70％ +21.4％

[0111] Table 2

[0112] Kinetic energy recovery rate: The energy recovered by elevators with energy efficiency optimization modules during the descent process is significantly higher than that of elevators without optimization modules.

[0113] Power consumption: Power consumption is significantly reduced with the optimization module enabled.

[0114] Acceleration / deceleration time: The acceleration and deceleration process of elevators with energy efficiency optimization modules is more efficient, reducing energy waste.

[0115] Total operating energy efficiency: The overall energy efficiency of the energy efficiency optimization module is improved by 21.4%.

[0116] Experimental Example 3:

[0117] Experiment on optimization effect of adaptive learning module

[0118] Purpose:

[0119] Verify the optimization role of the adaptive learning module in elevator scheduling, especially the scheduling effect in different time periods and special events (such as holidays, activities, etc.).

[0120] Experimental steps:

[0121] Experimental environment setting: Select a high-rise commercial building with relatively complex elevator scheduling requirements, and divide it into multiple experimental scenarios such as weekdays, holidays, and specific activities.

[0122] Experimental group setting: The system is divided into two groups for comparative experiments:

[0123] Experimental group: applied adaptive learning module (reinforcement learning).

[0124] Control group: applied the traditional rule-based elevator dispatching system.

[0125] Data collection: record passenger flow, waiting time, elevator stop time, etc. in different time periods.

[0126] Data analysis: Analyze the dispatch efficiency, passenger waiting time, energy consumption and other indicators of the two systems during holidays, large-scale events and peak hours.

[0127]

[0128] Table 3

[0129] Passenger waiting time: The waiting time for passengers in the experimental group was significantly shortened, which improved the passenger experience.

[0130] Elevator utilization and empty load rate: Through intelligent scheduling, the utilization rate of elevators is improved while the empty load rate is reduced.

[0131] Energy consumption: The energy consumption of the elevator system in the experimental group was greatly reduced, showing excellent energy-saving effects.

[0132] Passenger comfort score: The comfort of passengers in the experimental group's elevator system was significantly improved, indicating that scheduling optimization and energy efficiency control can significantly improve user experience.

[0133] Working principle: The intelligent elevator passenger flow analysis and optimization system of the present invention integrates multiple intelligent modules to achieve optimized scheduling, energy efficiency management and passenger experience improvement of elevator operation. The working principle of the system is based on technical means such as real-time monitoring data collection, data analysis, prediction model, optimized scheduling, energy efficiency regulation and adaptive learning, aiming to improve the operating efficiency of the elevator group, reduce the empty load rate, reduce energy consumption, and optimize the passenger waiting experience; first, the system monitors the passenger flow in the elevator waiting area in real time through the flow monitoring module. The module captures the number and distribution information of passengers in the waiting area through sensors installed in the elevator waiting area (such as infrared sensors, laser rangefinders, cameras, etc.), and uploads the data to the data processing module in real time. The flow monitoring module provides accurate basic data for subsequent analysis and prediction through high-frequency data collection; next, the data processing module analyzes the passenger flow data, uses machine learning algorithms, and combines historical flow data, time period, weather conditions and other factors to predict the changes in passenger flow in the future period. This module continuously optimizes the prediction accuracy by training the prediction model, helping the elevator dispatching system to prepare response strategies in advance to avoid congestion or empty load caused by passenger flow fluctuations; the elevator dispatching module adopts a multi-objective optimization algorithm based on the prediction results of the data processing module, and formulates the elevator dispatching strategy based on the real-time passenger flow data and prediction results. This dispatching strategy not only takes into account the waiting time of passengers to ensure that the elevator can quickly respond to passenger needs and reduce waiting time, but also optimizes the operation path and dispatching sequence of the elevator to achieve the optimization of energy efficiency. Through the goals of shortest waiting time and minimum energy consumption, the elevator dispatching module can reasonably allocate tasks among multiple elevators, thereby improving the efficiency of the entire elevator group; in the energy efficiency optimization module, the system automatically adjusts the acceleration and deceleration process of the elevator by real-time monitoring of the elevator's operating status to ensure that the elevator runs at the most appropriate speed to reduce energy consumption. This module uses kinetic energy recovery technology. When the elevator decelerates or brakes, it recovers part of the kinetic energy and converts it into electrical energy, which is stored in the battery for subsequent use. This not only reduces power consumption, but also improves the green environmental performance of the elevator system; in addition, the present invention also includes an adaptive learning module, which is based on a reinforcement learning algorithm and dynamically adjusts elevator scheduling and energy efficiency optimization strategies through real-time collected traffic data, elevator operation status, passenger behavior and other information. The adaptive learning module can perform self-optimization under different operating conditions and continuously improve the scheduling efficiency and energy utilization efficiency of the elevator system. Through the feedback mechanism of reinforcement learning, the system can quickly adapt to different passenger behavior patterns and external environmental changes, thereby improving the flexibility and intelligence level of the system; on this basis, the elevator group scheduling module uses cross-building data sharing to achieve collaborative scheduling of multiple elevator groups. This module optimizes the scheduling strategy of elevators between multiple buildings by analyzing real-time passenger flow data and historical operation data between different buildings, so as to reduce no-load operation and improve the utilization rate and overall efficiency of elevators.Real-time resource allocation can be carried out among multiple elevator groups according to actual needs to ensure that passenger flow is not restricted during peak hours and reduce passenger congestion and waiting time caused by insufficient elevator resources; finally, the control system integrates traffic monitoring, data processing, elevator scheduling, energy efficiency optimization, and adaptive learning modules, responsible for the overall data collection, transmission, processing and decision-making. The control system ensures the coordination of all modules by providing real-time feedback on the operating status of the elevator and the working data of each module. Through intelligent scheduling and real-time adjustment, the control system can respond to changes in system operation in a timely manner, automatically optimize the operating parameters of the elevator, and ensure the efficient operation of the elevator group in various complex scenarios.

Claims

1. An intelligent elevator passenger flow analysis and optimization system, characterized in that: The system comprises: The flow monitoring module is used to monitor the passenger flow in the elevator waiting area in real time and obtain passenger flow data; A data processing module, used to analyze the passenger flow data and predict the passenger flow in future time periods; The elevator dispatching module optimizes the dispatching strategy of the elevator according to the prediction results of the data processing module to improve the efficiency of passengers getting on and off the elevator; The energy efficiency optimization module adjusts the acceleration and deceleration process of the elevator according to the operating status of the elevator to reduce the energy consumption of the elevator.

2. The intelligent elevator passenger flow analysis and optimization system according to claim 1 is characterized in that: The data processing module predicts future passenger flow based on historical flow data, time period, and weather factors through machine learning algorithms.

3. The intelligent elevator passenger flow analysis and optimization system according to claim 1 is characterized in that: The elevator dispatching module adopts a multi-objective optimization algorithm to dispatch elevators based on the goals of shortest waiting time and minimum energy consumption to optimize passengers' waiting experience.

4. The intelligent elevator passenger flow analysis and optimization system according to claim 1 is characterized in that: The energy efficiency optimization module reduces power consumption by adjusting the acceleration and deceleration process of the elevator and applying kinetic energy recovery technology.

5. The intelligent elevator passenger flow analysis and optimization system according to claim 1 is characterized in that: The system also includes an adaptive learning module, which automatically adjusts scheduling and energy efficiency optimization strategies through real-time traffic data and elevator operation status, and continuously optimizes system performance based on a reinforcement learning algorithm.

6. The intelligent elevator passenger flow analysis and optimization system according to claim 5, characterized in that: The adaptive learning module is based on a deep learning algorithm and combines elevator group dispatch data to predict the impact of different time periods, floors and passenger behaviors on elevator operation, and automatically adjusts the dispatch and energy efficiency optimization strategies.

7. The intelligent elevator passenger flow analysis and optimization system according to claim 1 is characterized in that: The system also includes an elevator group scheduling module, which coordinates the scheduling of multiple elevator groups based on cross-building data sharing to improve overall operating efficiency and reduce idle loads.

8. The intelligent elevator passenger flow analysis and optimization system according to claim 1 is characterized in that: The elevator group dispatching module dynamically adjusts the elevator operation strategy according to real-time passenger flow data and historical data, and optimizes the elevator dispatching between multiple buildings.

9. The intelligent elevator passenger flow analysis and optimization system according to claim 1, characterized in that: The system collects, transmits and processes real-time data through the control system, automatically optimizes the elevator's scheduling and energy efficiency strategy, and adjusts the elevator's operating parameters based on predicted traffic.

10. An intelligent elevator passenger flow analysis and optimization system according to claim 9, characterized in that: The control system provides real-time feedback on the operating status of the elevator through integrated multiple data sources and sensors, and automatically adjusts the elevator scheduling algorithm through a computing platform to ensure the overall efficiency of the elevator group operation.

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