Energy-saving elevator control system

Abstract

The present invention discloses an energy-saving elevator control system, which relates to the field of energy-saving elevator technology and includes a historical data collection and analysis module, a periodic self-inspection scheduling module, a real-time data acquisition module, a key data extraction and processing module, a deep learning evaluation and status classification module, and a dynamic self-inspection cycle adjustment module: the historical data collection and analysis module collects and analyzes the historical operating data of each elevator, sets a different self-inspection cycle for each elevator, and ensures that each elevator performs an appropriate inspection frequency according to its actual operating needs, avoiding potential faults due to untimely self-inspection. The present invention intelligently evaluates the operating status of the elevator and dynamically adjusts the self-inspection cycle. This solution improves the fault warning and maintenance efficiency of high-frequency operating elevators and reduces potential faults and safety risks. At the same time, optimizing the self-inspection cycle reduces unnecessary inspections and energy consumption, improves the operating efficiency of the elevator, achieves the goal of energy conservation and emission reduction, and improves operational efficiency while ensuring elevator safety.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving elevators, and in particular to an energy-saving elevator control system. Background Art

[0002] Energy-efficient elevator control refers to technology that reduces energy consumption and improves overall energy efficiency by optimizing elevator operation and control strategies in multi-elevator environments, such as shopping malls and office buildings. Specifically, energy-efficient elevator control systems achieve efficient operation through precise scheduling algorithms, intelligent prediction, and dynamic adjustment. For example, in a multi-elevator environment, the system can rationally allocate elevator operating time and load capacity based on real-time demand, reduce empty runs, and optimize the order of elevator up and down flights, ensuring balanced and efficient operation between floors. Furthermore, the system can flexibly adjust elevator speeds and dwell times based on floor demand forecasts to reduce unnecessary stops and waiting. Modern energy-efficient elevators also widely utilize variable frequency drive technology, enabling the elevator motor to dynamically adjust power based on actual load and speed requirements, thereby avoiding energy waste. Furthermore, coordinated multi-elevator scheduling can further improve overall efficiency, reduce power waste, and achieve energy conservation and emission reduction goals. The combined application of these technologies not only significantly reduces elevator energy consumption but also enhances building operational efficiency and user experience.

[0003] The existing technology has the following deficiencies:

[0004] Existing technologies typically perform a unified periodic self-test on all elevators to identify any deviations from normal operation. However, the operating conditions and workloads of each elevator vary, so a unified self-test solution may not meet the testing needs of high-frequency elevators. Due to the frequent starts and stops and the higher workloads, key components of high-frequency elevators (such as motors, braking systems, transmissions, and door control systems) are more susceptible to fatigue and premature aging. In this case, a unified periodic self-test may not meet the specific needs of high-frequency elevators, resulting in excessively long self-test cycles or infrequent inspections, missing critical fault warning windows. Faults that go undetected can gradually develop into serious problems, ultimately leading to elevator downtime, service interruptions, and even safety incidents. Therefore, a more flexible and targeted self-test strategy, tailored to the actual operating conditions of each elevator, is needed to ensure that high-frequency elevators operate optimally and avoid potential failures and safety risks.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of this invention is to provide an energy-saving elevator control system. By intelligently assessing the elevator's operating status and dynamically adjusting the self-check cycle, this solution effectively improves elevator fault warning capabilities and maintenance efficiency, ensuring timely inspections of high-frequency elevators and reducing potential faults and safety risks. Furthermore, rationally adjusting the self-check cycle optimizes elevator resource utilization, reduces unnecessary inspections and energy consumption, improves elevator operating efficiency and energy efficiency, and achieves energy conservation and emission reduction goals. This solution not only ensures elevator safety, but also improves operational efficiency and energy savings, thereby addressing the aforementioned background technology issues.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an energy-saving elevator control system, comprising a historical data collection and analysis module, a periodic self-check scheduling module, a real-time data acquisition module, a key data extraction and processing module, a deep learning evaluation and status classification module, and a dynamic self-check cycle adjustment module:

[0008] The historical data collection and analysis module collects and analyzes the historical operating data of each elevator, sets different self-inspection cycles for each elevator, and ensures that each elevator is inspected at an appropriate frequency based on its actual operating needs, thus avoiding potential failures due to untimely self-inspections.

[0009] The regular self-inspection scheduling module ensures that all elevators conduct regular self-inspections according to the set self-inspection cycle, ensuring that each elevator is fully inspected within the preset self-inspection cycle, and that potential problems are discovered and addressed in a timely manner to maintain the normal operation of the elevator.

[0010] Real-time data acquisition module, which collects the operation data of each elevator in real time during the operation of the elevator;

[0011] The key data extraction and processing module extracts key features reflecting the high-frequency operation of each elevator from the real-time collected operating data during the monitoring window, analyzes the extracted key features, and inputs the analyzed features into a pre-trained deep learning model to analyze the elevator's operating data and evaluate the elevator's current operating status.

[0012] The deep learning evaluation and status classification module divides the current elevator operation status into two categories: "high-frequency operation" and "stable operation" based on the evaluation results of the deep learning model;

[0013] The dynamic self-inspection cycle adjustment module continues to perform routine self-inspections according to the preset self-inspection cycle for elevators assessed as "smoothly running". For elevators assessed as "high-frequency running", the module dynamically adjusts their self-inspection cycle based on the evaluation results of the deep learning model, shortens the self-inspection interval, detects and handles potential faults in advance, and reduces the risk of downtime and safety accidents.

[0014] Preferably, for each elevator, key features reflecting the high-frequency operation of the elevator are extracted from the operating data collected in real time. The extracted features include the time difference of the elevator operation cycle and the frequency of the elevator position adjustment. During the set monitoring window, after analyzing the extracted features, an elevator operation time difference index and an elevator position adjustment index are generated respectively. The elevator operation time difference index quantifies the time difference required for the elevator to go from one floor to another, reflecting the response speed and efficiency of the elevator under high-frequency operation; the elevator position adjustment index quantifies the frequency of fine-tuning of the elevator's position during the process of going up and down floors, reflecting the precise control and adjustment requirements of the elevator under high-frequency operation.

[0015] Preferably, in the monitoring window, the specific steps of analyzing the elevator operation cycle time difference and generating the elevator operation time difference index are as follows:

[0016] In the monitoring window, the time difference data extracted from the elevator operation cycles between multiple floors is used to generate the time difference fluctuation. The calculation expression is as follows:

[0017]

[0018] Where, Indicates the The time difference between the two running cycles, is the time difference of the last running cycle, Is the weighting coefficient, which is used to adjust the influence of the relative rate of change. is the time difference fluctuation value, indicating the The relative change in the time difference between the current operating cycle and the previous operating cycle;

[0019] In order to further understand the operating status of the elevator, a cumulative index of time difference fluctuation is constructed, and the calculation expression is as follows:

[0020]

[0021] Where, is the cumulative index of time difference fluctuations, i.e. the time point The cumulative index of time difference fluctuation at the moment, It's the time point. is the monitoring window length, is the exponential decay factor, is the natural exponential function, is the decay time constant, is the time series index;

[0022] Finally, by using the time difference volatility accumulation index Further processing is performed to generate the elevator running time difference index, and the calculation expression is as follows:

[0023]

[0024] Where, is the elevator running time difference index, is the scaling factor, is the sensitivity adjustment factor.

[0025] Preferably, in the monitoring window, the frequency of elevator position adjustment is analyzed to generate the elevator position adjustment index in the following specific steps:

[0026] In the monitoring window, first collect the elevator operation data in real time. Based on the acquired data, define the dynamic frequency factor for elevator position adjustment. The calculation expression is as follows:

[0027]

[0028] Where, It's the elevator in the monitoring window Number of position adjustments within It's the elevator in the monitoring window The total number of floors traveled within is the monitoring window duration, It's the elevator in the monitoring window The load factor within is the elevator position dynamic frequency factor;

[0029] When evaluating the frequency of elevator position adjustment, a weighting factor for the adjustment amplitude is introduced to enhance the assessment of the need for fine-tuning during frequent elevator operation. The calculation expression is as follows:

[0030]

[0031] Where, is the adjustment amplitude weight factor, It is The magnitude of the adjustment, Monitoring Window The maximum adjustment range within is the amplitude-weighted index;

[0032] Dynamic frequency factor based on elevator position and adjustment amplitude weighting factors Generate the elevator position adjustment index, the calculation expression is as follows:

[0033]

[0034] Where, It is The duration of the position adjustment, is the elevator position adjustment index.

[0035] Preferably, the elevator operation time difference index and the elevator position adjustment index generated by analyzing the elevator operation cycle time difference and the frequency of elevator position adjustment extracted from the elevator operation data are input into a pre-learned deep learning model, and the working frequency index is generated by the deep learning model, and the current elevator operation status is intelligently evaluated by the working frequency index.

[0036] Preferably, the operating frequency index generated when the elevator operating state is intelligently evaluated by a pre-learned deep learning model during the monitoring window is compared with a pre-set operating frequency index reference threshold value to divide the current elevator operating state. The division steps are as follows:

[0037] If the operating frequency index is greater than or equal to a preset operating frequency index reference threshold, the current elevator operation state is classified as high-frequency operation;

[0038] If the operating frequency index is less than a preset operating frequency index reference threshold, the current elevator operation state is classified as stable operation.

[0039] Preferably, for elevators assessed as "high-frequency operation", the specific steps of dynamically adjusting their self-test cycles and shortening the self-test intervals are as follows based on the evaluation results of the deep learning model:

[0040] The elevator is assessed to be in high-frequency operation state, and the self-inspection cycle is dynamically adjusted to shorten the self-inspection interval. Based on the evaluation results of the deep learning model and the elevator's operating frequency index Calculate the adjusted self-test period using the following expression:

[0041]

[0042] Where, It is the preset self-test cycle. is the regulating factor, is the operating frequency index reference threshold, is the adjusted self-test cycle;

[0043] After adjusting the self-test cycle, the operating status of the elevator is tracked in real time, and the fault warning is quantified. The calculation expression is as follows:

[0044]

[0045] Where, is the fault warning value, is the weight factor, Is a performance indicator The weight factor, It is The performance indicators of each elevator are: is the total number of performance indicators;

[0046] Based on the dynamically adjusted self-check cycle and health status assessment results, the elevator maintenance and service strategy will be adjusted accordingly to generate the maintenance priority. The calculation expression is as follows:

[0047]

[0048] Where, It is the maintenance priority of the elevator, is the fault warning weight factor, is the operating frequency weighting factor.

[0049] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0050] This invention uses a deep learning model to intelligently assess the operating status of elevators, accurately identifying potential risks in high-frequency elevator operation. When an elevator is in high-frequency operation, the system dynamically adjusts its self-test cycle to shorten the interval. This strategy ensures that high-frequency elevators can undergo more frequent inspections at an early stage, thereby promptly detecting potential fatigue or aging of key components (such as motors, braking systems, and transmissions). This not only effectively prevents the accumulation of faults and reduces the risk of elevator downtime, but also ensures that the elevator remains in optimal operating condition throughout its lifecycle. Compared with traditional, unified periodic self-tests, this dynamically adjusted self-test strategy can significantly improve elevator maintenance efficiency and fault warning capabilities, reduce the occurrence of faults, and avoid safety incidents and operational interruptions caused by undetected faults.

[0051] Through intelligent evaluation and analysis of elevator operating data, the energy-saving elevator control system of this invention can rationally adjust the self-test cycle based on the actual workload and operating frequency of the elevator. This allows elevators with high operating frequencies to receive more attention and resources, while maintaining a standard self-test cycle for elevators with low frequency and stable operation. This strategy ensures elevator safety while avoiding excessive inspections and reducing unnecessary maintenance, thereby saving operating costs. Furthermore, the system monitors the elevator's operating frequency and operating status in real time, adjusting operating parameters (such as speed and dwell time) based on actual needs, further improving the elevator's energy efficiency. This energy-saving elevator control solution optimizes elevator operating efficiency, ensuring equipment safety and reliability while significantly reducing energy consumption and achieving the goal of energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0053] Figure 1 This is a module diagram of the energy-saving elevator control system of the present invention. DETAILED DESCRIPTION

[0054] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0055] The present invention provides Figure 1 The energy-saving elevator control system shown in the figure includes a historical data collection and analysis module, a periodic self-inspection scheduling module, a real-time data acquisition module, a key data extraction and processing module, a deep learning evaluation and status classification module, and a dynamic self-inspection cycle adjustment module:

[0056] The historical data collection and analysis module collects and analyzes the historical operating data of each elevator, sets different self-inspection cycles for each elevator, and ensures that each elevator is inspected at an appropriate frequency based on its actual operating needs, thus avoiding potential failures due to untimely self-inspections.

[0057] By personalizing the self-inspection cycle, each elevator is ensured to undergo an appropriate inspection frequency based on its actual operating needs, avoiding potential failures in high-frequency elevators due to untimely self-inspections, and also avoiding the waste of resources in low-frequency elevators due to excessive self-inspections.

[0058] The regular self-inspection scheduling module ensures that all elevators conduct regular self-inspections according to the set self-inspection cycle, ensuring that each elevator is fully inspected within the preset self-inspection cycle, and that potential problems are discovered and addressed in a timely manner to maintain the normal operation of the elevator.

[0059] All elevators undergo regular inspections according to their assigned self-inspection cycles. This ensures that each elevator receives a comprehensive inspection at specified intervals, allowing potential problems to be promptly identified and addressed. This ensures that the elevators remain in good working condition during daily operation, preventing malfunctions or accidents due to malfunctions or lack of maintenance. Each elevator's self-inspection cycle is set based on its specific usage (such as frequency of use, load, and operating hours), so the timing and frequency of inspections will vary. This approach ensures the long-term reliability and safety of the elevators. Regular inspections also help identify problems promptly, preventing the accumulation of faults that could lead to significant losses or safety hazards.

[0060] The preset self-inspection cycle needs to be appropriately set based on factors such as the elevator's frequency of use, load, operating environment, and historical fault history. The cycle should be short enough to ensure timely inspection and maintenance before potential faults occur, preventing minor issues from developing into serious problems due to neglect. The self-inspection cycle should be set not only based on the actual usage intensity of the elevator but also aligned with the performance degradation patterns of key components (such as the motor, braking system, and door control system). Regular self-inspections ensure that each elevator maintains optimal operating condition during operation, promptly identifying anomalies that could pose safety hazards. Timely maintenance can prevent elevator outages and passenger safety threats, thereby maintaining long-term stable operation.

[0061] Real-time data acquisition module, which collects the operation data of each elevator in real time during the operation of the elevator;

[0062] These sensors, including current sensors, temperature sensors, load sensors, speed sensors, and door control sensors, monitor the elevator's motor load, operating speed, temperature fluctuations, door opening and closing frequency, and load. All collected data is transmitted to a central monitoring system via the network, ensuring real-time and accuracy. Real-time data acquisition provides the foundation for subsequent operational status assessments, ensuring that every detail of elevator operation is accurately monitored and recorded, supporting intelligent evaluation and adjustments to self-test cycles.

[0063] The key data extraction and processing module extracts key features reflecting the high-frequency operation of each elevator from the real-time collected operating data during the monitoring window, analyzes the extracted key features, and inputs the analyzed features into a pre-trained deep learning model to analyze the elevator's operating data and evaluate the elevator's current operating status.

[0064] For each elevator, key features reflecting the high-frequency operation of the elevator are extracted from the real-time collected operation data. The extracted features include the time difference of the elevator operation cycle and the frequency of the elevator position adjustment. During the set monitoring window, the extracted features are analyzed to generate the elevator operation time difference index and elevator position adjustment index respectively. The elevator operation time difference index quantifies the time difference required for the elevator to go from one floor to another, reflecting the response speed and efficiency of the elevator under high-frequency operation state; the elevator position adjustment index quantifies the frequency of fine-tuning of the elevator's position when going up and down floors, reflecting the precise control and adjustment requirements of the elevator under high-frequency operation state.

[0065] When the elevator's cycle time difference increases, it generally indicates that the elevator is operating at a high frequency. The elevator's cycle time difference reflects the fluctuation in the time required to travel from one floor to another. Under high-frequency operation, the elevator may start and stop frequently. This can significantly increase the time difference, especially under conditions of uneven load, high passenger volume, or when the elevator is constantly traveling up and down floors. This is because the elevator's operation is affected by various factors, such as load fluctuations, floor distance, and inertia during starting and stopping, resulting in slight variations in each run time. As the elevator's usage increases, especially during busy periods or under high load conditions, the fluctuation in the time difference becomes more pronounced, reflecting the elevator's operating state under prolonged, high-frequency operation.

[0066] In the monitoring window, the specific steps for analyzing the elevator operation cycle time difference and generating the elevator operation time difference index are as follows:

[0067] In the monitoring window, the time difference data extracted from the elevator operation cycles between multiple floors is used to generate time difference fluctuations to reflect the degree of change in the elevator operation status. The calculation expression is as follows:

[0068]

[0069] Where, Indicates the The time difference between the two running cycles, is the time difference of the last running cycle, Is the weighting coefficient, which is used to adjust the influence of the relative rate of change. is the time difference fluctuation value, indicating the The relative change in the time difference between the current operating cycle and the previous operating cycle;

[0070] In this way, larger fluctuation values ​​will be more prominent, reflecting the complexity and instability of elevators at high frequency operation.

[0071] In order to further understand the operating status of the elevator, a cumulative index of time difference fluctuation is constructed. This index performs weighted accumulation of fluctuation values ​​in multiple time periods to reflect the overall operating trend of the elevator within the monitoring window. The calculation expression is as follows:

[0072]

[0073] Where, is the cumulative index of time difference fluctuations, i.e. the time point The cumulative index of time difference fluctuation at the moment, It's the time point. is the monitoring window length, is the exponential decay factor, is the natural exponential function, is the decay time constant, is the time series index;

[0074] Exponential decay is used to weight fluctuations within a short distance, simulating the impact of different time periods during elevator operation, thereby accurately evaluating the elevator's operating status.

[0075] Finally, by using the time difference volatility accumulation index Further processing is performed to generate the elevator running time difference index, and the calculation expression is as follows:

[0076]

[0077] Where, is the elevator running time difference index, is the scaling factor, which adjusts the sensitivity of the formula, is the sensitivity adjustment factor, which is used to control the rate of attenuation.

[0078] In the monitoring window, a larger value for the Elevator Run Time Difference Index, generated by analyzing the time differences in elevator run cycles, generally indicates high-frequency operation. During high-frequency operation, frequent starts and stops, as well as load fluctuations, increase the time differences between each run cycle. Larger time differences indicate significant fluctuations in the elevator's run time between floors, often a result of prolonged, high-frequency operation. For example, frequent elevator response to passenger demand, increased load and start / stop times, can lead to inconsistent run cycles. Conversely, when the elevator is in stable operation, the run cycle time differences are smaller, and the run time between floors tends to be consistent, indicating a lightly loaded and infrequently used elevator.

[0079] When an elevator's position is frequently adjusted, it usually indicates that the elevator is in a high-frequency operation state. The precise position adjustment of the elevator is to ensure that the elevator can accurately stop at each floor and ensure that passengers get on and off the car smoothly. In the case of high-frequency operation, the elevator needs to start and stop frequently and may carry different loads. With frequent starts, stops, and passenger transportation, the elevator's control system will require more fine-tuning and position corrections, especially in environments with high loads or large passenger flow. The increase in the frequency of elevator fine-tuning is usually due to load fluctuations, frequent passengers getting on and off, or the elevator itself is operating under a heavy load, resulting in the elevator requiring more adjustments each time it arrives at a floor to ensure accurate positioning and smooth operation.

[0080] In the monitoring window, the frequency of elevator position adjustment is analyzed and the specific steps to generate the elevator position adjustment index are as follows:

[0081] In the monitoring window, first collect the elevator's operating data in real time, especially the elevator's position adjustment during operation. Based on the acquired data, define the dynamic frequency factor of the elevator's position adjustment. The calculation expression is as follows:

[0082]

[0083] Where, It's the elevator in the monitoring window Number of position adjustments within It's the elevator in the monitoring window The total number of floors traveled within is the monitoring window duration, It's the elevator in the monitoring window The load factor within is the elevator position dynamic frequency factor;

[0084] The purpose of this step is to normalize the elevator's adjustment frequency with the load, travel distance, and time to avoid errors caused by different time spans or numbers of traveled floors, and to ensure the accuracy of the calculation results.

[0085] When evaluating the frequency of elevator position adjustment, considering only the number of adjustments cannot fully reflect the high-frequency operation of the elevator. A weighting factor for the adjustment amplitude is introduced to enhance the assessment of the need for fine-tuning during frequent operation. The calculation expression is as follows:

[0086]

[0087] Where, is the adjustment amplitude weight factor, It is The magnitude of the adjustment, Monitoring Window The maximum adjustment range within is the amplitude weighting index, which is used to control the impact of each position adjustment amplitude on the weight factor;

[0088] The purpose of this step is to weight each adjustment amplitude so that a larger adjustment amplitude has a higher weight in the calculation of the frequency factor, thereby more accurately reflecting the actual performance of the elevator under high-frequency operation conditions.

[0089] Dynamic frequency factor based on elevator position and adjustment amplitude weighting factors Generate the elevator position adjustment index, the calculation expression is as follows:

[0090]

[0091] Where, It is The duration of the position adjustment, is the elevator position adjustment index.

[0092] In the monitoring window, the Elevator Position Adjustment Index (EPI) performance value, generated by analyzing the frequency of elevator position adjustments, indicates high-frequency operation. A lower Elevator Position Adjustment Index value generally indicates stable operation. Frequent elevator position adjustments indicate frequent starts and stops, load fluctuations, or significant passenger flow between floors, requiring the control system to perform more fine-tuning to ensure accurate floor alignment at each stop. These frequent adjustments typically occur when the elevator is operating at high frequency for extended periods, such as during peak hours or with high passenger volume in a building. Conversely, when the elevator is operating more steadily, with less load fluctuation and a low start-stop frequency, the need for position adjustments is reduced, resulting in a smaller adjustment factor.

[0093] The elevator operation time difference index and elevator position adjustment index generated by analyzing the elevator operation cycle time difference and the frequency of elevator position adjustment extracted from the elevator operation data are input into the pre-learned deep learning model. The working frequency index is generated by the deep learning model, and the current elevator operation status is intelligently evaluated based on the working frequency index.

[0094] A pre-learned deep learning model is a deep neural network model trained and optimized using historical data from the long-term accumulation and analysis of elevator operation data. This model can intelligently analyze elevator operating status. During the training phase, the model learns from a large amount of elevator operation data, including various operating characteristics of the elevator (such as the time difference between elevator operation cycles and the number of position adjustments), as well as the relationship between these characteristics and actual operating conditions such as elevator faults, load, and frequency. By learning from this historical data, the deep learning model can extract underlying patterns and patterns, enabling it to accurately determine the operating frequency, operating intensity, and potential failure risks of elevators. Specifically, the model learning process considers data from multiple dimensions, such as the operating environment, load conditions, and operating frequency of different elevators. The model continuously optimizes weights using a backpropagation algorithm until the model's prediction accuracy on the test data reaches the expected level.

[0095] After model training is complete, the pre-learned deep learning model can be used to analyze elevator operating data in real time. When new elevator data is input into the model, it intelligently assesses the current elevator operating status based on its learned knowledge and patterns, and generates an operating frequency index. This operating frequency index is a quantitative indicator of the elevator's operating status, reflecting whether the elevator is currently operating at a high frequency, whether there are potential failure risks, and whether more frequent self-inspection and maintenance are needed. Through deep learning model evaluation, managers can obtain real-time and accurate feedback on elevator status, allowing them to adjust self-inspection cycles and optimize elevator scheduling strategies based on the actual needs of each elevator, further improving elevator operating efficiency and safety. Furthermore, the application of deep learning models eliminates the reliance on traditional fixed rules for elevator monitoring, allowing dynamic adjustments based on the actual operating conditions of each elevator, ensuring optimal performance under different workloads and usage scenarios.

[0096] The deep learning model is not limited here and can realize the elevator running time difference index and elevator position adjustment index Perform comprehensive analysis to generate the operating frequency index In order to realize the technical solution of the present invention, the present invention provides a specific implementation method;

[0097] Operating frequency index The generation formula is as follows:

[0098]

[0099] Where, 、 Elevator running time difference index and elevator position adjustment index The preset scaling factor of 、 Both are greater than 0.

[0100] It can be seen from the working frequency index performance value that, under the monitoring window, the larger the elevator operation time difference index performance value generated after analyzing the elevator operation cycle time difference, and the larger the elevator position adjustment index performance value generated after analyzing the frequency of elevator position adjustment, the larger the working frequency index performance value generated when the elevator operation status is intelligently evaluated through the pre-learned deep learning model under the monitoring window, indicating that the current elevator is in a high-frequency operation state. Conversely, it indicates that the current elevator is in a stable operation state.

[0101] The deep learning evaluation and status classification module divides the current elevator operation status into two categories: "high-frequency operation" and "stable operation" based on the evaluation results of the deep learning model;

[0102] The operating frequency index generated by intelligently evaluating the elevator operating status using a pre-learned deep learning model during the monitoring window is compared with the pre-set operating frequency index reference threshold to classify the current elevator operating status. The classification steps are as follows:

[0103] If the operating frequency index is greater than or equal to a preset operating frequency index reference threshold, the current elevator operation state is classified as high-frequency operation;

[0104] If the operating frequency index is less than a preset operating frequency index reference threshold, the current elevator operation state is classified as stable operation.

[0105] The high-frequency operation state means that the elevator is subjected to high loads and frequent operations for a long time, usually experiencing more starts and stops, shorter operation cycles and frequent inter-floor scheduling; the stable operation state means that the elevator operates under conditions of lower loads and less operating frequency, usually occurring during off-peak periods or when the usage load is light.

[0106] Dynamic self-test cycle adjustment module: for elevators assessed as “smooth running”, regular self-tests will continue according to the preset self-test cycle;

[0107] For elevators assessed as "smoothly operating," continuing to perform routine self-inspections according to the pre-set self-inspection cycle ensures the elevator maintains its safety and reliability during stable operation. Even if the elevator does not show obvious signs of high-frequency operation or malfunctions, regular self-inspections can still promptly identify potential problems or emerging hazards, such as wear and tear of mechanical components, minor glitches in the electronic control system, or a slight decrease in elevator operating efficiency. This continuous self-inspection ensures that each elevator system is always in optimal working condition, proactively identifying and resolving potential hazards and preventing the accumulation of minor problems that could lead to major failures. This extends the elevator's service life, reduces the occurrence of sudden failures, and improves passenger safety and user experience. Furthermore, regular self-inspections optimize elevator maintenance and management, ensuring that equipment meets safety standards at all times and preventing overlooked potential safety risks during smooth elevator operation.

[0108] For elevators assessed as "frequently operating," the system dynamically adjusts its self-inspection cycle based on the evaluation results of the deep learning model, shortens the self-inspection interval, and detects and addresses potential faults in advance, reducing the risk of downtime and safety accidents.

[0109] For elevators assessed as "high-frequency operation," the self-test cycle is dynamically adjusted based on the evaluation results of the deep learning model. The specific steps to shorten the self-test interval are as follows:

[0110] The elevator is assessed to be in high-frequency operation state, and the self-inspection cycle is dynamically adjusted to shorten the self-inspection interval. Based on the evaluation results of the deep learning model and the elevator's operating frequency index Calculate the adjusted self-test period using the following expression:

[0111]

[0112] Where, It is the preset self-test cycle. It is a regulating factor used to control the influence of the operating frequency index on the self-test cycle. is the operating frequency index reference threshold, is the adjusted self-test cycle;

[0113] If the elevator is assessed to be in a high-frequency operation state, the self-inspection cycle will be shortened, thereby ensuring that the high-frequency operation state of the elevator can be monitored and maintained more frequently and potential faults can be discovered in time.

[0114] After adjusting the self-check cycle, the elevator's operating status is tracked in real time, and health status assessments are continuously performed within the new self-check cycle. To this end, the deep learning model will continue to track the elevator's high-frequency operating characteristics and perform regular inspections based on the new self-check cycle to quantify fault warnings. The calculation expression is as follows:

[0115]

[0116] Where, is the fault warning value, Is the weight factor, controlling the operating frequency index and fault warning value The relationship between Is a performance indicator The weight factor represents the influence of various elevator health parameters on fault warning. It is The performance indicators of each elevator, including but not limited to, the operating temperature of the motor, the working pressure of the brake system, the load condition of the elevator, is the total number of performance indicators;

[0117] Through comprehensive calculation, a fault warning value is obtained , used to determine whether the elevator is about to malfunction or have safety issues. If the preset threshold is exceeded, a fault warning is triggered and maintenance response is accelerated.

[0118] Based on the dynamically adjusted self-check cycle and health status assessment results, the elevator's maintenance and service strategy will be adjusted accordingly. In the case of high-frequency elevator operation, not only will the self-check cycle be shortened, but the daily scheduling and operating load of the elevator will also need to be adjusted to generate maintenance priorities. The calculation expression is as follows:

[0119]

[0120] Where, It is the maintenance priority of the elevator, is the fault warning weight factor, which is used to adjust the impact of fault warning on maintenance priority. is the working frequency weight factor, which controls the influence of the working frequency index on the maintenance priority.

[0121] Higher A low value indicates that the elevator requires more urgent maintenance and inspection to ensure stable operation and minimize service interruptions and safety hazards caused by elevator failures. This dynamic adjustment mechanism optimizes elevator service strategies and improves elevator operation safety and efficiency by comprehensively considering the health status and workload of the elevator.

[0122] By introducing a maintenance priority adjustment formula, we are able to optimize the service schedule of elevators based on their current status

[0123] For elevators assessed as "frequently operating," the self-test cycle is dynamically adjusted based on the deep learning model's assessment results. Shortening the self-test interval aims to address the increased risk of failures that may arise from frequent elevator use. Frequently operating elevators experience greater loads and frequent starts and stops, which can cause fatigue, wear, or aging of components (such as motors, braking systems, and transmissions), potentially leading to potential failures. By shortening the self-test interval, the elevator's operating status can be monitored more frequently, allowing for timely detection of hidden issues caused by frequent operation and preventing minor failures from escalating into major problems. This allows for the early identification and resolution of potential failures, reducing the risk of equipment downtime, ensuring continued safe and efficient operation of the elevator, and minimizing downtime and safety incidents, thereby enhancing user safety and user experience.

[0124] This invention uses a deep learning model to intelligently assess the operating status of elevators, accurately identifying potential risks in high-frequency elevator operation. When an elevator is in high-frequency operation, the system dynamically adjusts its self-test cycle to shorten the interval. This strategy ensures that high-frequency elevators can undergo more frequent inspections at an early stage, thereby promptly detecting potential fatigue or aging of key components (such as motors, braking systems, and transmissions). This not only effectively prevents the accumulation of faults and reduces the risk of elevator downtime, but also ensures that the elevator remains in optimal operating condition throughout its lifecycle. Compared with traditional, unified periodic self-tests, this dynamically adjusted self-test strategy can significantly improve elevator maintenance efficiency and fault warning capabilities, reduce the occurrence of faults, and avoid safety incidents and operational interruptions caused by undetected faults.

[0125] Through intelligent evaluation and analysis of elevator operating data, the energy-saving elevator control system of this invention can rationally adjust the self-test cycle based on the actual workload and operating frequency of the elevator. This allows elevators with high operating frequencies to receive more attention and resources, while maintaining a standard self-test cycle for elevators with low frequency and stable operation. This strategy ensures elevator safety while avoiding excessive inspections and reducing unnecessary maintenance, thereby saving operating costs. Furthermore, the system monitors the elevator's operating frequency and operating status in real time, adjusting operating parameters (such as speed and dwell time) based on actual needs, further improving the elevator's energy efficiency. This energy-saving elevator control solution optimizes elevator operating efficiency, ensuring equipment safety and reliability while significantly reducing energy consumption and achieving the goal of energy conservation and emission reduction.

[0126] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. Energy-saving elevator control system, characterized in that, It includes historical data collection and analysis module, regular self-inspection scheduling module, real-time data acquisition module, key data extraction and processing module, deep learning evaluation and status classification module, and dynamic self-inspection cycle adjustment module: The historical data collection and analysis module collects and analyzes the historical operating data of each elevator, sets different self-inspection cycles for each elevator, and ensures that each elevator is inspected at an appropriate frequency based on its actual operating needs, thus avoiding potential failures due to untimely self-inspections. The regular self-inspection scheduling module ensures that all elevators conduct regular self-inspections according to the set self-inspection cycle, ensuring that each elevator is fully inspected within the preset self-inspection cycle, and that potential problems are discovered and addressed in a timely manner to maintain the normal operation of the elevator. Real-time data acquisition module, which collects the operation data of each elevator in real time during the operation of the elevator; The key data extraction and processing module extracts key features reflecting the high-frequency operation of each elevator from the real-time collected operating data during the monitoring window, analyzes the extracted key features, and inputs the analyzed features into a pre-trained deep learning model to analyze the elevator's operating data and evaluate the elevator's current operating status. The deep learning evaluation and status classification module classifies the current elevator operation status into "high-frequency operation" and "stable operation" based on the evaluation results of the deep learning model; The dynamic self-inspection cycle adjustment module continues to perform routine self-inspections according to the preset self-inspection cycle for elevators assessed as "smooth operation"; for elevators assessed as "high-frequency operation", the module dynamically adjusts their self-inspection cycle based on the evaluation results of the deep learning model, shortens the self-inspection interval, detects and handles potential faults in advance, and reduces the risk of downtime and safety accidents.

2. The energy-saving elevator control system according to claim 1, characterized in that: For each elevator, key features reflecting the high-frequency operation of the elevator are extracted from the real-time collected operation data. The extracted features include the time difference of the elevator operation cycle and the frequency of the elevator position adjustment. During the set monitoring window, the extracted features are analyzed to generate the elevator operation time difference index and elevator position adjustment index respectively. The elevator operation time difference index quantifies the time difference required for the elevator to go from one floor to another, reflecting the response speed and efficiency of the elevator under high-frequency operation state; the elevator position adjustment index quantifies the frequency of fine-tuning of the elevator's position when going up and down floors, reflecting the precise control and adjustment requirements of the elevator under high-frequency operation state.

3. The energy-saving elevator control system according to claim 2, characterized in that: In the monitoring window, the specific steps for analyzing the elevator operation cycle time difference and generating the elevator operation time difference index are as follows: In the monitoring window, the time difference data extracted from the elevator operation cycles between multiple floors is used to generate the time difference fluctuation. The calculation expression is as follows: Where, Indicates the The time difference between the two running cycles, is the time difference of the last running cycle, Is the weighting coefficient, which is used to adjust the influence of the relative rate of change. is the time difference fluctuation value, indicating the The relative change in the time difference between the current operating cycle and the previous operating cycle; In order to further understand the operating status of the elevator, a cumulative index of time difference fluctuation is constructed, and the calculation expression is as follows: Where, is the cumulative index of time difference fluctuations, i.e. the time point The cumulative index of time difference fluctuation at the moment, It's the time point. is the monitoring window length, is the exponential decay factor, is the natural exponential function, is the decay time constant, is the time series index; Finally, by using the time difference volatility accumulation index Further processing is performed to generate the elevator running time difference index, and the calculation expression is as follows: Where, is the elevator running time difference index, is the scaling factor, is the sensitivity adjustment factor.

4. The energy-saving elevator control system according to claim 2, characterized in that: In the monitoring window, the frequency of elevator position adjustment is analyzed and the specific steps to generate the elevator position adjustment index are as follows: In the monitoring window, first collect the elevator operation data in real time. Based on the acquired data, define the dynamic frequency factor for elevator position adjustment. The calculation expression is as follows: Where, It's the elevator in the monitoring window Number of position adjustments within It's the elevator in the monitoring window The total number of floors traveled within is the monitoring window duration, It's the elevator in the monitoring window The load factor within is the elevator position dynamic frequency factor; When evaluating the frequency of elevator position adjustment, a weighting factor for the adjustment amplitude is introduced to enhance the assessment of the need for fine-tuning during frequent elevator operation. The calculation expression is as follows: Where, is the adjustment amplitude weight factor, It is The magnitude of the adjustment, Monitoring Window The maximum adjustment range within is the amplitude-weighted index; Dynamic frequency factor based on elevator position and adjustment amplitude weighting factors Generate the elevator position adjustment index, the calculation expression is as follows: Where, It is The duration of the position adjustment, is the elevator position adjustment index.

5. The energy-saving elevator control system according to claim 2, characterized in that: The elevator operation time difference index and elevator position adjustment index generated by analyzing the elevator operation cycle time difference and the frequency of elevator position adjustment extracted from the elevator operation data are input into the pre-learned deep learning model. The working frequency index is generated by the deep learning model, and the current elevator operation status is intelligently evaluated based on the working frequency index.

6. The energy-saving elevator control system according to claim 5, characterized in that: The operating frequency index generated by intelligently evaluating the elevator operating status using a pre-learned deep learning model during the monitoring window is compared with the pre-set operating frequency index reference threshold to classify the current elevator operating status. The classification steps are as follows: If the operating frequency index is greater than or equal to a preset operating frequency index reference threshold, the current elevator operation state is classified as high-frequency operation; If the operating frequency index is less than a preset operating frequency index reference threshold, the current elevator operation state is classified as stable operation.

7. The energy-saving elevator control system according to claim 6, characterized in that: For elevators assessed as "frequently operating," the self-test cycle is dynamically adjusted based on the deep learning model's evaluation results. The specific steps for shortening the self-test interval are as follows: The elevator is assessed to be in high-frequency operation state, and the self-inspection cycle is dynamically adjusted to shorten the self-inspection interval. Based on the evaluation results of the deep learning model and the elevator's operating frequency index Calculate the adjusted self-test period using the following expression: Where, It is the preset self-test cycle. is the regulating factor, is the operating frequency index reference threshold, is the adjusted self-test cycle; After adjusting the self-test cycle, the operating status of the elevator is tracked in real time, and the fault warning is quantified. The calculation expression is as follows: Where, is the fault warning value, is the weight factor, Is a performance indicator The weight factor, It is The performance indicators of each elevator are: is the total number of performance indicators; Based on the dynamically adjusted self-check cycle and health status assessment results, the elevator maintenance and service strategy will be adjusted accordingly to generate the maintenance priority. The calculation expression is as follows: Where, It is the maintenance priority of the elevator, is the fault warning weight factor, is the operating frequency weighting factor.

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